Variation in the Human Genome (Novartis Foundation Symposia)

VARIATION IN THE HUMAN GENOME

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Ciba Foundation Symposium 197

VARIATION IN THE HUMAN GENOME

1996

JOHN WILEY & SONS Chichester . New York . Brisbane . Toronto . Singapore

8 Ciba Foundation 1996

Published in 1996 by John Wiley & Sons Ltd B a f i s Lane, Chichester West Sussex PO19 IUD, England Telephone National (01243) 779777 International (+44) (1243) 779777 All rights reserved. No part of this book may be reproduced by any means, or transmitted, or translated into a machine language without the written permission of the publisher. Other Wiley Ediiorial Oflces

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Conte nts Symposium on Variation in the human genome, held at the Ciba Foundation, London 13-15 June 1995 This symposium is based on a proposal made by K. M . Weiss and R . H . Ward Editors: Derek Chadwick (Organizer) and Gail Cardew

K. M. Weiss Introduction

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R. H. Ward and D. Valencia Phylogeographic variability in traditional societies 6 Discussion 19 P. Donnelly Interpreting genetic variability: the effects of shared evolutionary history 25 Discussion 40 N. B. Freimer and M. Slatkin Microsatellites: evolution and mutational processes 51 Discussion 67

C. R. Scnver, S. Byck, L. Prevost, L. Hoang and the PAH Mutation Analysis Consortium The phenylalanine hydroxylase locus: a marker for the history of phenylketonuria and human genetic diversity 73 Discussion 90 J. Bertranpetit and F. Calafell Genetic and geographical variability in cystic fibrosis: evolutionary considerations 97 Discussion 114

G. R. Sutherland and R. I. Richards Unusual inheritance patterns due to dynamic mutation in fragile X syndrome 119 Discussion 126 A. Cao, M. C. Rosatelli and R. Galanello Control of b-thalassaemia by carrier screening, genetic counselling and prenatal diagnosis: the Sardinian experience 137 Discussion 15 1

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H. Nagase, S. Bryson, F. Fee and A. Balmain Multigenic control of skin tumour development in mice 156 Discussion 168 W. F. Bodmer and I. Tomlinson Population genetics of tumours Discussion 189

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J. Cohen, A. Gaw, R. I. Barnes, K. T. Landschulz and H. H. Hobbs Genetic factors that contribute to interindividual variations in plasma low density lipoprotein-cholesterol levels 194 Discussion 206 C. F. Sing, M. B. Haviland and S. L. Reilly Genetic architecture of common multifactorial diseases 21 1 Discussion 229

J. Bodmer World distribution of HLA alleles and implications for disease 233 Discussion 253 A. R. Templeton Cladistic approaches to identifying determinants of variability in multifactorial phenotypes and the evolutionary significance of variation in the human genome 259 Discussion 211 G. M. Lathrop Quantitative phenotype analysis for localization and identification of disease-related genes in a complex genetic 2 background 8 Discussion 293

D. J. Weatherall The genetics of common diseases: the implications of population variability 300 Discussion 308 Final discussion 312 Summary 315 Index of contributors Subject index 320

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Participants J. Armour Department of Genetics, University of Leicester, Adrian Building, University Road, Leicester LEI 7RH, UK A. Balmain CRC Beatson Laboratories, Department of Medical Oncology, Alexander Stone Building, University of Glasgow, Garscube Estate, Switchback Road, Bearsden, Glasgow G61 IBD, UK

P. Beighton Department of Human Genetics, University of Cape Town Medical School, Observatory 7925, Cape Town, South Africa J. Bertranpetit Laboratorio d’Antropologia, Facultat de Biologia, Universitat de Barcelona, Av Diagonal 645, E-08028 Barcelona, Catalonia, Spain

J. Bodmer Imperial Cancer Research Fund, 44 Lincoln’s Inn Fields, London WC2A 3PX, UK Sir W. F. Bodmer Imperial Cancer Research Fund, 44 Lincoln’s Inn Fields, London WC2A 3PX, UK A. M. Bowcock Department of Pediatrics, University Of Texas, Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75235-8591, USA A. Cao Istituto di Clinica e Biologia dell’Eta Evolutiva, Universita degli Studi di Cagliari, Via Jenner s/n, 1-09121 Cagliari, Italy

R. Chakraborty Human Genetics Center, School of Public Health, University of Texas, PO Box 20334, Houston, TX 77225, USA A. Chakravarti Department of Genetics, Case Western Reserve University, School of Medicine, BRB Rm 721, 10900 Euclid Avenue, Cleveland, OH 44106-4955, USA A. Clark Department of Biology, Penn State University, 208 Mueller Building, University Park, PA 16802, USA vii

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P. Donnelly Departments of Statistics, and Ecology and Evolution, University of Chicago, 5734 University Avenue, Chicago, IL 60637, USA J. H. Edwards Genetics Laboratory, Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, UK N. B. Freimer Neurogenetics Laboratory and Center for Neurobiology and Psychiatry, Department of Psychiatry and Programs in Genetics and Biomedical Sciences, University of California, San Francisco, CA 941430984, USA

D. L. Hart1 Department of Organismic & Evolutionary Biology, Harvard University, 16 Divinity Avenue, Cambridge, MA 02138, USA N. Hardman Ciba Pharmaceuticals, Wimblehurst Road, Horsham, West Sussex, RH12 4AB, UK P. S. Harper Institute of Medical Genetics, University of Wales College of Medicine, Heath Park, Cardiff CF4 4XN, UK

H. H. Hobbs Departments of Internal Medicine and Molecular Genetics, University of Texas, Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75235, USA K. K. Kidd Department of Genetics, Yale University School of Medicine, 333 Cedar Street, PO Box 3333, New Haven, CT 06510-8005, USA G. M. Lathrop The Wellcome Trust Centre for Human Genetics, University of Oxford, Windmill Road, Oxford OX3 7BN, UK A. R. Linares (Bursar) Departamento de Bioquimica, Facultad de Medicina, Universidad de Antioquia, AA 1226, Medellin, Colombia C. R. Scriver McGill University-Montreal Children’s Hospital Research Institute, 2300 Tupper Street, A-717, Montreal, Quebec H3H 1P3, Canada

C.F. Sing Department of Human Genetics, School of Medicine, University of Michigan, Medical Sciences I1 M4708, Ann Arbor, MI 481094618, USA

G. R. Sutherland Department of Cytogenetics and Molecular Genetics, Centre for Medical Genetics, Women’s and Children’s Hospital, North Adelaide, SA 5006, Australia A. R. Templeton Department of Biology, Washington University, St Louis, MO 63 130-4899, USA

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R. H. Ward Department of Human Genetics, 2100 Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT 84112, USA Sir D. J. Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK K. M. Weiss (Chairman) Department of Anthropology, The Pennsylvania State University, 409 Carpenter Building, University Park, PA 16802-3404, USA R. Zechner Institute of Medical Biochemistry, University of Graz, Harrachgasse 21, A-8010 Graz, Austria

Other Ciba Foundation Symposia:

No. 130 Molecular approaches to human polygenic disease Chairman: Sir David Weatherall 1987 ISBN 0 471 91096 1 No. 149 Human genetic information: Science, law and ethics Chairman: Sir Gustav Nossal 1990 ISBN 0 471 92624 8 No. 194 Genetics of criminal and antisocial behavour Chairman: Sir Michael Rutter 1996 ISBN 0 471 95719 4

lntroduction Kenneth M. Weiss Department of Anthropology, The Pennsylvania State University, 409 Carpenter Building, University Park, PA 16802-3404, USA

London is an appropriate place for a symposium in this field because it’s essentially the home of the idea of evolution. During the last century, London was at the centre of a debate about the interpretation of similarities among animal species. Most of the evidence was based on morphology because there was no biochemistry in those days, and there was basically an essentialist or platonic philosophy about structural archetypes among different animals. The problem was to explain the origin and the reality of those archetypes, i.e. whether they were actually present in animals or whether they were just ideals. These issues were addressed by Darwin, who, through the study of variation, proposed the theories of evolution, phylogeny and common descent. These theories did not answer all the questions, but they showed, at least in general terms, how the archetypal concepts could be interpreted in terms of ancestry. The Human Genome Project, which will create a stereotype of human genetic structure, is in a sense history’s greatest exercise in platonic essentialism. It is a stereotype that neither Plato nor Linnaeus would recognize because it is a composite, made up of pieces of chromosomes from different people. It is intended to represent the normal human genome, although there is no guarantee that those who donated their chromosomes for analysis will find out subsequently that they are susceptible to a particular disease. Although this is a stereotype, it is probably one of the most important single projects in biology ever undertaken, and it has already proven to be immensely important. Data that have been generated in association with the Human Genome Project, together with our understanding of evolution, show that the genome did not arise from a ‘great chain of stereotypes’. Genetic variation has been fundamental, not incidental, to the evolutionary generation of our genome. In this introduction I would like to outline a few relevant ideas about evolution and some principles that may be discussed at this symposium. First, we heavily use the concept of phylogeny, i.e. that present variation in the genome is due to descent with modification. This has now been shown clearly by the results of DNA sequencing. Second, Victorian biologists were aware of modularity and common body plans in nature. They advanced hypotheses about the central role of 1

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anatomical segmentation in animal evolution (they did not know about modularity in physiological systems). Basic segmental body plans were considered to represent the essence of animal organization, and it was debated whether major advances in evolution occurred by saltations or by large, rapid changes involving these forms. These notions were laughed at for much of this century, but the laughing is now starting to subside because we’re realizing that in some ways their ideas about modularity were correct. We now know that the genome itself is modular, from nucleotides and codons to gene families and other higher order structures, and that duplication of whole genes (or clusters of genes) with subsequent modification provides the material for complexity. We now use these concepts routinely in the process of trying to understand complex traits at the level of genetic and molecular physiology. Third, we are becoming more aware of interactions both between genes, and between genes and their environment. These interactions result in the production of particular somatic phenotypes, but of course only genetic factors are contained within the germline and are, therefore, inherited. The distinction between the ‘information’ inherited in the germline and the traits that are realized by that information in an individual lifetime is an important distinction that can be traced back to the nineteenth century. Today it takes many forms, including the nature versus nurture debate regarding causation of chronic diseases that involve both genes and environmental exposures. Recent discoveries have shown that more is inherited than just the DNA sequence itself, but the long-term implications of those findings are not yet clear. Fourth, I would like to mention that evolution has proceeded by a crude form of empirical ‘sieving’. That is, natural selection operates on phenotypes, not genotypes, and it accepts any genotype whose phenotype passes the competitive standard. Wallace and Darwin debated whether this standard was, in general, established by the environment (Wallace) or by competition among individuals (Darwin). The former was probably most important for the majority of traits of interest to contemporary biomedical genetics. The critical point is that any genotype that generated an acceptable phenotype was acceptable to selection. There was also undoubtedly a strong component of luck (or genetic drift) in the process. As we shall see in this symposium, one result of evolution by phenotypic rather than genotypic selection is that the genetic basis of simple as well as complex traits is variable: different individuals can have a similar disease for a diverse set of genetic reasons. A number of generalizations have arisen as a consequence of DNA analysis. All of us here are aware of them, implicitly or explicitly, but they have not always been built explicitly into our models of variation. We do not have a good systematic understanding of the meaning of some of these generalizations, but there are at least some general principles. (1) There are many alleles at any given locus, not just two. In the past we have conceptually thought of most loci as being diallelic but we now know that there are

Introduction

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hundreds of alleles at a given locus. (2) There is a quantitative relationship between genotype and phenotype, even at single loci. Simple concepts such as dominance and recessiveness are becoming obsolete in many ways because we now see that there is a more or less continuous relationship between various alleles at even a single locus and the phenotypes associated with them. This was not fully anticipated until we started looking at DNA. (3) The polygenic model, which originated in the last century, described complex quantitative traits as aggregates of individually unidentifiable genetic components traditionally referred to as polygenes. However, it is only in the last few years that we have been able to identify the quantitative trait loci pertaining to the variation in these traits. We don’t yet know how to interpret most of these data. However, at some of these loci what we find are one or two alleles with strong effect and many with minor effect on phenotypic variation. In this sense, modern genetics has unified the previously considered disparate ways in which qualitative and quantitative phenotypes were produced at the gene level. Most mutations are unique at the haplotype DNA level. These mutations generate cladistic sequence patterns among copies of the given gene in a population, and they retain a strong trace of history. I would say these patterns reflect a type of ‘weak law of nature’. There is not the precise relationship between alleles and phenotypes that is generally evoked by ideas of genetic adaptation to environments. Instead, from the point of view of DNA sequences, the genotype to phenotype relationship is rather forgiving or statistically noisy. However, the cladistic structure of DNA sequences themselves retains a reasonably strong trace of population history, and it seems likely that that history, rather than any deterministic force such as adaptive natural selection, is responsible for most of the pattern that we see today. We will see in this symposium how this fact can be used to increase our power to detect the genotype to phenotype signal that exists in any given system. Genetic identity by state is now usually interpreted as being roughly equivalent to identity by descent, which is different from what I was taught when I was a student. Identical DNA sequences are typically assumed to be descended from a common ancestral chromosome with that sequence (important caveats are needed for some regions of the genome in which real recurrent mutation seems to occur, for various chemical reasons). As a result, when we see different individuals in the same population with a similar disease, we can say that the diseases are clonal, in the sense that they’re caused by copies of an allele which can be traced back to a common ancestor. Alleles with a strong effect on the risk of disease are usually rare. For most alleles, their effect on risk is modest, complex and fairly uncertain. From a public health perspective, the effects of these modest alleles may be more important; however, the alleles with strong effects are the ones that are easily studied by standard scientific methods.

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We had not anticipated that the germline genotype is so dynamic from generation to generation. The situation is more complicated than the simple genetic beads on a string model. This has added a new kind of richness to our understanding of genetics. Somatic genotypes are dynamic during the life of an individual, and the germline is also dynamic across generations in complex ways. The discovery of regulatory genetic elements has also altered what we know about genes and the original beads on a string model of genetics. Short response elements recognized by transcription factors to switch a gene on or off in appropriate tissues may act together as a separate kind of non-coding gene, which may evolve separately from its neighbouring coding sequence. Phenotypic changes may result from mutations in the response elements or in the coding sequence itself. In our general model of evolution, genotypes are generated by a random process of mutation. Natural selection provides a sieving mechanism on phenotypes only, as mentioned earlier, and the important point again is that any genotype whose associated phenotype can get through that sieve will be an acceptable genotype. In that sense, the phenotypic variation that we see among individuals was produced by a process that went from phenotype indirectly down to genotype. But in applied biomedicine, we’re trying to identify genotypes that predict phenotypes accurately. So are we trying to do something that isn’t what Nature did to produce us? Nature was only interested in the fact that you can get here, which represents a very different perspective. In this sense, human genetics turns Nature on its head and tries to make a causal connection, from genotype to phenotype, that was not rigorously built into the system of variation as it arose. We refer to the struggles this entails by using terms such as ‘complexity’, and we will see what this means to biomedicine in many presentations at this symposium. A new field that is being called ‘evolutionary medicine’ is generating a lot of interest, at least in the USA. The idea is that a diversity of human traits, including host-pathogen relationships, allergic reactions, anxiety, menstruation and fever, must have had their origin in adaptive evolution by natural section. There is a highly (I would say ‘hyper’) deterministic approach to human phenotypes, which views pathological variation as something that should be approached with an understanding of adaptive origins so that therapy does not violate the built-in function of the system. For example, if fever is an adaptive response to infection, it should be interfered with only with caution. Closed explanations are appealing, and no one can deny that dysfunction can be understood best in the context of normal function. There will probably be fervid advocacy of this point of view in the near future. However, not all of life, nor all of disease, reflects a tightly deterministic natural world. This symposium will consider another, more problematic, aspect of evolutionary medicine. The probabilistic role of population history in generating the pattern of genetic variation associated with disease, and the

Introduction

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statistical relationship between specific genotypes and specific phenotypes, pose challenges for a field that hopes to identify the specific causes of human disorders. Genetic variants, even frequent ones, need have no adaptive meaning or ‘explanation’. The contingent nature of biological variation and its phenotypic relationships, the essential product of much of evolution at the gene level, is not always so tidy as the adaptationist perspective would suggest. This symposium is organized into several topical categories, designed to address the issues I have discussed in a systematic way. The first group of presentations will address molecular variation in human populations and its evolution in general as a topic; the second group will address variation in the Mendelian diseases that should be simplest to understand genetically; the third group will address genetic variation and complex causation for traits that we know involve many genes; the fourth group will consider evolutionary principles and methods for aetiological inference that can take advantage of the historical origin of existing variations; and finally, David Weatherall will give us an overview that illustrates most of the ideas contained in the symposium in terms of an elegant example.

Phylogeographic variability in traditional societies Ryk H. Ward* and Diana Valenciat

*Department of Human Genetics, 2100 Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT84112, USA a n d ?Departamento de Biologia, Universidad de Puerto Rico, San Juan, PR 00931, Puerto Rico

Abstract. Our perception of the amount, and distribution, of human genetic diversity is becoming radically altered by the introduction of sophisticated molecular techniques into the field of evolutionary biology. Along with the exponential increase in the number of informative DNA markers, has come an increased precision in estimating the evolutionary relationships between populations. Evaluating DNA variability in terms of the phylogenetic analysis of sequence variability at the population level has been especially informative in illuminating the long-term ancestry of our species. An extension of this strategy, phylogeography, aims to evaluate the evolutionary ancestry of specific genomic regions in terms of the geographic distribution of phylogenetic lineages within and among populations. We have started to apply this approach by studying the distribution of mitochondria1 DNA sequence diversity within and among a variety of Amerindian tribes. These data provide an illuminating contrast to regional data on sequence variability, especially when analysed within the theoretical framework of the coalescent. To interpret these results, we have analysed a simple model in which the rate of coalescence between subpopulations varies with respect to the rate of coalescence of individual ancestry. The model indicates that extensively isolated subpopulations will have distinct distributions of ancestry, and hence of sequence variability, compared to subpopulations which arise due to a series of rapid fissioning events. Subpopulations within tribes, such as bands, appear to correspond to the latter category, whereas tribal populations appear intermediate between the two extremes. 1996 Variation in the human genome. Wiley, Chichester (Ciba Foundation Symposium 197) p 6-24

Evolutionary biology has experienced two major revolutions in the past 30 years: each sparked by advances in laboratory techniques. The first was the ability to apply gel electrophoresis routinely to identify protein variability, leading to direct estimates of genetic heterozygosity in natural populations (Harris 1966, Lewontin & Hubby 1966). This innovation opened new vistas in population genetics and provided a wealth of empirical data to displace 6

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theoretical speculation. Evolutionary biology benefited from unexpected insights and the development of new research directions. However, some of the presumed benefits of the new technology proved surprisingly elusive and data acquisition was sometimes driven more by rote than by hypothesis testing (Lewontin 1991). The application of molecular techniques to allow direct assessment of genomic variability at the level of DNA represents the second innovation. Although still in its infancy, this new technology promises to dwarf completely the impact of protein electrophoresis. With the ability to resolve genetic variability at the level of a single nucleotide, evolutionary biology is poised to embark on an explosive renaissance. However, Lewontin’s (1991) retrospective assessment of protein electrophoresis suggests that it will be difficult to predict the direction in which this new research might develop. Despite some obvious applications, it is likely that many fundamental questions will require a paradigm shift before data can be properly collected and analysed. With the advent of the Human Genome Project, the rate of data acquisition and the development of new techniques will have a profound impact on the strategies that are developed to assess the evolutionary ancestry of our species (Cavalli-Sforza 1990). As new types of data are described, long-held assumptions about the origin of human diversity will be challenged. This will have relevance for both the individual and society: a comprehensive evolutionary description of genomic variability not only defines ancestry but also illuminates the genetic potential for important phenotypes, such as disease. A primary theme of this symposium is to examine ways in which molecular strategies can provide insights about the evolutionary heritage of individuals and populations, and how that heritage translates into disease susceptibility and other phenotypes with societal relevance. This paper evaluates the distribution of molecular data at the level of traditional human communities, corresponding to what is loosely called a ‘tribe’. Results that are beginning to emerge from the study of small-scale traditional populations promise to provide a perspective that will help interpret data derived from large-scale communities. The distribution of molecular variability in tribal populations helps indicate how methods, such as molecular phylogeography, can be used to interpret variability at a higher level. Phylogeography and ancestral coalescence Although molecular techniques allow a huge increase in the number of DNA markers (Cavalli-Sforza 1990), the potential to construct genealogies for specific genomic regions is even more important. Application of phylogenetic analysis provides important clues about evolution in terms of how ancestry coalesces backwards in time. Gene genealogies, singly or collectively, can lead

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to powerful inferences about the evolutionary history of populations. Consequently, the analysis of the spatial and community-specific distribution of a set of genomic phylogenies has led to the development of a new paradigm in evolutionary biology: phylogeography (Avise et a1 1987). Following the construction of intraspecific molecular phylogenies, a phylogeographic analysis allows the distribution of gene genealogies to be traced in space and time. Assessing the coalescence of lineage ancestry within and among populations provides information about the origin of contemporary genetic variation. In particular, the distribution of genomic phylogenies can be used to draw conclusions about the relative impact of deterministic forces compared to the influence of drift. Phylogeographic analyses can also be used to investigate the influence of past demographic fluctuations (Slatkin & Hudson 1991, Rogers & Harpending 1992). However, within this relatively novel area, the intersection between theory and observation still needs considerable development. In this context, application of phylogeography to small-scale, traditional human populations can help to identify some of the pressing problems that need resolution. However, not all types of molecular variability are equally informative for phylogenetic reconstruction. Although biallelic DNA polymorphisms, such as restriction fragment length polymorphisms (RFLPs), represent an obvious extension of classical genetic markers, they suffer from the same constraints: individual loci provide relatively little information about gene genealogies. Consequently, such loci are relevant to modern population geneticists only because of their vast number. Classical genetic markers have already proved exceptionally informative about population affiliations on a global scale (Cavalli-Sforza et a1 1994). Hence, the incorporation of many more markers will give a substantial increase in both precision and discrimination, as demonstrated by the use of RFLP data to estimate the relative impact of selection and migration on the genetic composition of major ethnic groups (Bowcock et a1 1991). Information content increases with the number of alleles, so that highly variable loci, such as minisatellites and microsatellites, hold even more promise. With only 30 microsatellite loci it is possible to define informative phylogenies of genetic kinship between individuals (Bowcock et a1 1994), which would be difficult to duplicate with biallelic loci. The critical requirement for estimating a phylogeny for a specific genomic region is that mutational events must be distinguishable. Although this is wellnigh impossible for microsatellites, the development of the minisatellite variant repeat mapping strategy (Jeffreys et a1 1991) holds promise for minisatellites. This technique could be exceptionally useful for defining phylogenies for otherwise uninformative regions, such as the Y chromosome. The ancestral state of mobile elements (e.g. Alu elements) can be defined; therefore, these loci hold considerable promise for developing population phylogenies (Batzer et a1 1994). However, individual elements are relatively uninformative for a single

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genomic region. The incorporation of multiple loci into haplotypes also holds promise, but more data are needed on the relative rate of recombination versus mutation. Ultimately, the sine qua non for phylogenetic reconstruction is sequence data. Although the vast bulk of phylogeographic studies are based on mitochondrial DNA sequences, there is a growing body of sequence data for nuclear regions (Fullerton et al 1994). Consequently, there is hope that population phylogenies will soon be based on a number of independent gene genealogies, which will help overcome the intrinsic problem of trying to infer population history from only a single realization of evolution (Slatkin & Hudson 1991, Majoram & Donnelly 1994). Mitochondria1 DNA variability in Amerindian tribes The phylogenetic analysis of mitochondrial DNA has had a major impact on contemporary theories of human evolution. High resolution sequence analysis of the mitochondrial DNA control region suggested a relatively recent origin for anatomically modem Homo supiens and a surprisingly rapid dispersal from Africa with little if any matrilineal contribution by the human groups that previously occupied large tracts of Eurasia (Vigilant et a1 1991). The unimodal distribution of sequence differences and occurrence of a star-like phylogeny suggested that this early migration was associated with a considerable population expansion (Di Rienzo & Wilson 1991). These observations led to methods for estimating the timing and magnitude of past demographic expansions (Rogers & Harpending 1992), with the conclusion that the major demographic expansion of ancestral human populations occurred 80 000 to 30000 years ago, depending on the ethnic group (Harpending et al 1994). Despite the numerous data on regional populations, few studies have evaluated sequence variability within small-scale, traditional societies. This is unfortunate, as these populations approximate the ancestral breeding structure that characterized much of our species’ recent evolution. Our initial study of mitochondrial DNA sequence variation in the Nuu-Chah-Nulth, an Amerindian tribe of the Pacific Northwest, identified a high level of intratribal sequence variability (Ward et a1 1991). The analysis of 63 maternally unrelated Nuu-Chah-Nulth revealed 26 variable positions in a 360 nucleotide stretch of the mitochondrial DNA control region. These variable positions defined 28 mitochondrial lineages. The average sequence diversity among these 28 Nuu-Chah-Nulth lineages was 80% of the value observed in a sample of 62 Japanese, and 60% of the values observed in a sample of 94 sub-Saharan Africans. These data indicated that, as was true for classical markers (Nee1 & Ward 1970), an appreciable proportion of human molecular variability is contained within tribal populations. As indicated in Fig. 1, the majority of Nuu-Chah-Nulth mitochondrial DNA lineages fell into four phylogenetic clusters. The existence of these clusters,

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FIG. 1. Phylogeny of 28 mitochondria1 DNA lineages found in a sample of 63 NuuChah-Nulth, after Ward et a1 (1991). The branch lengths in the phylogeny are scaled proportionally to sequence divergence, with the total depth of the tree being 3% sequence divergence. Open boxes denote multifurcations, where the hierarchical branching order cannot be assigned with statistical confidence. Lineages are numbered as in Ward et a1 (1991), and the three starred lineages are found in tribes throughout the Pacific Northwest. The four major mitochondrial DNA Amerindian clades are indicated with roman numerals.

which appear to predate the colonization of the New World, accounts for a significant fraction of the molecular diversity within the tribal sample. Apart from implying that the ancestral population which entered the Americas contained elements of formerly isolated populations (Ward et a1 1991), the existence of these clusters can be used to aid the phylogeographic analysis of mitochondrial DNA variation in Amerindian populations. However, in order to interpret the resulting data from a more intensive analysis, an assessment is needed of the relative importance of the temporal stability of local populations and also of the effect of different levels of genomic variation.

The influence of population coalescence Like molecular lineages, populations also have an evolutionary history. Population history influences the distribution of coalescent events for the individual genetic lineages; therefore, it will also influence the cladistic structure of molecular phylogenies. This will affect the phylogeographic pattern of molecular lineages within and among extant populations. Demographic change is perhaps the most obvious factor, as phylogenies derived from

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populations that had experienced a marked expansion tend to be star shaped with densely bifurcating tips. Such phylogenies result in a strongly unimodal distribution of pairwise sequence differences (Majoram & Donnelly 1994, Rogers & Harpending 1992, Slatkin & Hudson 1991). Most human populations also display marked extensive population structure. Although characteristic of many natural populations, the cultural dimension of our species tends to magnify the development of population substructure. Moreover, for most tribal populations, the existence of subpopulations is an extremely dynamic process. Local demes (bands, villages etc.) have relatively short histories and tend to evolve through a dynamic process of population interactions that are largely dominated by the sociopolitical relationships between groups. For many tribal populations, this dynamic process can best be described as a ‘fission-fusion’ process whereby larger populations have a tendency to split into smaller units, with periodic accretion of small subunits into larger ones. From the perspective of a small number of generations, this fission-fusion process can have a marked influence on the magnitude and structure of intratribal genetic differentiation (Ward & Nee1 1970, Ward 1972). The few investigations of the influence of population subdivision on coalescence times (Majoram & Donnelly 1994) have tended to use Wright’s paradigm of an island in which subpopulations are fixed, invariant entities. Although instructive, these studies may be less relevant to the human situation than studies which focus on the relationship between coalescence times of individual lineages and population coalescence which involves the coalescence of entire collections of individual lineages. Accretion of small populations into larger ones is a special case of migration that is already incorporated into many existing models (Marjoram & Donnelly 1994, Slatkin & Hudson 1991). When population coalescence occurs over a much longer time-scale than lineage coalescence, widely divergent clades characterize each subpopulation (Fig. 2). Within each subpopulation, the distribution of coalescence times approximates the standard model and will depend on population size (Kingman 1982, Tavark 1984). By contrast, the coalescence time between lineages from different subpopulations will be much longer, resulting in phylogeographic patterns that fail to reflect the true population history. Thus, lineages 6 1 2 , represented by the dashed lines in Fig. 2, have been lost, as have lineages 18-20. Hence, population isolation tends to exaggerate the normal process of lineages loss, with consequent inflation of the pairwise sequence differences. The converse situation is obtained when population coalescence occurs much more rapidly than lineage coalescence (Fig. 3). Here, representations of the same lineage tend to be distributed across closely related subpopulations, resulting in extensive lineage sharing. Related populations will only differ in the frequency of their lineages rather than having characteristic lineages.

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C

1 2, 3 , 4 , 5

13, 14, 15, 16, 17

21,22, 23, 24, 25

I

FIG. 2. Representation of 15 molecular lineages distributed in a set of three subpopulations that coalesce to population ancestry at a substantially slower rate than lineage coalescence. This corresponds to populations that have been isolated for long periods of time. Dashed lines indicate the ten molecular lineages that have been lost by random drift.

POPULATION LINEAGES

A 1,2

B 1,2

C 3,4

D 3,4

E 3,4

FIG. 3. Representation of four molecular lineages distributed in a set of four subpopulations that coalesce to population ancestry at a much faster rate than lineage coalescence. Dashed lines indicate molecular lineages that have been lost.

Phylogeographic variability

13

TABLE 1 Influence of relative rate of population coalescence on distribution of sequence diversity among subpopulations

Relalive rate of population coalescence

Lineage sharing bet ween populations 1&3

Lineage sharing between populations 2&3

Proportion of sequences dgfering by 0

Propor tion of sequences Number of dijfering variable by 3 positions

0.01 0.10 1.oo 10.00 100.00

0.3 2.3 19.1 63.7 87.0

0.1 1.o 10.4 47.3 75.1

13.4 13.1 15.6 24.6 29.9

5.2 5.7 9.5 10.7 9.4

129 65 24 15 13

To evaluate the influence of changing rates of population coalescence, we carried out a series of simulations using the same sample parameters that characterized the Nuu-Chah-Nulth data, plus the mutation rates estimated by Lundstrom et a1 (1992). Relative to the rate of lineage coalescence, the rate of population coalescence ranged over four orders of magnitude; from a 100-fold faster coalescence to a 1% rate of coalescence. The results are summarized in Table 1. They indicate that different rates of population coalescence have a marked impact on the degree of lineage sharing: little lineage sharing occurs among isolated populations (slow coalescence), whereas 1&19% of lineages are shared when the rate of population coalescence equals the rate of lineage coalescence. When coalescence of population ancestry is very short, 75-87% of lineages may be shared between populations. The distribution of pairwise sequence differences is also influenced by the rate of population coalescence (Table 1). Although the proportion of identical sequences rises monotonically, the proportion of sequences that differ by a specific number of nucleotides has a maximum that depends on the parameters in the model. Thus, the proportion of sequences that differ by three nucleotides rises from 5.2% to a maximum of 11.2%, when the rate of population coalescence is five times the rate of lineage coalescence, then declines. Also, for a given mutation rate, the relative rate of population coalescence has a marked influence on the number of variable sites: isolated populations in this simulation had 129 variable sites (35.8% variability), whereas ephemeral populations had only 13 variable sites (3.6% variability). An additional issue is the impact of varying mutation rates on the ability to estimate coalescent events from molecular data. Observations from relatively invariant genomic regions will give sparse molecular phylogenies, whereas highly variable regions will yield dense molecular phylogenies. The more complex dense phylogenies will tend to give a more accurate reflection of the

14

Ward & Valencia

pattern and frequency of coalescent events. An example is afforded by the observation that no sequence variation at the ZFY region was detected in a sample of 38 males (Dorit et a1 1995). Although application of the appropriate statistical techniques allows some inference about the time to the most recent common ancestor (Donnelly et a1 1995), these data provide much less information about human evolutionary history than an equivalent sample of mitochondrial DNA sequences. Genomic variability and ancestral inference in lhe Nuu-Chah-Nulth To determine whether differing levels of genomic variability influenced estimates of ancestral coalescence, we evaluated sequence variability at three mitochondrial DNA regions in the same set of 60 Nuu-Chah-Nulth. The regions, selected to give a range of variability, were as follows: most variable, 360 nucleotides at the 5' end of the control region (HVS1 of Vigilant et a1 1991); intermediate, 200 nucleotides at the 3' end of the control region (HVS2); and least variable, 18 RFLPs scattered around the mitochondrial DNA molecule. The restriction sites, chosen for their informativeness in Amerindians (Torroni et a1 1992), were equivalent to assaying 510 nucleotides (Valencia 1992). With eight RFLPs being invariant in this sample, the remaining ten sites, representing 2% sequence variability, defined 16 mitochondrial DNA lineages (Table 2). These restriction sites gave low levels of ancestral resolution because 53% of the sample was defined by lineages that occurred eight or more times, and only 10% of the sample was defined by unique lineages. By contrast, both sets of sequence data gave more resolution, Although the 5' segment of the control region had slightly lower levels of sequence variability than the 3' region, it identified almost twice as many mitochondrial DNA lineages, indicating that sequence variability alone may not be the best predictor of phylogeographic informativeness: the resolution of ancestral coalescence is more dependent on the pattern of variable nucleotides than the number. When the 3' and 5' data are combined, the number of lineages increases to 40, and the frequency spectrum of lineage distribution changes appreciably (Table 2). Only nine lineages are unique in the 3' control region sequence data (15% of the sample), whereas two lineages occur nine times each (30% of the sample). When both sets of sequence data are combined, 48% of the sample is defined by 29 unique lineages and the most common lineage occurs only five times (8% of the sample). Somewhat unexpectedly, the information about ancestral coalescence varies considerably between segments. It seems reasonable that adding more sequence data will mostly increase resolution at the tips of the phylogeny, with little impact on estimates of the coalescence to distant ancestors, This assumption is not borne out. Addition of 5' sequence data to the two 3' lineages that each occur nine times, gives seven and four new lineages, respectively, with mean

9.5%

8.9%

10.5%

2.0%

2 4 27%

4

2 2 3 7% 10% 20%

0

0

3

29 5 4 48% 17% 20%

1 7%

0

0

6 0 0

7

1 8%

0 0

0 0

0 0

0 0

1 1 0 8% 10% 0

0

0

5

15 3 4 2 2 25% 10% 20% 13% 17%

9 15%

6 3 10% 10%

1

Number of occurrences

‘18 restriction fragment polymorphisms scattered around the mitochondrial DNA molecule.

18 RFLPsa No. of lineages % of sample 3’ (HVS1) No. of lineages YOof sample 5’ (HVS2) No. of lineages YOof sample 3’ & 5’ No. of lineages % of sample

Genomic region

Sequence variability

9

10

0 0

0

0

0 0

0 0

0

.o

1 0 15% 0

2 0 30% 0

1 1 0 13% 15% 0

8

Total lineages

0

0

0 0

0 0

40

31

17

1 16 25%

15

TABLE 2 Frequency spectrum of mitochondrial DNA lineages in a sample of 60 Nuu-Chah-Nulth as a function of the genomic region sampled. Tabulation of number of lineages in terms of the number of occurrences of each lineage was observed

16

Ward & Valencia

pairwise sequence differences within these ostensibly identical lineages of 3.8f2.5 and 2.6k3.6. This level of sequence divergence (0.7-1.0%) is nearly half the total sequence divergence observed in the entire sample. Further, one of these 3' lineages falls into three of the four major clades that were defined by the 5' sequence data (Fig. 1). Conversely, addition of the 3' data to the lineage that occurs nine times in the 5' data set results in five new lineages, with an average pairwise difference of 2.3 f 1.7. Similar results were also obtained by analysing sequence variability within the Chibcha tribes of Central America (Kolman et a1 1995, Santos et a1 1994). Overall, these results suggest that estimates of ancestral coalescence are likely to have unacceptably large standard errors unless quite large mitochondrial DNA segments are sequenced. Further work will be required to determine whether the approximately 600 nucleotides at the mitochondrial DNA control region is sufficient to give stable estimates of ancestry at the tribal level. Intratribal phylogeography A more intensive study of the Nuu-Chah-Nulth analysed sequence data for 119 individuals, sampled from 40 1 four-generation matrilines, identified by genealogical analysis. These individuals were selected to represent seven bands, with an average sample size of 17& 11.7. Sequencingthe 360 nucleotides at the 5' end of the control region identified 36 mitochondrial DNA lineagesdefined by 35 variable sites, with an average pairwise sequence difference of 1.5% f 0.7%. Although the number of lineages observed within each band was relatively small, ranging from six to 13, the mean pairwise sequence difference within bands was virtually identical with the tribal value, ranging from 1.3% f 0.7% to 1.8% f 1.O%. Further, the average pairwise sequence difference between different bands was also identical to the tribal average, suggesting that mitochondrial DNA lineages are randomly distributed among the Nuu-ChahNulth bands. More detailed analyses failed to identify any association between sequence divergence and geography, language dialect, or sociopoliticalgrouping (Valencia 1992). This suggests that, analogous to Fig. 3, the coalescence of band ancestry occurs on a much shorter time-scale than coalescence of lineage ancestry. This implies that when using mitochondrial DNA sequences to evaluate ancestral coalescencein Amerindian populations, the tribe, rather than the band (or village), is the more appropriate unit for analysis. Intertribal phylogeography Following the analysis of sequence diversity within the Nuu-Chah-Nulth, we evaluated the distribution of mitochondrial DNA lineages within and among Amerindian tribes of the Pacific Northwest (Ward et a1 1993) and the circumarctic area (Shields et a1 1993). In both cases, the proportion of

Phylogeographicvariability

17

mitochondrial DNA lineages shared among tribes (1.1%) is substantially lower than the proportion shared among Nuu-Chah-Nulth bands. However, if the majority of lineages within a tribe tend to be unique, it is relevant to ask whether tribally specific lineages form a distinct clade, similar to the situation depicted in Fig. 1. Analysis of 41 lineages found in 144 individuals sampled from three Amerindian tribes of the Pacific Northwest gave no indication that mitochondrial DNA lineages clustered by tribe (Ward et a1 1993). The two Amerindspeaking tribes shared only four lineages, but they had no tribally specific lineage clusters. This was also true for the third (Na-Dene) tribe. Further, the shared lineages occupied a nodal position in the tree (and are marked with an asterisk in Fig. 1). Rather than having been dispersed by admixture, these lineages are likely to be ancestral lineages maintained in all three populations. A similar result is obtained from evaluating 33 lineages found among 90 circumarctic individuals sampled from a wide geographic range (Greenland to Siberia) and involving representatives of three language phyla (Na-Dene, Eskimo-Aleut, Chukchi-Kamchatka). The phylogeny for these lineages had no evidence of clades that corresponded to geography or language (Shields et al 1993). Hence, the intertribal distribution of mitochondrial DNA lineages is intermediate between the situation depicted in Figs 2 and 3, suggesting that the rate of coalescence of ancestry among tribes may occur at roughly the same rate as the rate of lineage coalescence.

Conclusion While demonstrating the potential of the phylogeographic approach, these results indicate that the relative rate of population coalescence can exert an important influence on estimates of ancestral coalescence. More detailed models are required to characterize this effect, and analysis of additional sequence data from local populations will provide a guide to the probable magnitude of effect in human populations. The data for Amerindian tribes suggest that bands (or villages) are fairly ephemeral with short coalescence times resulting in a high degree of lineage sharing. Analysis of lineage frequency, rather than phylogeographic structure, is most likely to be informative. However, tribes appear to be more stable entities, with considerable scope for phylogeographic analysis. The degree of sequence variability represents another variable that needs further study at both the theoretical and empirical level. Therefore, the degree of lineage sharing between bands is consistent with the concept that the coalescence times for bands is appreciably shorter than that for lineages. Acknowledgement We thank Ken Weiss, not merely for chairing the meeting with infectious enthusiasm, but also for his input in ensuring that the central theme of the symposium would develop along a consistent and productive direction.

18

Ward & Valencia

References Advise C, Arnold J, Ball RM et a1 1987 Intraspecific phylogeography: the mitochondrial DNA bridge between population genetics and systematics. Annu Rev Ecol Syst 18:489-522 Batzer MA, Stoneking M, Alegria-Hartman M et a1 1994 African origin of humanspecific polymorphic A h insertions. Proc Natl Acad Sci USA 91:12288-12292 Bowcock AM, Kidd JR, Mountain JL et a1 1991 Drift, admixture and selection in human evolution: a study with DNA polymorphisms. Proc Natl Acad Sci USA 88~839-843 Bowcock AM, Ruiz-Linares A, Tomforhde J, Minch E, Kidd JR, Cavalli-Sforza LL 1994 High resolution of human evolutionary trees with polymorphic microsatellites. Nature 368:455-457 Cavalli-Sforza 1990 How can one study individual variation for three billion nucleotides of the human genome? Am J Hum Genet 46:649451 Cavalli-Sforza LL, Menozzi P, Piazza A 1994 The history and geography of human genes. Princeton University Press, Princeton Di Rienzo A, Wilson AC 1991 Branching pattern in the evolutionary tree for human mitochondrial DNA. Proc Natl Acad Sci USA 88:1597-1601 Donnelly P, Tavark S, Balding DJ, Griffiths RC 1995 On the time since Adam. Science, in press Dorit RL, Akashi H, Gilbert W 1995 Absence of polymorphism at the ZFY locus on the human Y chromosome. Science 268:1183-1185 Fullerton SM, Harding RM, Boyce AJ, Clegg JB 1994 Molecular and population genetic analysis of allelic sequence diversity at the human fl-globin locus. Proc Natl Acad Sci USA 91:1805-1809 Harpending HC, Sherry ST, Rogers AR, Stoneking M 1994 The genetic structure of ancient human populations. Curr Anthrop 34:483-496 Harris H 1966 Enzyme polymorphism in man. Proc R SOCLond Ser B Biol Sci 164:2983 10 Jeffreys AJ, Macleod A, Tamaki K, Neil DL, Monckton DG 1991 Minisatellite repeat coding as a digital approach to DNA typing. Nature 354204-209 Kingman JFC 1982 On the genealogy of large populations. J Appl Prob 19:27A43A Kolman CJ, Bermingham E, Cooke R, Ward RH, Arias TD, Guionneau-Sinclair F 1995 Reduced mtDNA diversity in the Ngobk Amerinds of Panama. Genetics 140~275-283 Lewontin RC 1991 Twenty-five years ago in genetics: electrophoresis in the development of evolutionary genetics: milestone or millstone? Genetics 128:657662 Lewontin RC, Hubby JL 1966 A molecular approach to the study of genetic heterozygosity in natural populations. 11. Amount of variation and degree of heterozygosity in natural populations of Drosophila pseudoobscura. Genetics 54595609 Lundstrom R, Tavark S, Ward RH 1992 Estimating substitution rates from molecular data using the coalescent. Proc Natl Acad Sci USA 895961-5965 Majoram P, Donnelly P 1994 Painvise comparisons of mitochondrial DNA sequences in subdivided populations and implications for early human evolution. Genetics 136:673483 Nee1 JV,Ward RH 1970 Village and tribal genetic distances among American Indians and the possible implications for human evolution. Proc Natl Acad Sci USA 65: 323-330

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19

Rogers AR, Harpending HC 1992 Population growth makes waves in the distribution of pairwise genetic differences. Mol Biol Evol 9:552-569 Santos M, Ward RH, Barrantes R 1994 mtDNA variation in the Chibcha Amerindian Huetar from Costa Rica. Hum Biol 66:963-977 Shields GF, Schmiechen AM, Frazier BL et a1 1993 mt DNA sequences suggest a recent evolutionary divergence for Beringian and northern North American populations. Am J Hum Genet 53:549-562 Slatkin M, Hudson RR 1991 Pairwise comparisons of mitochondrial DNA sequences in stable and exponentially growing populations. Genetics 129:555-562 TavarB S 1984 Line-of-descent and genealogical processes and their applications in population genetics models. Theor Popul Biol 25:119-164 Torroni A, Schurr TG, Yang C-C 1992 Native American mitochondrial DNA analysis indicates the Amerind and the Na-Dene populations were founded by two independent migrations. Genetics 130:153-162 Valencia D 1992 Mitochondria1 DNA evolution in the Nuu-Chah-Nulth population. MSc thesis, University of Utah, Salt Lake city, UT, USA Vigilant L, Stoneking M, Harpending HC, Hawkes K, Wilson AC 1991 African populations and the evolution of human mitochondrial DNA. Science 253: 1503-1 507 Ward RH 1972 The genetic structure of a tribal population, the Yanomama. V. Comparison of a series of genetic networks. Ann Hum Genet 36:2143 Ward RH, Nee1 V 1970 Gene frequencies and micro-differentiation among the Makiritare Indians. IV. A comparison of a genetic network with ethno-history and migration matrices: a new index of genetic isolation. Am J Hum Genet 22:538-561 Ward RH, Frazier BL, Dew-Jaeger K, Paabo S 1991 Extensive mitochondrial diversity within a single Amerindian tribe. Proc Natl Acad Sci USA 88:872&8724 Ward RH, Redd A, Valencia D, Frazier BL, Paabo S 1993 Genetic and linguistic differentiation in the Americas. Proc Natl Acad Sci USA 90:10663-10667

DISCUSSION Bowcock: Have you used other molecular markers to look at these tribes? Ward: We have used standard genetic markers, and we’re just starting to use other molecular markers. There seems to be about the same level of diversity, possibly slightly less, in microsatellite dinucleotide repeats as in Caucasian populations. We’ve also looked at the distribution of two retroviral elements, human T cell lymphotropic virus (HTLV) I and 11, and we have observed patterns analogous to the mitochondrial data, but with a much greater level of divergence, which is one of the reasons why we thought of the concept between the relative rates of coalescence at the epimolecular level and a t the population level. Retroviral elements probably evolve at a slower rate than the mitochondrial control region. Bowcock: Is it possible that your conclusions may be biased by looking only at mitochondrial data in these tribes? Because they are a small population. Ward: My personal view is that we are not getting an intrinsic bias. The distribution of diversity within tribes is a function of the distinctiveness of the

20

Discussion

tribes, and it is the amount of migration that relates to the overall evolutionary ancestry of a tribe. Weiss: How does the existence of admixture affect your interpretation of population coalescence? Ward: My view is based on the classical fission-fusion model, rather than straightforward population coalescence, where one small population may grow in size and then split to become two populations. The admixture event can be depicted as having a reticulate distribution. Templeton: The idea of a gene tree versus the population tree is a critical issue. Gene trees exist within a species, but there may not be a bifurcating population tree because of the exchange of genes by gene flow. The appropriate null hypothesis would be that there is no population tree, and that the observed patterns of variation can be explained simply by recurrent forces, such as gene flow. It is possible that gene flow is restricted, so that differentiation may occur at any given time. The use of the word split has been grossly abused by human geneticists. The idea of population coalescence is based upon a set of assumptions that, for the most part, has not been tested. We have tested for coalescence in humans, and we have no evidence for population trees, even at the level of the major racial groups (Templeton 1993). W . Bodmer: Migration and separation occur, so there must be an element of coalescence if one looks backwards. Templeton: Separation does not have to occur after migration. If migration is a recurrent event, then there is no split. W . Bodmer: Then why are there major differences between the population groups? Templeton: If gene flow is restricted, at any given time a population structure will be created that shows genetic differentiation among the groups, but this does not mean that there is a split between them. W . Bodmer: Assuming that the original population migrated out of Africa into other areas, how can you say that a split has not occurred between Oriental populations and African populations, for example? Templeton: If the original population left Africa, some of them returned and in a subsequent generation some left again, then this represents recurrent gene flow and, by the systematic definition of the word split, there was no split. W .Bodmer: The same is true for speciation, which does not occur as a result of a single,definitive event, but as a series of gradual events. It’s only when one looks back over a long evolutionary period that a speciationevent seems instantaneous. Templeton: But we can look at patterns of recurrent events within gene trees. For example, by looking at mitochondria1 DNA, we have found that there were recurrent genetic contacts between Africans, Europeans and Asians throughout their entire evolutionary history (Templeton 1993, 1994). Bertranpetit: Moore (1994) discusses the issue of the origin and formation of populations from an anthropological, rather than from a phylogenetic, point

Phylogeographic variability

21

of view. Moore proposes that it is not possible to reconstruct or even suggest a tree of descent for the formation of several Amerindian groups because the merging events have been more important than the splitting events, of which only the latter can be uncovered by phylogenetic analysis. There are more events involved in the ethnogenesis than simply splitting and migrating events, and we should try to look at these suggestions from a genetic point of view. I would also like to mention a relevant issue on the interpretation of class zero in the pairwise difference distribution among sequences, that is, when the same sequence is found more than once in a population. In many cases this could simply be due to a sampling bias; for example, by extensive sampling in a given location. In general if there is some degree of geographic stratification of the genetic variation, a sampling strategy that does not take it into account may influence the pairwise distribution, especially in its zero class. Ward: The issue of sampling is clearly important, and we are ready to understand how effects such as sampling and population structure might interact with each other. One has to continue modelling, recognizing that models are idealized and are, therefore, an unfaithful representation of what is occurring in the real world. However, one can still use models to interpret the results that one observes. Weiss: It is possible that there is a quantitative aspect of migration, such that one migrant every N number of generations may be sufficient to unify populations from a theoretical population genetics point of view over a long period of time (e.g. at equilibrium) but over a shorter period of time, a small number of migrants may only have a small quantitative effect. Chakraborty: Migrations that have occurred in the past leave a remnant that can be observed in present day populations. For example, the study by Bowcock et a1 (1991) on restriction fragment length polymorphisms found traces of admixture and gene flow among European Caucasian populations that can be traced back to 50000 years ago. It is important to take into account the sampling variation that is either due to the limited number of nucleotides sequenced or the small number of individuals from whom DNA sequencing was done; however, some degree of confidence can be found in the phylogeny obtained from such samples. One can look at the data that Ryk Ward presented from another point of view. Each of the mitochondria1 DNA lineages he identified can also be characterized by sets of a small number of conserved nucleotides among sequences within each lineage. These can be called diagnostic nucleotides. All of the sequences in his study can then be grouped by lineages defined by these diagnostic nucleotides, and they also result in the same tree topology that he obtains. The phylogenetic relationships between lineages defined by the diagnostic nucleotides can be reanalysed in different ways to corroborate other findings relating to the age of the lineages. Also, sharing of sequences with a common set of diagnostic nucleotides can be informative about gene flow between populations. For

22

Discussion

example, in the mitochondria1 DNA hypervariable region, three of the four lineages (characterized by three sets of diagnostic nucleotides) are found not only in North Western Amerindians, but also as far south as in the Pehuenche Indians of Chile and Mapuche Indians of Argentina. Weiss: But how can you infer that migration has occurred, as opposed to ancient lineage sharing, i.e. lineages shared by two populations since their common founding population? Didn’t you say that you can identify a migration event by finding exogenous sequences in a group? How do you know it’s exogenous? Chakraborty: A sequence carrying the same set of diagnostic nucleotides will undergo ‘background’ mutations at other sites. Ancient sharing will accumulate these background mutations separatelyin different isolated populations, whereas recent gene flow will have similar background mutations. Discrimination of ancient sharing versus recent migration can be inferred from such distinctions. Weiss: But history is shallow in most populations, so how do you know that it’s migrant, as opposed to ancient shared variation? Chakraborty: Shallow history is an artefact of migration. Chakravarti: Ryk Ward has presented some data that demonstrate population relationships based on evolutionary trees built accurately from pairwise relationships between sequences. It is likely that, although we are interested in the relationships between sequences, we are much more interested in the relationships between populations. Therefore, the perceived population relationships, such as the apparent population split between Caucasians and Orientals, may be too simplistic. These questions are probably not going to be resolved on the basis of a single locus, they will need the sequences of hundreds of genes. The population tree would then represent the genealogy of all of the individual genes taken together, actually representing the history of a population. This information would distinguish whether ancestral haplotypes may have survived at low frequency or whether they are recent migrants. I would also like to comment on the distinctions between identity by state (IBS) and identity by descent (IBD). In shallow pedigrees, or in any genealogy that’s extremely short, such as the Amish or Mennonites, distinguishing IBD from IBS is not a problem. However, in deeper pedigrees, such as the populations that Ryk Ward is studying, rare haplotypes across small genomic regions (so that the expected rate of recombination is low) may allow us to distinguish IBD from IBS. Ward: The ability to make reasonable conclusions about IBD depends on the frequency of the IBD that one observes. It is difficult to distinguish between IBS and IBD for traditional genetic markers, and to some extent alleles that are relatively frequent, because one does not know whether a particular copy is a result of migration, so that it is identifiable by state as opposed to by descent. I agree that frequencies are the critical issue and that we need multiple gene genealogies to reconstruct population trees. The rate of change in different

Phylogeographic variability

23

genealogies is, of course, likely to differ from one genetic region to another. For example, the rate of change of mitochondrial DNA is relatively fast, whereas it is relatively slow for nuclear DNA. This is really a technological issue because of the increased amount of work involved in analysing nuclear DNA as opposed to mitochondrial DNA. For example, for the haemoglobin gene region, one would need to sequence about 3 kb to produce the same number of variant sites that one can observe in approximately 300 nucleotides of mitochondrial control region. With the current advances of molecular biology, this may be within reach in two or three years time. Chakravarti: In addition, different regions of the genome may be influenced by different mutation rates. Therefore, we may have to look at non-coding DNA sequences to obtain enough variability to distinguish IBD from IBS. Weiss: How many genes would have to be sequenced for us to answer some of the questions relating to population history? Chakravarti: I don’t know. We may decide to take a number of regions irrespective of whether they evolve quickly or slowly. This approach may not establish the pattern of the genome but it may provide the basis to answer the question objectively. Kidd: I would like to make two comments. First, no matter how well the gene genealogy for mitochondrial DNA is defined, it is only one locus. Therefore, its pattern is only one realization of a stochastic process and, as such, population estimates of relationships may not necessarily be accurate. In terms of needing multiple estimators, Judith Kidd and I have found, in collaboration with Anne Bowcock and Luca Cavalli-Sforza, that for nuclear DNA the gene genealogies all pre-date the dispersal of modern humans, such that essentially every allele that we look at in Europeans is present in every population (Bowcock et a1 1991, Kidd et a1 1991, 1993). The allele frequencies differ but all the alleles are present. Therefore, at the nuclear DNA level, the gene genealogies are not necessarily going to tell us anything about how the populations have dispersed. Second, we’ve used the word migration in two different ways during this discussion, and I’d like to clarify this distinction. There is a difference between migration between existing populations, or gene flow, and the history of the migration of a particular species as it has spread around the world. For example, the migration into the New World represents one bifurcating event, so that prior to 500 years ago there was no gene flow between populations in central Asia and central America. There may also be other bifurcating events that we can define historically. Of course, withm the New World, gene flow and migration occurred among the populations. Templeton: I agree that data from multiple loci are required, but significant information can still be gained from studying just one locus. We have shown that there are different signals for recurrent gene flow versus range expansion (which corresponds to your second use of the word migration) versus a

24

Discussion

traditional systematic split (Templeton et a1 1995). We described an example in tiger salamanders in which all of these events have occurred. We found that patterns of genetic variation are often determined by the joint action of range expansion, splits and gene flow, and that they’re not mutually exclusive alternatives. A technique is required that can discriminate and localize each effect, but information from just one locus can be helpful. When we applied the same techniques to humans, we found evidence for range expansion but not for systematic splits (Templeton 1993). Therefore, human genetic patterns seem to be determined by range expansion into new areas coupled with gene flow. However, this gene flow is restricted, so that genetic differentiation occurs. One common error in human genetics is to equate genetic differentiation with splits. There are many ways of getting genetic differentiation that do not involve population splits, although splits are often assumed. The null hypothesis should be that a split has occurred. W. Bodmer: Genetic terminology is often used loosely. For example, what is a locus? Is it a base pair, an exon or is it a region defining a whole protein? When one is looking at sequence differences within a gene, one is really looking at a haplotype. The extent of convergencedepends on the length of the sequences that one observes:the larger the haplotype, the less likely one is going to find a unique haplotype because of recombination. Therefore, we must be careful in our use of genetic terminology, and we should not talk about the whole of the mitochondrial genome as being a single locus or a single gene. It is a length of nucleotide sequence. People are concentrating far too much on mitochondria. Higher resolution, in terms of frequency of events,would be obtained by studying recombination, rather than looking at haplotypes over relatively large distances. References Bowcock AM, Kidd JR, Mountain JL et a1 1991 Drift, admixture, and selection in human evolution: a study with DNA polymorphisms. Proc Natl Acad Sci USA 88:839-843 Kidd JR, Black FL, Weiss KM, Balazs I, Kidd KK 1991 Studies of three Amerindian populations using nuclear DNA polymorphisms. Hum Biol 63:775-794 Kidd JR, Pakstis AJ,Kidd KK 1993Global levels of DNA variation. Proceedings of the 4th International Symposium on Human Identification 1993, Promega, Madison, WI, p 21-30 Moore JH 1994 Putting anthropology back together again: the ethnogenetic critique of cladistic theory. Am Anthropol 96:925-948 Templeton AR 1993 The ‘Eve’ hypothesis: a genetic critique and reanalysis. Am Anthropol95:Sl-72 Templeton AR 1994 ‘Eve’: hypothesis compatibility versus hypothesis testing. Am Anthropol96: 141-147 Templeton AR, Routman E, Phillips C 1995 Separating population structure from population history: a cladistic analysis of the geographical distribution of mitochondria1 DNA haplotypes in the tiger salamander, Ambystoma tigrinum. Genetics 140:767-782

Interpreting genetic var iabiIity: the effects of shared evolutionary history Peter Donnelly Departments of Statistics, and Ecology and ,Evolution, University of Chicago, 5734 University Avenue, Chicago, IL 60637, USA

Abstract. Data from different individuals at a single locus are positively correlated because of the shared genealogy of the sampled genes. This paper illustrates the qualitative effects on genealogical trees of assumptions about population demography, and it considers the consequences for genetic variability. An understanding of these effects is invaluable in the interpretation of data and for inferences about population history. In contrast, traditional genetic measures of diversity and approximation methods do not seem well suited for addressing the problem.

1996 Variation in the human genome. Wiley, Chichester (Ciba Foundation Symposium 197) p 25-50

Patterns in genetic data result from the superposition of two random mechanisms. The first is the underlying genealogical tree, which describes the ancestral history of the sampled genes. The second is the effect of mutation in changing genetic types. In non-neutral models these two forces interact: the action of mutation on genetic type in turn affects the structure of the genealogical tree as a consequence of natural selection. In contrast, for neutral models the two processes can be treated separately: one can first study properties of genealogies, and then superimpose the effects of mutation. Little is known about genealogy for selective models; however, genealogy is now reasonably well understood under neutrality. This separation of the effects of genealogy and mutation, via so-called ‘coalescent methods’, has provided powerful tools for studying neutral models, both analytically and by simulation. It also provides valuable insights into the patterns and correlations within population genetic data. From the point of view of using neutral genetic data to make inferences about population history, a genealogical approach is extremely natural. Different scenarios for population demography effect only the first of the underlying random mechanisms: they change the probability distribution of the underlying genealogical trees. Thus, if one could observe the trees 25

26

Don nelly

themselves, inference about demographic history would be a direct, though rather complicated, statistical problem. In fact, what one observes is the result of a noisy filter, namely mutation, acting on the genealogical tree. Our perspective throughout this paper is that to understand the consequences. of demographic assumptions, and hence to make inferences about them, one should first study their effects on genealogical structure. One can then ask, separately, how certain tree structures will be reflected in patterns in genetic data. The inference problem is far from simple. Data on different individuals at a single locus are highly correlated, and for most models there is substantial variability in realized genealogical trees. The variability means that even if one were to observe the entire gene tree associated with a sample, precise inferences about population history would be impossible. In any case, because of the dimensionality of the underlying trees, statistical analysis is technically challenging. The problem is compounded by the substantial additional noise imposed by the mutation process. On the other hand, data from unlinked loci have independent genealogies, so that some replication is available. Traditional summary measures based on pairwise comparisons, such as heterozygosity and pairwise sequence divergence, ignore most of the information in genetic data. They are not well suited to the problem of understanding human population history. Instead, more sophisticated methods, which make better use of information in the data, may be desirable. Further, traditional approximations of more realistic population models, via effective population sizes, can be seriously misleading in this context. Instead, one is now able to study, in some cases analytically and always by simulation, properties of reasonably complicated evolutionary models. Such an approach would appear essential for understanding the consequences of the many simplifying assumptions made in current analyses. The next section describes and illustrates both the coalescent and the qualitative effects on genealogical structure of various demographic assumptions. (For a more detailed and more quantitative treatment, see Tavark 1984, Ewens 1990, Hudson 1990, 1992, Donnelly & Tavark 1995, and references therein.) The consequences of genealogical structure on genetic variability will also be examined, and the implications for inference about population history will be discussed. Neutral loci will be considered throughout the paper, and recombination within a locus will be ignored. (For details of the effects of recombination, see Hudson 1990, 1992, Marjoram & Griffiths 1995.) Much less is known of genealogy in the presence of selection (see, for example, Kaplan et a1 1988, 1989, Hudson & Kaplan 1994).

Genealogical trees The coalescent (Kingman 1982a,b,c) can be thought of as a random tree. Its distribution is a close approximation to that of the genealogical tree associated

27

Interpreting genetic variability

with a sample of genes taken from a large panmictic population which has been of the same size throughout its evolution. The approximation is valid for a variety of demographic models. Throughout this paper, trees are drawn vertically from the number of sampled genes (n) at the tips of the tree up (i.e. backwards in time) to their most recent common ancestor (MRCA). At any time between the present and the MRCA of the sample, the coalescent tree will have one branch for each gene in the population that is an ancestor of at least one of the genes in the sample. Two branches in the tree coalesce each time the corresponding ancestral genes themselves share a common ancestor, until the entire sample is traced back to the single ancestral gene from which all are descended. The coalescent has a particularly simple structure. Time is measured in units of N/02generations, where N is the number of haploid genes in the population and o2is the variance of the number of direct copies in the next generation of a gene in the current generation. Throughout this paper I will assume, as is common, that the value of o2is 1. Note, however, that this need not be true for early human populations, particularly for genes carried in males. In the coalescent, the times q ,j = n,n - 1, . . . , 2 for which the tree has exactly j branches are independent, exponentially distributed random variables with

Thus, Var( T,) =

4 j * (j - 112.

It follows that the total depth of the tree, i.e. the time until the MRCA of the sample, has mean 2(1 - l/n) and variance between 1 and about 1.16, the latter being a good approximation for n larger than 4.Note that most of the depth of the tree, and almost all of the variability in t h s depth, is due to the times for which there are only a small number of ancestors. In particular, the expected time for which there are exactly two ancestors is over half the expected depth of the tree, and the variance of this time is 1. Each time the number of branches in the tree decreases, the coalescence is equally likely to involve any of the possible pairs of branches in the tree at that time. When there are j ancestors of the sample, the joint distribution of their numbers of descendants in the sample is uniform. In particular, the distribution of the number of descendants in the sample of a particular one of the final two ancestral genes before the MRCA is uniformly distributed on { 1,2, . . . ,n - 1). Figure 1 shows six realizations of coalescent trees. The timescaling is that for an autosomal locus in a population of 5000 diploid individuals. For

Donnelly

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6I-L A-7i-I

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f

I-2 Ah rn

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FIG. 1. Six realizations of the gene genealogy for a sample of size n panmictic population of a constant size of 5000 individuals.

=

56

6 genes from a

populations of different sizes, all that is necessary is to change the vertical time units. Note the variability, and the fact that it is common to see trees dominated by the final two branches before the MRCA. All of the figures in this paper relate to small sample sizes. The reason for this is that under most assumptions, ‘early’coalescences, i.e. those near the tips of the trees, occur quickly. For larger sample sizes, or indeed for the whole population, the structure of the trees would be similar to those presented here except for dense branching near the tips of the tree. An exception to this is the scenario of continual rapid population growth underlying Fig. 2. For panmictic populations that have not maintained constant (or approximately constant) sizes, genealogical trees have a different distribution. In general, these trees arise as non-linear time changes of coalescent trees. The nature of this time change depends on aspects of the demography of the population (P. Donnelly & T. G. Kurtz, personal communication 1994). (For early work on the effects on genetic data of variation in population size see Chakraborty 1977.) The simplest and best understood case applies, for example, to populations in which the variation in the population size has been ‘exogenous’ in a certain sense, or in which it results from independent reproduction by different individuals. This case is, therefore, moderately general, and it appears to have been assumed, at least implicitly, in all published work that uses genealogical techniques to study human evolution.

29

Interpreting genetic variability

I I m 4 6 3 2 5 1 3 4 2 6 5

m 5 3 6 4

1 2

1

T

5000 generations

k

m m m

1

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1 2

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FIG. 2. Six realizations of the gene genealogy for a sample of size n = 6 genes from a panmictic population that has grown exponentially, throughout its history, to a current size of 250 000 000 individuals. The value taken by the population size 2500 generations ago is 5000.

However, it may not apply to early human populations; therefore, results described here and elsewhere on the effects of variation in population size should be interpreted with caution. One way of thinking about the coalescent approximation to constant-sized populations is that each generation of real time corresponds to 1/N units of coalescent time. For the class of variable population size models considered here, a generation t for which the haploid size of the population is Nl accounts for l/Nl units of coalescent time. Consider a population, such as the human population, that has grown in size with time. Recent generations correspond to large population sizes, in which coalescences are less likely than in the smaller-sized generations in the past. The effect of this, which can be quite marked, is to stretch the usual coalescent tree near its tips, and to shrink it near its root. If a population has grown rapidly from a small size, the result is to make the associated genealogical trees resemble a star phylogeny. The intuition behind this is that, going backwards in time, no coalescences occur until the population reaches a relatively small size, at which point all of the coalescences occur in close succession. Figure 2 shows realizations of six sample genealogies for a panmictic population that has grown continuously in an exponential fashion. Note the similarity in the shapes of the trees for all SIX realizations, in marked contrast to the variability in the other figures presented here. The parameters, although perhaps not the continual exponential growth, may be plausible for the human population. Any rapid growth, from a small size, will tend to produce the same effect. The exact form of growth is immaterial. It is important to note that this effect of star-shaped genealogies depends crucially on the fact that the population has grown from a small size. In contrast, for a population that was of approximately constant size before

30

Donnelly

Lm

r-TT5I-5

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m m Ah

1 6 3 2 5 4

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FIG. 3. Six realizations of the gene genealogy for a sample of size n = 6 genes from a panmictic population. The population is assumed to have been at a constant size of 5000 individuals until 2500 generations ago, before growing exponentially to a current size of 250 000 000 individuals.

growing exponentially, the shape of the trees will depend on the size of the population before growth. Unless this is small, say fewer than 500 individuals, the trees will again resemble those of the coalescent. Figure 3 illustrates this effect. It shows six trees from a population that had a constant size of 5000 until 2500 generations ago, from which point it grew in size exponentially to a current value of 250 000 000. The trees in Fig. 3 are much more similar to the standard coalescent trees of Fig. 1 than to those of Fig. 2. The demographic difference between the settings of Figs 2 and 3 is that in the latter case the population is assumed to be of constant, non-trivial size before growth. This may be more plausible for the human population than an assumption that it has grown exponentially from a small value. Genealogical properties are also well understood for certain models of geographically structured populations. These models posit a population consisting of large, partially isolated colonies. Each such colony is randomly mating, with gene flow resulting from migration of individuals between colonies. The model is then specified by describing the relative sizes of the colonies and the rates and patterns of migration between them.

Interpreting genetic variability

31

Attention is restricted here to the qualitative effects on genealogy of such spatial structure. (For a fuller description of the models and the associated genealogical processes, see for example Donnelly & Tavark 1995 and references therein. For details of genealogy in the presence of this form of spatial structure and variation in population size, see Marjoram & Donnelly 1996.) The models described in the previous paragraph may well not capture important features of early human evolution, so that conclusions drawn from them in this context should be interpreted with caution. It seems likely that many of the qualitative effects on genealogy of geographical population structure may nonetheless hold for other forms of spatial structure, although at this stage little has been established. If the migration rates are high enough, gene trees from any of the spatial models under consideration will resemble those from panmictic populations. For lower migration rates, the effect of the spatial structure, loosely speaking, is opposite to that of population growth. That is, gene trees tend to be compressed near their tips and stretched near their root. The intuition behind this is that the early coalescences (those near the tips) occur between genes within colonies and these are more rapid because each colony is smaller than the whole population. On the other hand, because coalescences can only occur when the relevant ancestral genes are in the same colony, the final few coalescences in the tree can take substantially longer than in the panmictic case because they must first wait for migration to bring the ancestral genes into the same colony. The extent of this stretching near the root increases as the mutation rate decreases or the extent to which the population is structured increases. A further effect of population structure, in contrast to the setting of population growth, is that there can be enormous variability between realizations in gene trees. Figure 4 shows six realizations of genealogical trees for a sample of 12 genes from a structured population. The simulations assume a 3 x 3 stepping-stone model for the population structure. That is, the population is assumed to consist of nine colonies arranged as a square lattice (in fact as a torus to avoid edge effects) with migration allowed only between a colony and its four immediate neighbours: above; below; left; and right. For each gene, per generation, the probability of migration is taken to be In the event of a migration the destination is chosen uniformly from the four neighbours. The sample of 12 genes consists of six genes, labelled 1-6 from one colony, and six additional genes, labelled 7-12 from a second, neighbouring colony. The assumptions about population size are as for Fig. 3. Note that the vertical scale in Fig. 4 is different from that in the earlier figures. The trees in Fig. 4 tend to be much deeper than in the panmictic case (Fig. 3), although the depth of the tree at the bottom right (17449 generations) is comparable with those for panmictic populations of the same size. Note also the extent to which the trees are dominated by the times for the final one (or for the top left tree, two) coalescence(s). Relative to the panmictic trees, even the

Donnelly

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1 _ _ L 1 n

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11129

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10911

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FIG. 4. Six realizations of the gene genealogy of a sample of size n = 12 genes from a geographically structured population. The population is structured according to a 3 x 3 stepping-stone model with migration probability of lo-' per gene per generation. The population is assumed to have been at a constant size of 5000 individuals until 2500 generations ago, before growing exponentially to a current size of 250 000 000 individuals. The sample consists of six genes from each of two neighbouring colonies. Genes labelled 1-6 are from one colony. Those labelled 7-12 are from the second colony.

bottom right tree exhibits this effect: the time for which there are exactly two ancestral genes (about 13000 generations) is longer than for all but one of the trees in Fig. 3. The effect is extremely marked for the other trees. In the structured population, the early (first nine or 10) coalescences tend to occur more rapidly than the early (first four) coalescences in the panmictic case. In most of the trees, all the genes from within the same colony have a recent common ancestral gene within that colony. The exceptions are the top left tree, in which gene 9 is descended from a recent migrant from one of the

33

Interpreting genetic variability

A

1

r-l

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-:-d 3

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2 7

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I

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10

FIG. 5. Six realizations of the gene genealogy of a sample of size n = 12 genes from a geographically structured population. The population is structured according to a 3 x 3 stepping-stone model with migration probability of per gene per generation. The population is assumed to have been at a constant size of 5000 individuals until 2500 generations ago, before growing exponentially to a current size of 250 000 000 individuals. The sample consists of six genes from each of two neighbouring colonies. Genes labelled 1 4 are from one colony. Those labelled 7-12 are from the second colony.

non-sampled colonies, and the top right tree in which gene 1 is descended from a recent migrant from the other sampled colony. There is also great variability in the depth of the trees in Fig. 4. This results from the variability induced by the migration process in the times for the final coalescences. Figure 5 shows six genealogical trees for a population identical to that underlying Fig. 4, except that the migration probability has been increased to Again, the genes are sampled from two neighbouring colonies. The scale in Fig. 5 is different from that in earlier figures. The increase in the migration rate has a substantial effect on the structure of the trees. Their shape is more similar to those from panmictic populations: they are dominated less by the final coalescence time and more by the times for the final two or three coalescences. Although genes sampled from the same colony are more likely to share recent common ancestors than those from different

34

Donnelly

colonies, this is no longer nearly as definitive as in Fig. 4. The increased migration rate means that in a relatively short time in the past the ancestral genes are scattered throughout the population. Final coalescences still rely on migration bringing ancestral genes together, so that the trees in Fig. 5 still tend to be much deeper, and exhibit much more variability in depth, than those for panmictic populations, although these effects are less marked than for lower migration rates. Genealogical trees were also simulated under the same demographic conditions as in Figs 4 and 5 , with the migration probability increased to (P. Marjoram, personal communication 1995). These trees were similar in structure to those from a panmictic population of the same sizes. They were also more similar in total depth to the panmictic case than were the trees in Figs 4 or 5 , but still somewhat longer, with an average depth of about 23 000 generations. The variability in this total depth was comparable with, or perhaps slightly less than, that for trees from panmictic populations. Consequences for genetic variability

The patterns observed in genetic data from within populations reflect the effects of mutation at the locus in question superimposed on the genealogy of the sampled genes. Although it is not directly observed, the MRCA of the sampled genes will be of a particular allelic type. In the absence of mutation, all the sampled genes would be of this type. The variation actually present in the sample results from mutations along the lineages leading down from the MRCA to the sample. This paper is confined to a qualitative discussion of genetic variability. In fact, one of the advantages of a genealogical approach is that it often greatly simplifies quantitative analysis. In addition, it provides an efficient and simple method for simulating neutral evolution for quite general mutation mechanisms. (For further details, see, for example, Hudson 1990, 1992, Donnelly & Tavark 1995 and references therein.) A mutation that occurs on a particular branch of the genealogical tree will affect all the genes in the sample which are descended from the ancestral gene associated with that branch. For example, under the ‘infinite sites’ assumption that no back mutation has occurred between the MRCA and the sampled genes, each mutation on the tree will correspond to a segregating site in the sample. For a particular such mutation, all the genes descended from the ancestral gene that underwent mutation will have one base at a particular site, whereas all the other genes will have a different base, that of the MRCA, at that site. Mutations that occur higher up the tree, closer to the MRCA, will tend to be represented in more of the sampled genes. Patterns in the gene tree will thus tend to produce patterns in the genetic data. Consider, for example, the top left tree in Fig. 1. Because they share a

interpreting genetic variability

35

recent common ancestor, genes 1 and 2 will be similar or identical. The same is true for genes 3, 4, 5 and 6. However, unless the mutation rate is very small, genes 1 and 2 will tend to be different from genes 3, 4, 5 and 6, with the difference reflecting the mutations that occurred on the lineages leading from the MRCA to the common ancestor of genes 1 and 2, and from the MRCA to the common ancestor of genes 3,4,5 and 6. Thus, for such a gene tree, the data will tend to consist of two groups of genes, with great similarity within groups and possibly substantial differences between groups. Such an induced pattern is common for coalescent trees from constant-sized populations. For example, it is also likely to apply to each of the trees in the bottom row of Fig. 1. Trees for populations that were of constant, non-trivial size before recent rapid growth will tend to resemble those from a similar population that has not grown, except that the tips will be lengthened by an amount similar to the number of generations since the onset of growth. If the relative effect of this lengthening is small, as it would be for populations whose size before growth was more than five to 10 times the number of generations since the onset of growth, the induced patterns will be similar to those for constant-sized populations. The effect of this lengthening is not insignificant for the demographic assumptions underlying Fig. 3. Nonetheless, for example, several of the trees may result in two distinct groups of alleles in the sample, for the reasons described in the previous paragraph. In some sense, the total amount of diversity in the sample will reflect the total length of the tree. One consequence then of the substantial increase in tree depth caused (unless migration rates are large) by geographical subdivision will be a substantial increase in genetic diversity within the sample, compared to the panmictic case, for the same mutation rates. In addition, for small migration rates, subdivision can greatly accentuate the clustering of genetic types within the sample. For example, for the first five trees of Fig. 4, one would expect great similarity within genes sampled from the same colony, relative to the differences between the colonies (with the exception of gene 9 in the top left tree). Because of their increased depth, the trees in Fig. 5 should also result in greater diversity than in a panmictic population. The two trees in the top row will tend to induce a clustering of the sample into two distinct groups of genes, but now the groups will not correspond to the colonies from which the genes are sampled. Figure 2 illustrates the effect of continual exponential growth, from a small size, in making gene trees star shaped. Such trees will tend to result in quite different patterns in genetic data from those in the other figures. For an appropriate range of mutation rates, the probability of at least one mutation between the root and a particular tip of the tree will be bounded away from 0 and 1. In this case, there may be a single group of identical genes in the sample, those which are identical by descent to the MRCA. The other sampled genes (or all genes in the sample for higher mutation rates) will tend to be ‘equally

36

Donnelly

different’ from each other. Of course the randomness inherent in the mutation process will mean that observed samples will not contain faithful reproductions of this ‘equally different’ property, and chance effects may result in some clustering amongst these genes.

Discussion In contrast to the setting of classical statistics, single-locus genetic data from different individuals do not consist of independent observations. Rather, population genetic data of this sort is highly dependent because the genes in question share the same underlying genealogy. Recall that all the genes would be identical to the (random) type of their MRCA were it not for the effects of mutation since the MRCA. Thus, there is limited information about the underlying evolutionary and demographic processes in genetic data of this kind. As a consequence, there is a premium on making maximal use of the information in the data. In any case, inferences based on the complete data, or suitable sufficient statistics, will be more efficient, and more reliable, than those based on other summary measures. Unfortunately, many traditional genetic measures do not provide efficient summaries of the data. In particular, this is true of measures that are based on pairwise comparisons of the genes within a sample, such as sample heterozygosity or the average pairwise sequence difference. For example, in straightforward problems, such as the estimation of mutation rates under simple assumptions about mutation, estimates based on these measures are not even consistent, in contrast to those which make better use of the data. (For a more detailed discussion see Donnelly & Tavari 1995.) Fortunately there has been exciting recent progress in the development of full likelihood-based inference procedures for these models (see Griffiths & Tavari 1994a,b,c, Kuhner et a1 1996). There is substantially more independence between observations, and hence more information about evolutionary parameters, in a star-shaped genealogy than in the other tree structures described here. Provided sensible methods are used, inference in such a setting is, therefore, more reliable than in the other cases. Furthermore, under the demographic assumption of continual rapid growth from a small size, there is also much less variability in the shape and depth of the tree between realizations of evolution, and hence less variability in observed genetic data. This reduced variability also has the effect of substantially increasing the precision of inference procedures. For growing populations, such star-shaped trees will only arise if the population has grown rapidly from a small size; for example, 500 or fewer individuals. This may be thought a priori to be unlikely for the human population, at least within the last 100000 or 200000 years; for example, in

Interpreting genetic variability

37

view of the fossil evidence as to the spread of the population (Aiello 1993) and perhaps levels of diversity at certain loci (Takahata 1993). A severe bottleneck effect in the population, which reduces the population size to 100 or fewer individuals, will tend to produce the same effect.This may also be unlikely for a widespread population and it may be inconsistent with some observed levels of diversity. Star-shaped genealogies can also arise for a neutral locus that is closely linked to a selective locus at which a favourable allele sweeps through the population (P. Marjoram, personal communication 1995). The growth in the number of genes linked to the favourable allele will mimic population growth at the linked neutral locus, and the initial frequency of the favourable allele is small. Myself and others (Excoffier 1990, Di Rienzo & Wilson 1991, Marjoram & Donnelly 1994) have argued elsewhere that such a sweep, either at a locus on the mitochondrial genome or on the X chromosome, may be important in interpreting observed patterns in human mitochondrial data. One traditional approach to population modelling under more realistic assumptions than panmixia and constant population size has been via the concept of effective population size. In this approach, instead of studying the model of interest, one approximates it by a panmictic population with a suitably chosen effective population size. There are several different definitions of effective population size. Loosely speaking, one focuses on some particular one-dimensional summary of the population and defines the effective population size to be the size of a panmictic population for which aspects of the chosen summary behave similarly to those for the more complicated population, over a fixed time horizon. The above discussion of genealogy allows an assessment of the usefulness of this approach in various situations. For example, for a panmictic population of fixed size in which the variance of the number of descendant genes is different from the value of one in our coalescent approximation, use of an effective population size, defined as the actual number of genes divided by this variance, provides exactly the correct compensation. More generally, however, use of effective population sizes can be quite misleading, except possibly as an informal summary of the extent of genetic diversity in a population. Gene trees in a constant-sized panmictic population are described by the coalescent. Changes to the value of the population size simply change the vertical scale in these trees. For example, if the population size underlying Fig. 1 was increased by a factor of 10, all that would be necessary would be to increase the vertical scale by a factor of 10. Coalescent trees have a certain intrinsic structure and associated variability. Patterns in genetic data reflect the structure of the underlying gene tree. It follows that if the demographic assumptions about a population are such as to induce gene trees that do not share the structure, or variability, or both, of coalescent trees, then there will be no value of an effective population size for which the patterns in the sample will resemble those from a constant-sized panmictic population.

38

Donnelly

The trees in Fig. 2 are fundamentally different from those in Fig. 1. There is no way that a linear change in the vertical scale of the coalescent trees in Fig. 1 will result in a close resemblance to those of Fig. 2. In other words, no choice of effective population size will give a good approximation to the patterns observed in populations that have undergone continual rapid growth. Similarly, there is no linear change of scale that will induce coalescent trees to resemble those of the structured population underlying Fig. 4. Again, approximation of the structured model via effective population size could be extremely misleading. One consequence of recent progress on genealogical methods is that it is now possible to undertake reasonably sophisticated modelling of evolving neutral populations. An understanding of genealogical structure often provides invaluable insights. In some cases, even for relatively complicated models, analytical progress is possible. Most importantly, efficient simulation of such models is always possible via genealogy, so that it is in any case no longer necessary to resort to possibly misleading approximations. With the exception of Fig. 2, one striking feature of the simulated genealogical trees within each figure is the variability that they exhibit. This is discouraging from the point of view of using genetic data to infer aspects of human population history. Changes in the underlying population demography induce changes in the distribution of the associated gene trees. In general, one is not able to observe the gene trees themselves. Rather, one sees the consequences of an additional random process, namely mutation, superimposed on the underlying gene tree. If one were able to see through the noise added by the mutation process and reconstruct genealogical trees exactly, then, from a single locus, one would have a sample of size one of an object (the tree) whose distribution depends in a complicated way on the underlying demographic process. Note that statistical methods which reconstruct gene trees from data do not have this property. Inference about the demography from this single observation is far from straightforward, and therefore one should be cautious. This applies to demographic inference from data (regardless of the number of individuals involved) at a single locus, such as the mitochondrial genome. It is difficult to construct plausible demographic scenarios in conventional neutral models for the human population that are consistent with some aspects of observed human mitochondrial DNA (Marjoram & Donnelly 1994, 1996). Some of the inferences as to early human demography on the basis of mitochondrial data should be interpreted with caution until further information from nuclear loci is available. Data from unlinked loci will have independent genealogies. Thus, although it is impossible to get around the correlations between individuals within a locus, one can gain independent samples from the underlying genealogygenerating mechanism by sampling different loci. Data of this kind for the

Interpreting genetic variability

39

human population, from a variety of nuclear loci, is now becoming available. The problems associated with the efficient, or even the systematic, use of these data remain open, and one needs to have a better understanding of the consequences for inference of the various simplifying assumptions made in evolutionary models. Nonetheless, the patterns in such data, if not traditional summary measures of them, are potentially extremely informative. They should prove enormously valuable in the understanding of our population’s demographic history. Acknowledgements Thanks to Paul Marjoram for generating the trees presented in the paper and to Mitzi Nakatsuka for producing the figures. This work was supported in part by a Block Grant from the University of Chicago.

References Aiello LC 1993 The fossil evidence for modern human origins in Africa: a revised view. Am Anthropol 95:73-96 Chakraborty R 1977 Distribution of nucleotide differences between two randomly chosen cistrons in a population of variable size. Theor Popul Biol 11:ll-22 Di Rienzo A, Wilson A 1991 Branching pattern in the evolutionary tree for human mitochondria1 DNA. Proc Natl Acad Sci USA 88:1597-1601 Donnelly P, Tavark S 1995 Coalescents and genealogical structure under neutrality. Annu Rev Genet 29:401-421 Ewens WJ 1990 Population genetics theory-the past and the future. In: Lessard S (ed) Mathematical and statistical developments of evolutionary theory. Kluwer Dordrecht, Amsterdam, p 177-227 Excoffier L 1990 Evolution of human mitochondria1 DNA: evidence for departure from a pure neutral model of populations at equilibrium. J Mol Evol 30: 125-1 39 Griffiths RC, Tavark S 1994a Sampling theory for neutral alleles in a varying environment. Phil Trans R SOCLond B Biol Sci 344:403410 Griffiths RC, Tavark S 1994b Ancestral inference in population genetics. Stat Sci 9:307319 Griffths RC, Tavark S 1994c Simulating probability distributions in the coalescent. Theor Popul Biol46:131-159 Hudson RR 1990 Gene genealogies and the coalescent process. In: Futuyama D, Antonovics J (eds) Oxford surveys in evolutionary biology, vol7. Oxford University Press, Oxford, p 1-44 Hudson RR 1992 The how and why of generating gene genealogies. In: Takahata N, Clark AG (eds) Mechanisms of molecular evolution. Sinauer, Sunderland, MA, p 2336 Hudson RR, Kaplan N 1994 Gene trees with background selection. In: Golding GB (ed) Alternatives to the neutral model. Chapman Hall, London, p 140-153 Kaplan N, Darden T, Hudson RR 1988 The coalescent process with selection. Genetics 120:819-829 Kaplan N, Hudson RR, Langley CH 1989 The ‘hitchhiking effect’ revisited. Genetics 123:887-899

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Kingman JFC 1982a On the genealogy of large populations. J Appl Probab 19:7A43A Kingman JFC 1982b The coalescent. Stochastic Processes Appl 13:235-248 Kingman JFC 1982c Exchangeability and the evolution of large populations. In: Koch G, Spizzichino F (eds) Exchangeability in probability and statistics. North-Holland, New York, p 97-1 12 Kuhner MK, Yamato J, Felsenstein J 1996 Applications of Metropolis-Hastings genealogy sampling. In: Donnelly P, TavarC S (eds) Progress in population genetics and human evolution. Springer-Verlag, in press Marjoram P, Donnelly P 1994 Pairwise comparisons of mitochondria1 DNA sequences in subdivided populations and implications for early human evolution. Genetics 136:673-683 Marjoram P, Donnelly P 1996 Human demography and the time since mitochondria1 Eve. In: Donnelly P, Tavare S (eds) Progress in population genetics and human evolution. Springer-Verlag, in press Marjoram P, Griffiths RC 1995 An ancestral recombination graph. In: Donnelly P, Tavart S (eds) Progress in population genetics and human evolution. SpringerVerlag, in press Takahata N 1993 Allelic genealogy and human evolution. Mol Biol Evol 10:2-22 Tavark S 1984 Line-of-descent and genealogical processes, and their applications in population genetics models. Theor Popul Biol46: 119-164

DISCUSSION Chukruborty: I would like to make two points. First, coalescent theory is mathematically rigorous and can be applied to many complicated gene histories. However, we cannot observe the coalescent tree without the superimposition of mutation events. If a DNA region is hypermutable, then can we say that star-shaped trees reflect the history of this region, or are they just artefacts of the mutation rate? Second, I would refrain from using the term ‘correlation’when talking about sequence similarities because it has a different connotation in the context of the genetic structure of populations. The shared evolutionary history of genes (detected by sequence similarities) should be regarded as different from the correlation of genes between individuals. Donnelly: Let me take your second point first. This is purely a question of terminology. I did not refer to the term ‘correlation’ in the same way as human geneticists. Rather, I used the term in the same way as statisticians. The observation of one particular gene in a population is not independent of observations of other genes in the population. Additional information about ancestral history can be gained simply by increasing the sample size; however, each additional observation conveys less information than the previous one. In fact, in the context of coalescent theory, we do not get significantly more information when the sample size is increased from six to 100. Therefore, there’s a trade-off between sequencing extra individuals for a given region and

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sequencing either a different region or a longer region and, in this case, one is better off doing the latter. Your first point addresses whether star-shaped trees can be an artefact of high mutation rates. I am referring to the underlying ancestral tree, but your point is that this cannot be seen in isolation. The relevant question is that if one takes the tree generated with certain assumptions about demography, and mutations are superimposed on that tree, then what patterns will be observed? Consider the modelling of microsatellite mutation mechanisms. One can still see the effects of the tree. Trees that have a long period when there are only two ancestors will result in ‘clumps’ of similarly sized alleles in the sample. Highly mutable loci will also have clumps but the differences between the clumps will be larger under a generalized stepwise mutation mechanism, for example. If one draws a histogram of allele lengths in a population, then for microsatellite loci or minisatellite loci, there may be two peaks. This phenomenon is consistent with the clumping involved in coalescents. It does not require there to be two different mutation mechanisms. W . Bodmer: I’m an old-fashioned population geneticist who hasn’t quite come to terms with understanding coalescent theory. Although, intuitively, I would expect similar results from more conventional theories. Please can you clarify the nature of the simulation. How can you simulate gene genealogies without introducing mutations? Donnelly: Let me explain this by illustrating an alternative simulation. I could just take demography into account in order to simply keep a track of how many descendants a particular gene has in the next generation. W . Bodmer: But you’re not talking about genes, you’re talking about individuals. Donnelly: At a given locus, I can talk about genes because a gene in a particular generation will be descended from one gene in the previous generation, although the genes will be in pairs in the individuals. Therefore, I can simulate the whole population by just keeping track of which genes are descended from which. I can go to the present and look at two particular genes, and then I can look backwards to see how far I have to go before I find that they’re descended from a single ancestral gene. W . Bodmer: You have to assume that each gene is different to start with. A gene has to be identified or somehow labelled because it’s in a particular individual and not because of its sequence. Donnelly: But a DNA sequence in a given region has an ancestral history that is related only to a particular demography on which mutations are being superimposed. W . Bodmer: Two sequences could be the same. You are actually putting a label on an individual’s gene without reference to the sequence of that gene. Weiss: Wouldn’t it have been simpler to say that you’re constructing a population pedigree?

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Donnelly: Yes, I’m labelling the genes by saying that each one is from a particular individual. They may be the same allele or they may be different. The pictures I drew are realizations of what we would see if we just traced this ancestral history. W. Bodmer: Are your simulations forwards or backwards? Donnelly: The simulations are backwards. I could have simulated the whole population and just looked at the relevant bits, but that’s inefficient. W. Bodmer: But you would get the same answers, so it’s no different from conventional simulation, except in terms of efficiency, which I’m prepared to accept. Donnelly: Yes, you’re correct. The answers are no different from conventional simulation. However, the approach of focusing first on the gene tree and then asking how will mutations affect that gene tree is different from the usual population genetics approach. One of the advantages of this approach is that the dependencies in population genetics data are there not because of mutation, but because of the shared ancestral history. The patterns are the result of mutation on top of that. Hartl: This coalescent process is the backwards version of the classical Wright-Fisher model (Hartl & Clark 1989). One of the results from the classical Wright-Fisher model is that mutation rates or migration rates cannot be decoupled from the effective population size because the governing parameter is the product. However, in your answer to Ranajit Chakraborty’s question, you seemed to suggest that, by looking at gene genealogies, one could in fact decouple these, and I’m suspicious of that implication. Donnelly: You have every right to be suspicious. That problem cannot be solved by looking at gene genealogies. Coalescent trees can be converted into real time trees, which requires an estimate of the population size, so that mutations can be superimposed with a real time mutation rate. If one halved the population size, so that the trees were half as deep and the mutation rate was doubled, one would observe the same pattern of variability. Chukraborty: Irrespective of how inefficient the classic population genetic summary measures are, there are summary measures (e.g. heterozygosity, number of alleles conditioned on the sample size) that allow the decoupling of those parameters by combining data from different loci. Coalescent theory creates problems because different genes have different coalescent histories, which are due to the superimposition of different mutation processes on the same demography. Donnelly: If one looks at two different, unlinked loci, although the demography of the underlying population of a group of individuals is the same, the gene genealogy is independent because of the time-scale on which it operates. Dan Hartl also mentioned that coalescent theory is the backward genealogy of the Wright-Fisher model. I would like to give you some examples of insights

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that can be obtained by focusing on this. Under the usual Wright-Fisher assumptions of random mating and constant population size, we shouldn’t be surprised to observe two ‘clumps’ of alleles in the genetic data. The similarity within clumps and differences between the clumps are easier to define by looking at the trees than by using the forward equations of the Wright-Fisher diffusion. The patterns are there because of the effects of mutation on the trees, and so we can get some insights into the structure of the data by focusing on the shape of the trees. Also, if one uses the data to estimate the shape of the trees, then what do these shapes tell us about population history? One way to answer that is to ask the question the other way round, i.e. what tree shape would be expected under various sorts of assumptions? Chakravarti: There are variations in coalescent patterns under any given set of assumptions, and these variations change if the assumptions are altered. It would be much more interesting to determine the coalescent corresponding to a region that has been sequenced. If you consider the sequences of different loci, are you suggesting that there is a single coalescent and the differences between the loci are due to different mutations, or are you suggesting that different loci will have an entirely different coalescent patterns? Donnelly: At a single locus, the expected coalescent depends on the demographic assumptions. With the same assumptions for two unlinked loci, however, one would expect two independent trees. These independent trees would be star shaped if the population grew exponentially from a small value. Therefore, the loci are independent but the probability distributions change when the demographic assumptions change. Chakravarti: Are you referring to the demography of the alleles or the populations? Donnelly: I’m talking about the demography of the populations. Chakravarti: So for a given set of populations it would be the same? Donnelly: The probability distribution will be the same, but there would be independent choices from that probability distribution. Sing: Using data from the French Canadian population we have estimated a star-shaped tree, or cladogram, for the APOAI/CIII/AIV gene cluster (Haviland et a1 1995). This is consistent with your theoretical expectation. However, when we looked at the apoB gene, we found that it was not star shaped. Therefore, there are two genes within the same French Canadian population, which I believe expanded rapidly within the last hundred years, that have different patterns of allelic variations. Clark: Is it possible that the founding population had greater genetic diversity at the APOB locus? Sing: Yes. We’re talking here in general about the expected differences in the shape of the trees between populations with different demographic histories. Our experience may reflect different allele demographies within a particular population.

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Donnelly: If the founder size was two or three individuals, then all trees would be star shaped. If the founder size was a thousand individuals, then it is likely that no trees would be star-shaped. One possible explanation is that there is a range of founder sizes in-between where some trees would be star shaped and some wouldn’t. Scriver: The differences in shapes may also depend on where you did your sampling. Charlie Sing took samples only from French Canadians, so that a small number of people had a disproportionately large effect on the diversity that one sees today (Heyer & Tremblay 1995). However, if French Canadians from eastern and western regions of the province are studied, because they have different demographic histories in those two regions, there is likely to be genetic stratification. Also, studying gene-related diseases is an important application of this work. It’s not strictly a question of anthropology or population history per se. Harper: The question of what can be inferred from data on a single locus becomes extremely important when dealing with genetic diseases. This was apparent at the European Science Foundation meeting in Strasbourg (November 1993) on genes and genetic diseases in European populations, where there was some confusion between clinical geneticists and population geneticists. A number of people presented valuable data on disease genes in different populations. They traced the spread and evolution of these disease genes with great accuracy but then they proceeded to generalize from the behaviour of these disease genes to the migration and development of entire populations. However, the spread of different disease genes produced different patterns, so that making generalizations about the behaviour of the whole population became impossible. These results also contrasted with more broadly based population studies that involved several loci. Therefore, studies of disease genes are exceptionally valuable in terms of their own particular locus, and they may also form a part of the history of the population, but a multilocus approach is definitely necessary. Also, because of selective forces and other influences that may be involved, the study of disease genes may not give the most accurate answers about the development of the whole population. Weiss: It’s important to look at whether every disease must be studied strictly on its own merits, or whether generalizationscan be made that will help in the study of the next disease. How can selection be incorporated into your coalescent approach? Donnelly: We know a little bit about genealogy for certain sorts of selection. The advantage of the coalescent approach is that in a neutral model, one can focus first on genealogy and then superimpose the genetic types. However, it is not possible to do this with selection because the genetic types are affecting reproductive success and hence demography. The general effect of selection on genealogical structure is really an unsolved problem.

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Chakravarti: But wouldn’t you expect that alleles which are being strongly selected against would have a short history, so that they probably wouldn’t affect the coalescent in a serious way? Donnelly: Unless the mutation rates are high enough so that, at any given time, some of those alleles are present. Chakravarti: This suggests that the major effects will be either for deleterious recessive alleles, because they are sheltered within surviving populations for long periods of time, or for common disease-predisposing alleles that have a small effect. The latter alleles may even be the common gene polymorphisms that have survived within the human population for a long period of time. Is it possible that weak selection, either for or against, for a long period of time is likely to have a stronger effect on the coalescent? Donnelly: It’s possible because the time-scale is long and the effect of genetic drift is weak. Chakravarti: Summary measures, such as heterozygosity and numbers of alleles, have supported the neutral theory. If all alleles are subject to small levels of selection throughout evolution, then are the trees going to look considerably different? In other words, is coalescence going to detect these effects? Donnelly: If the null hypothesis is a constant-sized population with random mating, then we can do statistical tests with those assumptions. However, problems may arise when tests for neutrality result in the rejection of the null hypothesis not because the neutral assumption has changed but because the other assumptions have changed. Hurtl: I suspect that part of the reason that neutral theory looks good from the summary statistics point of view is that it lacks the power to detect departures from neutrality. In my opinion, if we want to find out the effects of selection on particular genes in the human genome, then we have to look not only at human polymorphisms, but also at the divergence between human and primate genes in comparison with the level of polymorphism in other primates. Kidd: We have found that, relative to chimpanzees and gorillas, humans are depauperate of genetic variation on a species-wide level. We have exhaustively examined a DNA sequence about 1 kb long, and we have found more common polymorphisms in a few chimpanzees and gorillas than in several-fold larger samples of humans stratified to represent the whole species (Ruano et a1 1992, Deinard & Kidd 1995). In a less exhaustive study, we have also found higher rates of polymorphism in a single troop of baboons than in humans (Rogers & Kidd 1993, 1995) I would like to return to Peter Donnelly’s simulations, where he assumed a single demography and had a torus-shaped migration matrix model, and ask whether he has attempted to simulate the actual history of humans. For example, it may be possible to start with the original model, allow the simulation to run for a few generations, and then look at the population in one

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comer of the 3 x 3 matrix. This corner could be expanded into one corner of a new 3 x 3 matrix that is gradually filled by expansion of that population. This situation could represent the migration out of Africa, where there is a clear founder effect with some migration across the point where the two matrices join, and also migration (expansion) into the new matrix representing Eurasia. The opposite corner of this second matrix could even be expanded into a third matrix that would represent the New World. Migration would occur within that region, but there would only be a little migration between matrices through the connecting comer. How can the coalescent model explain this in a backward simulation? Donnelly: The short answer is that I haven’t looked at this. The more substantial point you’re making, which I’m in entire agreement with, is that the traditional models cannot explain the spread of major human populations. We need to study populations that have smaller groups of migrating individuals. One approach is to think of something realistic and simulate it, but there would be too many variables, so it would be difficult to interpret the conclusions. Another approach is to start with the simplest model, make it slightly more general and look at the quantitative effects, then make it slightly more general and so on. The coalescent story is going through this process, but it is only part way through and not enough is known to study human populations in realistic models. Weiss: Rogers & Harpending (1992) have performed similar simulations with mismatched distributions. They have evidence for relatively recent (few tens of thousands of years) geographic expansions in every major region of the world. In common with other investigators’models, their model finds that even a small amount of migrations among regions effectively homogenizes different populations. Donnelly: The approach of picking up signatures of population expansions through pairwise differences has at least three problems. The first is that just looking at pairwise differences can ignore other information in the data. The second is that some population growth models may not be realistic and completely different conclusions may be drawn from more realistic models of human evolution. The third is that the patterns that one sees as a consequence of population growth are broadly the same patterns as one sees as a consequence of selective sweeps. This is particularly applicable to the mitochondrial data, which is what the majority of this work is focused on. One would have to be brave to rule out selection on mitochondria. It’s difficult to fit realistic neutral models to the human mitochondrial data. Weiss: The Rogers-Harpending model is at an early stage but they’re also trying to relate it to archaeological evidence for relevant cultural advances or other kinds of ancillary data. W . Bodmer: It’s not difficult to incorporate selection into these models. Luca Cavalli-Sforza made a fundamental point many years ago, which was that

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whatever the population structure, if there are families of genes that are behaving differently, then they also have to be influenced differently by selection (Cavalli-Sforza & Bodmer 1972). Results from the analysis of the HLA system support this statement: the pattern of variation at the DNA level in non-synonymous versus synonymous substitutions in particular parts of the genes is so different from other parts of the genes that there has to be selection for one and not for the other (Bodmer et a1 1986). At the DNA level, it is possible to define neutral variations in most regions; for example, intron regions, flanking regions, CA repeats and synonymous positions. Therefore, it is possible to define families of differences which are neutral, look for the similarities amongst those and then look for differences in the pattern of variation of sequences that may be subject to selection. The pattern of selection for HLA, for example, is probably a recurrent frequency-dependent selection that selects different variants at different times and can explain the maintenance of combinations of differences that go back through evolutionary time. Bowcock: Cavalli-Sforza, Ken Kidd and I did some work a few years ago on restriction fragment length polymorphisms (RFLPs) that are mainly derived from non-coding regions, and we found that as many as 30% of the alleles could be subject to selection (Bowcock et a1 1991). W. Bodmer: But if one looks at RFLPs within the HLA region, RFLPs that are defined by polymorphic genes have a different pattern than those defined by genes that are not polymorphic. Therefore, if RFLPs are picked up by functional genes, they are in linkage disequilibrium. Kidd: I would like to amplify the point that Anne Bowcock made. We found that no more than 30% of the loci are subjected to selection, assuming neutrality and taking the population relationship structure that we inferred from the data. In other words, we found higher values of FST, the standardized variance, than we expected on our simple model for about 30% of the loci. W. Bodmer: But you shouldn’t base these calculations on any a priori assumptions. If you’re looking at FST values, you should forget about any model and simply ask whether there is any evidence for bimodality or multi-modality. And if there is, then at least one category has to have been subjected to selection. Kidd: But we found a distribution skewed towards higher FSTvalues at more loci than we would have expected. W. Bodmer: What is the source of the sequences that you are using for picking them up? Most RFLPs are based on cDNA sequences. If one were to take a cosmid containing a complete gene that isn’t polymorphic, for example the HLA-DRA gene, then one would obtain a different pattern of RFLPs than one would if one took a polymorphic gene, for example HLA-DRBl. In the first case little or no variation is detected, whereas in the second there is extensive variation that correlates with HLA-DR serology because of linkage disequilibrium.

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Clark: I agree with Walter’s comment that comparing data from different loci can be extremely informative. The Hudson-Kreitman-AguadC test, in the field of Drosophilu molecular population genetics is based on this idea (Hudson et a1 1987). It examines the levels of polymorphism and interspecific sequence divergence for pairs of loci. Under neutrality, one expects the pattern of polymorphism and divergence to be similar across loci, and divergence from this can be detected by using the x2 test. However, Peter Donnelly was incorrect to say that no work on the coalescent with selection has been done. Some work has been done on the coalescent properties of a gene subjected to selection. For example, John Gillespie (1989) showed that his SAS-CFF (stochastic additive scale-concave fitness function) model can produce a neutral coalescent. Also Takahata (1990) showed that under conditions of strict symmetrical overdominance, one still gets an expected neutral coalescent, i.e. a geometric distribution of the time back to a common ancestor, except that the time-scale is expanded. He also showed that the time-scale can be calculated, based on diffusion. The expected time depth of the coalescent is deeper for a symmetrical overdominance model than under neutrality. This is also true for selfincompatibility loci (Clark 1992), which show a pattern of selection that produces allelic variation having geometric distribution of coalescence times, but with extraordinarily ancient times of coalescence. Coalescence times are so ancient, in fact, that they pre-date speciation, and trans-species polymorphisms are observed (Ioerger et a1 1990, Clark & Kao 1991). A second example is the work of Dick Hudson and Norm Kaplan (Hudson & Kaplan 1995). They proposed a hitch-hiking situation where there is a neutral locus with deleterious mutations hitting the genome at linked sites, and again a neutral coalescent is expected. This represents one situation where the coalescent relates to the population size because the removal of deleterious variation elsewhere in the genome reduces the effect of population size and, therefore, reduces the expected coalescent for that neutral site. Hurtl: I would like to raise to Walter’s bait by first conceding a point, and then ask a question. I don’t doubt that a number of methods can be deployed for detecting selection coefficients on the order of 10-fold the effective population size, which is probably what’s occurring with sickle-cell anaemia and P-thalassaemia. Selection coefficients that are on the order of the reciprocal of the effective population size may be more common than larger selection coefficients. My question is: are selection coefficients on the order of the reciprocal of the effective population size of any interest or are we only interested in larger values that might be of some immediate clinical significance? W. B o h e r : We do not fully understand relatively large effects, so we should look at those first. We should also take note of the power that we have to decide what is likely to be neutral a priori at the DNA level. We are in a novel situation. Previously, we only had results from gel electrophoresis and blood

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group differences, and we could not be sure that selection was not operating. We should analyse the exact nature of the differences at the DNA level, and use that to determine the neutral situation, whatever model it may fit. Weiss: There’s a difference between the seriousness of a disease and whether it has any selective effects on biochemical communities. Many serious diseases are treated because they reduce your physical ‘fitness’ at a certain age but this doesn’t mean that they have a evolutionary effect, i.e. that they affect your Darwinian fitness. Harper: Small to moderate selective effects are also important, especially in recessive disorders. For instance, cystic fibrosis and phenylketonuria are two classic examples where people have argued whether heterozygotes have a selective advantage, and if they do, what is it or what might it have been in the past. The fact that these issues are difficult to resolve means that they’re worth studying. Scriver: The cystic fibrosis story has developed sufficiently for us to propose the process and target of selection, and perhaps even the historical period over which the selection process could have taken place. One problem with the phenylketonuria story is that we do not know what the selective agent may have been in the environment, and we do not know the target phenotype in the host. Therefore, we can only begin to guess how old or how current the process might be-if selection has played any role at all. Beighton: Part of the problem is to define the nature of the selective forces, and whether they are positive or negative. We tend to look only at the present and not in the past. For instance, Gaucher disease in the Ashkenazi Jewish population is found at a high frequency in South Africa because of a founder effect, which reflects the earlier situation in Lithuania (Goldblatt & Beighton 1979). The question then arises as to what has been going on in Lithuania over the past 200G3000 years. Also, we regard selection as a gradual generation-to-generation process but is a big-bang, one-off event possible instead? Take Gaucher disease again as an example. It is possible that, in the middle ages, the plague decimated the population of Europe, but did not kill off Gaucher disease heterozygotes selectively. Therefore, within one generation the gene frequency could increase. W.Bodmer: That idea has been around for a long time in the field of HLA and disease.

References Bodmer WF, Trowsdale J, Young J, Bodmer J 1986 Gene clusters and the evolution of the major histocompatibilitysystem. Phil Trans R SOCLond B Biol Sci 312:303P-315P Bowcock A, Kidd J, Mountain J et a1 1991 Drift, admixture and selection in human evolution: a study with DNA polymorphisms. Proc Natl Acad Sci USA 88:839-843

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Cavalli-Sforza LL, Bodmer WF 1972 The genetics of human populations. Ann Hum Genet 36:239-240 Clark AG 1992 Evolutionary inferences from molecular characterization of selfincompatibility alleles. In: Takahata N, Clark AG (eds) Mechanisms of molecular evolution. Sinauer, Sunderland, MA Clark AG, Kao T-H 1991 Excess nonsynonymous substitution at shared polymorphic sites among self-incompatibility alleles of Solanaceae. Proc Natl Acad Sci USA 88:9823-9827 Deinard AS, Kidd KK 1995 Levels of DNA polymorphism in extant and extinct hominoids. In: Brenner S, Hanihara K (eds) The origin and past of modern humans as viewed from DNA. World Scientific, Teaneck, NJ, p 149-170 Gillespie JH 1989 Molecular evolution and polymorphism: SAS-CFF meets the mutational landscape. Am Natural 134:638458 Goldblatt J, Beighton P 1979 Gaucher disease in South Africa. J Med Genet 16:302-305 Hart1 DL, Clark AG 1989 Principals of population genetics. Sinaeur, Sunderland, MA Haviland MB, Kessling AM, Davignon J, Sing CF 1995 Cladistic analysis of the apolipoprotein AZ-CZIZ-AZV gene cluster using a healthy French Canadian sample. I. Haploid analysis. Ann Hum Genet 59:211-231 Heyer E, Tremblay M 1995 Variability of the genetic contribution of Quebec population founders associated to some deleterious genes. Am J Hum Genet 56:97&978 Hudson RR, Kaplan N 1995 Deleterious background selection with recombination. Genetics 141:1605-1617 Hudson RR, Kreitman M, AguadC M 1987 A test of neutral molecular evolution based on nucleotide data. Genetics 116:153-1 59 Ioerger TR, Clark AG, Kao T-H 1990 Polymorphism at the self-incompatibility locus in Solanaceae predates speciation. Proc Natl Acad Sci USA 87:9732-9735 Rogers AR, Harpending HC 1992 Population growth makes waves in the distribution of pairwise genetic differences. Mol Biol Evol9:552-569 Rogers J, Kidd KK 1993 Nuclear DNA polymorphisms in a wild population of yellow baboons (Papio hamadryas cynocephalus) from Mikumi National Park, Tanzania. Am J Phys Anthropol90:477486 Rogers J, Kidd KK 1995 Nucleotide polymorphism, effective population size and dispersal distances in the yellow baboons (Pupio hamadryas cynocephalus) of Mikumi National Park, Tanzania. Am J Primatol, in press Ruano G, Rogers J, Ferguson AC, Kidd KK 1992 DNA sequence polymorphism within hominoid species exceeds the number of phylogenetically informative characters for a HOX2 locus. Mol Biol Evol 9575-586 Takahata N 1990 A simple genealogical structure of strongly balanced allelic lines and trans-species evolution of polymorphism. Proc Natl Acad Sci USA 87:2419-2423

Microsatellites: evolution and mutational processes Nelson 6.Freimer and Montgomery Slatkin* Neurogenetics Laboratory and Center for Neurobiology and Psychiatry, Department of Psychiatry and Programs in Genetics and Biomedical Sciences, University of California, San Francisco, CA 94143-0984 and *Department of Integrative Biology, University of California, Berkeley, CA 94720-3140, USA

Abstract. Microsatellites (simple sequence repeats) are ubiquitous in eukaryotic genomes, and they are highly polymorphic. They are currently the primary tools for most genetic mapping and for studies comparing the differentiation of human and other mammalian populations. More and more inherited human diseases are now recognized as resulting from mutations in particular microsatellites, and such microsatellite mutations can serve as markers for some cancers. The majority of microsatellite mutational changes probably consist of insertion or deletion of one or a few repeat units through replication slippage, whereas larger (much rarer) changes are important in producing observed allele distributions. Comparisons of microsatellite allele frequencies between humans and chimpanzees suggest that there are constraints on the overall length of microsatellites. Sequence analyses of microsatellites in diverse human and non-human populations indicate that the structure of many repeats may not be as simple as previously believed, in that alleles differ in base composition as well as in repeat length. Single base changes that result in long uninterrupted repeats may lead to increased mutation rates, including the extreme trinucleotide repeat instability responsible for several inherited diseases.

1996 Variation in the human genome. Wiley, Chichester (Ciba Foundation Symposium 197) p.51-72

Microsatellites are tandemly repeated arrays of one to six nucleotides. They have also been termed ‘simple sequence repeats’ in recognition of their distinction from the previously discovered satellite and minisatellite arrays, which consist of much longer and more complex repeat elements (Tautz 1993). As we shall discuss in this paper, this term may be a misnomer, as microsatellites are probably not as simple as once thought. Consideration of the biology of microsatellites is vital in the context of this symposium, which is focused on human variation and disease. These sequences are not only ubiquitous in the human genome and extraordinarily polymorphic (Weber 1990), but when mutated they are also an important cause of human 51

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neurological and neuromuscular disorders, and they are markers for certain types of human cancers (Caskey et a1 1992, Thibodeau et a1 1993, Aaltonen et a1 1993, Tautz & Schlotterer 1994). Investigation of the genetic basis of these human disorders has, in turn, enhanced our understanding of the mutational processes of microsatellites in general. We will discuss combined theoretical and empirical population genetic approaches to understanding the evolution and mutational processes of microsatellites. Much of the impetus for these studies derives from observations on microsatellite variability made in the course of constructing genetic maps of the entire genomes of humans, as well as of several eukaryotic model systems (Dietrich et a1 1994, Weissenbach et a1 1992, Cooperative Human Linkage Center 1994), and in using these maps to localize disease genes. Observations of mutations in numerous different microsatellites have stimulated the development of experimental approaches to elucidate their molecular mechanisms. Studies in a wide variety of systems have shown that replication slippage is the predominant means by which the number of simple repeats in a microsatellite array changes, and that such alterations most commonly consist of insertion or deletion of one or two repeat units. This evidence has accrued from in vitro syntheses of a variety of simple sequence motifs (Schlotterer & Tautz 1992) and from investigations based on introducing microsatellite motifs into both prokaryotic and eukaryotic cloning vectors (Levinson & Gutman 1987, Strand et a1 1993) and assessing mutation rates b y observing frameshifts. (Models for the mechanics of slippage have been discussed in Tautz & Schlotterer 1994.) Although other mechanisms of mutation (such as unequal crossing over) have been demonstrated in minisatellites (Jeffreys et a1 1991), they have not yet been observed in microsatellites. It is clear, however, that certain phenomena of microsatellites, in particular the dramatic expansion associated with certain trinucleotide repeats (Caskey et a1 1992, Kunst & Warren 1994, Imbert et a1 1993, Orr et a1 1993, Gacy et a1 1995), are inadequately explained by slippage models. As such events become more thoroughly documented at the molecular level it is likely that more sophisticated mechanistic models will be developed; for example, it has recently been proposed that the ability to achieve threshold stability for hairpin structures may underlie the susceptibility of certain sequences to unstable expansion in repeat number (Gacy et a1 1995). As slippage events during replication lead to base pair mismatching, several investigators have suggested, and subsequently demonstrated, that microsatellite mutation rates increase in cells deficient for mismatch repair (Levinson & Gutman 1987, Strand et a1 1993). It was subsequently observed in humans that microsatellites from several chromosomes exhibited extreme instability in certain tumour cells in both inherited and sporadic colorectal cancer syndromes. This observation led to the hypothesis, which was soon confirmed,

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that these cancers might result from mutations in human homologues of bacterial and yeast mismatch repair genes (reviewed in Karran & Bignami 1994). Microsatellite instability has subsequently been noted in a wide variety of tumour cells, and it can now be considered as a pointer for mismatch repair defects. In addition, microsatellite alterations observed in primary tumours are likely to occur subsequently in other tissues, providing early evidence of cancer spread (Ma0 et a1 1994). Observations of microsatellites in tumour cells may also provide an excellent in vitro system for studying specific repeat loci of special interest in humans, such as trinucleotide repeats that undergo unstable expansions (Toomajian et a1 1994). This system enabled the observation of substantial variation in mutation rates between microsatellites. When it was recognized that microsatellites were hypervariable, a number of attempts were made to evaluate their mutation rates (Weber & Wong 1993). The finding that particular trinucleotide repeats demonstrate extraordinarily high mutation rates (in some cases virtually one per generation) in association with inherited human diseases has further fuelled interest in microsatellite mutation rates and processes (Caskey et a1 1992). Investigation of these processes has proceeded through a variety of in vivo and in vitro experimental strategies. We have used population genetic approaches, through a series of theoretical and empirical studies, to elucidate these processes. Although we have hypothesized that trinucleotide repeat instability reflects mutational mechanisms that are characteristic of microsatellites in general, we will not discuss such instability in detail as it is the focus of other work presented at this symposium.

Modelling the mutation process

As noted above, empirical studies have shown that most microsatellite mutations result in a change of one or two in the number of repeat units. This observation suggests that analysis of allele frequencies at microsatellite loci should not be based on a model, such as the ‘infinite alleles’ model commonly used in population genetics, which assumes that all mutants are equally dissimilar. Furthermore, the relatively high mutation rates found at many microsatellite loci suggest that mutation may not be much weaker than other population genetic forces (such as selection, gene flow and genetic drift) and, hence, that many of the simplifications in population genetic models that follow from the assumption of extremely low mutation rates may not be valid in models of microsatellite loci. Instead, population genetic models of microsatellite loci must make explicit assumptions about mutation rates and the spectra of mutations that can occur. Fortunately, a wide range of possibilities can be subsumed in a single class of models.

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Freimer & Slatkin

Several groups have used direct observations of mutations to support the assumption of the stepwise mutation model for microsatellites (Deka et a1 1991, Valdes et a1 1993, Shriver et a1 1993, Goldstein et a1 1995). The stepwise (‘one-step’, ‘charge state’ or ‘ladder rung’) mutation model assumes that mutations occur at a rate m,and that when a mutation occurs, it has an equal probability of increasing or decreasing the number of repeat units by one. The stepwise mutation model was introduced in the 1970s to model the mutation process for alleles distinguishable by protein electrophoresis (Ohta & Kimura 1973, Wehrhahn 1975). Later empirical studies showed that the stepwise mutation model was not suitable for electrophoretic alleles (Ranshaw et a1 1979); it seemed appropriate, however, for microsatellites. The stepwise mutation model makes several assumptions that we will focus on in turn:

(1) a small change in the number of repcat units; (2) an equal probability of increase or decrease in repeat number; (3) no constraint on allele size; (4) the rate and magnitude of mutation is independent of repeat number. The irregular distributions of allele sizes observed in some populations initially seemed inconsistent with the stepwise mutation model, but Valdes et a1 (1993) and Shriver et a1 (1993) showed that similarly irregular distributions could be obtained under the stepwise mutation model in a population of constant size (Fig. 1). The irregularity of the distribution can arise from the shape of the gene genealogy in a population of constant size, rather than from the occurrence of mutations of large effect (Valdes et a1 1993). In such a population, a gene genealogy with two or more distinct clades separated by long internal branches is often found. Enough mutational events could occur on each of the internal branches so that allele sizes in the different clades could be quite different, thereby creating a bimodal or even trimodal distribution of allele sizes, as observed. The similarity of the simulated and observed distributions did not in itself verify the assumptions of the stepwise mutation model, especially because most human populations have not been of constant size. Instead, evidence from mitochondrial DNA from several human populations is consistent with the assumption of a relatively rapid increase in population size in the recent past (Di Rienzo & Wilson 1991, Slatkin & Hudson 1991). To provide a better test of the stepwise mutation model, Di Rienzo et a1 (1994) examined data from a single population, the Sardinians, for which some demographic information was available. The mitochondrial DNA data of Di Rienzo & Wilson (1991) suggested that Sardinians had undergone a rapid increase in population size. Slatkin & Hudson (1991) showed that in such a population, the gene genealogy of neutral genes would be quite different in character from that in a population of constant size. In a rapidly growing

Microsatellites

55

population, one would expect to see a star-like genealogy, with long terminal branches and much shorter internal branches. The opposite is expected in a population of constant size. A star-like genealogy results in a less irregular distribution of microsatellite allele frequencies under the stepwise model. Di Rienzo et a1 (1994) genotyped a series of unrelated Sardinians using 10 unlinked microsatellite loci, chosen to represent a wide diversity of repeat number (and thus, possibly, of mutation rate). They showed that for eight out of 10 loci, the stepwise model could be rejected in favour of a ‘two-phase’ model, in which most mutations are one-step mutations but occasional mutations that result in larger changes in repeat number also occur. The conclusions of Di Rienzo et a1 (1994) were based on their demonstration that mutations causing large changes in the number of repeat units contribute more to the variance in repeat number than one-step mutations do, whereas they change the homozygosity by the same amount. Their simulations showed that for eight of the 10 loci examined the homozygosities were too large relative to the variance in repeat number to be consistent with the stepwise model. For example, for the marker mfd32 the variance is 15.4 and the homozygosity is 0.305; according to the stepwise model the confidence interval for homozygosity given this variance is 0.071-0.091, whereas according to the two-phase model the confidence interval is 0.155-0.383. Empirical support for the two-phase model has recently been provided by Farber et a1 (1994) who inserted a dinucleotide repeat microsatellite into a mouse cell line (using a reporter system that indicated frameshift mutations). Although about 80% of the detected mutations involved changes of one or two repeat units, alterations of eight and 10 repeat units were observed, although rarely. The suggestion that alterations in repeat number occur in distinct classes (rare, large changes and common, single or two-step ones) may be relevant in understanding the genesis of some human diseases. For example, some colorectal tumours are apparently characterized by either small or large changes, but not both (Thibodeau et a1 1993). Furthermore, linkage disequilibrium analyses between the CTG triplet that is unstably expanded in myotonic dystrophy and microsatellites that flank this repeat indicate that a rare jump from five to 19 repeat units may be important in determining susceptibility to repeat expansion in this disorder (Imbert et a1 1993). Both the stepwise and the two-phase models of mutation place no upper or lower limit on the number of repeat units in an allele. This assumption seems in conflict with the fact that the number of repeat units in polymorphic loci is generally within the range five to 50, except for the unstable alleles associated with diseases. It is possible that there is a constraint on the mutation process that would cause allele size to remain within a certain limit. The constraint could be either that there are absolute upper and lower limits to allele size, as modelled by Goldstein et a1 (1995), or that there is a tendency for larger alleles

56

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FIG. 1. (a) Frequency distributions for 10 of the microsatellite loci described in the text. The most frequent allelic sizes are 192 bp for mfdl, 167 bp for mfd4, 96 bp for mfdll, 152bp for mfdl9, 269bp for mfdlol, 83bp for mfdlO9, 198bp for mfdl16, 142bp for mfdll7, 89 bp for mfd23 and 131 bp for mfdl00. These are all dinucleotide repeat loci; adjacent bars in the histograms indicate frequencies of alleles that differ in

57

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size by one repeat unit (2 bp). (b) Frequency distributions for 10 independent replicates of the one-step model with 4Nu = 10, where N is the population size and u is the mutation rate, and the sample size is 100. The relative allele sizes are all centred on 0. Adjacent bars on the histograms indicate the frequencies of alleles that differ by one repeat unit. Figures from Valdes et a1 1993.

Freirner 8,Slatkin

58

TABLE 1 Statistics of allele frequency distributions of eight microsatellite loci ~

LOCUS

Population N

Mean

Variance

Mfd 3

Africa

46 84 88 29

17.348 17.155 17.557 17.052

5.172 5.012 4.78 1 8.661

Mfd 32

EHPt Sardinia Chimp Africa

46 82 92 32

9.216 9.876 15.791 2.194

40 88 92 32

14.543 14.451 15.446 17.344 27.475 27.568 27.043 24.953

46 89 90 32

Egypt Sardinia Chimp Mfd 38

Mfd 59

Africa Egypt Sardinia Chimp Africa

Mfd 75

EYgPt Sardinia Chimp Africa Egypt Sardinia Chimp

Mfd 104 Africa Egypt Sardinia Chimp Mfd 139 Africa Egypt Sardinia Chimp Mfd 142 Africa Egypt Sardinia Chimp

No. of Range alleles

H,,

10 8 7 10

0.162 0.275 0.328 0.146

7 7 7 6 12 11 9 16

0.339 0.304 0.305 0.201

9.274 13.586 6.139 12.287

9 8 7 9 10 10 11 5 15 21 11 17

22.371 23.792 24.033 15.281

5.201 84 9 6.382 34.358

10 13 8 22

9 13 9 14

0.171 0.128 0.236 0.101 0.203 0.116 0.127 0.132

44 83 92 32

38.1 14 35.952 35.826 24.500

4.646 12.022 9.122 6.000

10 19 12 10

11 14 12 7

0.152 0.159 0.152 0.303

38 52 92 32

29.632 29.308 30.891 40.781

37.601 26.944 29.119 37.733

17 17 17 24

12 11 10 15

0.216 0.213 0.226 0.101

46 77 90 32 42 80 82 32

19.826 20.591 20.501 12.719

22.176 28.316 30.601 0.983 1.769 1.848 1.945 0.746

9 15 13 6 6 9 7 4

0.179 0.230 0.237 0.344

20.571 2 1.206 21.134 18.062

18 22 21 5 10 9 8 3

0.419 0.254 0.225 0.314

N, the number of chromosomes examined; the mean, variance and the range refers to the number of repeats; H,, homozygosity calculated as Zqi2 where qi represents the frequency of the ith allele.

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59

to decrease in allele size and for smaller alleles to increase in size under mutation, as modelled by Garza et a1 (1995). To test whether there is evidence for constraints on allele size, Garza et a1 (1995) compared distributions of allele sizes at several loci in chimpanzees and humans (eight of the microsatellites previously used by Di Rienzo et a1 [1994]). Under both the stepwise and two-phase models of mutation, the squared difference in the average repeat number increases linearly with time. Furthermore, Garza et a1 (1995) showed that the difference in the average repeat number in different species would follow a normal distribution with a mean of zero and a variance that increases linearly with time. This theoretical result provides the basis for a statistical test of a model that assumes no constraints. Garza et a1 (1995) found that, at the eight loci they examined, there was a tendency for the absolute difference in average allele size to be smaller than expected, but that at only one locus was the difference in allele size between the two species so small that one could reject the assumption that there are no constraints. A comparison of allele frequencies between chimpanzees and three distinct human populations is depicted in Table 1. Thus, data that are currently available suggest that there are constraints on allele size, but it appears that those constraints are not strong, even when considered on a time-scale of several million years. This finding suggests that ignoring constraints when modelling the evolution of a microsatellite locus within a species is a reasonable starting assumption. The assumption that the mutation rate does not depend on repeat number has been less closely examined theoretically. Valdes et a1 (1993) showed that there was no obvious relationship between the variance in repeat number, which should be proportional to the mutation rate, and the average allele size, but their analysis did not provide a strong test because the data they had available were not the repeat numbers per se, but the overall sizes of the PCR products for each allele. Because each locus has flanking sequences of different lengths, variation in the length of flanking sequences could obscure a relationship between the mean and the variance in allele size. As discussed below, a number of lines of evidence indicate that the total length of the microsatellite may have less of an effect on the mutation rate than the length of uninterrupted or perfect repeats. Exploiting the mutation models

As we and others have demonstrated, the one-step model (or its generalization to the two-phase model) provides an adequate description of the mutation process at most microsatellite loci, at least when considering processes within a species. This finding is fortunate because some simple and useful results can be derived for these models using coalescent theory. The general approach can be illustrated by considering two copies of a microsatellite locus. We can trace the

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17

13

FIG. 2. Illustration of the use of coalescent theory to model the evolutionary process of a microsatellite locus. The numbers refer to the number of repeats at the locus.

ancestry of those two copies through generations until their ancestral copies are derived from a single common ancestor at time t in the past. That is, between now and time t , there are two independent lineages leading from the common ancestor to the copies we observe. Any differences between these two copies had to accumulate independently under mutation on these two lineages after time t. This is the essence of the coalescent approach. We represent the history of these two copies by a 'gene tree' (Fig. 2). The gene tree represents the ancestry of the two copies and depends on who mates with whom. Then, once the gene tree is specified, mutations are added to the tree to determine the genetic states of the two copies. Hudson (1990) provides an excellent introduction and review of coalescent theory and its applications. Let the numbers of repeat units in the two alleles we are considering be a1 and a2. The two copies are then separated by a branch of a gene genealogy of total length 2t. During that time, the number of mutations that occur is a random variable drawn from a Poisson distribution with mean 2pt. Let a be the number of mutations that have occurred, and let xn be the increment in repeat number of the nth mutational event. Then:

where each xn is drawn independently from a distribution with mean 0 and variance .",= 1, which is the variance of the change in allele number under

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mutation. For the one-step model = 1 , and for the two-phase model .",> 1. Taking the expectation of the distribution of x,, E(al - a2) = 0, and 2

2

E[(W - a2) I = &Urn

(2)

then, as E(a) = 2pt,

What this result means is that on each lineage the allele size undergoes a random walk where the average change in size is 0 and the variance in the change is pu; per unit time. Consequently, the variance in the difference increases linearly with time. The power of this result can be seen when we consider a sample of copies of a microsatellite locus. Between every pair in the sample, there is a time of the most recent common ancestor, t, which is called the coalescence time. It is possible to predict the expected values o f t under different hypotheses about the population(s) from which the sample was drawn. As a simple example, consider the case in which samples are drawn from two populations that were separated at time z in the past. If we have samples from both populations, then we can consider separately the average coalescence times within each population t l and i2,and the average coalescence time when one copy is from one population and the other is from the other i12. If we let S1 and S2 be the average of the squared difference in allele size within each population, and SI2 be the average squared difference between populations, then equation (3) tells us that Si = /L~;:u, 2 ( i = 1,2)

(44

thus,

If the two populations and the ancestral population are all of effective size N, then the average coalescence time within each population tl is 2N, and the average coalescence time between populations is z + 2N. Then:

This provides us with a simple measure of genetic distance that increases linearly with time (Goldstein et a1 1995).

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It is possible to use this approach on models in which populations are exchanging migrants to derive a statistic that is equivalent to Wright’s FST statistic (Slatkin 1995a). This statistic, called RST, is the proportion of the variance in allele size that is among populations, and it can be used to estimate the effective migration rate when populations exchange migrants. Slatkin (1995a) showed that RST performs better than FST on the same simulated data because it takes explicit account of mutation processes at microsatellite loci. He showed that FST tends to show too much migration because it does not take into account the genetic similarity in different populations generated by mutation alone. Slatkin (1995b) used the same approach for modelling a microsatellite locus closely linked to a selected locus. As reviewed by Hudson (1990), selection will change the distribution of allele sizes in closely linked loci and, hence, should be reflected in the variances at closely linked microsatellite loci. Furthermore, Slatkin (1995b) showed that overdominant selection at the linked locus induces a significant partitioning of variance among copies of the microsatellite locus linked to one or the other of the selected alleles. This phenomenon is called ‘associative overdominance’. Thus, closely linked microsatellite loci might provide evidence of selection to preserve genetic variability. Treating changes in allele size as a random walk also provided the basis for the theoretical results of Garza et a1 (1995). When viewed on a sufficiently long time scale, the population average allele size undergoes a random walk at a rate of per generation. Because differences in the average allele number between species are the result of numerous mutation events, their sum will approach a normal distribution because of the law of large numbers, which leads to the test developed by Garza et a1 (1995) and is described above. Empirical evidence for microsatellite complexity The models used by us and others to explain microsatellite mutational processes have all rested on the assumption that allelic differences are due entirely to changes in the number of repeat units. These models are based on empirical studies conducted using the usual means of detection of microsatellite alleles, namely PCR amplification of the repeat and flanking unique sequence with detection of polymorphism based on the migration of the PCR product on denaturing acrylamide gels. As this procedure yields size resolution to the single base pair level, the observation was made that almost all alleles differ from one another by multiples of the repeat unit. However, even at this level of analysis, it is clear that many alleles differ from one another by a single base pair (Weissenbach et a1 1992), suggesting that other forms of mutational change occur. Several studies have used direct sequencing of microsatellite alleles to show that alterations in the base composition of repeats is an important component

63

Chimp 4: 50bp (CA)i(TA)l I(CA)13 FIG. 3. Sequence variation of two human and four chimpanzee alleles for the microsatellite locus mfdli9. The sequence of the TA region is shown for the two human alleles to illustrate the nature of the imperfections. of the variation between individuals and species (Garza et a1 1995, BlanquerMaumont & Crouau-Roy 1995). Interspecific comparisons are possible because the vast majority of microsatellites amplify between closely related species (Stallings et a1 1991, Schlotterer et a1 1991); this observation indicates an ancient origin for most microsatellites. Garza et a1 (1995) sequenced multiple alleles for two microsatellites that showed a substantial difference in allele size range between humans and chimpanzees. They discovered that these differences result from variation in the composition of the repeat element, rather than simple changes in the number of repeats or insertions or deletions in the sequences that flank the repeat (see Fig. 3 for examples). A similar analysis was undertaken using three microsatellites from the major histocompatibility complex region in two families of primates (Blanquer-Maumont & Crouau-Roy 1995). In this investigation, as in that of Garza et a1 (1999, interspecific variation included transversions that interrupted perfect repeats. Such transversions were also observed within some species, although most intraspecific variation is due to simple addition or deletion of repeat units. Allele size changes due to insertions or deletions of unique sequence adjacent to the repeats were also observed. Direct sequencing also reveals that microsatellite alleles may be identical in length but not sequence. For example, M.-C. Grimaldi, P. Avoustin & B. Crouau-Roy (unpublished results 1995) have identified human alleles in a single microsatellite that were the same length but differed in the number of CA repeat units (alleles with fewer repeats contain an equally sized insertion in

64

Freirner & Slatkin

the flanking sequence). A similar finding has been obtained by J. C. Garza, M. Slatkin & N. B. Freimer (unpublished results 1995) for human and chimpanzee alleles at other microsatellites. These observations have several implications. As the ubiquity and hypervariability of microsatellites permit mapping to be attempted for genetically complex traits, for which standard linkage analysis is poorly suited, there is great interest in strategies based on identifyingmarker alleles that are inherited identically by descent along with the trait. The power of these approaches could be substantially improved if it were possible to distinguish between alleles that are inherited by patients from a common ancestor (identical by descent) from those that are merely common in the population (identical by state). Direct sequencing may be useful to increase the information content of particular loci. For example, for the FMRl repeat, direct sequence analysis indicates that by combining the information obtained from variation in the number of the predominant CGG repeats with that from the position of interspersed AGG triplets, it is possible to distinguish more than 90 different alleles in humans (Kunst & Warren 1994). Direct sequencing may also be useful in comparative population studies; such studies using microsatellites have already provided substantially more information on population divergence than those undertaken using other markers (Bowcock et a1 1994). Direct sequencing of microsatellites has not only yielded information regarding mutation processes, but has also provided important insights into mutation rates. Comparison of sequences between individuals demonstrating or transmitting trinucleotide repeat expansion and control subjects indicates that removal of imperfect repeats is the key determinant in the initiation of repeat instability, at least for SCAl and FMRl genes (Orr et a1 1993, Kunst & Warren 1994). It appears that long, uninterrupted repeats are necessary for extreme expansion to occur. Several studies indicate that a similar relationship between the length of uninterrupted repeats and mutation rate applies to microsatellites in general (Weber 1990, Blanquer-Maumont & Crouau-Roy 1995, Garza et a1 1995). The relatively frequent changes in the base composition of microsatellites between species probably accounts for the observation that loci which are highly polymorphic in one species may be monomorphic in another one. Summary

It is likely that most mutations in microsatellite loci involve a change in size of one or two repeat units, presumably by slippage. Infrequent events cause changes in size of several repeat units. Although such large changes in repeat number could also occur by slippage, the evidence for this is less clear than it is for small changes, and alternative models have been proposed, such as hairpin formation. Direct sequence data indicate that many ‘simple repeat sequences’ are not so simple and that mutations occur through alterations in base

Microsatellites

65

composition. In particular, the conversion of cryptic to perfect repeats is responsible for an increased mutation rate of microsatellites, including the unstable expansions observed in some trinucleotide repeats associated with inherited human diseases. These observations suggest that mutation rates may differ substantially between different alleles at a single locus; so far this hypothesis has only been tested for loci with unusually high mutation rates. Several issues related to microsatellite mutation and evolution remain unresolved. For example, it is not yet certain whether some microsatellite alleles are particularly predisposed to the removal of imperfect repeats, leading in some cases to unstable repeat expansion. Furthermore, although the role of slippage in usual microsatellite mutations is well established, the possible role of other mutational mechanisms remains unclear. Given that mutation rates are not constant between microsatellites (let alone between different alleles at a single locus), is there any way of predicting which loci will demonstrate the highest mutation rate? Although it has been suggested that longer repeat motifs (e.g. tetranucleotides) are characterized by higher mutation rates than dinucleotides (Mahtani & Willard 1993), this hypothesis has not yet been tested systematically. If it is correct, why is it correct? This issue is important because the most widely used human genetic maps are almost exclusively based on either tetranucleotide (Cooperative Human Linkage Center 1994) or dinucleotide (Weissenbach et a1 1992) repeats. Finally, and perhaps most importantly, do microsatellites have a biological function which remains unrecognized? The sequencing of whole eukaryotic chromosomes may shed light on this question; answering it is likely to explain the evolution of microsatellites as well as the constraints on their variation at both the cellular and population levels. Acknowledgements We would like to thank B. Crouau-Roy for sharing unpublished results with us, and L. Bull and J. C. Garza for helpful comments. N. B. F. is supported by a Scientist Development Award from the National Institutes of Health and by a Young Investigator Award from the National Alliance for Research on Schizophrenia and Depression. M. S. is supported by award GM40282 from the National Institutes of Health.

References Aaltonen LA, Peltomaki P, Leach FS et a1 1993 Clues to the pathogenesis of familial colorectal cancer. Science 260:812-816 Blanquer-Maumont A, Crouau-Roy B 1995 Polymorphism, monomorphism and sequences in conserved microsatellites in primate species. J Mol Evol 41:492497 Bowcock AM, Ruiz-Linares A, Tomfohrde J, Minch E, Kidd JR, Cavalli-Sforza LL 1994 High resolution of human evolutionary trees with polymorphic microsatellites. Nature 368:455-457

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Caskey CT, Pizzuti A, Fu Y-H, Fenwick, RG, Nelson DL 1992 Triple repeat mutations in human disease. Science 256:784-789 Cooperative Human Linkage Center 1994 A comprehensive human linkage map with centimorgan density. Science 2652049-2054 Deka R, Chakraborty R, Ferrell RE 1991 A population genetic study of six VNTR loci in three ethnically defined populations. Genomics 11:83-92 Dietrich WF, Miller JC, Steen et a1 1994 A genetic map of the mouse with 4,006 simple sequence length polymorphisms. Nat Genet 7:22&245 Di Rienzo A, Wilson AC 1991 Branching pattern in the evolutionary tree for human mitochondria1 DNA. Proc Natl Acad Sci USA 88: 1597-1601 Di Rienzo A, Peterson AC, Garza JC, Valdis AM, Slatkin M, Freimer NB 1994 Mutational processes of simple-sequencerepeat loci in human populations. Proc Natl Acad Sci USA 91:3166-3170 Farber RA, Petes TD, Dominska M, Hudgens SS, Liskay RM 1994 Instability of simple sequence repeats in a mammalian cell line. Hum Mol Genet 3:253-256 Gacy AM, Goellner G, Juranic N, Macura S,McMurray CT 1995 Trinucleotide repeats that expand in human disease form hairpin structures in vitro. Cell 81:533-540 Garza JC, Slatkin M, Freimer NB 1995 Microsatellite allele frequencies in humans and chimpanzees with implications for constraints on allele size. Mol Biol Evol 12: 594-603 Goldstein DB, Linares AR, Cavalli-Sforza LL, Feldman MW 1995 An evaluation of genetic distances for use with microsatellite loci. Genetics 139:463471 Hudson RR 1990 Gene genealogies and the coalescent process. Oxf Surv Evol Biol 7: 1-44 Imbert G, Kretz C, Johnson K, Mandel J-L 1993 Origin of the expansion mutation in myotonic dystrophy. Nat Genet 4:72-76 Jeffreys AJ, MacLeod A, Tamaki K, Neil DL, Monckton DG 1991 Minisatellite repeat coding as a digital approach to DNA typing. Nature 354204-209 Karran P, Bignami M 1994 DNA damage tolerance, mismatch repair and genome instability. BioEssays 16:833-839 Kunst CB, Warren ST 1994 Cryptic and polar variation of the fragile X repeat could result in predisposing normal alleles. Cell 77:853-861 Levinson G, Gutman GA 1987 Slipped strand mispairing: a major mechanism for DNA sequence evolution. Mol Biol Evol 4203-221 Mahtani MM, Willard H F 1993 A polymorphic X-linked tetranucleotide repeat locus displaying a high rate of new mutation: implications for mechanisms of mutation at short tandem repeat loci. Hum Mol Genet 2:431437 Mao L, Lee DJ, Tockman MS, Erozan YS, Askin F, Sidransky D 1994 Microsatellite alterations as clonal markers for the detection of human cancer. Proc Natl Acad Sci USA 91~9871-9875 Ohta T, Kimura M 1973The model of mutation appropriate to estimate the number of electrophoretically detectable alleles in genetic population. Genet Res 22201-204 Orr HT, Chung M, Banfi S et al 1993 Expansion of an unstable CAG repeat in spinocerebellar ataxia type I. Nat Genet 4221-226 Ranshaw JAM, Coyne JA, Lewontin RC 1979 The sensitivity of gel electrophoresis as a detector of genetic variation. Genetics 93: 1019-1037 Schlotterer C, Tautz D 1992 Slippage synthesis of simple sequence DNA. Nucleic Acids Res 20:211-215 Schlotterer C, Amos B, Tautz D 1991 Conservation of polymorphic simple sequence loci in cetacean species. Nature 3546345

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Shriver MD, Jin L, Chakraborty R, Boerwinkle E 1993 VNTR allele frequency distributions under the stepwise mutation model. Genetics 134:983-993 Slatkin M 1995a Hitchhiking and associative overdominance at a microsatellite locus. Mol Biol Evol 12:473430 Slatkin M 1995b A measure of population subdivision based on microsatellite allele frequencies. Genetics 139:457-462 Slatkin M, Hudson RR 1991 Pairwise comparisons of mitochondria1 DNA sequences in stable and exponentially growing populations. Genetics 12935-562 Stallings RL, Ford AF, Nelson D, Torney DC, Hildebrand CE, Moyzis RK 1991 Evolution and distribution of (GT), repetitive sequences in mammalian genomes. Genomics 10307-815 Strand M, Prolla TA, Liskay RM, Petes T D 1993 Destabilization of tracts of simple repetitive DNA in yeast by mutations affecting DNA mismatch repair. Nature 365:274-276 Tautz D 1993 Notes on the definition and nomenclature of tandemly repetitive DNA sequences. In: Pena SDJ, Chakraborty R, Epplen JT, Jeffreys AT (eds) DNA fingerprinting: state of the science. Birkhauser Verlag, Basle, p 21-28 Tautz D, Schlotterer C 1994 Simple sequences. Curr Opin Genet & Dev 4:832-837 Thibodeau SN, Bren G, Schaid D 1993 Microsatellite instability in cancer of the proximal colon. Science 260:81&819 Toomajian M, Sisk B, Haines K, Barch D, Di Rienzo A 1994 Variability in microsatellite instability in sporadic colon cancer. Am J Hum Genet (suppl) 55:72A Valdes AM, Slatkin M, Freimer NB 1993 Allele frequencies at microsatellite loci: the stepwise mutation model revisited. Genetics 133:737-749 Weber JL 1990 Informativeness of human (dC-dA),- (dG-dT), polymorphisms. Genomics 7524-530 Weber JL, Wong C 1993 Mutation of human short tandem repeats. Hum Mol Genet 2:1123-1128 Wehrhahn C 1975 The evolution of selectively similar electrophoretically detectable alleles in finite natural populations. Genetics 80:375-394 Weissenbach J, Gyapay G, Dib C et a1 1992 A second-generation linkage map of the human genome. Nature 359:794-801

DISCUSSION Armour: I would like to make a few remarks about how we can obtain the answers to questions concerning mutation at short repeats. Replication slippage is a popular explanation for mutational changes at short repeat loci, but I don’t know of any evidence that positively identifies it as being the mechanism which actually operates in real human germlines. Nelson Freimer presented some results on somatic cells taken from colorectal tumours and from plasmids in mouse somatic cells, but we do not know how relevant this is to events that occur in the germline. The other point I would like to raise is the strength of the evidence for estimates of the mutation rate at any one of these loci and estimates of the effective population size, because predictions made by these models are only as good as the numbers put into them. As far as I know, numerous papers have

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been published on microsatellite mutation (Kwiatkowski et a1 1992, Weber & Wong 1993, Mahtani &Willard 1993, Talbot et a1 1995). These papers suggest that only a few mutations have been observed at any one locus. Similarly, there are many loci where, despite a large number of observations, not a single germline mutation has been observed. If we’re going to accept that there are loci which mutate more frequently than others, how can we estimate these rates? Possibly the most difficult question to answer is, if the effective population size that is required is not the modern one but an aggregate of historical population sizes, how can we estimate a realistic figure for that parameter? Freimer: What are your reasons for thinking that what occurs in the germline is different from what occurs in somatic cells? Armour: The germline mutation rates of some minisatellites are about an order of magnitude higher than in somatic cells, and these mutations are also apparently operating by different mechanisms. W. Bodmer: I agree with the comment that tumours are composed of somatic cells and, as such, could be different from the germline. I’m slightly puzzled about replication slippage. This was an idea that came from Streisinger et a1 (1964), who looked at phage T4 mutations. There are a lot of data on germline and somatic mutations in tumour suppressor genes, such as the data accumulated for the APC (adenomatous polyposis coli) gene. These data, although not directly relevant to CA repeats, suggest indirectly that a high frequency of mutations is due to replication slippage. A small number of these are insertions, but they are mostly deletions, which result in an out-of-phase, non-functional gene. There are also extensive data that compare the spectrum of mutations somatically with the germline, because there are germline mutations and somatic mutations that generate tumours by the same gene (Miyaki et a1 1995). Looking for a function is hopeless because of the battle between mutation rates and both the rate and efficiency of repair following replication errors. Therefore, the number of CA repeats that are observed is that which is tolerated by a reasonably efficient mismatch repair process, where the major element of mutation is due to replication errors. All the CA repeats that are used as polymorphisms are in non-coding regions. It is unusual to have a polymorphism in the numbers of CA repeats in coding regions because this would create a frameshift and, therefore, a nonfunctional protein. Chakraborty: I would like to raise a couple of points. First, we should not make any gross generalizations regarding the functions of microsatellites based on the data on size variations and their relationship within and between populations. We simply do not know enough about the functions of most of the tandem repeat loci. The pattern of mutations also varies drastically between different loci. For example, Jin (1994) has presented a compilation of mutation rates and patterns of variation at different repeat loci that are categorized by their repeat motif and length of repeat units.

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Second, it is not necessary to invoke multiphase mutation models to all microsatellite loci. The statistical behaviour of most microsatellite loci can be explained by a simple single-step mutation model that extends over a finite number of steps. However, it is useful to rank the microsatellite alleles by their sizes because such size-ordered ranking of alleles helps to discriminate between different mutation models. Weiss: Is the reason why you don’t need a two-step model because genetic drift will affect some of these alleles, which may be far from the original mode, so that they increase in numbers enough to constitute another mode? Chakraborty: Yes. And there is no direct evidence for large changes in the size of microsatellite alleles that have no functional relevance. Armour: Not many mutations have been observed, so it’s no wonder that the rarer mutations have not been observed. Kidd: I would argue that, in some of the cases in humans, the distribution is independent of the mutation rate and that it is due entirely to random genetic drift. In studies of two separate loci-DRD2 (Castiglione et a1 1995) and CD4 (S. Tishkoff & K. K. Kidd, unpublished work, see Kidd & Kidd 1995)-we found the microsatellite to be in strong disequilibrium with flanking markers, so that the same microsatellite allele can be followed identically by descent. We found that the population variation of allele frequencies at the microsatellite is reflected completely by the variation in the haplotype frequencies. Therefore, within the time-span of many modem human populations, we may not be looking at mutational distributions at all. Bertranpetit: I agree that the allele distribution can be entirely independent of the mutation pattern which produced it, and that it can be shaped by stochastic processes. When postulating a two-step model, you suggested (in Di Rienzo et a1 1994) that the mutation frequency for changes involving a successive number of repeats (adding or subtracting two, three or more units) follows a geometric distribution. But you do not have empirical data for estimating the parameters of this geometric distribution, apart from the irrelevant allele distribution. In this sense any model seems to be possible. Freimer: It’s not a completely geometric distribution but a distribution that has constraints on the frequency of the size changes. Bowcock: In terms of evidence for other molecular events that can give rise to alleles which differ by more than one repeat and that should be considered when constructing these models, we have an ovarian cancer cell line that exhibits microsatellite instability in culture. We have subcultured this cell line, and we have looked at the number of repeats in different microsatellitecontaining loci in each subculture (Orth et a1 1994). We found that for some loci the cell line starts off as a heterozygote and then at the next passage, it looks as though recombination rather than slippage has occurred between its two alleles. W . Bodmer: Is this a microsatellite variant of the cell line?

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Bowcock: Yes. W . Bodmer: Does it have to have a mutator phenotype? Bowcock: Yes, it has an MSH2 phenotype. W. Bodmer: Then this is totally irrelevant because it lacks a mismatch repair system. Bowcock: The point I am making is that at some loci a mechanism other than slippage is occurring to generate new alleles. Therefore, one should exercise caution in employing a two-step model. W . Bodmer: Another point that may be relevant is that, if CA repeats are near a functional gene, then linkage disequilibrium may affect the process. This may explain some of the heterogeneity. Sutherlund: But the number of alleles at a particular microsatellite locus is related to the length of the perfect repeat, so if there is a CA repeat with 30 perfect copies, for example, then there is a higher level of heterozygosity than if there are one or two imperfections, or if it is a short perfect repeat. If selection and drift were involved, then this relationship between the level of heterozygosity and the length of perfect repeat sequences would not exist. This suggests that a molecular mechanism involving slippage is operating. W . Bodmer: My comments are consistent with that observation. I was simply trying to explain the occasional situation of apparent bimodality, which could have arisen by selection acting on a particular subset of repeat giving rise to heterogeneity. The mechanisms that you described could be acting around that event. Kidd: This is also consistent with what I was saying because the alleles could have been generated in the past, but the distribution of the frequencies of the different alleles could be the result of recent random genetic drift and not the result of mutation in a stable constant population. Weiss: Is it true that the larger the stretch of repeats, the more physically different ways there are to slip? A short segment would not have as many ways to slip, so there wouldn’t be as many different alleles that misalignment could produce. Armour: Any tandem array is an equally large target for any of the possible mutational processes, so I’m not sure that this allows us to say what mechanism is operating. However, I agree that a bigger target would generate more mutations. One general point which seems to be emerging is that sequencing around these repeats is going to be more useful than simply measuring their length. This applies to both the internal workings of these repeats and in a haplotypic context, which allows us to go from the length on a gel to having some idea about the real lineage in which it’s occurring. Chukraborty: There are at least two studies that support this theory. First, we have shown that intron 40 of the von Willebrand factor gene contains two tetranucleotide (GATA) repeat polymorphic sites which are physically

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separated by 212 bp (Pena et al 1994). The polymorphism patterns at each of these sites are in agreement with some form of mutation-drift balance. With the aid of an internal primer, when haplotypes of these two site polymorphisms are defined, we observed more haplotypes than expected from a mutation-drift balance model. In a second study, Hanis & Bertin (1992) reported the existence of two tandem repeat polymorphic sites (with repeat motifs AAAT and TC, respectively) near exon 3 of the insulin receptor locus. These two sites are separated by only three conserved nucleotides (CCC). Again, although the individual sites are in a mutation-drift balance, the observed haplotype diversity in Mexican Americans is larger than expected. The common feature of both of these two systems is that the larger alleles at one site are positively associated with the larger alleles at the other linked site, and the same is true for the smaller alleles. We postulated that these co-operative changes between loci could either be due to gene conversion or a common control mechanism (probably residing somewhere in the flanking sequence region of these sites) which produces new variation at these repeat polymorphism sites (Pena et al 1994). Ward: Are there any situations where there’s been a reduction in the amount of variability at specific loci? Because understanding what diminishes variability might be just as informative as understanding what enhances it. There seems to be two lines of study on which to base this information. First, Nelson Freimer showed that in chimpanzees, two out of 12 loci were quite diminished in their variability. It would interesting to know what might be associated with that. Second, in Amerindian populations, for example, variability seems to be constrained only at specific loci. We don’t yet know the sequences of these loci. Freimer: We don’t yet know why there is reduced variability at the two chimpanzee loci. We are sequencing these loci now in order to find out. Chukravarti: In some cases, there are good reasons to believe that population structure might be the reason for the decreased variability. For example, we have studied 150 microsatellites in the Mennonites, and we have shown that heterozygosity decreases by about 5%. This would be understandable if founder effects were responsible. Chukruborty: We have looked at eight dinucleotide, 10 trinucleotide and 10 tetranucleotide loci in 16 human populations (Deka et al 1995a,b). We observed that the loci with perfect repeat alleles have a higher interlocus variance of heterozygosity in small populations, but the small populations are not necessarily extremely invariant at any of these loci. Chukravarti: These types of loci haven’t been studied extensively. It’s unlikely that these kinds of questions will be answered unless we look at specific populations where the gene genealogy is known in some detail. Population geneticists have always had to deal with the embarrassment of having hundreds of models, but not enough data to prove their validity.

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References Castiglione CM, Deinard AS, Speed WC et a1 1995 Evolution of haplotypes at the DRD2 locus. Am J Hum Genet, in press Deka R, Jin L, Shriver MD et a1 1995a Population genetics of dinucleotide (dC-dA), (dG-dT), polymorphisms in world populations. Am J Hum Genet 56:46 1 4 7 4 Deka R, Shnver MD, Yu LM, Ferrell RE, Chakraborty R 1995b Intra-population and inter-population diversity at short tandem repeat loci in diverse populations of the world. Electrophoresis 16:1659-1664 Di Rienzo A, Peterson AC, Garza JC, ValdCs AM, Slatkin M, Freimer NB 1994 Mutational processes of simple-sequence repeat loci in human populations. Proc Natl Acad Sci USA 91:3166-3170 Hanis CL, Bertin T K 1992 Juxtaposed short sequence repeat types and haplotypes near exon 3 of the insulin receptor locus among Mexican Americans. Genomics 12:842845 Jin L 1994 Population genetics of VNT loci and their applications in evolutionary studies. PhD thesis, University of Texas Graduate School of Biomedical Sciences, Houston, Texas, USA Kidd KK, Kidd JR 1995 A nuclear perspective on human evolution. In: Boyce AJ (ed) Molecular biology and human diversity, Cambridge University Press, Cambridge, in press Kwiatkowski DJ, Henske EP, Weimer K, Ozelius L, Gusella JF, Haines J 1992 Construction of a GT polymorphism map of human 9q. Genomics 12:229-240 Mahtani MM, Willard H F 1993 A polymorphic X-linked tetranucleotide repeat locus displaying a high rate of new mutation: implications for mechanisms of mutation at short tandem repeat loci. Hum Mol Genet 2:431437 Miyaki M, Tanaka IS,Kikuchi-Yanoshita R, Muraoka M, Konishi M 1995 Familial polyposis: recent advances. Crit Rev Oncol Hematol 19:l-31 Orth K, Hung J, Gazdar A, Bowcock A, Mathis M, Sambrook J 1994 Genetic instability in human ovarian cancer cell lines. Proc Natl Acad Sci USA 91:9495-9499 Pena SDJ, de Souza KT, de Andrade M, Chakraborty R 1994 Allelic associations of two polymorphic microsatellites in intron 40 of the human von Willebrand factor gene. Proc Natl Acad Sci USA 91:723-727 Streisinger G, Edgar RS, Denhardt G H 1964 Chromosome structure in phage T4. I. Circularity of the linkage map. Proc Natl Acad Sci USA 51:775-779 Talbot CC Jr, Avramopoulos D, Gerken S et a1 1995 The tetranucleotide repeat polymorphism D21S1245 demonstrates hypermutability in germline and somatic cells. Hum Mol Genet 41193-1199 Weber JL, Wong C 1993 Mutation of human short tandem repeats. Hum Mol Genet 2: 1123-1 128

The phenylalanine hydroxylase locus: a marker for the history of phenylketonuria and human genetic diversity Charles R. Scriver*, Susan Byck, Lynne Prevost, Liem Hoang and the PAH Mutation Analysis Consortium McGill University-Montreal Children's Hospital Research Institute, 2300 Tupper Street, A-717, Montreal, Quebec H3H 1P3 a n d *Department of Human Genetics, Faculty of Medicine, NU13 Stewart Biology Building, McGill University, Montreal, Quebec H3A 1B1, Canada

Abstract. Disease-producing allelic variation describes one aspect of human genetic diversity. Phenylketonuria, the major type of hyperphenylalaninaemia and formerly a functional genetic lethal, has a 2% carrier frequency in temperate-zone populations. Newborn screening for hyperphenylalaninaemia (incidence of 1 in 10000) has made it one of the most widely ascertained human Mendelian traits; 99% of hyperphenylalaninaemia mutations map to the PAH (phenylalanine hydroxylase) gene on 12q24.1, and most cause phenylketonuria. The gene is well characterized. Analysis of 3986 mutant chromosomes by 81 investigators in 26 countries has identified 243 different mutations in 788 different associations (with polymorphic intragenic haplotypes [seven diallelic sites, one short tandem repeat, one variable number of tandem repeats], populations and regions). These data are compiled on a database accessible on the World-Wide Web or as a stand-alone software package. A few phenylketonuria alleles occur at high relative frequencies in particular populations on one or only a few haplotypes, suggesting positive selection in the past. Additional mechanisms (founder effect, drift and recurrent mutation) can explain frequencies and distributions of particular alleles. Allele stratification in Europeans and Orientals implies that mechanism(s) accounting for distribution and high frequencies of PAH alleles were acting before and during demic expansion in Europe and after the European and Oriental radiations.

1996 Variation in the human genome. Wiley, Chichester (Ciba Foundation Symposium 197) p 73-96

Genes can be viewed as biological memories connecting individuals, families and communities with the past. Populations and genes share contingent histories, and contemporary human genomes, particularly those of defmed populations, trace past migration patterns and reflect mutational events, 73

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74 0 0 0

*

E

A ad tu h

FIG. 1. Disease-producing alleles in the PAH gene (90kb of genomic DNA on chromosome 12, region q24.1) are associated with hyperphenylalaninaemia (mainly phenylketonuria). Introns are numbered in the boxes, the corresponding exon is the vertical bar preceding the box. Seven polymorphic diallelic restriction sites, a multiallelic short tandem repeat (STR) region and a multiallelic variable number of tandem repeats region (VNTR) are indicated in boxes. Mutations affecting splicing are shown below the gene; all others above it. Mutations are named according to convention (Beaudet & Tsui 1992). Known associations between allele and polymorphic haplotype (e.g. haplotype 1 [Hl]) are given. Silent alleles exist; several in the promoter region, others in exons (Q2324, V245V, L385L, V399V, R400R, Y414Y) and some in introns (e.g. IVS2ntl9). Data from PAH Mutation Analysis Consortium Database or published reports representing 3986 mutant chromosomes). RE, restriction enzyme site generated or deleted by mutation.

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4

I

75

5

6

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selection and drift. Initially, The Human Genetic Diversity Project (HUGO 1994) relied heavily on methods of indirect detection of nuclear gene diversity, but DNA-based methods for detection of polymorphic markers and diseaseproducing allelic variation are now the major sources of genetic data for the project. The authoritative catalogue of human genes and genetic diseases (McKusick 1994) records disease-producing genetic and allelic variation; 432 loci contain more than one mutant allele and 30 loci have more than 25 alleles (in some cases, more than 500 alleles). The phenylalanine hydroxylase ( P A W locus contains more than 240 alleles ascertained through probands expressing the hyperphenylalaninaemia trait. Phenylketonuria, the major disease form associated with hyperphenylalaninaemia, is an autosomal recessive phenotype (Scriver 1994, Scriver et a1 1995) that is prevalent with mutant gene frequencies exceeding 1% in certain European populations, and approaching that figure in several Oriental populations. Phenylketonuria was a severe heritable disorder until a generation ago, when a new treatment neutralized the effects of the mutant gene. The most striking symptom, mental retardation, is the consequence of deficient phenylalanine hydroxylase activity and it reflects two histories: an ultimate event (mutation) and a proximate one (exposure to the essential amino acid Lphenylalanine). Dietary normalization of hyperphenylalaninaemia permits near-normal cognitive development. Universal newborn screening for early diagnosis and treatment of affected probands is one of the great successes of applied human genetics. Treatment will eventually affect PAH allele frequencies. In the meantime, heterozygote advantage (selection) and the historical accidents of migration, genetic drift and recurrent mutation, alone or together, have contributed to the high prevalence of disease-producing PAH alleles in many geographical regions and human populations. However, this high prevalence could not be explained until molecular genetic techniques became available, although the reason for the high prevalence of phenylketonuria carriers has been an abiding interest for half a century (Penrose 1946). Hyperphenylalaninaemia reflects impaired phenylalanine hydroxylation. A small fraction (about 1%) of cases have disorders affecting tetrahydrobiopterin synthesis or recycling, which impairs the hydroxylating process; the remainder have mutations that map to the PAH locus on chromosome 12q24.1.

Hyperphenylalaninaemia,notably phenylketonuria, as a paradigm Together with the a- and 8-thalassaemias, sickle-cell anaemia (Flint et a1 1993) and cystic fibrosis (Morral et a1 1994), phenylketonuria offers a special opportunity to study the mechanisms by which disease-producing allelic

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variation has achieved high frequencies in human populations. Phenylketonuria has both advantages and limitations for such an analysis. Advantages. (1) Frequencies of hyperphenylalaninaemiaand phenylketonuria have been extensively documented in human populations through newborn screening programmes (Scriver et a1 1995). The classification of probands has been confirmed by follow-up programmes. (2) The genetic (molecular) causes of hyperphenylalaninaemia are becoming known because the PAH gene is well characterized (Fig. 1) and mutation detection methods (Cotton 1993), notably denaturing gradient gel electrophoresis and sequencing, detect mutations efficiently (Guldberg & Guttler 1994). (3) Associations exist between particular mutations and polymorphic haplotypes, and the locus has an extensive suite of intragenic markers. (4) The PAH Mutation Analysis Consortium (81 investigators in 26 countries) maintains a relational database accessible through an Internet/World-Wide Web site (http://www.mcgill.ca/pahdb)or via a software package (WINPAHDB, available from us) as a stand-alone system executable on IBM-compatible hardware with Microsoft Windows. ( 5 ) The effect of mutation on phenotype can be analysed by in vifro expression analysis. Limitations. (1) Probands are ascertained only through the variant metabolic trait; alleles present but not causing it are not identified. (2) Sampling from human populations is not random, being dependent on the existence and efficiency of the newborn screening programme. (3) Mutation detection is incomplete, and only 65% of mutant chromosomes have been characterized since the development of denaturing gradient gel electrophoresis; accordingly, allele frequencies are relative, not absolute. (4) Mutation detection methods currently scan only 3% of the genomic PAH sequence. Nonetheless, the frequency of ‘missed’ mutations cannot be great, because denaturing gradient gel electrophoresis analysis detected 99% of hyperphenylalaninaemia/phenylketonuria alleles in two representative populations (Guldberg et a1 1993a,b). (5) Parental alleles are not uniformly reported, hence the number of de novo mutations is probably underestimated.

Mutations and polymorphic haplotypes at the PAH locus Mutations

More than 240 mutations have been identified in the PAH gene, within which they are non-randomly distributed. The majority of mutations affect the 3‘ half of the gene, which encodes domains required to form a functional PAH homotetramer, the putative active site and the tetrahydrobiopterin binding site (Scriver et a1 1995). The MUTPRED program (Cooper & Krawczak 1993),

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from codon 231453

FIG. 2. The profile for mutability of codons (in arbitrary units) in the PAH gene predicted using the MUTPRED program (see Cooper & Krawczak 1993). Filled circles on the abscissa indicate locations of CpG dinucleotide sequences (on template and atemplate strands); X indicates CpG dinucleotide sites without an identified mutation.

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79

TABLE 1 PAH gene: mutation types (%)a Mutation

Unambiguousb

Deletion Insertion

12.5 1.7 10.4 7.1

Splice Nonsense Missense Polymorphism in exonsd Polymorphism outside exons

Ambiguous‘

Silent

61.7 3.3 3.3

‘From the database maintained for the PAH Mutation Analysis Consortium by C. R. Scnver, L. Hoang, S. Byck & L. Prevost. bLoss of function evident. “Lossof function not uniformly proven, yet presumed or shown: (1) not to be polymorphic;and (2) to be the only mutation on the haplotype. ‘silent’ polymorphism (R400R)could affect splicing because the codon spans intron 1 1 .

which predicts the mutability profile of the PAH gene (Fig. 2), shows that predicted and actual distributions of disease-producing alleles are not necessarily synonymous (compare Figs 1 and 2). Mutation types (Table 1) can be classified as unambiguous, ambiguous or silent, according to their predicted effect. Unambiguous mutations include deletions with frameshifts, codon deletions, insertions with frameshifts, nonsense and splice mutations. Unambiguous mutations (of which there are 76) have been predicted to cause loss of function. This has been confirmed for some by expression analysis (see PAH Mutation Database). Missense mutations (of which there are 151) affecting residues involved in folding of the protein, the active site, the site for cofactor binding, and residues involved in post-transcriptional regulation of PAH activity should impair PAH function; but which residues they are is not yet known-hence the interest in mutation and expression analysis, in the absence of which it is advisable to show that a ‘novel’ missense mutation is not a simple polymorphism and is the only mutation in the gene. Eight silent alleles in exons (Fig. 1, Table 1) and eight polymorphisms in the 5’ region of transcription regulation or in the introns of PAH (Fig. 1) can be used for haplotype analysis (Lichter-Konecki et a1 1994) (see below). The ‘silent’ R400R (AGG 4CGG) allele involves a codon split between exons 11 and 12 and it may affect splicing. Of the 46 CpG sites on the template and atemplate strands in the PAH gene, 20 harbour no mutations. CpG sites in codons 53,71, 155, 169 and 413 are very mutable according to MUTPRED (Fig. 2) and the apparent absence of C + T or G -+ A mutations in these CpG dinucleotide pairs is intriguing. All possible

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CpG alleles in exon 7 harbour mutations. A CpG allele on the atemplate (coding) strand in codon 408 (R408W allele) producing classic phenylketonuria is the most prevalent and widely distributed disease-producing PAH allele in the world. The corresponding CpG allele on the template strand (R408Q) produces a more benign form of hyperphenylalaninaemia and is prevalent only in Scandinavians. The most prevalent PAH alleles in Europeans and Orientals all cause loss of function and result in the phenylketonuria phenotype.

Haplo types

Seven diallelic polymorphic restriction sites exist in the PAH gene (Lidsky et a1 1985) (Fig. l), five of which can be analysed by PCR. The seven sites are in linkage disequilibrium (Feingold et a1 1993). The 3’ untranslated region contains a multiallelic variable number of tandem repeats (VNTR) region, which can be analysed by PCR (Goltsov et a1 1992): haplotypes harbouring three, six, seven, eight, nine, 11, 12 and 13 copies of the 30bp cassette have been reported. In addition to length variation in the allelic series, there is sequence variation within the cassette (Byck et a1 1994). The 5’ end of the gene contains a multiallelic short tandem repeat (STR) comprising (TCTA), (Goltsov et a1 1993), which can be analysed by PCR. Analysis of PAH haplotype differences has been less informative than analysis of rare diseaseproducing allelic variation in describing population differences, in our experience. Molecular analysis exposes the evolutionary process at work in the PAH gene. For example, in a sample of 242 phenylketonuria chromosomes from Ulster, the 165T allele occurs 46 times on a series of different STR alleles ranging in size from 238 to 258bp on haplotype 1.8, and three times on haplotype 1.12 (STR 238 bp) (Zschocke et a1 1995). The high mutability of the STR (Goltsov et a1 1993) implies that the I65T allele is probably older than its polymorphic haplotype, which has been mutating with time. In the same population, one particular haplotype, H1 (containing STR - 242 and VNTR -8), carries seven mutations: R408W (58 copies), L348V (12 copies), IVS12ntl (eight copies), F39L (six copies), R252W (four copies), I65T (one copy) and A300S (one copy), implying that an ancient haplotype has collected multiple mutations. The PAH mutations (n = 243 at the time of this symposium, but for the latest update see the PAH Mutation Database) have 788 different associations for haplotype, population and region. Only a few alleles are prevalent (relative frequencies 5-30%); each of these tends to be found in a particular geographical region or population, and is associated with only one or a few haplotypes. The pattern suggests that a process of positive selection has occurred.

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The view from Quebec: histories of a population and histories of genes Settlers to Quebec, and French Canadians in particular, are among the bestdocumented populations in the world (McEvedy & Jones 1978). Histories of populations and histories of genes are contingent on one another in this region (Bouchard & De Braekeleer 1991, Scriver 1993). Settlement of New France began in earnest in the early-17th century, and with natural increase led to demic expansion. Samuel de Champlain (founder [in 16081 and first Lieutenant Governor of Quebec) died in 1635; by the end of the 1630s, 160 French Europeans had immigrated to New France; the total influx of settlers over a century and a half was less than 15000. Today, more than 5000000 people claim French-Canadian ancestry. This extraordinary expansion took place in relative genetic isolation. Not all contemporary Quebeckers are French Canadian. In 1759 Wolfe established his English troops on the Plains of Abraham and defeated Montcalm in a decisive battle. The discontinuity in political control had an enormous effect on North-American history, and it was followed by new waves of migration bringing English, Scots and Irish settlers to Canadian North America from the late-18th up to the early-20th centuries. In the 20th century, other settlers came, first from eastern and then from southern Europe and, most recently, from Asia. A million or more contemporary Quebeckers can claim non-French ancestors. Quebec introduced universal newborn screening for hyperphenylalaninaemia in 1971. The specificity of the screening test is 100% (Lambert 1994), 34 different PAH mutations have been identified so far (Table 2), and the effects of founders, genetic drift, migration and recurrent mutation on their frequencies and distributions are apparent. PAH alleles are different in contemporary Quebec and France (Table 1). French Canadians residing in eastern and western regions of Quebec province have different demographic histories and different mutation patterns. The Montreal region, which received most of the non-French settlers and immigrants, has yet another pattern of PAH mutations. The allelic stratification apparent here illustrates how demographic history and the histories of genes are contingent on one another. The finding of novel mutations in Quebec raised questions about their origins. The R408W mutation, prevalent in Europe on haplotype (H) 2 (Eisensmith et a1 1992), occurred with approximately 50% prevalence on H1 in Quebec (John et a1 1990, Treacy et a1 1993, Rozen et a1 1994) (Table 2). Genealogical analysis of Quebec families harbouring the novel R408W[H1] allele, and the I65T mutation also identified first in Quebec (John et a1 1992), led to the discovery of Celtic ancestors in both lineages (Treacy et a1 1993). Subsequent studies demonstrated high frequencies of these two alleles in Scotland, Ireland and north-western Europe (Eisensmith et a1 1995, L. Tyfield,

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82 TABLE 2 PAH mutations discovered in Quebec and Francea Quebec

Western Eastern Province Quebec Montreal Quebec Franceb

Chromosomes (n) Mutations [haplotypelc (relative frequency) MlV F39L K42I L48S I65T S67P T92I delI94 A104D R157N Rl58Q R243X V245A L249F R252W A259V R261Q G272X S273F Y277D E280K[H1] E280K[H2] P28 1L IVS7ntl F299C A300S IVS8ntl A309D L311P

145

65

40

40

372

5.5 2.1 0.7 0.7 4.8 0.7 0.7 0 0.7 0.7 3.4 0 0.7 0 0.7 0 3.4 0.7 0 0 1.3 2.1 2.1 0 4.8 0.7 0.7 2.1 0

1.5 4.6 0 1.5 3.1 0 1.5

0 0 2.5 0 7.5 2.5 0

1.1

-

-

17.5 0 0 0 2.5 0 0 0 0 2.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.5 0

0 0 3.1 0 1.5

2.5 0 2.5 -

0

-

-

1.5

0

-

-

1.5 1.5 0 0 0 4.6 1.5 0 9.2 1.5 0 3.1 0

10.0 0 -

4.0 0 4.0 0 2.5 -

2.5

-

-

-

0.5 -

0 1.1 0 -

1.3 -

0.5 6.2 0.5 0.5 0.3 4.8 0

-

-

0.5 (continued)

Allelic variation in PAH

83

TABLE 2 (cont.) D338Y L348V S349P 1064delG (352fs) 1054\55delG (363fs) IVSlOnt 1 1 IVS 10nt3 A403V R408W[H1] Y414C D415N IVS 12nt1 Identified (%)

0.7 2.1 4.1 0.7

1.5 3.1 3.1 1.5

0.7

0

0.7 0 0.7 7.0 0 0.7 13.1 74.6

0 0 1.5 4.6 0 1.5 15.3 74.8

-

0 2.5 10.0 0

-

2.5

0

-

2.5 0 0 10.0 0 0 7.5 75.5

0 0 0 7.5 0 0 15.0 62.5

-

-

0 -

-

-

0.3 -

5.6 30.4

“Adaptedfrom Rozen et a1 (1994). New data provided by K. Carter (unpublished results 1995). bData from Abadie et a1 (1993); F. Rey & J. Rey have extended the findings in France (see PAH Mutation Database); differences between Quebec and France remain. ‘Mutations listed are those identified or specifically searched for.’ 0, searched for and absent; -, not searched for.

personal communication 1995) suggesting that R408W[H1] and I65T trace migrations to Quebec. The MlV mutation, also discovered first in Quebec (John et a1 1989) and most prevalent in the eastern region of the province (Table 2), has been found on only four other chromosomes, all in France. Genealogical reconstructions in Quebec identified putative ancestors in the historical province of MortagnePerche in north-western France (Lyonnet et a1 1992), a region that contributed only 227 settlers to New France, of whom 202 migrated before 1670. Although these settlers contributed disproportionately to the gene pool of contemporary French Canadians in eastern Quebec (Heyer & Tremblay 1995), the possibility exists that the MlV mutation arose in a New France gamete, which would imply that returned settlers account for its appearance in Europe today. The age of the MlV mutation might be ascertained if there is linkage disequilibrium for highly polymorphic markers over a relatively large genetic distance around it (see Risch et a1 1995). Two mutations, R408W and E280K, were found on unconventional haplotypes in Quebec (Rozen et a1 1994). R408W on H1 could have originated from H2 (or vice versa) by a single recombination at a site just 3‘ to the mutant

Scriver et al

a4

TABLE 3 Origins of PAH allele frequencies and distributions Relative frequency (%)

Population

Deletion of exon 3 M1V

Very high

Yemenite Jewsa Avigad et a1 1990

17.6

French Canadian (eastern Quebec)

M1I Many

Rare

Norwegian Canadian (Quebec)

Mechanism

Allele

Founder effect

De novo Drift

-

Australian (Victoria) Danish Sicilian Migration (traces)

L48S IVS 12nt1

13 6

R408W[H 11

7-20b.'

I65T

5-18

IVS 12nt1

15-16

IVSlOnt546 15-32 R408WW21, 10-30 IVSlOnt546 and IVS12ntl Mutation R408Ww1,2] Prevalent Recurrent or R408WW4,41, Prevalent recombinant? 441 E280Kw 1,2]

Prevalent

SouthernNorthern Italian SouthernNorthern Italian Canadian (Quebec) Australian (Victoria) Australian (Victoria) Latin American Tatarian European Oriental European

Reference

Lyonnet et a1 1992, Rozen et al 1994, K. Carter, unpublished data 1995 Eiken et al 1992 Rozen et a1 1994, K. Carter, unpublished data 1995 Ramus et a1 1995 Guldberg et a1 1993a Guldberg et a1 1993b Dianzani et al 1994 Dianzani et a1 1994 Treacy et a1 1993 Ramus et a1 1995 Ramus et a1 1995 Perez et a1 1994 K W n et a1 1995 Byck et a1 1994 Several (see PAH Mutation Database) S. Byck, unpublished data 1995 (continued)

Allelic variation in PAH

85

TABLE 3 (cont.) Slippage

Selection (putative)

Short deletions (many) R408W[Hl]

Rare

R408W[H2]

40-70

IVS 12nt1

10-35

IVSlOnt546

1545

I65T

10-25

R243Q

> 10

R413P

> 10

Y204C

> 10

IVS4nt 1

> 10

IVS4ntl

> 10

1040

Widespread in gene

Several (see PAH Mutation Database) North-westem Treacy et a1 1993, European Eisensmith et a1 1995 Eastern Eisensmith et a1 European 1992, PAH Mutation Database Northem Eisensmith et a1 European and 1992, PAH British Mutation Database South-eastern Eisensmith et a1 and southern 1992, PAH European Mutation Database Western and Desviat 1993 south-western European Northern Wang et a1 1991, Chinese Okano et a1 1992 Northern Wang et a1 1991, Chinese Okano et a1 1992 Northern Wang et a1 1991, Chinese Okano et a1 1992 Southern Wang et a1 1991, Chinese Okano et a1 1992 Korean Wang et a1 1991, Okano et a1 1992

"Carrier frequency for the allele is approximately 2.5% in the population (50000 Yemenite Jews in Israel). bAllelefrequencies are high in the 'emigrant' population (UK and Ireland) (L. Tyfield, personal communication 1995). T h e higher values are found in Australia (versus Quebec). -, not searched for.

codon in exon 12 (John et a1 1990). Although the most prevalent phenylketonuria allele in the world might be adaptive through a directional recombinant process (Cairns et a1 1988), recurrence at the hypermutable CpG dinucleotide in this codon is more likely, although less interesting. R408W chromosomes in Quebecker and European families have been analysed using polymorphic markers 5' and 3' relative to codon 408 (Byck et a1 1994). The 5'

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Scriver et al

STR markers are different on the two haplotypes bearing R408W, but this difference could be explained by slippage in the repeat sequences. Accordingly, recombination could not be ruled out, unless the VNTR marker at the 3' end of the locus was informative. Sequence variation in the fifth cassette of the eighth VNTR allele differentiated mutant and normal H1 chromosomes, a feature that could not be easily explained by a single recombinational event. The most likely explanation for the occurrence of R408W on two haplotypes in Europeans (and on H4, H41 and H44 in Orientals [Fig. 11) is recurrent mutation. A corresponding analysis of Quebecker and European chromosomes harbouring the E280K allele on H1 and H2 is also compatible with recurrent mutation (S. Byck, L. Tyfield & C. R. Scriver, unpublished results 1995). A world view of the PAH locus

The distribution of PAH alleles in Quebec reflects the histories of genes and of populations. The effects of founders (M 1V allele), drift with stratification (many alleles) and hypermutability (R408W and E280K) are each evident. The effects of migration (gene flow) are also apparent in that the ratio of European settlers to aboriginal residents was high; moreover, within the population of European settlers, there are migration traces (R408W[Hl], I65T) for those who came after 1759, bringing not only gametes, but also language and culture different from that of the resident population (Bouchard 1990, Rudin 1985). The microcosm of Quebec provides a useful but short and selective view of human history. The larger question remains: how much allelic diversity at the PAH locus is explained by historical accidents as opposed to adaptation by selective advantage in heterozygotes? Examples of random historical events affecting the distribution of PAH alleles abound in the world beyond Quebec (Table 3). Founder effect is seen in the deletion of exon 3, a mutation that is carried by 2.5% of Yemenite Jews, who trace their ancestry to the Yemen capital of San'a in the early-18th century (Avigad et a1 1990). A de now mutation (MlI) has been reported in Norway (Eiken et a1 1992). Genetic drift is reflected in the very low incidences of phenylketonuria in Finland, Israel and Japan (Scriver et a1 1995). Migration traces are evident in Victoria State, Australia (Ramus et a1 1999, in Brazil and in Latin America as a whole (Perez et a1 1994), in Tataria (Kuzmin et a1 1995), in Denmark (Guldberg et a1 1993a) and in Sicily (Guldberg et a1 1993b). Dispersion of the R408W mutation on different haplotypes in Europeans (H1 and H2) and in Orientals (H4, H41 and H44) can be explained by recurrent mutation or recombination. Most of the small, rare deletions apparently occurred by slippage in short tracks of direct repeats. However, when the accidental mechanisms of founder effect, genetic drift, migration and mutation are taken into account there remains a vast number of human phenylketonuria

Allelic variation in PAH

87

chromosomes, about two-thirds of the total, carrying just a few PAH mutations (four in Europeans). Globally, a few PAH mutations account for most of the total pool of phenylketonuria alleles, and these few show distinct regional clustering where they are found in association with only one or a few haplotypes (Table 3). These alleles, confined to human populations that experienced demic expansion in temperate climates, might best be explained as the result of selection. The argument, based on frequency distributions, is familiar (Flint et a1 1993). However, because phenylketonuria is much less frequent than other genetic recessive conditions for which selection has been proposed, its effects may either have dissipated or be too small to detect. At present, we do not know what agent acted on phenotype to select the objects (PAH alleles); whether the selection process is still active; the trait upon which the process acted; and whether the process altered gamete selection, embryo/fetal wastage or reproductive rates of heterozygotes (Scriver et a1 1995, see p 1037-1038). A hitchhiking effect seems unlikely from inspection of closely-linked known genes on chromosome 12q. Ochratoxin A, a mycotoxin in contaminated lentils and grains, has been proposed as the selective agent (Woolf 1986), but it is unclear whether this would apply to Oriental and European populations alike and there is no contemporary evidence for it. The process apparently discriminated between phenylketonuria and non-phenylketonuria hyperphenylalaninaemia phenotypes, because the latter usually occur at lower frequencies and independently of the former (Vogel & Motulsky 1992). ‘Selected’ alleles (e.g. R408W, IVS12nt1, IVSlOnt546) cause severe loss of function, yet the I65T allele-also a candidate for selection-leaves considerable residual function. The segregation pattern of major phenylketonuria alleles in Europeans and Orientals suggests that the process began after the separate radiations of Europeans and Orientals, yet before and during the demic expansion of Europeans. There are at least two reasons why it is worthwhile soon to determine whether phenylketonuria alleles were adaptive for Homo sapiens. First, as human populations merge and fracture, the genetic structures upon which the arguments rest will change. Second, parents of probands wish to know the ultimate cause of their child’s problem while they modify proximate experience to cope with it; they may find it easier to adapt if they know that a gene, seen as ‘bad’ now, was (or is) a ‘good’ one in the history of our species. Acknowledgements The authors thank members of the PAH Mutation Analysis Consortium (see PAH Mutation Database for a directory); Linda Tyfield (Bristol) and Bernd Dworniczak (Munich) assisted us in the studies of recurrent mutation; Kevin Carter and Rima Rozen provided data for Quebec. Jean Rey (Paris) and Savio Woo (Houston)

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generously nominated us as Curators of the PAH Mutation Database. Discussions with Randy Eisensmith (Houston) and Kenneth Morgan (Montreal) have been stimulating. This work is supported by the Medical Research Council of Canada (Group in Medical Genetics), the Canadian Genetic Diseases Network (Networks of Centers of Excellence, Canada), the Quebec Network of Genetic Medicine and Le Fonds de la recherche en sante du Quebec.

References Abadie V, Lyonnet S, Melle D et a1 1993 Molecular basis of phenylketonuria in France. Dev Brain Dysfunct 6:120-126 Avigad S, Cohen BE, Bauer S et al 1990 A single origin of phenylketonuria in Yemenite Jews. Nature 334168-170 Beaudet AL, Tsui LC 1992 A suggested nomenclature for designating mutations. Hum Mutat 2:245-248 Bouchard G 1990 Representations de la population et de la societt quebecoises: l’apprentissage de la diversite. Cah Quebec Demogr 19:7-28 Bouchard G, De Braekeleer M 1991 Histoire d’un ginome: population et gknetique dans I’est du Qutbec. Presses de 1’Universitk du Quebec, Sillery, Quebec Byck S, Morgan K, Tyfield L, Dworniczak B, Scriver CR 1994 Evidence for origin, by recurrent mutation, of the phenylalanine hydroxylase R408W mutation on two haplotypes in European and Quebec populations. Hum Mol Genet 3: 1675-1677 Cairns J, Overbaugh J, Miller S 1988 The origin of mutants. Nature 335142-145 Cooper DN, Krawczak M 1993 Human gene mutation. BIOS Scientific, Oxford, p 141144 Cotton RGH 1993 Current methods of mutation detection. Mutat Res 285125-144 Desviat LR, Perez B, Ugarte M 1993 Phenylketonuria in Spain: RFLP haplotypes and linked mutations. Hum Genet 92:254-258 Dianzani I, Giannattasio S, de Sanctis L et a1 1994 Genetic history of phenylketonuria mutations in Italy. Am J Hum Genet 55:851-853 Eiken HG, Knappskog PM, Apold J, Skjelkvble L, Boman H 1992 A de nova phenylketonuria mutation: ATG (Met) to ATA (Ile) in the start codon of the phenylalanine hydroxylase gene. Hum Mutat 1:388-391 Eisensmith RC, Okano Y, Dasovich M et a1 1992 Multiple origins for phenylketonuria in Europe. Am J Hum Genet 51:1355-1365 Eisensmith RC, Goltstov AA, O’Neill C et al 1995 Recurrence of the R408W mutation in the phenylalanine hydroxlase locus in Europeans. Am J Hum Genet 56:278-286 Feingold J, Guilloud-Bataille M, Feingold N, Rey F, Berthelon M, Lyonnet S 1993 Linkage disequilibrium in the human phenylalanine hydroxylase locus. Dev Brain Dysfunct 6:26-3 1 Flint J, Harding RM, Clegg JB, Boyce AJ 1993Why are some genetic diseases common? Distinguishing selection from other processes by molecular analysis of globin gene variants. Hum Genet 91:91-117 Goltsov AA, Eisensmith RC, Konecki DS, Lichter-Konecki U, WOO SLC 1992 Associations between mutations and a VNTR in the human phenylalanine hydroxylase gene. Am J Hum Genet 51:627-636 Goltsov AA, Eisensmith RC, Naughton ER, Jin L, Chakraborty R, Woo SLC 1993 A single polymorphic STR system in the human phenylalanine hydroxylase gene

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permits rapid prenatal diagnosis and carrier screening for phenylketonuria. Hum Mol Genet 2:577-581 Guldberg P, Guttler F 1994 Mutations in the phenylalanine hydroxylase gene: methods for their characterization. Acta Paediatr Suppl 407:27-33 Guldberg P, Henriksen KF, Guttler F 1993a Molecular analysis of phenylketonuria in Denmark: 99 % of the mutations detected by denaturing gradient gel electrophoresis. Genomics 17: 141-146 Guldberg P, Romano V, Ceratto N et a1 1993b Mutational spectrum of phenylalanine hydroxylase deficiency in Sicily-implications for diagnosis of hyperphenylalaninemia in Southern Europe. Hum Mol Genet 2:1703-1707 Heyer E, Tremblay M 1995 Variability of the genetic contribution of Quebec population founders associated to some deleterious genes. Am J Hum Genet 56:970-978 HUGO 1994 The Human Genome Diversity (HGD) Project summary document. International Planning Workshop, HUGO Europe, London John SWM, Rozen R, Laframboise R, Laberge C, Scriver CR 1989 Novel phenylketonuria mutation on haplotype 2 in FrenchCanadians. Am J Hum Genet 45:905-909 John SWM, Rozen R, Scriver CR, Laframboise R, Laberge C 1990 Recurrent mutation, gene conversion, or recombination at the human phenylalanine hydroxylase locus: evidence in FrenchCanadians and a catalog of mutations. Am J Hum Genet 6:97&974 John SWM, Scriver CR, Laframboise R, Rozen R 1992 I n vitro and in vivo correlations for 165T and MlV mutations at the phenylalanine hydroxylase locus. Hum Mutat 1: 147-1 53 Kuzmin AI, Eisensmith RC, Goltsov AA, Sergeeva NA, Schwartz EI, Woo SLC 1995 Complete spectrum of PAH mutations in Tataria: presence of Slavic, Turkic and Scandinavian mutations. Eur J Hum Genet, in press Lambert DM 1994 The genetic epidemiology of hyperphenylalaninemia in Quebec. MSc thesis, McGill University, Montreal, Quitbec, Canada Lichter-Konecki U, Schlotter M, Konecki DS 1994 DNA sequence polymorphisms in exonic and intronic regions of the human phenylalanine hydroxylase genes aid in the identification of alleles. Hum Genet 94307-3 10 Lidsky AS, Ledley FD, DiLella AG et a1 1985 Extensive restriction site polymorphism at the human phenylalanine hydroxylase locus and application in prenatal diagnosis of phenylketonuria. Am J Hum Genet 37:619-634 Lyonnet S, Melle D, De Braekeleer M et a1 1992 Time and space clusters of the French Canadian M1V phenylketonuria mutation in France. Am J Hum Genet 51: 191-196 McEvedy C, Jones R 1978 Atlas of world population history. Penguin, Harmondsworth, England McKusick VA 1994 Mendelian inheritance in man: a catalog of human genes and genetic disorders, 1 lth edn. Johns Hopkins University Press, Baltimore, M D Morral N, Bertranpetit J, Estivill X et al 1994 The origin of the major cystic fibrosis mutation (AF508) in European populations. Nat Genet 7: 169-175 Okano Y, Hase Y, Lee DH et al 1992 Frequency and distribution of phenylketonuric mutations in Orientals. Hum Mut 1:21&220 Penrose LS 1946 Phenylketonuria-a problem in eugenics. Lancet I:949-953 Perez B, de Lucca M, Desviat LR et a1 1994 Distribution of the phenylketonuria mutation 165T in Spain and Latin America. Am J Hum Genet 55:931(abstr)

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Ramus SJ, Treacy EP, Cotton RGH 1995 Characterization of phenylalanine hydroxylase alleles in untreated phenylketonuria patients from Victoria, Australia: origin of alleles and haplotypes. Am J Hum Genet 56:1034-1041 Risch N, de Leon D, Ozelius L et a1 1995 Genetic analysis of idiopathic torsion dystonia in Ashkenazi Jews and their recent descent from a small founder population. Nat Genet 9: 152-1 59 Rozen R, Mascisch A, Lambert M, Laframboise R, Scriver CR 1994 Mutation profiles of phenylketonuria (PKU) in Quebec populations: evidence of stratification and novel mutations. Am J Hum Genet 55321-326 Rudin R 1985 The forgotten Quebeckers: a history of English-speaking Quebec, 17591980. Institut QuCbecoise de recherche sur la culture, QuCbec. Scriver CR 1993 Every disease has two histories. Trans R SOCCan IV:23-38 Scriver CR 1994 Science, medicine and phenylketonuria. Acta Paediatr Suppl407: 11-18 Scriver CR, Kaufman S, Eisensmith RC, Woo SLC 1995 The hyperphenylalaninemias. In: Scriver CR, Beaudet AL, Sly WS, Valle E (eds) The metabolic and molecular bases of inherited disease, 7th edn. McGraw-Hill, New York, p 1015-1075 Treacy E, Byck S, Clow C, Scriver CR 1993 ‘Celtic’ phenylketonuria chromosomes found? Evidence in two regions of Quebec province. Eur J Hum Genet 1:220-228 Vogel F, Motulsky 1992 Human genetics: problems and approaches, 2nd edn. SpringerVerlag, Berlin, p 181-183 Wang T, Okano Y , Eisensmith RC et a1 1991 Identification of a novel phenylketonuria (PKU) mutation in the Chinese: further evidence for multiple origins of PKU in Asia. Am J Hum Genet 4k628-630 Woolf LI 1986 The heterozygote advantage in phenylketonuria. Am J Hum Genet 38:713-175 Zschocke J, Graham CA, Carson DJ, Nevin NC 1995 Phenylketonuria mutation analysis in Northern Ireland: a rapid stepwise approach. Am J Hum Genet ST1311-1317

DISCUSSION Beighton: Charles Scriver has addressed several areas. The first of these is ascertainment. If a genetically determined disease is to be used in order to address wider issues, it is important that the condition should be chosen carefully. Phenylketonuria is an excellent choice because it is relatively common and the phenotypic effects, notably the mental retardation that occurs in the homozygote, are important. Phenylketonuria can also be defined accurately by biochemical procedures, and in the developed world, at least, all newborns are tested for phenylketonuria. In Africa, however, including South Africa, this test is not carried out regularly, so accurate information concerning the frequency of phenylketonuria in these countries is lacking. Diagnostic screening has been undertaken in institutions for the mentally retarded in some of these countries but few, if any, instances of phenylketonuria have been encountered in the indigenous population. It is generally accepted that the English-speahng population of South Africa has a similar frequency of phenylketonuria to that of western Europe, which is exactly what one would expect. Also, the Afrikaansspeaking population, who are a homogeneous, endogamous isolate, have a

Allelic variation in PAH

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similar frequency of phenylketonuria to that of the French Canadian population. It is not known whether the same allele frequencies are present in these populations but I would suggest that they are probably different, not because of independent mutation in South Africa and Canada, but because of the founder effect, which reflects the situation in Holland and France prior to emigration some 300 years ago. Phenylketonuria is rare, if not absent, in the indigenous Black population of southern Africa. A similar situation is apparent for the common and important autosomal recessive disorder, cystic fibrosis. There are, therefore, profound implications at the population genetics level. The parallels between the French Canadian population of Quebec and the Afrikaners of South Africa are remarkable. The French Canadians settled in Canada in 1630 and the Afrikaners arrived in South Africa in 1652. Today there are three million Afrikaners, of whom about 90% share the same 30 surnames, so they are a genetically homogeneous population. The arrival of General Wolfe in Quebec changed the structure of the French Canadian population. Equally, the revocation of the Edict of Nantes in 1685 brought the Huguenots to South Africa; these persons were absorbed into the Afrikaansspeaking population (Nurse et a1 1985). Another incident occurred when the British abolished slavery in South Africa and the Afrikaners trekked northwards into the Transvaal province. This movement parallels the separation of the eastern and western French Canadians in Quebec, although the separation in South Africa was later and probably did not make as much difference to the relative distribution of the gene in the Afrikaners population. Weiss: How common is phenylketonuria in North American Blacks? Scriver: Phenylketonuria is certainly less frequent in North American Blacks than it is in the corresponding Caucasian population. This observation originates in the data from state-operated screening programmes. Beighton: General historical and population genetic evidence suggests that the west African population and the indigenous African population of southern Africa both originated in central Africa. It has been has estimated that the two populations separated 1500-2000 years ago (Beighton & Botha 1986). Scriver: Phenylketonuria is a disease of ‘temperate-zone’ populations, including those of the Middle East. Penrose (1946) commented on this. It is still true at the level of case finding, and it is the case wherever newborn screening differentiates between Caucasians and Asians on one hand, and African Blacks and Aboriginals on the other. Weiss: It’s strange that all the mutations are in the European populations and not in the African population. Beighton: I’m not saying that they’re not present. I’m saying that there’s a vast disparity in gene frequencies between the populations of Africa and Europe. Scriver: Among North American Blacks the observed frequency of phenylketonuria ascertained by screening, which reaches virtually everyone

92

Discussion

in the USA, is about one tenth of the frequency in the American Caucasian population. It will be worthwhile to characterize the phenylketonuria alleles to find out their origins. W. Bodmer: The R408W allele is located at a CpG site, so one would expect it to have a high rate of mutation; but only three or four alleles account for most cases of phenylketonuria. Scriver: In Europeans four alleles account for 62% of mutant chromosomes. W. Bodmer: If there is a ratio of 1:3 between overall allele frequencies in Africans and Europeans, then the homozygotes will be one ninth of the frequency. Therefore, I wonder how good the data are which suggest that there’s a fundamental a difference in the patterns of mutations in people of African and European origin. It is possible that this is an ascertainment bias, and that what needs to be explained is the relatively high frequency of a few alleles. Scriver: This is probably not the case for the USA because universal newborn screening reaches all registered births, whatever their race or ethnic identity. W. Bodmer: But about one tenth is exactly what one would expect from Hardy-Weinberg calculations. If common alleles are not present in other populations, whereas the rare alleles are, and if there is a ratio of 1:3, this would give one ninth. The presence of three or four common alleles explains all the differences between the populations, and the rest is the background pattern of mutations. Is R408W one of the common alleles? Scriver: Yes, it accounts for one third of the total number of mutant alleles and about 4000 mutant chromosomes recorded in the Phenylalanine Hydroxylase (PAH) Mutation Analysis Consortium Database. W. Bodmer: Is the R408W allele found in the African Black population? Scriver: We don’t know this yet. W. Bodmer: You would expect to find it if it’s a high frequency mutation. It is possible that there were a few isolates of European origin that increased in frequency and that contributed to the European population, but that this did not occur for other populations. Chakravarti: Walter Bodmer’s point is correct in one sense because cystic fibrosis mutant alleles, at least in North American Blacks, are essentially the European mutation without AF508. This represents the classical idea of mutationselection balance. However, one would still need to explain the high frequency of the cystic fibrosis AF508 allele in Caucasians. W. Bodmer: That could just be due to a single chance event. There is a huge ascertainment bias. Chukravarti: But it’s an ascertainment bias that’s allele specific. W. Bodmer: Cavalli-Sforza and I (1972) pointed out that the observed mutations are biased by the frequency of observed mutations in the population. Therefore, the mutations that you observe will be the ones

Allelic variation in PAH

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associated with alleles that happen to have increased in frequency to a greater extent. Weiss: What is the situation in Asia? Scriver: The Asian data are not as robust as the European data. On the basis of the few centres where there’s been relatively extensive screening, the incidence of phenylketonuria is 1:lOOOO. The low frequency of the phenylketonuria allele in Japan is probably a result of drift. For example, the phenylketonuria allele occurs at ‘European’ frequencies in South Korea and North China. Weiss: Is the frequency much lower than in Europe? Scriver: No, the frequency is not much lower but the allele profile is different. The low frequency in Japan is analogous to the European case of Finland; it is probably an effect of genetic drift. Weatherall: If your screening ascertainment technology were relatively insensitive, then would you not just be looking at the tip of an iceberg? Scriver: This is unlikely. Deborah Lambert (unpublished results 1994) has demonstrated 100% sensitivity in phenylketonuria detection over 25 years for over two million tests. It is probably the case for most screening programmes that sensitivity, wherever measured, approaches 100%. Harper: But wouldn’t that be designed not to identify the fairly mild forms of hyperphenylalaninaemia? Scriver: The test identifies all individuals who have an elevated level of phenylalanine in their blood. We showed that in Quebec, but whether all alleles at the PAH locus cause significant hyperphenylalaninaemia is another matter. Harper: Exactly. But wouldn’t it also be possible that there are other alleles which cause minimal elevations of phenylalanine? Scriver: Yes, there is an ascertainment bias in the screening test. When we obtain the complete profile of mutations in a population, we might be able to determine the absolute frequencies of the alleles and calculate how many cases were missed. Weatherall: Does any relationship exist between particular alleles and the seventy of the disease? Scriver: Everyone in this field hoped that there would be a solid genotype/ phenotype correlation, but it is not standing up to what we had expected. It depends on the phenotype studied. There is an inverse correlation between the effects of the mutation on PAH activity and blood phenylalanine levels (Okano et a1 1991). This correlation breaks down when IQ scores and genotypes are compared in untreated cases. Hartl: I would like to raise the question of random genetic drift among a set of essentially equivalent mutant alleles. The distribution of wild-type isoalleles is uneven, which is expected from random sampling from the Dirichlet distribution. If there is a set of functionally equivalent mutant alleles, all of which are equally bad, the distribution of allele frequency is

94

Discussion

exactly the same within that class, as it is among the wild-type isoalleles. Therefore, one would also expect an uneven distribution of allele frequencies, and it would not be unusual to have three or four alleles that accounted for 60% of all the mutant alleles, depending on the population history. I haven't seen any evidence presented here, from the frequencies of the alleles or their geographical distribution, which supports the involvement of selection. Scriver: Ken Kidd wrote the first commentary on genetic mechanisms underlying phenylketonuria (Kidd 1987). I would like to hear his comments in 1995. Kidd: There were two Danish studies of two different mutants that were

present at a high frequency and accounted for the majority of the phenylketonuria patients (DiLella et a1 1986, 1987). These mutant alleles were each in strong disequilibrium with nearby markers, but they were in different haplotypes. I considered this to be an unusual distribution that was difficult to explain, except by selection (Kidd 1987). My thinking now is much more along the lines of what Dan Hartl was just saying, i.e. that it is possible to observe diverse distributions, with unequal frequencies of mutant alleles, within a functional class. These phenylketonuria alleles were both essentially null mutants, so the pattern of haplotype frequencies may be the consequence of selection, but I now feel the frequencies themselves do not provide compelling evidence that selection is occurring. Harper: It used to be stated that genetic drift had its main effect on very small populations. Has this opinion changed over the years? Because we've been discussing studies where the population size has been several thousands. The distinction between the random factors involved does not seem to have been made. W . B o h e r : Rare events do occur, and there may be an enormous selection bias because the events that are common may be picked out selectively. There is no inconsistency. Weiss: There has also been a lot of non-random and differential population growth and turbulence in human history. Chakravarti: The effects of drift are manifest at every generation, generating an amplification over time. Dan Hartl discussed selectively equivalent alleles all of which are mutations that are fairly strongly selected and most of them are not very old. The relative allele frequencies are due to drift and selection is not an issue. W. B o h e r : This may be true for a dominant allele, for example a tumoursuppressor gene such as the APC (adenomatous polyposis coli) gene. But, in the case of a recessive gene, then the effects of drift are much larger. Hartl: The framework of the result I gave you is one in which the population size is sufficiently large so that the deleterious frequency is maintained at the expected mutation-selection balance. However, because the set of deleterious

Allelic variation in PAH

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alleles is small relative to the whole population size, it’s particularly susceptible to random genetic drift. Donnelly: I have another technical comment about equilibrium. Under neutral conditions, the reason why some alleles are common and some are rare is that the rare alleles haven’t been around for as long. Weiss: Can we expect to find the same kind of complexity in every gene? Bertranpetit: Not necessarily, it depends on the strength of natural selection and on the time span in which overdominance has been active. Scriver: But there aren’t many recessive diseases that have 1% frequency of the mutant gene in the population. Beighton: That question can be answered by asking how many distinct sickle-cell alleles there are in Africa, although I don’t know whether this situation has been studied at the intramolecular level. Weatherall: The literature suggests that there are at least four, but the evidence is fairly soft. The best estimate is that there is one in Africa and another that arose in India or the Middle East (Flint et a1 1993). Sing: I would like to point out that the apoE (apolipoprotein E) gene has been studied extensively (Davignon et a1 1988, Gerdes et a1 1992) and certain allele combinations have been associated with reductions in the quality of health. This work on APOE represents more homogeneity in allelic effects than has been presented for the globin genes.

References Beighton P, Botha MC 1986 Inherited disorders in the black population of southern Africa. I. Historical and demographic background. S Afr Med J 69:247-249 Cavalli-Sforza LL, Bodmer WF 1972 The genetics of human populations. Ann Hum Genet 36:239P-24QP Davignon J, Gregg RE, Sing CF 1988 Apolipoprotein E polymorphism and atherosclerosis. Arteriosclerosis 8: 1-21 DiLella AG, Marvit J, Lidsky AS, Guttler F, Woo SLC 1986 Tight linkage between a splicing mutation and a specific DNA haplotype in phenylketonuria. Nature 322:799803 DiLella AG, Marvit J, Brayton K, Woo SLC 1987 An amino-acid substitution involved in phenylketonuria is in linkage disequilibrium with DNA haplotype 2. Nature 327:333-336 Flint J, Harding RM, Boyce AJ, Clegg JB 1993 The population genetics of the haemoglobinopathies. Clin Haematol6215-262 Gerdes LU, Klausen IC, Sihm I, Faergeman 0 1992 Apolipoprotein E polymorphism in a Danish population compared to findings in 45 other study populations around the world. Genet Epidemiol 9: 155-167 Kidd KK 1987 Phenylketonuria: population genetics of a disease. Nature 327:282-283

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Nurse GT, Werner JS, Tenkins T 1985 The peoples of southern Africa and their affinities. In: Research monograph on human population. Clarendon Press, Oxford, P 18 Okano Y, Eisensmith RC, Guttler F et a1 1991 Molecular basis of phenotypic heterogeneity in phenylketonuria. 324 1232-1238 Penrose LS 1946 Phenylketonuria-a problem in eugenics. Lancet I:949-953

Genetic and geographical variability in cystic fibrosis: evolutionary considerations Jaume Bertranpetit* and Francesc Calafell Laboratori d'Antropologia, Facultat de Biologia, Universitat de Barcelona, Av Diagonal 645, €48028 and */nstitut Catala de Salut P ~ b l i c a ,Barcelona, Catalo nia, Spa in

Abstract. We have studied cystic fibrosis from an evolutionary perspective by focusing on three main points: the high frequency of cystic fibrosis; the different spectrum of mutations that cause the disease in different areas of Europe; and the age of the AF508 mutation. Several hypotheses have been suggested to explain the high frequency of cystic fibrosis. We argue that the most plausible is heterozygote resistance to cholera and to other dehydrating intestinal diseases. Different population sizes in the past could explain the higher diversity among cystic fibrosis mutations found in southern Europe. Three microsatellite loci are found close to the AF508 mutation. The reconstruction of the original microsatellite haplotype in which AF508 arose, and of the genealogy of the microsatellite haplotypes bearing the mutation, allowed us to date the mutation event at around 50000 years ago. 1996 Variation in the human genome. Wiley. Chichester (Ciba Foundation Symposium 197) p97-118

The development of human molecular genetics has allowed our genome to be dissected precisely and therefore our understanding of the genetic basis of inherited disorders is growing at a remarkable pace. The clinical applications of this advance have opened new fields in molecular research and deserve attention, although some of the new open questions may be related to a general understanding of what is there and why things are the way they are. Why do inherited disorders occur at particular frequencies?Why are there variations in the specific genetic errors that underlie these disorders? Why do these disorders follow geographical patterns? Is this related to adaptive processes? These are questions about the natural history of genetic diseases, and they can be addressed from an evolutionary perspective in which the population is the unit of reference. This wider perspective may also provide clues that, indirectly, have clinical applications. 97

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Many papers on cystic fibrosis begin by stating that it is the most common autosomal recessive disorder in Caucasoids. Its approximate incidence among newborns is 1 in 2500 (Boat et a1 1989), which means a gene frequency for the recessive disease of q=O.O2 and a carrier frequency of 1 in 25. Precise geographic variation of the incidence within Caucasoids is not known. Most of the numerous manifestations of this fatal disease result from an abnormally viscous mucus, which interferes with pulmonary function, and from an insufficient production of pancreatic digestive enzymes, which causes nutritional deficiencies and developmental retardation. The concentration of Cl- is also increased, and males (and most females) are infertile. Defective C1transport across affected epithelia is the hallmark of the disease (Boat et a1 1989, Welsh & Smith 1993). The gene responsible for cystic fibrosis was cloned by Riordan et a1 (1989). It spans over 250 kb, is composed of 27 exons (Fig. 1) and encodes a 1480 amino acid protein, namely the cystic fibrosis transmembrane regulator (CFTR), which is a regulated C1- channel. The understanding of the relationship between the structure and function of the CFTR is growing (Welsh et a1 1992). More than 400 different point mutations have been described (see Cystic Fibrosis Genetic Analysis Consortium 1994), which have different frequencies and geographic heterogeneity. AF508 is the most common of these mutations,

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accounting for 66% of affected chromosomes worldwide (in 28948 out of 43 849 cystic fibrosis chromosomes analysed) but with a wide fluctuation, from around 50% in Spain and Italy to more than 80% in Denmark. Although early results pointed to a cline from southern to northern Europe in the frequency of AF508, the pattern seems to be much more complex. Most of the other CFTR mutations have been found only once or are rare, although some have been detected in distinct populations with a non-negligible frequency. For instance, G542X has been found in 2.4% of cystic fibrosis chromosomes, G551D in 1.6%, N1303K in 1.3% and W1282X in 1.2%. These frequencies vary geographically and they are important in the detection of new mutations when designing strategies for DNA testing of cystic fibrosis and when a population screening for heterozygotes is planned. This enormous genetic heterogeneity seems to have a common physiological target: the differential impairment of the C1- channel function. Although this varies from mutation to mutation, it usually results in an important loss of efficiency that triggers the disease and allows the analysis and detection of the genetic abnormality. Among the many questions that can be asked about cystic fibrosis and the numerous mutations that cause it, only those related to its natural history deal with the mechanisms that have been acting to shape its existing complexity. Therefore, mechanisms such as mutation, natural selection (including overdominance), drift (including the founder effect) and population history (including migration and population expansions) should be considered in order to understand the abundance of cystic fibrosis, the frequency distribution of mutations and their geographical variation, and the history of the disease. Why is cystic fibrosis so frequent?

Possible explanations for the high frequency of cystic fibrosis include: genetic drift following a founder effect in European populations; the selective advantage of heterozygotes, mediated by increased fertility or increased survival; a high mutation rate; and epistatic selection at other closely linked loci. We will consider all these hypotheses, but we will concentrate on overdominance, which is the most plausible. Basic population genetics models rule out the possibility that bottleneck effects in Europe caused the recessive lethal frequency to raise to a level as high as 2%. The elapsed time since the last severe reduction in European population (18 000 years ago during the last glacial maximum, see Soffer & Gamble 1990) and the size of the European population are probably both large enough to prevent any significant increase in a recessive lethal allele in the whole of Europe. A special mutation pattern or high mutation rate has not been observed for the gene encoding CFTR and its surrounding region. There are more than 400

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different mutations, but this is not, by itself, an argument for a higher mutation rate if gene length, number of meioses (number of individuals and of generations) and ease of detecting new mutations (evident from the study of affected homozygotes) are considered. The proportion of mutated alleles can be designated as ‘q’ and the proportion of wild-type alleles as ‘p’. Most mutated alleles occur within heterozygotes, and the proportion of mutations in homozygotes is only 2% (q2/q2+pq) of the total number of mutant alleles. Consequently, new mutations are likely to be found in large populations in the future. Some nucleotide positions have mutated more than once. Morral et a1 (1994a) have observed both different types of substitutions at the same position, and the same substitution in different haplotypes. These changes either involve one of the 29CpG dinucleotides in the sequence encoding CFTR, which is known to have a high mutation rate, or involve other nucleotides. Morral et a1 (1994a) have argued that the recurrence of mutations contributes to the high frequency of cystic fibrosis; however, the importance of this can be ruled out by an approximate calculation of the expected number of recurrences under a random process. The simplest model assumes that it is a random process with a fixed mutation rate, and that the 1480 codons (excluding mutations in introns and flanking regions) correspond to 2590 effective nucleotides for mutation (for each codon, 75% of changes in first position, all in the second and none in the third will produce a change in amino acid). In this model, if 400 distinct mutations have been observed, then 57 recurrent mutations would be expected to take place. Therefore, it predicts a higher rate of recurrence than has been observed to date. Consequently, although mutation has an intrinsic interest, it is of little help in understanding the frequency and distribution of cystic fibrosis. Cuppens et a1 (1994) have studied the different distributions of haplotype backgrounds in normal chromosomes and CFTR mutants, and they argue that their results support the hypothesis of selective advantage of a particular haplotype background in combination with mutations in the gene encoding CFTR. However, an alternative argument is that linkage disequilibrium of the different mutations arises because of a founder effect and that these mutations arose a long time ago in a population with a different background to th7t of the present population (Morral et a1 1994b). Moreover, other, less frequent mutations (i.e. E60X, G458V; see Table 3 in Cuppens et a1 1994) are found in linkage disequilibrium with other haplotypes, and they are unlikely to be associated with putative loci that are present in other cystic fibrosis chromosomes but not in normal individuals. The high frequency of cystic fibrosis can best be explained by overdominance. Increased fertility of heterozygotes was initially described by Knudson et a1 (1967), but it was of such a large magnitude that it was difficult

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to believe. Indeed, it was ruled out by a detailed demographic analysis of families affected with cystic fibrosis (Jorde 8z Lathrop 1988). An alternative explanation for the selective advantage of heterozygotes is that they have an increased resistance to a particular infectious disease. This theoretical possibility has been associated with resistance to cholera-induced secretory diarrhoea (Romeo et a1 1989). A direct relationship has been demonstrated by Gabriel et a1 (1994) in the cystic fibrosis mouse model, where the correlation between CFTR protein and cholera-induced C1- and fluid secretion suggests that cystic fibrosis heterozygotes might possess a selective advantage of resistance to cholera. The amount of this advantage, in terms of biological fitness (w) or selective coefficient (s = 1 -w), may be easily computed if equilibrium is assumed between the total loss of cystic fibrosis patients and partial loss of those not protected against cholera. The equilibrium allele frequency q is q = s/(l + s), which, for q = 0.02, gives s = 0.0204. Cholera was endemic in India and was attested in Europe for the first time in 1832 (McNeill 1976). No previous records of cholera have been found (M. Livi-Bacci, personal communication 1995). It ceased to be a problem at around the beginning of the 20th century, when water sanitation became widespread. Assuming that cholera deaths in Europe have not been significant for the last three to four generations and that all individuals affected with cystic fibrosis die without offspring, q would have equalled 0.023 three generations ago. If cystic fibrosis was then at equilibrium, it would have taken 670 generations .(rQughly 13400 years) for a mutant to increase in frequency from (one single mutant chromosome in the population) to the present level of mutations causing cystic fibrosis. Conversely, if cholera was the only selecting agent, considering the five generations during which it ravaged Europe, the selective coefficient against non-cystic fibrosis homozygotes would have been 0.68; that is, 68% of all reproductive individuals in a population would have died of cholera every generation. This is not the case (see McNeill 1976). Therefore, although Gabriel et a1 (1994) succeeded in identifying the protection conferred by cystic fibrosis mutations against cholera, this disease could not have been the sole selective agent that raised the frequency of cystic fibrosis in Europe. It is possible that a wider range of infectious diseases which kill by intestinal fluid loss could have been forestalled by the beneficial cystic fibrosis mutations. The study of cystic fibrosis in India and Bangladesh could cast new light on our understanding.

The spectrum of mutations Many attempts have been made to understand the geographical pattern of the AF508 mutation frequency. CFTR containing the AF508 mutation fails to mature to the fully glycosylated form (Cheng et a1 1990, Kartner et a1 1992)

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and the mutant protein is degraded (Welsh & Smith 1993); therefore, nonfunctional CFTR does not reach the apical membrane. This rules out the possibility that CFTR containing AF508 performs differently to other mutants. One may expect heterozygotes to have 50% of the normal amount of CFTR protein, which would result in decreased fluid secretion in response to cholera and, therefore, increased protection against dehydration (Gabriel et a1 1994). The present knowledge of the relationship between genotype and clinical phenotype (Welsh & Smith 1993) allows one to assume that the clinical picture is merely the outcome of the remaining, if any, activity of the CFTR. Mutations can be classified into four groups according to how they disrupt CFTR function. Class I and I1 mutant proteins are missing from the correct cellular location, and would be expected to have a severe phenotype. Class I11 and IV are correctly localized, and some retain significant residual function, whereas others have little measurable activity, resulting in a wide clinical heterogeneity. One significant feature of the latter mutants is that some individuals bear mutations which do not produce the classical symptoms of cystic fibrosis, and their only clinical manifestation is infertility (only 2-3% of males and probably more than 10% of females are fertile [Boat et a1 19891). These genes will therefore not be passed on by affected individuals. These arguments suggest that the possible selective differences among the various cystic fibrosis mutations need not be considered when attempting to understand their evolutionary history and geographic distribution. Although not every cystic fibrosis mutation would have the same selective importance (be it as an advantage in heterozygotes or as a disadvantage in recessive homozygotes), the final output can only be considered in terms of the lack of function of the CFTR as a channel. This allows the discussion of cystic fibrosis as a whole, and the presence of one or other of the mutants at a certain frequency will only be a product of chance-mutations already present in the population would increase when overdominance became important. Understanding the mutation spectrum seems to depend more on stochastic processes and population history than on differential mutational or selective pressures. It is possible that the model under which the mutation spectra can be understood is close to the infinite alleles model, where only a part of the total allele frequency (which equals 1) may be filled by the various alleles (which add up to q). The value of q may oscillate according to selective pressures. An argument favouring this is that several mutations in the CFTR occurred a long time ago in the population history (see below). Mutations have been described in many different nucleotide positions along the gene, and only very few recurrences have been attested (Morral et a1 1994a). Thus, the basic tenets of the infinite alleles model seem to be fulfilled in this case. Hart1 & Campbell (1982) developed a theoretical framework for such a situation.

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The geographical distribution of the various cystic fibrosis mutants may, therefore, be a case of multiple alleles at one locus. The best data set currently available has been published by the Cystic Fibrosis Genetic Analysis Consortium (1994). This includes mutation frequencies for up to 24 mutations in more than 37 European populations. However, two points must first be considered. Sample sizes vary from scores to thousands of individuals; therefore, the probability of detecting a rare mutant can differ from one sample to another. In addition, mutation frequencies do not add up to 1 because a variable fraction of mutations remains unknown. In northern Europe a smaller number of mutations accounts for a larger fraction of cystic fibrosis chromosomes than in southern Europe, where there is a greater diversity of mutations. The different proportions could simply be due to different population sizes through history. In the infinite allele model, the number of alleles for a given sample size is a function of 8=4Np, where N is the effective population size and p is the mutation rate. We have estimated 8 for some populations. First, we drew 1000 random subsamples from every population in order to match the size of the smallest sample used (Slovenia, 117 chromosomes), and we considered the average number of different mutations found in those subsamples. Cystic fibrosis chromosomes where mutations have not been identified could range between two extremes: in a given population, they could all have the same mutation or they could all be different. Therefore, we computed the minimum and maximum possible values of 6. It is likely that the true value lies closer to the maximum value rather than to the minimum. Results show a neat separation between northern and central Europe (Wales, Omax = 3.29; Denmark, Omax = 5.08; France, Omax = 9.13), and southern Europe (Bulgaria, Omax = 20.27; Italy, Omax = 24.1 1; Spain, Omax = 26.55). There is no reason to believe that mutation rates are higher in southern than in northern Europe; therefore, the higher values of 8 (and consequently of the number of alleles or a more diverse mutation spectrum) can be explained by the demographic history of Europe, long before the industrial revolution, and perhaps as long ago as the palaeolithic period. The geographical distribution of cystic fibrosis mutations in Europe can be analysed by standard population genetics techniques. Considering the 24 most common cystic fibrosis mutations in Europe as alleles at a single locus, we have computed genetic distances between 37 European populations. We have used the Reynolds et a1 (1983) co-ancestry coefficient, which approximates the time of separation for two populations provided that the main differentiating agent is drift. The genetic distance matrix obtained (not shown) does not seem to match the known genetic affinities among European populations. The distance matrix for the cystic fibrosis mutations has no significant correlation (r = - 0.095, p = 0.2740, Mantel test with 10 000 iterations) with the genetic matrix obtained with classical genetic markers (i.e. blood groups, protein electromorphs and HLA system; totalling 88 alleles). Therefore, population

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history does not seem to be the main factor in shaping the distribution of cystic fibrosis mutations. The genetic distance matrix has been represented with the two most popular tree-construction algorithms (average linkage and neighbour joining) and no clear geographical pattern can be seen. The neighbour-joining tree (Fig. 2) displays a nearly unidimensional structure with a general northsouth ordering, but exceptions are so numerous and striking that no interpretable pattern is observed. The correlation between genetic and geographical distances is significant (r = 0.235, p = 0.01 16, Mantel test with 10 000 iterations). Therefore, geography could explain at most 5.5% of variability in the spatial pattern of cystic fibrosis mutations. It has repeatedly been argued that there is a latitudinal cline in the relative frequency of the AF508 mutation. Lucotte & Loirat (1993) showed that it is not a clear or single cline as has sometimes been asserted, although a lower relative frequency is found in the south of Europe. The correlation between the frequency of the AF508 mutation and latitude is 0.4055 (p = 0.0006, data from Cystic Fibrosis Genetic Analysis Consortium 1994, based on 37 European populations). As discussed above, this cannot be interpreted as a selective cline, and it may simply result from different population sizes. Taking into account all the results presented so far, we suggest the following possible scenario for the geographical distribution of cystic fibrosis mutations. At a certain moment (probably in the distant past) the existing CFTR mutants were selected and those present in a given population expanded. Some (presumably the oldest; for example, the AF508 mutation), were present everywhere, and many others appeared later, randomly at different places in a number according to the population size. These late mutations were more frequent in the Mediterranean than in northern and north-western areas of Europe. Age of the AF508 mutation

A recent collaborative survey under the coordination of X.Estivill (Morral et a1 1994b) analysed three intragenic microsatellites (IVSSCA, IVS17BTA and IVSl7BCA, see Fig. 1) in a wide sample of 1738 AF508 chromosomes from numerous European samples. The joint study of the microsatellite variation in chromosomes carrying specific point mutations allowed us to analyse variation in microsatellites and their origin through mutation, and the origin of specific cystic fibrosis mutations through microsatellite variation. This is a key issue in the evolutionary comprehension of cystic fibrosis as a genetic complex, in which the manifestation of the disease is the main, but not the only, outcome. Moreover, the geographical variation in microsatellites may be analysed further to understand the spatial pattern of cystic fibrosis. The method for such an analysis is not yet well established. New microsatellites are being described continuously, many of them lying close,

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FIG. 2. Neighbour-joining tree based on genetic distances between 37 European populations according to frequencies of known cystic fibrosis mutations.

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or even within, many cloned disease genes. This increases the likelihood that this method will be applied to clarify the origin of many other point mutations in a wide variety of genetic diseases. The method proposed to achieve this goal is based on the following premises, applied here to the above-mentioned microsatellites and the AF508 mutation:

(1) external evidence should support a unique origin for the AF508 mutation; (2) the mutation rate for microsatellites should be known; (3) a pattern of mutation for microsatellites should be assumed, through data from informative meioses, from the allele or haplotype frequency distribution or from theoretical models; (4) the haplotype carried by the original chromosome where the AF508 mutation took place should be identified; ( 5 ) a maximum parsimony tree of all haplotypes, containing the minimum possible number of mutations, can be constructed from the original haplotype; and (6) by knowing the mutation rate of the neighbouring microsatellites and the amount of variation produced, it should be possible to estimate the age of the mutation, that is, the date from which microsatellite variation would have accumulated. A full discussion of all these topics is beyond the scope of the present study, but the consideration of every point is necessary to reach the proposed goal. Therefore, we will discuss them briefly to assess the validity of the conclusions and possible drawbacks.

The AFSO8 mutation is unique

It is not easy, or even possible, to prove that a given widespread mutation has occurred only once in the history of either a population or the whole of humanity, but some findings make a multiple origin extremely unlikely. The recurrence of a specific deletion of three base pairs is an improbable event (Cooper & Krawczak 1993). Early reports of restriction fragment length polymorphism (RFLP) markers linked to the then unknown gene encoding CFTR showed a remarkable linkage disequilibrium (with haplotype B of the XV-2C and KM19 markers, see Fig. 1). This was the first inkling of homogeneity among the cystic fibrosis chromosomes. The results, however, are clearer when more markers are analysed specifically in chromosomes with the AF508 mutation. When three RFLP markers (544, T854 and TUB20) within or very close to the gene (Fig. 1) are analysed, linkage disequilibrium increases markedly, reaching a total association of AF508 chromosomes with a single haplotype (represented as 1-1-2). This haplotype is present in all chromosomes

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with the AF508 mutation but only in 12% of normal chromosomes (Morral et a1 1994a). This complete linkage disequilibrium with low mutation markers would only be possible if the origin of the AF508 mutation was unique and occurred initially in a chromosome with the haplotype in which the mutation is presently found. It could, nonetheless, be argued that the mutation occurred twice in two chromosomes that shared the same haplotype, which, although impossible to dismiss, is highly improbable. The mutation rate of microsatellites

There is a growing amount of information on the microsatellite mutation rate (Weber & Wong 1993, Di Rienzo et a1 1994). Usually, rates of around per locus and per meiosis are assumed (Weber & Wong 1993) but variation in rates between microsatellites remains largely unknown. If the number of alleles is related to the mutation rate, which is not necessarily true, the ratios of the mutation rates of the microsatellites analysed (IVS8CA, IVSl7BTA and IVSl7BCA; see Fig. 1) would be 13 : 19:4 (3.2:4.7: 1) for alleles found in chromosomes with the AF508 mutation and 11 : 43 : 11 (1 : 3.9 : 1) for those in normal chromosomes. Intermediate values (a ratio of 2.3 :4.3 : 1) are obtained from the reconstruction of the haplotype genealogy (see below). However, variation among microsatellites seems to be much higher. For the tetranucleotide repeat IVS6aGATT in intron 6a of the gene encoding CFTR, there is a low variation (only two different alleles have been found) and a complete linkage disequilibrium with the AF508 mutation. These both support a low mutation rate. A direct estimation of the mutation rates of the three microsatellites may come from the observation that, in families with cystic fibrosis, no mutation has been observed in more than 3000 meioses studied in each locus. Assuming the same mutation rate for the three microsatellites (which can later be corrected by estimating three different values), the maximum mutation rate with a 95% confidence interval can be calculated according to a Poisson distribution: 1 -enp = 0.95, where p is the mutation rate and n is the number of observed events (9000 transmissions of a microsatellite). The maximum value of p is therefore 3.3 x Ten thousand meioses (and 30000 transmissions of a microsatellite) with no mutation should have been observed for a value of 10-4. Therefore, the value obtained is the maximum estimate of the mean microsatellite mutation rate. However, lower values are more likely. From this value, and considering a given relative value of the mutation rate (for example, for a ratio of 2.3 :4.3 : I), the three rates can be estimated as: 3.0 x IVS8CA; 5.6 x for IVS17BTA; and 1.3 x for IVSl7BCA. This correction may not be relevant, given the inaccuracy of the estimated value of

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the maximum mutation rate. These estimations of mutation rate have been the subject of much debate (Kaplan et a1 1994, Estivill et a1 1994). The pattern of microsatellite mutation

A complete analysis of the microsatellite mutation pattern would include: (1) the relative probability of an increase or a decrease in the number of repeats; (2) the frequency distribution of the number of repeats gained or lost; and (3) the dependence of the above on the initial number of repeats. Other external factors could also be considered, but these would seriously complicate the model. Several workers have described specific mutations (Weber & Wong 1993) and general models have been proposed (Di Rienzo et a1 1994, Goldstein et a1 1995). Experiments to elucidate the microsatellite mutation pattern, in a similar way to that performed by Jeffreys et a1 (1994) on minisatellites, have not yet been carried out. It appears that the most likely mutations are the ones in which one repeat is added or subtracted, although a small fraction of mutations could involve greater leaps (i.e. the addition or deletion of more than one repeat). Our own unpublished observations on the distribution of alleles and the frequencies of the three microsatellites in chromosomes with diverse cystic fibrosis mutations (not the set used here) are summarized as follows: there is a slightly higher probability of decreasing rather than increasing the size of the microsatellite (0.57 f0.07 versus 0.43 f0.07); and the most likely events are the addition or deletion of one repeat (65% of cases), followed by two repeats (15%), three repeats (5%) and occasional jumps of higher magnitude (from four to 15 or more; overall 15%). It is possible that the last of these values is too high and therefore it requires confirmation. The original microsatellite haplotype

The haplotype of the original chromosome in which the AF508 mutation occurred can be recognized by the present variation. Two different approaches are possible. The first approach, an intralocus variation approach, is based on observations of the allele distribution for each microsatellite in chromosomes with the AF508 mutation. All the present variation must have been produced from a single number of repeats; therefore, it is likely that the most frequent will be the oldest Patterson & Guess 1977). The second approach, an interlocus approach, is by examining, for each allele of each microsatellite, the number of haplotypes for the other two loci to which the allele belongs. This approach considers that the older an allele is, the greater number of mutations for the other two microsatellites will have occurred. Both distributions for each of the three microsatellites are shown in Fig. 3.

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There is no doubt that allele 31 is the oldest for IVS17BTA and allele 13 is the oldest for IVSl7BCA. For IVSSCA, however, either allele 17 or allele 23 could be the oldest. This observation is consistent with the similar distribution pattern of the decreasing number of chromosomes around both allele 17 and allele 23, formed by slippage of one or a few repeats. It is therefore likely that the second event (a mutation in IVS8CA) originated soon after the first event (the AF508 mutation), by a single mutation of six repeats, and variation accumulated afterwards around both of them. Further calculations are not significantly affected by taking one or the other of the haplotypes as the oldest (23-31-13 or 17-31-13). A maximum parsimony tree of haplotypes

A tree linking all the haplotypes (haplotype genealogy) can be constructed by following certain rules. The first rule is to have the minimum possible number of mutations. This number is related to the mutation pattern-the higher the number of repeats a mutation adds or removes, the lower the number of mutational events required, but their probability is lower than for smaller leaps. The probability that an allele with a given number of repeats is the output of others with varying numbers of repeats depends on their relative frequencies. This rule also has to be taken into consideration when building a parsimony tree. Thus, although haplotype 23-35-13 (frequency = 2/1738) could come from its neighbour 23-34-13, its low frequency (1/1738) makes it more probable that it originated from a more distant but more frequent haplotype, for example 2331-13 (frequency = 71 1/1738). When building the parsimony tree, the origin is taken as the haplotype for which the probability of the mutation type multiplied by the frequency of the haplotype is maximum. This procedure incorporates the allele or haplotype frequency in the determination of the evolutionary pattern, and it assumes that the haplotype frequency is constant in time. The discovery of new haplotypes would not change the topology of the tree. The parsimony tree for chromosomes carrying the AF508 mutation is shown in Fig. 4. This tree is similar to that previously obtained without strict parsimonious criteria (Morral et a1 1994b). For the following calculations, the exact structure of the tree is not of critical importance. Variation in the estimation of mutation rates is more critical. Age of the AF508 mutation

Once the haplotype genealogy and the mutation rate are known, it may be possible to estimate the age of the mutation, i.e. the date after which the microsatellite variation would have accumulated, under a constant-rate neutral

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FIG. 4. Maximum parsimony tree linking all the haplotypes found in chromosomes containing the AF508 mutation. Numbers in brackets refer to the number of chromosomes bearing each haplotype.

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mutation process. Mutations will accumulate in an inexorable fashion along lineages (Hudson 1990) and the number of mutations that would have accumulated in the individuals after t generations follows a Poisson distribution with a mean I = p t . For the 46 haplotypes derived from the original haplotype in which the AF508 mutation occurred, the mean number of mutations may be computed by considering the fraction of individuals that have each haplotype and the number of mutations that this haplotype has accumulated. Stochastic phenomena that could increase or decrease the frequency of only certain haplotypes would be difficult to take into account and are therefore not considered. The 1705 chromosomes carry, in total, 1477 microsatellite mutations, corresponding to a mean I = 0.866. With a mutation rate of 3.3 x this gives a total time (t=I/p) of 2625 generations, which, for a generation time of 20 years, is equivalent to 52 500 years. This is a mean estimation and its standard error may be large. If the mutation rates of the three microsatellites are different (see above), the number of generations becomes t = C(4/pJ, where i accounts for the number of mutations and mutation rate carried for each of the three microsatellites (Kaplan et a1 1994, Estivill et a1 1994). In this case, t becomes 2395 generations (48 000 years). This correction, however, has little value because the overall mutation rate is based only on a rough estimate.

Conclusions The most frequent mutation in cystic fibrosis, AF508, is an old mutation and could only have survived in human populations if it conferred an advantage to carriers. Both historical and genetic evidence suggests that carriers of this cystic fibrosis allele must be more resistant to cholera and other diseases which produce diarrhoea. This would therefore explain the high frequency of the AF508 mutation. Many other cystic fibrosis mutations seem to have had a similar role. The present distribution of these mutations, a key issue in clinical practice, appears to be the result of different (and irrelevant) demographic histories which would have shaped the genetic structure of human populations. This hypothesis, widely accepted for the distribution of neutral alleles and used in genetic diversity studies for the reconstruction of population histories, also seems to be relevant for the understanding of the distribution of alleles responsible for diseases. At least this appears to be the case for cystic fibrosis, the most common autosomal recessive disorder in Europe. Acknowledgments

Thanks to Xavier Estivill for first posing the population genetic problem of cystic fibrosis to us and therefore introducing us to this work. We were stimulated by the

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European Science Foundation conference on inherited disorders and their genes in European populations, which steered our efforts towards disease genes. This research is funded by the DGICYT (Spain) grant PB92-0722.

References Boat TF, Welsh MJ, Beaudet AL 1989 Cystic fibrosis. In: Scriver CR, Beaudet AL, Sly WS, Valle D (eds) The metabolic basis of inherited disorder. McGraw-Hill, New York, p 2649-2680 Cheng SH, Gregory RJ, Marshall J et a1 1990 Defective intracellular transport and processing is the molecular basis of most cystic fibrosis. Cell 63:827-834 Cooper DN, Krawczak M 1993 Human gene mutation. Bios Scientific, Oxford Cuppens H, Teng H, Raeymaekers P, De Boeck C, Cassiman J-J 1994 CFTR haplotype backgrounds on normal and mutant CFTR genes. Hum Mol Genet 3:607-614 Cystic Fibrosis Genetic Analysis Consortium 1994 Population variation of common cystic fibrosis mutations. Hum Mutat 4 167-177 Di Rienzo A, Peterson AC, Garza JC, Valdes AM, Slatkin M, Freimer NB 1994 Mutational processes of simple sequence repeat loci in human populations. Proc Natl Acad Sci USA 91:3166-3170 Estivill X, Morral N, Bertranpetit J 1994 Age of the AF508 cystic fibrosis mutationreply. Nat Genet 8:216-218 Gabriel SE, Brigman KN, Koller BH, Boucher RC, Stutts MJ 1994 Cystic fibrosis heterozygote resistance to cholera toxin in the cystic fibrosis mouse model. Science 266: 107-109 Goldstein DB, Ruiz Linares A, Cavalli-Sforza LL, Feldman MW 1995 An evaluation of genetic distances for use with microsatellite loci. Genetics 139:463-471 Hart1 DL, Campbell RB 1982 Allele multiplicity in simple mendelian disorders. Am J Hum Genet 34:866-873 Hudson RR 1990 Gene genealogies and the coalescent process. Oxf Surv Evol Biol 7: 1-44 Jeffreys AJ, Tanawi K, MacLeod A, Monckton DG, Neil DL 1994 Complex gene conversion events in germ line mutation at human minisatellites. Nat Genet 6: 136145 Jorde LB, Lathrop GM 1988 A test of the heterozygote-advantage hypothesis in cystic fibrosis carriers. Am J Hum Genet 42:808-815 Kaplan NL, Lewis PO, Weir BS 1994 Age of the AF508 mutation. Nat Genet 6:216-218 Kartner N, Augustinas 0,Jensen TJ, Naismith AL, Riordan JR 1992 Mislocalization of AF508 CFTR in cystic fibrosis sweat glands. Nat Genet 1:321-327 Knudson AG, Wayne L, Hallett WY 1967 On the selective advantage of cystic fibrosis heterozygotes. Am J Hum Genet 19:388-392 Lucotte G, Loirat F 1993 A more detailed map of the cystic fibrosis mutation AF508 frequencies in Europe. Hum Biol 65503-507 McNeill W 1976 Plagues and peoples. Anchor Press, Garden City, NY Morral N, Llevadot R, Casals T et a1 1994a Independent origins of cystic fibrosis mutations R334W, R347P, R1162X, and 3849 + lOKbc + T provide evidence of mutation recurrence in the CFTR gene. Am J Hum Genet 55:89&898 Morral N, Bertranpetit J, Estivill X et a1 1994b The origin of the major cystic fibrosis mutation (AF508) in European populations. Nat Genet 7: 169-175 Reynolds J, Weir BS, Cockerham CC 1983 Estimation of the coancestry coefficient: basis for a short term genetic distance. Genetics 105767-779

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Riordan JR, Rommens JM, Kerem B-S et a1 1989 Identification of the cystic fibrosis gene: cloning and characterization of complementary DNA. Science 245: 1066-1073 Romeo G, Devoto M, Galietta L-J 1989 Why is the cystic fibrosis gene so frequent? Hum Genet 84:1-5 Soffer 0, Gamble C (eds) 1990 The world at 18000 BP. Unwin Hyman, London Watterson GA, Guess HA 1977 Is the most frequent allele the oldest? Theor Popul Biol 11:141-160 Weber JL, Wong C 1993 Mutation of human short tandem repeats. Hum Mol Genet 2: 1123-1 128 Welsh MJ, Smith AE 1993 Molecular mechanisms of CFTR chloride channel dysfunction in cystic fibrosis. Cell 73:1251-1254 Welsh MJ, Anderson MP, Rich DP et a1 1992 Cystic fibrosis transmembrane conductance regulator: a chloride channel with novel regulation. Neuron 8:821-829

DISCUSSION Weiss: Some people, for example, Cuppens et a1 (1994), have suggested that a gene near to the CFTR (cystic fibrosis transmembrane regulator) gene is responsible for the high frequency of the AF508 allele. Is the linkage disequilibrium between nearby genes consistent with this data? Bertranpetit: No, it is not consistent. When we look at different point mutations for cystic fibrosis we do not find consistent linkage disequilibrium, although it may exist for each of the individual mutations. W . B o h e r : Why did you choose cholera as an explanation for selection? There are numerous infectious diseases that might be relevant which have been around for longer than cholera. Bertranpetit: I have chosen cholera because it is the cause proposed in the literature, and it is the only experimental case known and tested to date (Romeo et a1 1989, Gabriel et a1 1994). Scriver: The Gabriel et a1 (1994) paper was informative because it showed the effect of the mutant CFTR gene on C1- flux. But cholera is not the only agent that causes C1--losing diarrhoea. Weiss: Why has this not happened in Africa or Asia where diarrhoea and dysentery are also common? There are many mutations of this gene, and there would have been plenty of opportunity for selection to raise the frequency of cystic fibrosis alleles there, as in Europe. Scriver: I don’t know. It’s an interesting point. Hartl: What is the frequency of cystic fibrosis in South Africa? Beighton: In South Africa the Black population numbers about 30 000 000 but there are few, if any, instances of cystic fibrosis (Beighton & Botha 1986) In the pre-molecular era, the first step in the diagnosis of cystic fibrosis involved checking the electrolyte content of sweat. It is possible that heterozygotes have minor changes in electrolyte content. Many epidemic diseases are spread by biting insects, which are notoriously fickle in their choice of the persons whom

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they bite. My question is, could the selection pressure be the desirability of heterozygotes to these biting insects? Weiss: I don’t know if that hypothesis has been investigated, although some work has been carried out on the response of heterozygotes to tuberculosis infection (e.g. Meindl 1987). Tuberculosis is also a disease that is more common in urban Europe than elsewhere, which suggests that some cultural influences may also be involved. W. Bodmer: The mutations that you have studied are not equivalent, so there must be variations in the mutation rates, although I accept that AF508 doesn’t have a particularly high mutation rate. These mutations also do not have equivalent functions: the AF508 is an inframe deletion, which results in a partially functional product, and there are knockout mutations. Bertranpetit: Mutations can be categorized into four groups according to how they disrupt the CFTR function. There are over 400 known mutations and the effect is the same for most of them (lack of activity), although some do have a residual activity, but affected individuals in these cases are infertile. W. Bodmer: But you cannot assume that all mutations are equally likely. Therefore, your calculation of recurrence is not correct. Bertrunpetit: I agree that not all the mutations are equally likely. Our calculation of the expected number of recurrent mutations under a random model is approximate. If more parameters are introduced in the model, as for example the existence of 29CpG dinucleotides, this does not result in major differences. Armour: Is there any reason to reject the null hypothesis that the high frequency of the AF508 allele is just a result of drift? If a disease-causing allele of cystic fibrosis had not drifted to high frequency in Europe, we would be studying another disease. This is an ascertainment bias because cystic fibrosis is now so important in Europe. W. Bodmer: I agree. You have claimed that drift is impossible, but you did not explain why. Drift is an entirely plausible explanation. Bertrunpetit: I have not discussed drift thoroughly because in our opinion it is an unlikely phenomenon to consider when trying to understand a gene frequency as high as 2% for a lethal allele in a large population that has not suffered bottlenecks for millennia. The lethality would have decreased the allele frequency to much lower levels than 2% independently of the initial frequency. Under selection, it would decrease, for example, from 10% to 1% in only 90 generations (about 1800 years). W. Bodmer: I’m sure this can be simulated by random drift effects. Chukravarti: You cannot exclude that drift is involved. The amount of drift depends on certain assumptions on the time of origin of the mutation and the size of the population when the mutation arises. In a paper last year (Morral et a1 1994), you estimated the time of origin as 50 000 years ago, but a number of scientists have disagreed with this conclusion (Kaplan et a1 1994). I have a

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further problem in reconciling the phylogeny you presented with this estimate. Whenever one constructs phylogenies from sequence data, or microsatellite variation data as you have, problems can occur because different loci may have different mutation rates. In this case, this is definitely true because the evolutionary change at any microsatellite locus not only depends on its intrinsic mutation rate but also on how far it is from the target AF508 mutation. Therefore, a marker locus that’s a few hundred kilobases away from the target, for example, will evolve at a slower rate than a marker locus that is thousands of kilobases away. Your assumption that you can use the constancy of rate of change is incorrect. If one accounts for distance between marker and targets in the analysis, the estimates turn out to be of the order of 2500-5000 years ago. Weiss: If the aggregate frequency stays the same in different populations, but the mix of specific alleles changes, why doesn’t that argue against drift as the causal process? Bertranpetit: The aggregate frequency is the same in Spain and Denmark (about 2%), but it is achieved through different compositions of the various point mutations. Chakravarti: But most of the differences are due to the AF508 allele and not the other alleles. Bertranpetit: No, they are equally likely to result from other mutations: the AF508 represents a fraction of the 2% frequency that varies from one place to another, and the rest is filled up with the other alleles. It is not enough to take only the AF508 relative frequency into consideration. I do not see a way of understanding the presence of a lethal allele at a frequency as high as 2% in a large population. W. Bodmer: Drift can explain the increased frequency in a neutral heterozygote because a disadvantageous homozygote is so rare. Weiss: But if the estimates are accurate and the aggregate frequency is roughly the same in different parts of the population, although the alleles that comprise that frequency vary, do you agree that this would be evidence against a drift explanation for the overall phenomena? W. Bodmer: No. All the data that I’m aware of do not agree with that. Templeton: One line of evidence in Jaume Bertranpetit’s presentation that supports selection in this case is the genetic distance analysis of the different populations within Europe, because it does not agree with the other data. If these alleles were neutral, they should reflect the population structure in which they’re embedded, so that all the alleles should be equivalent. However, when one allele shows a different geographical pattern, this cannot be explained by a drift hypothesis. I would like to make another comment, about the identification of the oldest allele. Coalescent theory predicts that there is a tendency for the most frequent allele to be the oldest, but the allele’s age is also influenced by the allele’s

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position in the genealogical structure of the gene tree. As a consequence, there are often situations in which the oldest allele is not the most frequent allele (Castelloe & Templeton 1994). It is possible to estimate the age of the oldest allele under the assumptions of neutrality and constant mutation rates by using some equations generated by Tajima (1983), but the 95% confidence interval of this age is often large (Templeton 1993). Donnelly: Population expansion effects could be important in all of these issues, and in particular on the subject of drift, because population expansions cause a freeze in the gene frequencies, so it’s possible that drift could have occurred in a smaller population prior to the freeze. Kidd: It is plausible that when drift occurs in a smaller population, the gene frequency can increase to 2% and selection, even for a lethal, is extremely slow. Therefore, it would take hundreds of generations to reduce it. Positive selection, which would elevate the frequency, could also be affected. Therefore, in a cholera epidemic, if the gene frequency was close to 1-2%, selection could elevate the gene frequency significantly in one generation, but selection against the homozygotes would then be only slowly operating to reduce it. Freimer: Most of your analysis is based on the interpretation of the frequencies of alleles, which are defined by the length of the PCR product containing the microsatellite repeat. Sequencing the alleles, rather than just analysing allele size may be more informative because the flanking sequences might be important in indicating the lineage of particular alleles. Chakravarti: How could the lower frequency of AF508 in southern Europe compared with northern Europe have been established? Bertranpetit: The idea that I have put forward is that whether there is predominantly one allele or many other alleles depends only on the population size when the mutation spectrum is established. Under the infinite allele model, fully applicable for the various disease-causing alleles (Hartl & Campbell 1982), the number of alleles for a given sample size is only a function of 8, which is four times the mutation rate multiplied by the effective population size. Mutation rates do not vary geographically. In this case, the geographic distribution of 8 cannot be other than a function of population size when the mutants were positively selected. Depending on the population size, which had an enormous geographic variation in prehistoric Europe, some alleles would increase in frequency or new mutants would be added to those already existing (Hassan 1981, McEvedy & Jones 1978). Hurtl: The frequency of the AF508 allele is twice as high as the allele responsible for phenylketonuria, which means that it only has to have a fourfold higher mutation rate than phenylketonuria to be maintained by mutationselection balance. In the discussion following Charles Scriver’s presentation, we had no difficulty in accepting that the phenylketonuria allele may be maintained by mutation-selection balance, with variation among populations explained as random drift.

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It seems to me that the high frequency of AF508 in northern Europe, compared with southern Europe, favours random genetic drift, rather than selective forces, as the explanation for maintaining the frequency. Otherwise, one has to invoke a different selective regime in northern Europe than in southern Europe. Edwards: Cystic fibrosis is rarer in Sweden than in the UK, and it is rarer still in Finland. Ken Kidd said it would take a long time to die out, it seems to me it would die out quite quickly with that frequency, within a half-life of about two or three centuries. References Beighton P, Botha MC 1986 Inherited disorders in the black population of southern Africa. 11. Gene disorders. S Afr Med J 69:291-296 Castelloe J, Templeton AR 1994 Root probabilities for intraspecific gene trees under neutral coalescent theory. Mol Phylo Evol 3:102-113 Cuppens H, Teng H, Raeymaekers P, De Boeck C, Cassiman J-J 1994 CFTR haplotype backgrounds on normal and mutant CFTR genes. Hum Mol Genet 3:607414 Gabriel SE, Brigman KN, Koller BH, Boucher RC, Stutts MJ 1994 Cystic fibrosis heterozygote resistance to cholera toxin in the cystic fibrosis mouse model. Science 266: 107-109 Hart1 DL, Campbell RB 1982 Allele multiplicity in simple mendelian disorders. Am J Hum Genet 34866873 Hassan FA 1981 Prehistoric archaeology. Academic press, New York Kaplan NL, Lewis PO, Weir BS 1994 Age of the AF508 cystic fibrosis mutation. Nat Genet 8:2162 18 McEvedy C, Jones R 1978 Atlas of world population history. Penguin, Harmondsworth Meindl R 1987 Hypothesis: a selective advantage for cystic fibrosis heterozygotes. Am J Phys Anthropol 743945 Morral N, Bertranpetit J, Estivill X et a1 1994 The origin of the major cystic fibrosis mutation (AF508) in European populations. Nat Genet 7: 169-175 Romeo G, Devoto M, Galietta L-J 1989 Why is the cystic fibrosis gene so frequent? Hum Genet 84: 1-5 Tajima F 1983 Evolutionary relationship of DNA sequences in finite populations. Genetics 105:437-460 Templeton AR 1993 The ‘Eve’ hypothesis: a genetic critique and reanalysis. Am Anthropol 955 1-72

Unusual inheritance patterns due to dynamic mutation in fragile X syndrome Grant R. Sutherland and Robert I. Richards

Department of Cytogenetics and Molecular Genetics, Centre for Medical Genetics, Women’s and Children’s Hospital, North Adelaide, SA 5006, Australia

Abstract. Fragile X syndrome is the most common form of familial mental retardation and is ope of the world’s most common genetic diseases. The inheritance patterns of the disease have many unusual features. It is an X-linked disorder yet there are asymptomatic carrier males. The disease is expressed only when the gene is inherited from the mother. The risk of a carrier woman having a child with the syndrome depends upon her position in the pedigree (the Sherman paradox) and her own intellectual status. The discovery that the disease is due to dynamic mutation (which is a multistage process) that inactivates FMRI has provided an explanation for the unusual inheritance patterns. The finding of linkage disequilibrium between the fragile X mutations and closely linked DNA markers (haplotype) has required a reinterpretation of this phenomenon for dynamic mutations. Only a small number of normal alleles at the fragile X locus have long stretches of perfect repeat (2% with more than 24 copies) and these form a reservoir of alleles that can increase in length into the premutation range. Dynamic mutation is, so far, an exclusively human phenomenon, but this is probably because it has yet to be discovered in other species. Unusual inheritance patterns are a hallmark of dynamic mutation diseases.

1996 Variation in the human genome. Wiley, Chichester (Ciba Foundation Symposium 197) p 119-136

Fragile X syndrome is the most common form of familial mental retardation, affecting about 1 in 2500 children. It has been found in all ethnic groups exposed to western medicine and is, therefore, one of the world’s most common human genetic diseases. Its high prevalence and puzzling, unusual features were recognized in the early 1980s. The syndrome is named after the fragile site in chromosome band q27.3 on the end of the long arm of the X chromosome. The demonstration of this site was, for many years, the only diagnostic test for the syndrome because, apart from mental retardation, other abnormalities that characterize the disease are subtle and variable. The fragile site was often difficult to demonstrate cytogenetically, as many asymptomatic carriers of the 119

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disease did not show the fragile site and the inheritance in families departed from a classical X-linked pattern. The cloning of the fragile X mutation, which was found to be an amplification of a naturally occurring polymorphic CCG trinucleotide repeat, resulted in the discovery of the mechanism of dynamic mutation (Yu et a1 1991, Oberlk et a1 1991, Verkerk et a1 1991).The molecular properties of the fragile X mutation provided an explanation for its unusual genetics, and the concept of dynamic mutation offered explanations for other unusual genetic phenomena (Sutherland et a1 1991). Additional fragile sites, which have similar genetic mechanisms, have since been cloned, and a number of other neurological disorders have now also been found to be due to dynamic mutation of another trinucleotide (AGC) repeat (Sutherland & Richards 1994).

inheritance of fragile X syndrome

Fragile X syndrome is clearly an X-linked disorder because the fragile site and the mutation are on the X chromosome. However, early studies of families with fragile X revealed that the pattern of inheritance was unusual (see Sherman et a1 1984, 1985). These major segregation analyses revealed the following points. (1) The fragile X mutation arises only in sperm, with a mutation rate of which is the highest mutation rate estimated at a single locus in 7x humans. This mutation also has to be passed through an egg before it produces the disease. A corollary of this is that the mother of every child with fragile X syndrome is a carrier. (2) The daughters of fragile X carrier males do not have fragile X syndrome. The mothers of such carrier males have a much lower risk of having affected children than the daughters of these men. This is known as the Sherman paradox. (3) The penetrance of the mutation in the offspring of carrier women depends on their intellectual status. For carriers of normal intelligence, the penetrance in sons is about 0.75; whereas for even mildly affected mothers, it is 1.0. These features of fragile X inheritance defied explanation in terms of classical genetics as it was understood at that time. Many hypotheses were offered, including some that predicted two states for the mutation-i.e. a premutation and a full mutation (Pembrey et a1 1 9 8 5 j a n d a more complex and constantly evolving hypothesis involving imprinting (Laird 1987, 1993). It is only meaningful to discuss the inheritance pattern of fragile X in terms of its molecular genetics.

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Molecular genetics The fragile X mutation was characterized at the molecular level in 1991 by several groups (Oberlk et a1 1991, Yu et a1 1991, Verkerk et a1 1991). It was found to involve changes in the number of copies of a polymorphic CCG trinucleotide repeat in the 5' untranslated region of FMRl. This repeat varies from six to about 55 copies on normal X chromosomes. The copy number becomes unstable if it approaches the upper limit, so that chromosomes with more than 55 copies have a fragile X premutation. The rate of instability increases dramatically as the repeat copy number increases. The concept of dynamic mutation, therefore, is that DNA which has been changed once has a different risk of further change than the original sequence (Richards & Sutherland 1992). Once the number of copies of the repeat exceeds about 230, the CpG residues in the repeat and throughout the promoter region of FMRZ become methylated. This methylation is associated with transcriptional silencing of the gene (Pieretti et a1 1991) and phenotypic manifestation of fragile X syndrome (in all males and about 60% of females with the full mutation). Studies of the inheritance of the mutation (Rousseau et a1 1991, 1994) revealed the following points. (1) Premutations transmitted by males do not change in size. Subsequently, it was shown that males, even those with full mutations, only produce sperm which contain premutations (Reyniers et a1 1993). Thus, the daughters of males with fragile X mutations (of any type) are premutation carriers, and as such, do not have features of fragile X syndrome. (2) Premutations transmitted by females increase in size, and the chance that they increase to full mutations is a function of their size (Yu et a1 1992, Fisch et a1 1995). The probability that a premutation with less than 90 repeats develops into a full mutation is less than one, whereas it is a virtual certainty for premutations that have more than 90 repeats. Women with full mutations have children with full mutations. These observations explain the unusual inheritance patterns. The Sherman paradox is due to women in different generations having premutations at different stages of progression. The effect of a woman's intellectual status reflects the type of mutation she carries. The high mutation rate measures, to some extent, the change from a premutation to a full mutation.

Linkage disequilibrium The finding of linkage disequilibrium (Richards et a1 1992) at a locus thought to have a high mutation rate (it must be high to maintain a genetically lethal

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disorder at such a high frequency in the population) is surprising. In classical genetic (static mutation) terms, linkage disequilibrium indicates that all mutations of a particular haplotype are probably descended from a single mutational event. In fragile X syndrome, and indeed for dynamic mutation disorders in general, linkage disequilibrium cannot be interpreted in the same way as for conventional mutations. Analysis of the DNA sequence of normal alleles of the CCG repeat shows that they frequently contain imperfectionsprimarily a CCT trinucleotide about once every 10 repeat units. These imperfections are absent from some alleles, and some of the longer alleles have only one or two such interruptions at the 3' end of the sequence. Only 2% of normal alleles have a perfect repeat length of more than 23 trinucleotides (Kunst & Warren 1994). Similar findings were made by Eichler et a1 (1994), Snow et a1 (1994) and Hirst et a1 (1994), who showed that the alleles with long stretches of perfect repeat occurred more frequently on haplotypes overrepresented on fragile X chromosomes. An association between perfect repeat configuration and instability has also been demonstrated at the FRAZ6A locus (Nancarrow et a1 1995) and the SCAZ locus (Chung et a1 1993), suggesting a common basis for repeat instability. When does allele expansion occur?

Controversy surrounds this issue. Many individuals with full mutations have multiple bands or smears, indicating that postzygotic changes have occurred in repeat length. Studies of fibroblasts with full mutations show that these changes are stable in tissue culture (Wohrle et a1 1993). This, and other evidence from studies of monozygotic twins, indicates that gross instability is confined to a short period early in development. Small changes in copy number, possibly within the premutation range, probably occur prezygotically. There are, however, two views on the timing of the conversion of a premutation to a full mutation. The first view is that this event occurs during oogenesis and is followed by postzygotic instability, which could result in either an increase or a decrease in size. The second view is that full mutations are formed postzygotically. This hypothesis requires the involvement of some form of imprinting because, if it is correct, the zygote that receives a premutation from the sperm must treat it in such a way that it does not expand; whereas when the zygote receives the premutation from an ovum, the premutation must be given the opportunity to expand. Unfortunately, although it is known that sperm always contribute a premutation, nothing is known about the type of mutations in ova. A corollary to this hypothesis is that the germ cells of both sexes are protected from somatic expansion from premutations to full mutations, unless all full mutations in germ cells revert to premutations. Expansion occurs before extraembryonic membranes differentiate from cells in the inner cell mass that will form the

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fetus, so it is difficult to see how the germ cells could escape such expansion. Alternatively, the pattern of full mutation with multiple bands may be predetermined so that it can occur independently, yet virtually identically, in these two tissues. The first view is far less complex and would only require that mutations behave differently in meiosis in the two sexes. The meiotic process is different in males and females and may therefore be adequate to explain the differences. Other fragile sites Four other fragile sites have been characterized at the molecular level: FRAXE (Knight et a1 1993) and FRAXF (Parrish et a1 1994) are on the X chromosome, distal to the fragile X (FRAXA) site; FRAI6A (Nancarrow et a1 1994) is on the short arm of chromosome 16; and FRAIIB (Jones et a1 1995) is on the long arm of chromosome 11. These are all similar to the fragile X site in that they are dynamic mutations of naturally occurring polymorphic (CCG) repeats. Studies of FRA16A (Nancarrow et a1 1994) revealed that full mutations are hypermethylated, as are FRAXA full mutations, but that non-mutant alleles at the FRA16A locus are not normally methylated. This was an important observation because Laird (1987) had suggested that DNA methylation, which occurred as part of the X-chromosome inactivation process, was important for generating the full mutation in fragile X syndrome. F R A l l B is of particular interest because it is in the promoter region of the oncogene CBL2 (Jones et a1 1995). There has been considerable controversy about the role, if any, of fragile sites in the genesis of chromosomal rearrangements in malignant tissue, and this finding may reopen that debate. Two women with F R A l l B were ascertained because they had children with Jacobsen syndrome, a partial deletion of the long arm of chromosome 11. This is a very rare disorder and, although the breakpoint(s) on the long arm of this chromosome can vary in location, it is remarkable to find instances where they are at the fragile site. This would indicate that breakage at the fragile site can occur in viva The risk to any woman who has this fragile site of having a child with Jacobsen syndrome must, however, be small. Dynamic mutations in other species At present, dynamic mutations are an exclusively human phenomenon. The mouse homologues of human genes have shorter stretches of trinucleotides that are less polymorphic than they are in humans. This could, however, be due to the inbred nature of laboratory mice. The stability of the AGC trinucleotide is greater in transgenic mice containing an amplified AGC repeat in the androgen receptor gene than in humans (Bingham et a1 1995). A lower frequency of dynamic mutation diseases

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in mice may be due to their shorter life spans. The intracellular chemistry of neural cells probably does not differ greatly between species, so that short-lived animals, such as mice, may die before a disease, which has an onset in middle life in humans, can manifest itself.

Unusual inheritance and trinucleotide repeats Unusual inheritance patterns are associated with other dynamic mutation disorders that are distinct from fragile X syndrome. The rare folate-sensitive fragile sites that have been cloned are dynamic mutations of polymorphic CCG repeats, so it is reasonable to hypothesize that all members of this group of fragile sites have similar molecular mechanisms. Segregation analysis of these fragile sites (Sherman & Sutherland 1986) showed that when they were transmitted by women, they were fully penetrant (in terms of cytogenetic expression); whereas when they were transmitted by males, penetrance was only 25%. Insufficient families have been characterized at the molecular level to offer an explanation for this. Dynamic mutations of AGC repeats are associated with the phenomenon of anticipation. This is the tendency for an otherwise dominant disorder to onset at an earlier age, or be more severe in successive generations of a family. An example of this is in myotonic dystrophy (Harper et a1 1992), where there is a dynamic mutation of an AGC repeat in the 3' untranslated region of the gene encoding myotonin protein kinase. Some of the other neurological disorders (e.g. Huntington's disease, dentatorubral-pallidolyusian atrophy and spinocerebellar ataxia type l), which are due to dynamic mutations of AGC repeats in protein-coding (polyglutamine) regions of their respective genes, show paternal anticipation. Consequently, although unusual inheritance patterns could lead to suspicion that a disorder is due to a dynamic mutation, care must be exercised in concluding that anticipation is present on the basis of a few families. Ascertainment bias can undoubtedly mimic anticipation (Penrose 1948); and variation in age of onset, which many diseases exhibit, can also give the appearance of anticipation in small families.

Acknowledgement G.R.S. is an International Research scholar of the Howard Hughes Medical Institute.

References Bingham PM, Scott MO, Wang SP et a1 1995 Stability of an expanded trinucleotide repeat in the androgen receptor gene in transgenic mice. Nat Genet 9:191-196

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Chung M-Y, Ranum LPW, Duvick LA, Servadio A, Zoghbi HY, Orr HT 1993 Evidence for a mechanism predisposing to intergenerational CAG repeat instability in spinocerebellar ataxia type I. Nat Genet 5:254-258 Eichler EE, Holden JJA, Popovich BW et a1 1994 Length of uninterrupted CGG repeats determines instability in the FMRl gene. Nat Genet 8:88-94 Fisch GS, Snow K, Thibodeau SN et a1 1995 The fragile X premutation in carriers and its effect on mutation size in offspring. Am J Hum Genet 56:1147-1155 Harper PS, Harley HG, Reardon W, Shaw DJ 1992 Anticipation in myotonic dystrophy: new light on an old problem. Am J Hum Genet 57:lO-16 Hirst M, Grewal PK, Davies KE 1994 Precursor arrays for triplet repeat expansion at the fragile X locus. Hum Mol Genet 3:1553-1560 Jones C, Penny L, Mattina T et a1 1995 Association of a chromosome deletion syndrome with a fragile site within the proto-oncogene CLB2. Nature 376: 145-149 Knight SJL, Flannery AV, Hirst MC et a1 1993 Trinucleotide repeat amplification and hypermethylation of a CpG island in FRAXE mental retardation. Cell 74127-134 Kunst CB, Warren ST 1994 Cryptic and polar variation of the fragile X repeat could result in predisposing normal alleles. Cell 77:853-861 Laird CD 1987 Proposed mechanism of inheritance and expression of the human fragile-X syndrome of mental retardation. Genetics 117587-599 Laird C D 1993 Imprinting and imprint erasure as viewed through the fragile-X syndrome. Am J Med Genet 46:687488 Nancarrow JK, Kremer E, Holman K et a1 1994 Implications of FRA16A structure for the mechanism of chromosomal fragile site genesis. Science 264: 1938-1941 Nancarrow JK, Holman K, Mangelsclorf M et a1 1995 Molecular basis of p(CCG), repeat instability at the FRA16A fragile site locus. Hum Mol Genet 4:367-372 OberlC I, Rousseau F, Heitz D et a1 1991 Instability of a 550-base pair DNA segment and abnormal methylation in fragile X syndrome. Science 252: 1097-1 102 Pamsh JE, Oostra BA, Verkerk AJMH et a1 1994 Isolation of a GCC repeat showing expansion in FRAXF, a fragile site distal to FRAXA and FRAXE. Nat Genet 8:229235 Pembrey ME, Winter RM, Davies KE 1985 A premutation that generates a defect at crossing-over explains the inheritance of fragile X mental retardation. Am J Med Genet 21:709-717 Penrose LS 1948 The problem of anticipation in pedigrees of dystrophica myotonica. Ann Eugenics 14:125-132 Pieretti M, Zhang F, Fu Y-H et a1 1991 Absence of expression of the FMR-1 gene in fragile X syndrome. Cell 66:817-822 Reyniers E, Vits L, De Boulle K et a1 1993The full mutation in the FMR-1 gene of male fragile X patients is absent in their sperm. Nat Genet 4:143-146 Richards RI, Sutherland GR 1992 Dynamic mutations: a new class of mutations causing human disease. Cell 70:709-7 12 Richards RI, Holman K, Friend K et a1 1992 Evidence of founder chromosomes in fragile X syndrome. Nat Genet 1:257-260 Rousseau F, Heitz D, Biancalana V et a1 1991 Direct diagnosis by DNA analysis of the fragile X syndrome of mental retardation. N Engl J Med 325:1673-1681 Rousseau F, Heitz D, Tarleton J et a1 1994 A multicenter study on genotype-phenotype correlations in the fragile X syndrome, using direct diagnosis with probe StB12.3: the first 2,253 cases. Am J Hum Genet 55:225-237 Sherman SL, Sutherland GR 1986 Segregation analysis of rare autosomal fragile sites. Hum Genet 72: 123-128

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Sherman SL, Morton NE, Jacobs PA, Turner G 1984 The marker (X) syndrome: a cytogenetic and genetic analysis. Ann Hum Genet 48:21-37 Sherman SL, Jacobs PA, Morton NE et a1 1985 Further segregation analysis of the fragile X syndrome with special reference to transmitting males. Hum Genet 69:289299 (erratum: 1985 Hum Genet 71:184-186) Snow K, Tester DJ, Kruckenberg KE, Schaid DJ, Thibodeau SN 1994 Sequence analysis of the fragile X trinucleotide repeat: implications for the origin of the fragile X mutation. Hum Mol Genet 3:1543-1551 Sutherland GR, Richards RI 1994 Dynamic mutations. Am Sci 82157-163 Sutherland GR, Haan EA, Kremer E et a1 1991 Hereditary unstable DNA: a new explanation for some old genetic questions. Lancet 358:289-292 Verkerk AJMH, Pieretti M, Sutcliffe JS et a1 1991 Identification of a gene (FMR-1) containing a CGG repeat coincident with a breakpoint cluster region exhibiting length variation in fragile X syndrome. Cell 65:905-914 Wohrle D, Hennig I, Vogel W, Steinbach P 1993 Mitotic stability of fragile-X mutations in differentiated cells indicates early postconceptional trinucleotide repeat expansion. Nat Genet 4: 140-142 Yu S, Pritchard M, Kremer E et a1 1991 Fragile X genotype characterised by an unstable region of DNA. Science 252:1179-1181 Yu S, Mulley JC, Loesch D et a1 1992 Fragile X syndrome: unique genetics of the heritable unstable element. Am J Hum Genet 50:968-980

DISCUSSION Weiss: Could you clarify which trinucleotide repeats are present in fragile X? Sutherland: All the fragile sites are CCG repeats, whereas all the other dynamic mutations involve AGC repeats. When the AGC repeats are in the coding sequence, they code for polyglutamate tracts within the relevant proteins. In myotonic dystrophy the repeats are outside the coding sequence. The repeats responsible for this disorder were originally thought to have been in the 3' untranslated region of the gene encoding myotonin kinase, but another candidate gene has now appeared. Weiss: Isn't there a CAG repeat in Huntington's disease? Sutherland: The way the repeat is specified depends on the reading frame. There are only 10 trinucleotide repeats, and depending on which DNA strand you look at and where you start, you can get up to six versions of the same repeating sequence. The literature refers to dinucleotide repeats as AC repeats, CA repeats, GT repeats and TG repeats. I like to specify as close as possible to alphabetical order, in the 5' to 3' direction because it removes the confusion. Thus, the fragile X is CCG and the other dynamic mutations involve AGC repeats. Harper: I came into this field by studying two different dynamic mutation disorders: myotonic dystrophy and Huntington's disease. After studying them for 20 years, I was surprised to find that they had the same mechanism, and it has taught me that it can often be helpful to compare different conditions,

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partly to see the similarities, and also because there are real differences. In terms of population dynamics, there has to be an awareness of these differences. The critical difference, which Grant Sutherland has already alluded to, is that in two of these disorders, myotonic dystrophy and fragile X, the repeat sequence lies outside the translated region and doesn’t appear in the protein product. Therefore, the disease pathogenesis is probably either loss of, or at least interference with, the function of that gene. By contrast, in the case of repeats that are translated, the glutamine coded for by the trinucleotide repeat is introduced into the protein product, and it is likely to have a direct effect itself, which may not be immediately related to the nature of the gene involved. One interesting point is that these diseases appear to be in widely different genes, and yet they have numerous similarities. These similarities may relate to the common mutational process. In terms of the immediate origins of these diseases, there are no examples where they have arisen de novo from a trinucleotide repeat number that is clearly within the normal range. Although it is easier to see the process evolving in the dominant diseases like Huntington’s disease and myotonic dystrophy, in all these diseases new cases arise in individuals who have a borderline, or just below the borderline, number of repeats. A step by step increase then occurs until it reaches the pathological range. This is the immediate origin, but what is the original event underlying this instability? It is often possible to link large numbers of these families together with a single ancestry. Therefore, the number of original events is much fewer that what one would otherwise have thought of as being separate new mutations. We can now ask how many original events there were for Huntington’s disease, myotonic dystrophy and fragile X. This is still an open question which may be different for each disease. Myotonic dystrophy may have originated from a single jump in the number of repeats, although this is disputed. It’s important to recognize that step by step processes are not necessarily involved in the initiating events, unlike the later phases of the disease. In the case of myotonic dystrophy, for example, there is a similar haplotype seen in affected patients and in healthy individuals with a low number of repeats in the normal range. Analysis of the surrounding haplotype is also necessary to determine if it may be used to infer common descent and whether there are predisposing haplotypes for instability. These trinucleotide repeats are often called unstable sequences, but they may not be particularly unstable in their original form. Diseases such as myotonic dystrophy and possibly also Huntington’s disease don’t seem to have originated often, which suggests that only the later phase may be unstable. However, unstable may not be an appropriate word to give to the actual unaltered locus in its natural state. Unexpected departures from neutrality also confuse the population genetics of these diseases. For example, there is evidence for the preferential meiotic

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transmission of a larger allele size within the normal range in myotonic dystrophy (Carey et a1 1994). Also, in Huntington’s disease there is some evidence of non-random directedness of mutation towards a larger allele size. Therefore, both of these factors might work towards the perpetuation of these mutations and counteract any possible selective disadvantage (Rubinsztein et a1 1994). Sutherland: I agree with your point that the sequence in wild-type alleles should not be called an unstable sequence, but once the initial amplification occurs, it is unstable both between generations and somatically. There is some evidence that the instability may be confined to a small window of time early in development. Peter Steinbach’s group in Germany took a skin fibroblast culture from a fragile X patient with a complete smear of fragments and cloned out fibroblast lines. Each line had a single band on a Southern blot, suggesting that the allele was stable in cell culture at that point (Wohrle’et a1 1993). W . Bodmer: Is it possible that a particular protein binds to a tertiary configuration of the DNA, but that configuration can only be assumed when there are a certain number of copies. Sutherland: Yes, that is possible. Harper: I would like to caution against making generalizations about these diseases because they do behave quite differently. For example, the severe congenital form of myotonic dystrophy is almost exclusively maternally transmitted, whereas in Huntington’s disease the juvenile form is paternally transmitted. This may initially seem paradoxical, but it actually reflects the size of the expansion range. If one is looking in the range close to the borderline, in all of these diseases (with the possible exception of fragile X) a greater expansion occurs in male meioses, which reflects the preferential paternal origin. If one is looking at the grossly expanded levels apparent in many cases of myotonic dystrophy, one finds that they are transmitted by females because the male meioses cannot transmit an expansion beyond a certain limit. Bowcock: Another correlation relating to expansion is that when the repeat is in a non-coding sequence, the alleles are required to expand more extensively to cause disease than when the repeat is in a coding sequence. Harper: This occurs in myotonic dystrophy and fragile X, but not in the other diseases because it would probably be lethal in terms of the effect on the brain cells. Bowcock: But a larger expansion in the non-coding sequence is required for the disease phenotype. Harper: Yes, this is correct. Weiss: Is there any evidence that another locus has a trans effect on these sites? Harper: There is no evidence for primary heterogeneity, in the sense that these are specific mutations that account for the worldwide occurrence of the disease. However, there is evidence in Huntington’s disease that other loci have

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a profound effect on age at onset, for example. Therefore, there may be more than one locus involved in these diseases. W. Bodmer: The linkage disequilibrium data are striking and suggest that only a single allele or a few alleles were involved in the initiating events. Sutherlund: This may be the case for myotonic dystrophy but not for fragile X because this mutation occurs on every haplotype. W. Bodmer: But it could have been an ancient event. The original one could have been there for a long time, during which the different haplotypes were recombined. Freimer: It’s interesting that linkage was found early on for the major loci of these diseases, even though the cloning of the causative gene took a long time. I am not aware that any attempts have been made to look throughout the genome for other possible genes which modify the effect of the major locus for any of these disorders. Sutherlund: The gene responsible for myotonic dystrophy was mapped to chromosome 19 by linkage (Eiberg et a1 1983). We also mapped the location of the fragile X site by linkage because it was critical to determine whether fragile X syndrome was due to the fragile site or whether another locus was responsible. We found bizarre inheritance patterns, so we had to demonstrate that the phenotype could be mapped by linkage to the fragile site and to nowhere else. Freimer: Yes, but the genome maps weren’t completed until 1992 (Weissenbach et a1 1992), and so genome screening for modifying loci was not possible until then. Harper: A full genome map is not required if all the families you study show linkage to the same chromosome region. Freimer: I’m not talking about the major effect. I’m talking about possible modifying ones. My point is that if there are modifying effects somewhere else in the genome, we wouldn’t necessarily have found them because the major locus was mapped so quickly and minor variations haven’t been looked for. Harper: In all of these disease the major locus obviously has a strong effect. However, at least for Huntington’s disease, there’s a correlation between repeat length and age of onset. This is a strong correlation for young ages of onset, but for older ages, the Vancouver group have estimated that the actual fragment size only contributes about 7% of the variance in terms of age of onset in that group, and they have suggested that other loci are involved. We don’t know the nature of these loci yet (Kremer et a1 1993). Kidd: I would like to make a semantic point about all of the myotonic dystrophy coming from the same haplotype. In an ongoing study in our lab ( S . Tishkoff, J. R. Kidd & K. K. Kidd, unpublished work) we are examining normal haplotypes at the DM locus. We have found that all of the large size alleles in the 25-35 repeat range are on the same haplotype background. This background is different from those seen for alleles in the lower size range,

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except for the five repeat, which is on the same background as the large repeats. This background is found in all non-African populations that we have looked at, so it’s conceivable that the progression to disease state has occurred many different times from this background. My opinions are that the high frequency of this haplotype was established by genetic drift at the time modern humans came out of Africa, and that more than one chromosome from this group may have given rise to the disease-causing mutation. Chakraborty: Analysis of the only African Black family (from Yoruba, Nigeria) with myotonic dystrophy suggests that the expansion of CTG alleles in affected individuals of this family has occurred from a haplotypic background that is substantially different from the route suggested in DM individuals of non-African origin (Krahe et a1 1995). Armour: I would like to raise a different point concerning the context dependence of some of these events. It’s clear from the FRAXA work on the length of the perfect repeat, and from the perfect blocks of repeats in myotonic dystrophy and Huntington’s disease, that variant repeats do not seem to be involved in expansions at these loci (Richards et a1 1992, Hirst et a1 1994, Snow et a1 1994, Kunst & Warren 1994). However, if expansion is observed preferentially on a particular haplotypic background, it raises the question of whether the repeat block that finds itself on that haplotype background is important, or whether the haplotype background itself influences the mutational behaviour of the repeat block. There is clear evidence in minisatellites that particular haplotype backgrounds, and particular variant substitutional polymorphisms, correlate with the mutational behaviour of the adjacent repeat block (Monckton et a1 1994, C. A. May, A. J. Jeffreys & J. A. L. Armour, unpublished results 1995). Donnefly: This could explain why the repeats occur in humans but not in other species because the mutations may not have the required regions nearby. Armour: Absolutely. There may be something that switches on the mutation in that region but does not switch it on in other species. One important reservation is that the more we find out about short repeats and minisatellites, the more we find out how different they are. The minisatellite data are relevant simply from the point of view of a precedent, in showing a haplotypic context influence on the mutation of a repeat array. Clark: This context dependence also raises doubts about transgene experiments where human genes are inserted into mice bearing knockout mutations. Doesn’t the context of the transgene make interpretation difficult? Armour: Yes, that’s possible. If one clones out triplet repeats from random sequences in the human genome, one finds that a typical trinucleotide repeat locus in the genome is relatively short and quite stable. Therefore, even in their normal range, these disease repeat loci are at the more unstable, more active end of the spectrum for the genome. W . Boaher: Is the fragile X site present in chimpanzees conserved?

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Sutherland: Yes, it’s conserved but I don’t know if it’s polymorphic. Chakravarti: It is polymorphic in the great apes and many other primates. W. Bodmer: Is it comparable in size and number of repeats? Chakravarti: Yes, it’s comparable in size to the normal site in humans. Harper: It has been suggested that myotonic dystrophy originates from a low normal repeat number, i.e. five, because the same haplotype has been found universally. Although this has been found for nearly every population studied, is onejustifiedin concludingthat there was a single,ancestraljump from the original state of five repeats to 19 repeats? Because an alternative explanation is that a particular haplotype may be predisposed to repeat such changes independently. Kidd: We have some relevant data that we’re just collecting. In collaboration with Trevor Jenkins, our lab has been looking at several African and nonAfrican populations. We have found that African populations have a large array of haplotype backgrounds with different repeat sizes, but non-African populations have only a subset of that. The nature of the subset depends on the background. As I mentioned earlier, the five repeat allele and larger repeat alleles are found on a particular background, whereas middle repeat size alleles are found on a different background. From this perspective, there is no evidence of a jump from five repeats to 19; rather, chance has selected these few from a greater diversity of haplotypes in Africa and this particular subset has been preserved in all non-African populations. Armour: Are you saying that in addition to the five and the 19 repeats, in African populations there are intermediate repeat states on that background in normal individuals? Because if there are, then they may be intermediate states in the evolution of this locus that just happened not to have come out of Africa. Kidd: Exactly. There are more high repeat number CTG alleles in non-African populations, but even if one takes this into consideration, there are more haplotypes in the sub-Saharan African populations, and most of the ‘new’ CTG alleles in non-African populations are in the range of 19 repeats and above. Harper: It is possible that there was a single ancestral event, in terms of the origin of a mutation that caused the initial change, which resulted in an increased instability and a subsequent gradual stepwise change. W. Bodmer: But if there’s a single haplotype, then that haplotype has to have a distinguishing feature which gives rise to what’s being observed now. I don’t see how one can get away from the view that at some point there’s a single origin. If one takes CA repeats that go further and further away from the repeat, one can titrate out the extent to which there is linkage disequilibrium. These experiments have not been done in enough detail. They may tell us how long the repeat has been around. If they’re closer than a certain amount then one would expect the linkage disequilibrium to be preserved throughout all major human population groups. Chakravarti: Microsatellite repeat markers adjacent to the fragile X locus, as much as 500 kb apart, show strong linkage disequilibrium.

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W. Bodmer: 500 kb is small enough to give you linkage disequilibrium. Chakraborty: Several studies indicate, directly as well as indirectly, that more than one origin of the CTG expansions lead to myotonic dystrophy; for example, the study of the Yoruban kindred from Nigeria that I mentioned earlier (Krahe et a1 1995). Goldman et a1 (1995) have confirmed that a linkage disequilibrium occurs in the myotonin protein kinase gene of South African Blacks between specific alleles at the A h polymorphism (insertion/deletion), the HinfI polymorphism (HinfI- 1/HinfI-2) and the CTG repeat polymorphism. This disequilibrium had previously been reported in European and Japanese populations (Harley et a1 1992, Yamagata et a1 1992, Mahadevan et a1 1993, Imbert et a1 1993). However, the study by Goldman et a1 (1995) has uncovered several African haplotypes that do not exist outside of Africa. In addition, our unpublished work with R. Deka (University of Pittsburgh), together with a reanalysis of data reported in Goldman et a1 (1995), suggest that in several non-Caucasian populations (such as African Blacks from Benin, Pehuenche Indians of Chile and Papua New Guinea Highlanders, or even in Brahmins of Uttar Pradesh, India that are classified as Caucasians) these disequilibria are not exclusive. It is quite possible that CTG repeat expansions within the normal size range of alleles at the myotonin protein kinase gene may have occurred in different populations under a diverse set of haplotypic backgrounds. Thus, the reservoir of premutant CTG repeat alleles that are progenitors of the expanded alleles are not necessarily of the same evolutionary origin. Freimer: It is likely that in fragile X the removal of imperfect repeats at one end of the repeat is responsible for increasing the mutation rate. Have you observed the removal of these imperfections? Sutherland: On predisposing haplotypes there is a long stretch of perfect repeat, but as Peter Harper has pointed out we have never seen a new mutation. The premutation is present as far back in families as you can trace in living individuals. Therefore it is not possible to observe the transition from wild-type to premutation. Clark: Is the evidence for meiotic drive just an observed skew in the segregation of the gene itself or are there flanking sites that also show abnormal segregation? Harper: Evidence for this has come from the analysis of normal individuals rather than those affected with myotonic dystrophy (Carey et a1 1994). These studies have shown that if an individual is heterozygous for alleles with different repeat lengths, the larger of those alleles will be transmitted preferentially. This is more apparent in one sex than the other. These studies were carried out in normal individuals because there are too many biases involved in the study of affected individuals. Weiss: When you say transmitted, do you mean to live newborn offspring? Harper: Yes.

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Weiss: Is it possible that zygotes with the shorter allele are produced, but they don’t make it through gestation? Harper: Yes, it is possible. There may be some sort of deficit during development. Armour: Has anyone looked at PCR analysis of individual sperm? Harper: I don’t know whether people have reported results on this in terms of preferential transmission. Weatherall: Don’t you think it’s odd that these are all neurological diseases? Are there any speculations on the function of the repeats? Sutherland: The group of neurological diseases that have repeats in the coding region may have a similar pathogenetic mechanisms in neurons. On the other hand, the neurological diseases have attracted attention in human genetics, and there may be an ascertainment bias in the genes that have been cloned. Harper: I would like to go out on a limb and speculate that no more diseases associated with unstable expanded repeats will be discovered in non-coding regions because the degrees of anticipation would be so obvious that human geneticists would have stumbled across them by now. Any new repeat disorders will be those with defects in the coding region. Also, in my opinion all these repeats will be involved with neurological disorders because, if the pathogenesis of the disorder doesn’t relate directly to the specific nature of the gene, other than that it’s working in the brain, but relates more to the expanded glutamine repeat, which could have the same effect in any gene providing that it functions in the brain, then this situation will only show itself clinically in the brain. For years we have been asking what does the gene responsible for Huntington’s disease do, and what is missing in individuals affected with Huntington’s disease? However, this may not be the only question we should be asking. The gene involved with trinucleotide repeat disorders whose function we know most about is the gene encoding the androgen receptor, whose endocrine function is not greatly affected by the presence of a trinucleotide repeat expansion. W. Bodmer: Half of the genes work in the brain anyway. Harper: But these diseases form a remarkably similar group. Most of the differences are due to the distribution of cells in different parts of the brain. Sutherland: You suggested that anticipation would be noticed in other disorders. Penrose (1948) may have been wrong about ascertainment bias accounting for anticipation in myotonic dystrophy, but he was correct about ascertainment bias mimicking anticipation. It has also been suggested that bipolar affective disorder shows anticipation. Do you think that it does? Harper: I’m not an expert in this but I do share a fair amount of scepticism. Penrose was not wrong in relation to anticipation, he was perceptive, but he did not explain the whole picture. Edwarh: This publication (Penrose 1948) has been widely quoted, implying that Penrose regarded ascertainment as a sufficient explanation in myotonic

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dystrophy. In fact, after defining the obvious clearly but missing the fact that reproduction stops at death and is usually impaired by illness, he examined the data for several disorders. He found that myotonic dystrophy stood apart from the other disorders, and he advanced an ingenious explanation, which he explored and found wanting. Freimer: In contrast to bipolar affective disorder, myotonic dystrophy sometimes demonstrates anticipation over four generations, and all of those studies mentioned for bipolar affective disorder were based on evaluating two or at most three generations. If one looks at the few families that have been studied for four generations or more, there’s no evidence of anticipation. It is also well known that there has been an increase in the frequency of the diagnosis of bipolar affective disorder over the last two generations in the population as a whole, with decreasing age of onset across successive generations. There isn’t any compelling evidence that the families in which anticipation has been hypothesized are demonstrating any phenomena not seen in the whole population. Kidd: I agree. The so-called evidence for anticipation in bipolar affective disorder is not strong. No study has adequately excluded any of several other possible causes; ascertainment bias can be a cause of an anticipation-like pattern, even if it is the wrong explanation for myotonic dystrophy. In episodic disorders memory becomes a real bias against retrospective diagnosis in older individuals. Without a handle to put the families’ collective memory onto a diagnostic label, there is a tendency for the illness in older people not to have been recognized. Therefore, there are multiple ways of explaining small sets of data, but any of the large sets of data that have been looked at carefully do not show evidence of anticipation. Chukruvurti: We have to be more careful with complex trait phenotypes because they do not necessarily have simple modes of inheritance. For myotonic dystrophy the evidence can be evaluated directly because all pedigrees show a Mendelian pattern of inheritance, and anticipation really stands out. In contrast, for bipolar affective disorder, schizophrenia and other psychiatric disorders, anticipation is much more difficult to prove. Clark: In terms of animal models for anticipation, Drosophilu exhibits the phenomenon of magnification, whereby low copy numbers of ribosomal DNA undergo changes in copy number with a postmutational state that will affect future mutations (Ritossa 1968, Ritossa et a1 1971). This is similar to anticipation, except that it is directed towards recovering the normal phenotype. Are there any murine models of anticipation? Sutherlund: Not that I’m aware of. Bufmuin: We’ve noticed that there’s very little microsatellite instability in murine tumours (H. Nagase, C. Kemp &A. Balmain, unpublished observations 1994). In humans this is a relatively frequent phenomenon. Therefore, the lack of an effect in the mouse may be because of an intrinsic difference in the repair

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capacity of mice, so that these repeat sequences are replicated more efficiently than in humans. W. Bodmer: But microsatellite variation in tumours is due to mutations that have knocked out the function of one of the steps in the mismatch repair process. Whether or not you see those in mouse tumours may depend on the type of tumours. Microsatellite variation varies from one tumour to another. Balmain: Yes, but a reasonable number of those tumours have been looked at and, to my knowledge, nobody has reported any microsatellite instability. Zechner: Was the classical transgenic approach of microinjection used to introduce microsatellite sequences of different length into the mouse genome? Sutherland: Yes, that’s my understanding. Zechner: Wouldn’t it be possible to increase the number of repeats within the microsatellite sequences by gene targeting via homologous recombination and then look for changes in microsatellite instability? Sutherland: Yes, it would be possible. Armour: I think the practical difficulties are in making, for example, 50 CCG repeats and in being certain that you had actually introduced them all. Sutherland: It’s almost impossible to do this for CCG repeats because they are unstable and difficult to clone in almost any vector. It is slightly easier for the AGC repeats. We originally wanted to produce a CCG repeat construct and introduce it into cells to produce a fragile site, but this proved to be an impossible experiment to do. Zechner: But it worked for the traditional transgenic approach. Sutherland: Yes, but that’s a different repeat. They used the AGC repeat, which is more manageable.

References Carey N, Johnson K, Nokelainen P 1994 Meiotic drive at the myotonic dystrophy locus. Nat Genet 6:117-118 Eiberg H, Mohr J, Nielsen LS, Simonsen N 1983 Genetics and linkage relationships of the C-3 polymorphism: discovery of C3-SE linkage and assignment of LES-C3-DMSe-PEPD-Lu synteny to chromosome 19. Clin Genet 24: 159-170 Goldman A, Ramsay M, Jenkins T 1995 New founder haplotypes at the myotonic dystrophy locus in southern Africa. Am J Hum Genet 56:1373-1378 Harley HG, Brook JD, Rundle SA et a1 1992 Expansion of an unstable DNA region and phenotypic variation in myotonic dystrophy. Nature 355545-546 Hirst M, Grewal PK, Davies KE 1994 Precursor arrays for triplet repeat expansion at the fragile X locus. Hum Mol Genet 3:1553-1560 Imbert G, Kretz C, Johnson K, Mendel J-L 1993 Origin of expansion mutation in myotonic dystrophy. Nat Genet 4:72-76 Krahe R, Eckhart M, Ogunniyi AO, Osuntokun BO, Siciliano MJ, Ashizawa T 1995 De n o w myotonic dystrophy mutation in a Nigerian kindred. Am J Hum Genet 56: 10671074

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Kremer B, Squitieri F, Telenius H et a1 1993 Molecular analysis of late onset Huntington’s disease. J Med Genet 30:991-995 Kunst CB, Warren ST 1994 Cryptic and polar variants of the fragile X repeat could result in predisposing normal alleles. Cell 77353-861 Mahadevan MS, Foitzik MA, Surh LC, Korneluk RG 1993 Characterization and polymerase chain reaction (PCR) detection of an Alu deletion polymorphism in total linkage disequilibrium with myotonic dystrophy. Genomics 15:44&448 Monckton DG, Neumann R, Guram T et a1 Minisatellite mutation rate variation associated with a flanking DNA sequence polymorphism. Nat Genet 8: 162-170 Penrose LS 1948 The problem of anticipation in pedigrees of dystrophica myotonica. Ann Eugenics 14:125-132 Richards RI, Holman K, Friend K et a1 1992 Evidence of founder chromosomes in fragile X syndrome. Nat Genet 1:257-260 Ritossa F 1968 Unstable redundancy of genes for ribosomal RNA. Proc Natl Acad Sci USA 60~509-516 Ritossa F, Malva C, Boncinelli E, Graziani F, Polito L 1971 The first steps of magnification of DNA complementary to ribosomal RNA in Drosophilu melunoguster. Proc Natl Acad Sci USA 68:1580-1584 Rubinsztein DC, Amos W, Leggo J et a1 1994 Mutational bias provides a model for the evolution of Huntington’s disease and predicts a general increase in disease prevalence. Nat Genet 7525-530 Snow K, Tester DJ, Kruckeberg KE, Schnaid DJ, Thibodeau SN 1994 Sequence analysis of the fragile X trinucleotide repeat: implications for the origin of the fragile X mutation. Hum Mol Genet 3:1543-1551 Weissenbach J, Gyapay G , Dib C et a1 1992 A second-generation linkage map of the human genome. Nature 359:794-801 Wohrle D, Hennig I, Vogel W, Steinbach P 1993 Mitotic stability of fragile-X mutations in differentiated cells indicates early postconceptional trinucleotide repeat expansion. Nat Genet 4:140-142 Yamagata H, Miki T, Ogihara T et a1 1992 Expansion of unstable DNA region in Japanese myotonic dystrophy patients. Lancet 399:692

Control of P-thalassaemia by carrier screening, genetic counselling and prenatal diagnosis: the Sardi nian experience Antonio Cao, M. Cristina Rosatelli and Renzo Galanello lstituto di Clinica e Biologia dell'fta Evolutiva, Universita degli Studi di Cagliari, Via Jenner sln, 1-09121 Cagliari, Italy

Abstract. Homozygous b-thalassaemia in a number of at-risk populations (Greek and Turkish Cypriots, Greeks, Continental Italians and Sardinians) has been prevented at the population level by programmes based on carrier screening, genetic counselling and prenatal diagnosis. The Sardinian experience is based on a 20-year programme. Voluntary screening has been offered to prospective parents and, primarily, to women with an ongoing pregnancy. Education of the population at large, training of health personnel, and use of posters and informative booklets have been critical elements for the success of the programme. Genetic counselling has been camed out in a non-directive manner following well-established guidelines. The use of extended family screening magnified the efficacy of the screening programme, allowing the identification of the large majority of parents at risk by screening only 13% of the population at child-bearing age. Following counselling, the large majority of parents accepted prenatal diagnosis. Definition of the parents' mutation and prenatal diagnosis were carried out by a number of PCR-based procedures. The programme was effective, as indicated by the reduction of the birth rate of thalassaemia major from 1 : 250 live births to 1 :4000.

1996 Variation in the human genome. Wiley, Chichester (Ciba Foundation Symposium 197) p 137-155

The /3-thalassaemias are a group of autosomal recessive disorders characterized by the reduced (B') or absent (p)production of the /3 chains of the adult haemoglobin molecule (HbA). If homozygotes for this disorder are not treated, they develop severe anaemia, splenomegaly and skeletal modifications, they fail to thrive and they die within the first decade of life. Conventional treatment based on regular transfusions and iron chelation with daily subcutaneous 137

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TABLE 1 Demographic characteristics and frequency of Pthalassaemia in Sardinia Demographic characteristic

Freauencv

Population Couples at child-bearing age Target screening populationa Marriage Carrier frequency Expected couples at risk

1.6 million 170 000 700 000 10 000 per year 12.6% 2700 160 per year 1 :250

New couples at risk Incidence of homozygous state p1S40years.

Deferoxamine B (Ciba-Geigy, France) to control the transfusional iron overload result in an extended survival time beyond the third decade. At present, a limited number of patients can be cured definitively by bone marrow transplantation from HLA identical siblings. fl-Thalassaemias are among the commonest autosomal recessive diseases, and they occur with a high frequency in populations of the Mediterranean area, the Middle East, the Indian subcontinent and South-East Asia (World Health Organization 1982). In the island of Sardinia, which has a population of 1.5 million inhabitants, the birth rate of thalassaemia major, the homozygous condition of flthalassaemia, is 1 : 250 live births and the carrier rate is 12.6% (Table 1) (Cao et a1 1978, 1981, 1989, 1991). This indicates that one person in eight is a carrier of fl-thalassaemia and that in one couple out of 60, both parents are carriers and, therefore, risk producing children affected by thalassaemia major. The high frequency and the severity of fl-thalassaemia, the availability of simple procedures to detect the fl-thalassaemia carrier state and the development of methods to detect the disorder early in gestation led our group in 1977 to begin a genetic preventive programme, based on heterozygote screening and prenatal diagnosis, to control thalassaemia major in the Sardinian population. We would like to report the outline, the characteristics and the efficacy of such a programme. Previous reports have been published (Cao et a1 1989, Cao 1994).

Education and screening Voluntary screening was offered to young unmarried adults and prospective parents. At the beginning of the programme, the largest group of people requesting screening were women with a pregnancy in progress, although

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recently the number of individuals or couples without a pregnancy in progress present at our genetic services has increased. This obviously indicates a marked improvement in both the supply of information on thalassaemias and methodologies for its prevention at the population level (Cao et a1 1981, 1991). Only one member of each couple was tested, the other being examined only when his/her test showed a carrier state. Carrier screening and prenatal diagnosis were carried out in several genetic units set up in the main towns of the island. In addition, in the first three years of this programme we have also organized community screening in small villages or in large factories and shops. Education and sensitization of the population was realized mostly via local newspapers, radio, TV and magazines. In addition, lectures directed to the general public were carried out in factories as well as in large stores or shops. Meetings with parents’ associations, community leaders, family doctors, obstetricians, paediatricians, midwives, social workers and nurses were periodically organized to present the outline of the programme and to discuss in detail the social, technical and psychological aspects. Posters and information leaflets were left at marriage registry offices, general practitioners’, obstetricians’ and paediatricians’ offices, and in surgeries and family planning facilities. The informative booklets provided the following information: (a) who should be tested; (b) where to be tested; (c) that there is no disadvantage for carriers; (d) the natural history of the homozygous state; and (e) the availability of prenatal diagnosis. Education on inherited anaemias and specifically on thalassaemia was introduced in the last few years in elementary and secondary schools by means of educational videos. An informed consent by those screened was not requested, but prior to testing we informed each person to be tested about the nature of the illness, the meaning of the term ‘carrier’, the genetic implications of being a carrier and the reproductive alternatives available to those individuals found to be carriers. Critical analysis of our programme has recently shown that people and spouses were mostly informed about thalassaemia via the mass media (44%), followed by general practitioners (3 YO), obstetricians (23%) and midwives (2%). At present, we have screened 212899 individuals (about 13% of the population at childbearing age). In spite of this low percentage of people tested, we have identified 36 389 carriers, and we have found that 2310 out of 2700 couples (about 80%) are at risk (on the basis of carrier rate). The high efficacy of our screening depends on the large use of extended family testing whenever a B-thalassaemia carrier is identified. Counselling Genetic counselling was carried out by a private interview in a non-directive manner to both partners according to the well-established rules of accurate

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diagnosis, truth and confidentiality (Cao et a1 1989). The reproductive choices available were discussed in detail in the counselling sessions. In particular, we addressed the possibility of birth control, adoption, artificial insemination, mate selection and fetal testing. Information was also given about prenatal diagnosis, for which we dealt with the risk of fetal mortality, the eventuality of a failure to obtain sufficient material for diagnosis, the risk of misdiagnosis and the mortality and morbidity for voluntary interruption of pregnancy. Once identified, each carrier was informed about the implication of his/her carrier status for relatives, and education material was given to them. Relatives were informed in this way, and they were given the option to contact the centre if they desired further information or wanted to be screened. In the 1970s, when prenatal diagnosis was carried out by fetal blood analysis, which is performed at advanced gestation (18 weeks) and is associated with a high risk of fetal mortality, 7.4% of the couples counselled decided against prenatal diagnosis and continued the pregnancy. Following the introduction of chorionic villus sampling, which is carried out early in gestation (10 weeks) and has a low risk of fetal mortality, the uptake of prenatal diagnosis increased to about 99%. Only 95% of carriers were in favour of prenatal diagnosis when counselling was carried out in spouses without an ongoing pregnancy. a

Carrier detection methods A review of these methods can also be found in Cao & Rosatelli (1993). Heterozygotes for /?-thalassaemia are characterized by low mean corpuscular volume-mean corpuscular haemoglobin (MCV-MCH), increased HbA2 levels and unbalanced a//? globin chain synthesis. However, the haematological phenotype of heterozygous /?-thalassaemia may be modified by a number of genetic interactive factors (Galanello et a1 1979). The co-inheritance of heterozygous 8-thalassaemia and a-thalassaemia (either in the form of the deletion of two a-globin structural genes or non-deletion defects affecting the major a2 globin gene) may result in increased MCV and haemoglobin content per cell in such a way as to determine, in a certain proportion of double heterozygotes, normal values (Melis et a1 1983, Rosatelli et a1 1984). In our population, because of the high frequency of a-thalassaemia, about 3.5% of the /?-thalassaemia heterozygotes have normal erythrocyte indices and, therefore, %hey may be missed by carrier identification procedures based only on MCVMCH determination (Melis et a1 1983, Pirastu et a1 1982). Double heterozygotes for 8-thalassaemia and /?-thalassaemia either in cis or trans forms may have borderline or normal HbA2 levels, and they may be confused with a-thalassaemia (Pirastu et a1 1983, Paglietti et a1 1985). Carriers of mild /?-thalassaemia mutations may also have normal or borderline HbA2 levels. Finally, we should also take into account the existence of silent carriers, who have normal erythrocyte indices and normal HbA2 levels, and are defined

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only by unbalanced a/b globin chain synthesis. However, at least in our experience, the most common genetic determinant in our population, which is associated with the 8-thalassaemia silent carrier state and is a C - r T substitution at position - 101, usually shows borderline values of either MCH-MCV or HbA2. This suggests the presence of an atypical carrier that needs to be defined by more appropriate procedures (Table 2) (Murru et a1 1993). For these reasons we use the flow-chart depicted in Fig. 1, in which the first set of examinations includes HbA2 and HbF determination, and haemoglobin pattern by HPLC (high performance liquid chromatography). By this approach, only the triple a-globin gene arrangement and silent Bthalassaemia (which occur only rarely and are characterized by normal erythrocyte indices, normal HbA2 levels and unbalanced globin chain synthesis) may be missed. In the presence of low MCV-MCH and elevated HbA2, a diagnosis of b-thalassaemia carrier state is made. Whenever the person being tested shows microcytosis, low MCH and normal HbA2 levels, the second step is the exclusion of iron deficiency by determining the erythrocyte protoporphyrin levels and iron saturation. The above phenotype in the presence of normal iron results indicates a thalassaemia determinant of either a-thalassaemia, y6B-thalassaemia, mild B-thalassaemia or double thalassaemia. In order to discriminate between heterozygotes for 6 and these genotypes, we carry out globin chain synthesis analysis and a, fi and 6 globin gene analysis (see below). Definition of the presence and type of bthalassaemia in those carriers with low MCV-MCH and normal HbA2 levels is, however, carried out only when their partner is a typical 8-thalassaemia carrier. In those cases with normal or low MCV-MCH levels, normal-reduced HbA2 levels and high HbF we suspect the presence of 6B-thalassaemia, which should be differentiated from hereditary persistence of fetal Hb (HPFH). The distinction between 6b-thalassaemia and HPFH is carried out by the analysis of the distribution of HbF in peripheral blood, which is heterogeneous in 6sthalassaemia and homogeneous in HPFH. In doubtful cases, globin chain synthesis analysis and/or DNA analysis is also carried out (Galanello et a1 1989). /?, a and 6 globin gene analysis

In the Sardinian population the most common 8-thalassaemia mutation is the nonsense mutation (CAG-TAG) at codon 39 of the B-globin gene, which accounts for the molecular defects of 95.7% of the fi-thalassaemia chromosomes (Rosatelli et a1 1992). Other less common mutations are frameshifts at codon 6 (2.1%) and frameshifts at codon 76 (0.7%). Another rare mutation in Sardinians is the ‘Sardinian Gb-thalassaemia’ which is characterized by normal to low HbA2 levels and high HbF levels. This determinant results from the presence of two mutations in the same chromosome, one of which is

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TABLE 2 Atypical fithalassaemia carrier phenotypes in Sardinians Phenotype

Molecular mechanism( s )

Normal MCV-MCH levels

Heterozygous 8-thalassaemia and co-inherited athalassaemia Heterozygous 8-thalassaemia and co-inherited 6thalassaemia Mild thalassaemia (- 101 [C +TI, -87 [C + GI) - 101 (C + T)

Normal HbA2 levels

Normal MCV-MCH and normal HbA2 levels

MCH,mean corpuscular haemoglobin; MCV, mean corpuscular volume.

the P-globin codon 39 nonsense mutation, and the other is a C +T substitution at position -196 relative to the CAP site of the Ay-globin gene. The continuous production of Ay chains from the Ay gene in adulthood partially compensates for the absence of B-chain production and explains the mild phenotype associated with this mutation (Pirastu et a1 1984a). Other mutations, which are listed in Table 3, occur only rarely. Based on these studies, when we now identify couples at risk, we define the B-thalassaemia mutation in both parents, either by reverse oligonucleotide hybridization or primer-specific amplification using oligonucleotide probes or primers complementary to the most common mutations (Fig. 2) (Rosatelli et a1 1992). In those rare cases in which the mutation is not defined by this approach, we carry out denaturing gradient gel electrophoresis followed by direct sequencing on amplified DNA (Fig. 3). a-Globin gene analysis is performed on an amplified aglobin gene fragment. The presence of deletion a' or a+ thalassaemia is detected by PCR using primers flanking the a-globin gene, which results in the production of a unique amplified product only in the presence of the specific deletion. A DNA fragment from the normal chromosome is simultaneously amplified by using one of the primers flanking the breakpoint and a primer homologous to a DNA region deleted by the mutation. In addition to a' (- -meda, the deletion commonly detected in people of Mediterranean descent) and a+ (- -3.7a and rarely - -4%, where 3.7 and 4.2 represent deletions of 3.7 kb and 4.2 kb, respectively) a-thalassaemia deletion determinants in the Sardinian population, several non-deletion a-thalassaemias have been detected, of which the most common are the initiation codon mutation (ATG +ACG) FIG. 1. Flow chart for thalassaemia camer screening. DGGE, denaturing gradient gel electrophoresis; Hb, haemoglobin; HPFH, hereditary persistence of fetal haemoglobin; MCH, mean corpuscular haemoglobin; MCV, mean corpuscular volume; THAL, thalassaemia.

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FIG. 2. Molecular characterization of 8-thalassaemia mutations in a couple at risk. Top panel: reverse dot-blot analysis for the 10 most common mutations in the Italian population. Both subjects show hybridization with all probes for normal sequences (M, mutant; W, wild type); the male subject shows hybridization with the mutant oligonucleotide probe for the 8-thalassaemia G --t A mutation at nucleotide 1 of intron 2 (/j'I-'); and the female subject shows hybridization with the probe for frameshift at codon 6 (P6). Bottom panel: above results confirmed by primer-specific amplification analysis.

(Pirastu et a1 1984b) and the pentanucleotide deletions at the exon-intron 1 border of the a2 globin structural gene. To detect these non-deletion mutations, we also carry out selective amplification of a2 and a1 globin genes followed by NcoI and HphI restriction endonuclease analysis, which may detect the initiation codon mutation and the pentanucleotide deletion, respectively. In the Sardinian population, only three molecular defects in the 6 globin gene have been detected

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FIG. 3. Molecular characterization of the 8-thalassaemia mutation at nucleotide 844 of intron 2 (PIVSII-844). (A) Diagram of the j?-globin gene showing the site of both the mutation and the left (L) and right (R) primers used for denaturing gradient gel electrophoresis (DGGE) analysis. (B) DGGE analysis. (C) Sequencing analysis. The arrow on the left indicates the site of the mutation, the arrow on the right indicates the C .+ G change.

to date: 6+27 (G+T); Corfu 6B-thalassaemia (a small deletion in the $BS region); and a nonsense mutation at codon 37. The first of these is the most prevalent. Detection of 6-thalassaemia in the Sardinian population is, therefore, carried out by usinga limited number of specificprimers orprobes (Fig. 4) (Moi et a1 1988, Galanello et a1 1990, Gasperini et a1 1994). Prediction of the phenotype

The large majority of patients homozygous for the codon 39 nonsense mutation develop thalassaemia major. However, in about 10% of the cases, the

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7.2 kb deletion FIG. 4. Diagram of the d-globin gene with the localization of the d-thalassaemia mutations in the Sardinian population.

p39/p39 genotype results in non-transfusion-dependent thalassaemia intermedia (Table 4). The reasons for this mild phenotype have been investigated in the last few years. One factor able to ameliorate the clinical picture of homozygosity for codon 39 nonsense mutation is the co-inheritance of athalassaemia either in the form of the deletion of two a globin genes ( - a / - a genotype) or as a non-deletion lesion affecting the major a2 globin gene (Galanello et a1 1989). A second determinant conferring a mild phenotype, to which we have alluded before, is the co-inheritance of the C +T mutation at position - 196Ay, which results in high y chain production, that compensates

TABLE 3

&Thalassaemia mutations in Sardinians based on analysis of 3000 chromosomes

Type of mutation

C +T at codon 39 Frameshift at codon 6 (- 1 base pair) Frameshift at codon 76 (- 1 base pair) G + A at nucleotide 110 of intron 1 (IVSlntllO) C + G at nucleotide 745 of intron 2 (IVS2nt745) C + G at position - 87 T + C at nucleotide 6 of intron 1 Frameshift at codon 1 (- 1 base pair) G + A at nucleotide 1 of intron 1 (IVSl ntl) G + A at nucleotide 1 of intron 2 (IVS2nt 1) Unknown

Number of chromosomes

% of chromosomes

2872 66 20 15

95.7 2.1 0.7 0.5

12

0.4 0.2 0.1 0.1 0.03

1

0.03

0

0

6-Thalassaernia

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TABLE 4 Molecular analysis of &-thalassaemiaintermedia in Sardinians 8-Globin genotype

Cases

a-Globin genotype

AylGy promoter mutationa

/P39/jP39 $39/p39 /P39//F’39 /3”39/8”39 f3”39//P39 /P39//3O39 fP39//3O39 BOW86 PO39186 PO39186 BO39/86 86/86 /P39//3IVSI-6 8-87/p39 8-101//F’39

14 19 1 9 2 3 2 6

aalaa - a/ua - a/ua -a/-a

wild-type wild-type - 158(T)Cy wild-type

-a/-a

- 158cr)Cy wild-type - 196(T)Ay - 158(T)Cy

/P39/BA B”39/PA PWBN

4 4

1 1 1 1 1 3 2 4

aNcolu/aab ND au/au - a/ua -u/-a aNCo’a/aab - a/uu - a/aa -a/-a ND aa/uaa aaa/uau ND

- 158Cr)Cy - 158(T)Cy - 158(T)Cy - 158(T)Cy - 158cr)Cy - 158cr)Cy

ND ND ND ND

‘Mutation classifications: - 158(T)Gy, mutation associated with continuous expression of y chains in adults under erythropoietic stress (always heterozygous except in 86/86); - 196(T)Ay, mutation associated with continuous expression of y chains in adults. baNcO’, non-deletion a-thalassaemia results from initiation codon mutation (ATG + ACG) of the a2 globin gene. ND. not determined.

partially for the lack of fi chains (‘Sardinian Gfi-thalassaemia’). For a large number of patients with /Y39/B039 genotype, we have so far, however, not detected any molecular reason for the mild phenotype. Those patients homozygous for frameshift 6, or compound heterozygous /Y39/frameshift 6 develop thalassaemia intermedia. The reason for the mild phenotype is the presence of C + T at position -158Gy, either in the heterozygous or homozygous state, which has been associated with high HbF production at least in the condition of erythropoietic stress. Less commonly, thalassaemia intermedia results from the compound heterozygous state for a severe mutation (/Y39) and a mild mutation (-87 [C +GI, - 101 [C +TI). Sardinian heterozygotes for the codon 39 nonsense mutation develop thalassaemia intermedia even less often. In some cases the molecular mechanism accounting

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for this unusual severity of the phenotype for heterozygous /3-thalassaemia is the co-inheritance of the triple a-globin gene arrangement. However, in other cases we have detected no explanation for this clinical severity. For each couple at risk we carry out a-globin gene analysis and molecular screening to detect the - 196Ay (C +T) or - l58Gy (C +T) mutations so as to predict the development of thalassaemia intermedia instead of thalassaemia major. Prenatal diagnosis

The methodology for prenatal diagnosis has evolved over time. In the late1970s and early-l980s, prenatal testing was carried out by globin chain synthesis analysis using fetal blood obtained by placental aspiration or fetoscopy (Kan et a1 1975) and then, for a short period, by polymorphism analysis (Kan et a1 1980). As soon as the molecular bases of /3-thalassaemiahad been defined, the /3-thalassaemia mutations were detected directly by hybridization of allele-specific oligonucleotides to electrophoretically separated restricted non-amplified DNA fragments (Rosatelli et a1 1985). Since 1992, we have used allele-specific primers or probes defined by the analysis of parents’ DNA on fetal DNA obtained by transabdominal chorionic villus biopsy. Globin chain synthesis analysis has been shown to be a reliable procedure, with only two misdiagnoses (0.2%) out of 1131 cases monitored. The procedure was, however, associated with a high risk of fetal mortality. Prenatal diagnosis by DNA analysis has given reliable results to date. We have only had one misdiagnosis, which resulted from maternal contamination when we were using oligonucleotide hybridization on non-amplified DNA (Table 5). To reduce the occurrence of misdiagnosis we: dissect carefully the maternal decidua from fetal trophoblast under the inverted microscope; request an amount of chorionic villi not less than 3pg in order to reduce the effect of maternal contamination; carry out no more than 25 amplification cycles, thereby reducing the chance of coamplifying maternal DNA; analyse two different overlapping DNA fragments; and amplify a previously selected polymorphism in order to detect the presence of maternal contamination. Following prenatal diagnosis of an affected fetus, the large majority (99.9%) of the spouses decided to interrupt the pregnancy. The limited group opting for pregnancy continuation took this decision for ethical reasons. Efficacy of the programme

We monitored the efficacy of our programme by registering every new case of homozygous /3-thalassaemia born. The birth rate of homozygous fl-thalassaemia declined from 1 : 250 before prevention to the actual figure of 1 :4000 live births (Fig. 5). Considering the problem in absolute terms on the basis of

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TABLE 5 Prenatal diagnosis of pthalassaemia in Sardinia: overall results

Sample

Fetal DNA: PCRbased analysis Fetal DNA: enzymatically restricted DNA with AS0 probes Fetal blood

Pregnancies monitored

Homozygous fetuses

1666

416

1171

1131

Failures

Misdiagnoses

32 1

7 (0.6%)

1 (0.09%)

268

10 (0.90/)

2 (0.2%)

ASO, allelic specific oligonucleotide probes.

carrier frequency and the total number of newborns, the expected number of homozygotes per year should be 65, whereas only four or five new cases actually arose per year, with 94% of the cases prevented effectively. The new cases of thalassaemia major result from lack of information, decision of continuing the pregnancy without prenatal diagnosis, decision of not interrupting the pregnancy when a fetus affected has been detected and false paternity. Conclusions The experience of carrier screening and prenatal diagnosis of /J-thalassaemia presented here indicates that it is possible to control B-thalassaemia major at

I *expected I

100

E

0

t

2o 0

1

75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94

years

FIG. 5 . Fall in the birth rate of homozygous 8-thalassaemia in Sardinia.

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the population level by a preventive genetic programme. Similar results have also been obtained in other populations, such as Greek Cypriots, Greeks and Continental Italians. The most important challenge for the future will be the organization of such a preventive programme in those populations of the developing world where p-thalassaemia is prevalent but where the local resources at present preclude the development of such a programme. Finally, the control of B-thalassaemia by carrier screening and prenatal diagnosis in Sardinia may represent a model for the organization and delivery of prevention programmes for other common autosomal recessive disorders, primarily cystic fibrosis. Acknowledgements We thank Francesca Fodde and Rita Loi for editorial assistance. This work is supported by Assessorato Igiene e Sanita Regione Sardegna L. R. N. 11 30.04.1990 and N. 1526/ 4492 12.05.1994.

References Cao A 1994 1993 William Allan award address. Am J Hum Genet 54397402 Cao A, Rosatelli MC 1993 Screening and prenatal diagnosis of the hemoglobinopathies in clinical haematology. Bailliere Tindall, London Cao A, Galanello R, Furbetta M et a1 1978 Thalassemia types and their incidence in Sardinia. J Med Genet 14M3-447 Cao A, Furbetta M, Galanello R et a1 1981 Prevention of homozygous /3-thalassemia by carrier screening and prenatal diagnosis in Sardinia. Am J Hum Genet 33:592405 Cao A, Rosatelli C, Galanello R et a1 1989 The prevention of thalassemia in Sardinia. Clin Genet 36:277-285 Cao A, Rosatelli MC, Galanello R 1991 Population-based genetic screening. Curr Opin Genet & Dev 1:48-53 Galanello R, Melis MA, Ruggeri R et a1 1979 p-thalassemia trait in Sardinia. Hemoglobin 3:3346 Galanello R, Dessi E, Melis MA et a1 1989 Molecular analysis of p-thalassemia intermedia in Sardinia. Blood 74:823-827 Galanello R, Melis MA, Podda A et a1 1990 Deletion 8-thalassemia: the 7.2 kb deletion of Corfu G/3-thalassemia in a non-/l-thalassemia chromosome. Blood 75: 1747-1 748 Gasperini D, Perseu L, Cossu P et a1 1994 A novel 8"-thalassemia mutation: TGG +TAG (Trp + STOP) at codon 37. Hum Mutat 3:71-72 Kan YW, Golbus MS, Trecartin R F et a1 1975 Prenatal diagnosis of homozygous 8thalassemia. Lancet II:79&792 Kan YW, Lee KY, Furbetta M et a1 1980 Polymorphism of DNA sequence in the /3globin gene region: application to prenatal diagnosis of thalassemia in Sardinia. N Engl J Med 302:185-188 Melis MA, Pirastu M, Galanello R, Furbetta M, Tuveri T, Cao A 1983 Phenotypic effect of heterozygous a- and /3-thalassemia interaction. Blood 62~226-229 Moi P, Paglietti E, Sanna A et a1 1988 Delineation of the molecular basis of 6 and normal HbA2 /3-thalassemia. Blood 72530-533

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Murru S, Pirastu M, Sciarratta GV et a1 1993 A promoter mutation of the b-globin gene (- 101 C +T) has an age-related expression pattern. Blood 8 1:2818-28 19 Paglietti E, Galanello R, Addis M, Cao A 1985 Genetic counselling and genetic heterogeneity in the thalassemias. Clin Genet 28: 1-7 Pirastu M, Lee KY, Dozy AM et a1 1982 Alpha thalassemia in two Mediterranean populations. Blood 60509-512 Pirastu M, Galanello R, Melis MA et a1 1983 6’ thalassemia in Sardinia. Blood 62:341345 Pirastu M, Kan YW, Galanello R, Cao A 1984a Multiple mutations produce SgOthalassemia in Sardinia. Science 223:929-930 Pirastu M, Saglio G, Chang JC et a1 1984b Initiation codon mutation as a cause of a thalassemia. J Biol Chem 259:12315-12317 Rosatelli MC, Falchi AM, Scalas MT et a1 1984 Hematological phenotype of the double heterozygous state for a- and 8-thalassemia. Hemoglobin 8:25-35 Rosatelli C, Falchi AM, Tuveri T et a1 1985 Prenatal diagnosis of beta thalassemia with the synthetic-oligomer technique. Lancet I:241-249 Rosatelli MC, Dozy A, Faa V et a1 1992 Molecular characterization of B-thalassemia in the Sardinian population. Am J Hum Genet 50:422426 World Health Organization Working Group 1982 Hereditary anemias: genetic basis, clinical features, diagnosis, and treatment. Bull WHO 60:643460

DISCUSSION Weatherall: We have organized a DNA-based prenatal diagnosis programme for p-thalassaemia in the UK. We are envious of the Sardinian experience because it involves a single common mutation in over 90% of the population. In contrast, our heterogeneous population probably has about a hundred mutations, which creates obvious screening problems! As a result, we have even had to resort to sequencing the entire /?-thalassaemia gene for prenatal diagnosis. Our results are in broad agreement with Antonio Cao’s (Old et a1 1986, Weatherall 1991). However, there is a worrying risk of contamination of chorionic villus material with maternal cells, so it is essential to double check for that as part of a fetal DNA screening programme. It is important to establish what happens elsewhere. The results from Cyprus and the UK have been relatively impressive over recent years, although those from the UK are not as good as Sardinia (Alter 1990). The reasons for this are complex. They are partly due to religion, because of our large Islamic population, and also due to lack of awareness. Only southern regions of the UK have been educated into the importance of /?-thalassaemia, and education is a vital part of a successful screening programme. If one goes further afield, there are both economic and religious issues to consider. These issues can often be subtle. For example, in Sri Lanka not only are there financial problems, but also the type of Buddhism in that country opposes any interference with natural processes, so that screening is difficult and prenatal diagnosis may be virtually impossible. Recently, we’ve also been

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studying 8-thalassaemia in Indonesia (J. B. Clegg, D. J. Weatherall, unpublished observations), where the population exceeds two hundred million and there are enormous problems with /I-thalassaemia. For example, in Jakarta one clinic with a transfusion programme has over 900 affected children. Again, there are strong religious beliefs in Indonesia that one should not interfere. In contrast, prenatal diagnosis is accepted by Buddhists in Thailand, provided that it is performed early in pregnancy, so that a country-wide screening programme here is taking off. Another important issue is the question of heterogeneity. In one sense, it’s disappointing that in Sardinia, which is an island population with a well-defined series of P-thalassaemia mutations, there are still a number of genotypes from which it is difficult to predict the phenotypic outcome. We have tried to analyse some of these in sibships with both severe and mild forms of P-thalassaemia in which there is no obvious cause for the phenotypic variation (S. L. Thein, D. Reece, J. B. Clegg, D. J. Weatherall, unpublished observations). It seems that the main feature is the absolute amount of fetal haemoglobin that these sibs are producing-a variation of 2g of fetal haemoglobin can make the difference between normal health and a condition that requires blood transfusion. This is clearly a genetic issue but we have not identified all the genes that modify fetal haemoglobin production and that cause it to persist into adult life. We have evidence for at least one gene, which is not linked to the fl globin gene cluster, that is on chromosome 6 and is involved with the ability to make fetal haemoglobin after the neonatal period (Craig et a1 1995). I suspect that there are others. Weiss: I find it curious that mutations in regulatory genes have not arisen to high frequencies. Wouldn’t one expect mutations that inactivate regulatory proteins to have been selectively favoured? Weatherall: This is a difficult question because we are only just realizing what these mutations might do. A study by our group in Oxford recently defined an X-linked gene (XH2) that encodes a DNA helicase, mutations of which down-regulate 01 globin transcription (Gibbons et a1 1995). This is a subtle down-regulation, and it is clear that XH2 mutations also have widespread effects on fetal development. It is likely that many of these transacting factors regulate numerous genes. Weiss: Is there any evidence in Africa for unlinked mutations? Weatherall: The only evidence for unlinked mutations are the ones that are involved in up-regulating the expression of fetal haemoglobin. Coming back to Antonio Cao’s presentation, the uptake for prenatal diagnosis in the Sardinian population is 97%, and I’ve always believed that there’s no such thing as a neutral genetic counsellor, so I wanted to probe Antonio Cao as to how he has achieved such a high frequency, because no one else has even come close to that. Cao: There are several reasons for this high frequency of uptake in Sardinia. The Sardinian population is distributed into small villages (apart from the

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main town of Cagliari and two other small towns), where two to four individuals are affected by B-thalassaemia. There is, therefore, a strong awareness of the disease because everyone has a relative or friend who is affected. Also, the Sardinians are relatively pragmatic, even though they are Catholics, and the economic burden of the disease influences their decision to undergo prenatal diagnosis. There is no doubt that counsellors cannot be 100% non-directive, although we have tried to do our best. We do find that there are some people who do not accept prenatal diagnosis and others who accept prenatal diagnosis but refuse to interrupt the pregnancy if they have an affected child. Scriver: We have found two common Mediterranean B-thalassaemia mutations in French Canadians in Portneuf, Quebec. Genealogical reconstructions traced alleles to a small number of immigrants from France in the seventeenth century (Kaplan et a1 1990). The affected individuals had been diagnosed as originally having microcytic anaemia. An alert haematologist diagnosed the 8-thalassaemia heterozygous phenotype and he did not prescribe iron therapy. Ten per cent of this population carry a haemochromotosis allele, and to give iron to such individuals is inappropriate. Therefore, recognition of the /.I-thalassaemia phenotype in this region has medical relevance. There are also relevant epistatic effects in the Quebec population due to a coinheritance of /Iglobin and a globin gene mutations, which affect counselling accuracy (Scriver 1989). /?-Thalassaemia is prevalent in both the Mediterranean and Oriental populations in Montreal. We have identified the different mutations that occur in these two populations, and we can now design specific diagnostic reagents for purposes of reproductive counselling (Kaplan et a1 1991). We have had a fl-thalassaemia screening programme for 20 years in Montreal; it has had an effect similar to that described in Sardinia. The Italian Catholic and Greek Orthodox priests were among the principal advocates of the programme. They recognized that to have /3-thalassaemia in the North American context could be devastating. The families decide themselves whether or not to participate, and the programme is operated by the community. We provide only technical support. We did a community survey last year (J. Mitchell, unpublished results 1994), and every couple who received reproductive counselling or prenatal diagnosis did so as a direct outcome of the programme. The programme operates at the high school level. Beighton: There’s no doubt that the public health approach to the control of serious autosomal recessive diseases is well established. However, one of the issues that arose in the early approach to screening for Tay-Sachs, in particular, was the stigmatization of heterozygotes. Does social stigmatization of heterozygotes occur in Sardinia? CUO:We have found that only men encounter problems of this sort because the Italian Army has, until recently, refused to admit /3-thalassaemia carriers.

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Sardinia is a relatively poor country, and many young people consider the military service as one of the only options for employment, even if it is temporary. Following a wide discussion on this issue, a final agreement has been made to select individuals on the basis of their own haematological parameters and not on their carrier state. Weatherall: Do Sardinians have to be tested for 8-thalassaemia by law? Cao: No, there is no law for this in Italy. Freimer: A large number of single people are tested. Does the outcome of the test influence their choice of partner? Cao: That question is difficult to answer because it has not been studied. It is only recently that single people have been tested, before then it was just couples at risk or pregnant women. W. Boaher: My understanding is that the situation may be different in Cyprus. I have discussed these issues with a Greek Orthodox priest of a Cypriot community, who described his antagonism to abortion, but he discouraged marriages between carriers in the church. He felt that this made a major contribution to the reduction in the incidence of B-thalassaemia in Cyprus. Weatherall: This contrasts with Cyprus and Greece, where individuals may find it difficult to get married without being tested for B-thalassaemia. I’ve had the distinct impression from talking to Cypriots that it has had some effect on partner choice. Cao: I totally agree with you. Indeed, even if in Cyprus the test result is not legally important for the forthcoming marriage, it can, nevertheless, influence the couple psychologically. Scriver: In the ultraorthodox Hasidic community of Montreal a few mutations responsible for Tay-Sachs occur at high frequency. Testing of anonymous samples is done, and the results are given to a match-maker. When there are two carriers, the match-maker declares the match incompatible without giving a reason. This genetic match-making process is being requested for cystic fibrosis and Gaucher disease. Weatherall: If we’re going to all this trouble to define genotype/phenotype relationships for various genetic diseases, then we may encounter some problems with counselling. For example, how do you tell parents that their child has B-thalassaemia but of a less severe type, so that the child may have relatively good, trouble-free survival prospects? And what do the parents decide in this case? Cao: When the parents’ genotypes lead to a diagnosis of a mild form of flthalassaemia for the fetus, we explain carefully to the couple that there is a relatively good clinical prognosis for a child affected by mild /I-thalassaemia, but at the same time we try not to influence their decision. However, when a relatively normal clinical prognosis for the fetus cannot be given, the parents usually decide to interrupt the pregnancy.

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References Alter BP 1990 Antenatal diagnosis: summary of results. Ann NY Acad Sci 612: 237-250 Craig JE, Rochette J, Fisher CA et al 1996 Haemoglobin switch: dissecting the loci controlling fetal haemoglobin production on chromosomes l l p and 6q by the regressive approach. Nat Genet 12:58-64 Gibbons RJ, Picketts DJ, W a r d L, Higgs DR 1995 Mutations in a putative global transcriptional regulator cause X-linked mental retardation with a-thalassemia (ATR-X Syndrome). Cell 802337-845 Kaplan F, Kokotsis G, De Braekeleer M, Morgan K, Scriver CR 1990 j-thalassemia genes in French Canadians: haplotype and mutation analysis of Portneuf chromosomes. Am J Hum Genet 46:12&132 Kaplan F, Kokotsis G, Capua A, Scriver CR 1991 Quantification of B-thalassemia in Quebec immigrants of Mediterranean, Southeast Asian and Asian-Indian origin. Clin Invest Med 14:325-330 Old JM, Fitches A, Heath C et a1 1986 First trimester fetal diagnosis for haemoglobinopathies: report on 200 cases. Lancet II:763-767 Scriver CR 1989 The salience of Garrod’s ‘Molecular Groups’ and ‘Inborn factors of disease’. J Inher Metab Dis 12:9-24 Weatherall DJ 1991 Prenatal diagnosis of haematological disease. In: Hann I, Gibson B, Letsky E (eds) Fetal and neonatal haematology. Baillitire Tindall, London, p 285-314

Multigenic control of skin tumour development in mice1 H. Nagase, S. Bryson, F. Fee and A. Balmain CRC Beatson Laboratories, Department of Medical Oncology, Alexander Stone Building, University of Glasgow and Beatson Institute for Cancer Research, Garscube Estate, Switchback Road, Bearsden, Glasgow G61 1BD, UK

Abstract. Different inbred mouse strains vary greatly in their susceptibility to tumour development in a variety of tissues. Intraspecific and interspecific crosses can, therefore, be used to map the loci that control this predisposition. Crosses of Mus musculus with Mus spretus are highly resistant to tumour development in the skin, liver, lung and lymphoid system. M . spretus, therefore, has dominantly

acting resistance loci, which we have attempted to map. More than 350 interspecific backcross mice were followed for 18 months to assess susceptibility to development of chemically induced papillomas and carcinomas. The results were analysed using a combination of MAPMAKERIQTL analysis and multiple regression analysis for the determination of linkage in multigenic quantitative traits. The results showed clearly that at least three genes on chromosomes 5 and 7 control resistance to tumour development. Importantly, some genes confer resistance to benign tumours but they have relatively little effect on malignant progression. This suggests the existence of different classes of benign tumours: those that are capable of tumour progression and those that have only a very low probability of becoming malignant. Identification of these genes will improve our understanding of mechanisms of carcinogenesis and may provide a novel route to the identification of 'low-penetrance' human tumour susceptibility genes. 1996 Variation in the human genome. Wiley. Chichester (Ciba Foundation Symposium 197) p 156-180

Progress in the Human Genome Project has given enormous impetus to studies on the genetic basis of many common diseases, including cancer. The development of sophisticated mapping techniques has made it possible to detect genetic alterations that take place during tumour development in somatic cells, as well as germline alterations that can predispose to the development of a variety of tumour types (Knudson 1993). Genes in the latter category are generally thought of as high penetrance, low frequency genes 'This paper was presented by A. Balmain. 156

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within the human population, which cause a high probability of disease development in affected individuals. Much of the research on predisposition loci has, therefore, concentrated on genes such as the retinoblastoma gene, the p53 tumour suppressor gene and the APC (adenomatous polypopsis coli) gene. Germline alterations at these loci, however, account for only a small minority of the total human tumour burden, and it has been argued that, theoretically, a large number of low penetrance predisposition genes may be distributed at high frequency throughout the population and, therefore, play a substantial role in determining individual human cancer risk (Ponder 1990, Pet0 1980). It should be stressed that these predisposition genes may not represent mutant or nonfunctional alleles at any given locus, but rather a variety of alleles which could, for example, control the metabolic activation of carcinogens, capacity for efficient DNA repair, or susceptibility to the action of environmental mutagens or tumour promoters. Because of the lack of clear-cut familial inheritance patterns and the complexities implicit in the genetic-environmental interactions that may lead to tumour development in predisposed individuals, such genes may be extremely difficult to find on the basis of human genetic studies. Rodent systems, on the other hand, offer some distinct advantages for the detection of low penetrance tumour susceptibility loci. Different inbred mouse strains vary greatly in their susceptibility to the development of tumours of the lung, colon, skin, liver and lymphoid system (Demant 1992). Inbreeding has resulted in homozygosity at a number of tumour susceptibility or tumour resistance loci, which are randomly distributed among these different strains of mice. Classical genetic approaches can, therefore, be used to analyse segregation of such predisposition loci in backcrosses or F1 intercrosses between susceptible and resistant strains. It should be stressed that the genes which may ultimately be discovered as a consequence of such studies determine the nature of the interactions between the host’s genetic background and carcinogenic or mutagenic agents to which the host is exposed. As such, this represents a realistic model for the types of genes that may influence tumour predisposition in humans. The aetiology of tumour development in mice can be strictly controlled in terms of the types of agent used and the tissues targeted for exposure. The rodent model we have used for these studies is the classical two-stage mouse skin chemical carcinogenesis system. This system represents a realistic model for cancer development in humans because of the evidence that the known genetic aberrations in somatic cells are very similar in humans and mice. Genetic studies of multistep skin carcinogenesis have led to the identification of sequential genetic alterations during tumour initiation, promotion and progression (Fig. 1) (Brown et a1 1993). Nearly all benign papilloma and malignant carcinoma lesions induced by DMBA (7,12-dimethyl-l,2-benzanthracene)and TPA (12-0-tetradecanoylphorbol-13-acetate) in mouse skin exhibit mutational activation of H-rus by an

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A - + Ttransversion at codon 61 (Quintanilla et a1 1986, Bizub et a1 1986). Moreover, mouse chromosomes 6 and 7 are frequently trisomic in both papillomas and carcinomas, and chromosome 7, which carries the mutated H-ras on its distal part (Kemp et a1 1993a), is almost always duplicated (Bremner & Balmain 1990, Bianchi et a1 1990). This suggests that H-ras is not only involved in the initiation stage of carcinogenesis, but also in the promotion stage, by duplication or amplification of the mutated allele. H-rus is, therefore, considered a target gene for trisomy of chromosome 7. Although in humans mutation of this gene is observed in only 10% of bladder carcinomas (Bos 1989), the epidermal growth factor receptor, which lies upstream in the rm protein signalling pathway in keratinocytes, is frequently amplified in human epithelial tumours, including squamous cell carcinoma (Khazaie et a1 1993). Cyclin D1 is also amplified in both human squamous tumours (at 1lq13) and in chemically induced mouse skin tumours (distal part of chromosome 7) (Bianchi et a1 1993). The reasons for trisomy of chromosome 6 are unknown, but several candidate genes have been identified, including Met, Rufl, Tgfar (which encodes transforming growth factor a) and K-rus. The p53 gene, which is a common tumour suppressor gene located on chromosome 17p in humans, is mutated in a substantial number of more-advanced tumours, but not usually at the premalignant stage (Greenblatt et a1 1994). A similar situation is seen in mouse skin, where loss of heterozygosity on chromosome 11, on which p53 is located, is detected only in malignant skin tumours (Bums et a1 1991), and mice that lack functional p53 do not show any alterations in incidence or growth rate of benign tumours, but have greatly elevated rates of malignant progression (Kemp et a1 1993b). These results suggest that p53 is important in tumour progression, rather than promotion. Additional genetic alterations which occur during progression include loss of heterozygosity at the ~16'"" (MTS1) locus on human chromosome 9p and mouse chromosome 4 (Wiseman et a1 1994). This gene is frequently mutated in various human tumours (Kamb et a1 1994a) and germline mutations are responsible for familial malignant melanoma (Hussussian et a1 1994, Kamb et a1 1994b).

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All of these results suggest that the types of genetic alterations in human and mouse systems are very similar and, consequently, that the tumour predisposition loci, which may be detected in crosses between inbred mouse strains, may indeed be relevant to carcinogenesis in humans. Tumour predisposition in mouse strains The large variations in susceptibility to tumour development in different inbred mouse strains after exposure to carcinogens suggest that intraspecific and interspecific crosses between strains showing a high degree of genetic polymorphism can allow mapping of genes that control predisposition to tunaour development using classical linkage analysis. In fact, similar approaches have already led to the detection of susceptibility loci for chemically induced mouse tumours (Mock et a1 1993, Manenti et a1 1994, Gariboldi et a1 1993, 1994, Jacoby et a1 1994, Asada et a1 1994, Lee et a1 1995). Nevertheless, for skin carcinogenesis, which is one of the best characterized carcinogenesis models in terms of genetic alterations in tumours, very little is known about susceptibility loci. Previous reports of skin tumour promoter susceptibility using DBA/2 and C57BL/6 mice concluded that at least two genes are involved in susceptibility to phorbol ester treatment, and inheritance of susceptibility in F1 hybrids displays incomplete dominance (DiGiovanni et a1 1992). In our previous studies we noted that interspecific crosses between susceptible Mus musculus and Mus spretus are highly resistant to tumour development in the skin, liver, lung and lymphoid system (R. Bremner, C. J. Kemp & A. Balmain, unpublished results). This dominant resistance is an unusual observation because, in most mouse carcinogenesis experiments so far examined (including epidermal carcinogenesis) dominant or semi-dominant inheritance to tumour susceptibility was detected (Naito & DiGiovanni 1989). The genetic diversity between these two species, which diverged some 3 x lo6 years ago (Robert et a1 1985), allowed us to analyse genetic heterogeneity in offspring of these crosses using a large number of polymorphic markers (Love et a1 1990, Cornall et a1 1991, Dietrich et a1 1992). Linkage mapping of Mus spretus resistance (Spr) genes A large-scale study using mice from an interspecific cross between M. musculus and M. spretus was designed to detect dominantly acting resistance loci. NIH/ Swiss (OLAC, UK) mice are an inbred M. musculus strain that is highly susceptible to a two-stage skin carcinogenesis protocol (Fig. 2b). Figure 2 shows papilloma development at 20 weeks after initial treatment in each mouse strain. F1 mice (NIHxM. spretus) were highly resistant (Fig. 2a) and F1 backcross mice ([NIH x M. spretus] x NIH) showed an intermediate phenotype

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(Fig. 2c). If only one gene controlled susceptibility, the distribution of papilloma numbers in the F1 backcross would show a discrete pattern (Fig. 2d). However, the observed pattern (Fig. 2c) suggests that several genes are involved in resistance to carcinogenesis within this cross, in line with previous predictians. We treated 326 F1 backcross mice by the two-stage carcinogenesis protocol. and followed them for 18 months. We analysed predisposition to both promotion and malignant progression using separate data sets from the same mice. We assessed the phenotypes using papilloma numbers at 20 weeks after treatment as a quantitative trait, and carcinoma incidence before 46 weeks as a dichotomous trait. We have used over 130 microsatellite markers distributed over all autosomes, and we have employed two independent linkage packages based on the multiple interval and multiple regression methods. Figures 3A and 3B are schemes based on the results obtained from the MAPMAKER/ QTL program (Lander et a1 1987, Paterson et a1 1988). To date, we have identified three loci, designated Sprl and Spr2 on chromosome 7 and Spr3 on chromosome 5 , which show significant linkage with resistance to epidermal two-stage carcinogenesis (Nagase et a1 1995).

Genetic risk of malignant conversion Skin tumour progression takes place in a number of steps, passing through benign papillomas that are at either a ‘low risk‘ or a ‘high risk’ of malignant conversion. Predisposition to malignant conversion is affected by the genetic background, and several biochemical markers expressed at different levels in high risk and low risk papillomas have been detected (Yuspa 1994). However, it is not known whether passage through these different classes of papilloma is linear (Fig. 1) or involves independent pathways to high risk and low risk tumours (Fig. 4). The results we have presented suggest that these two papilloma types are under separate genetic control. Sprl and Spr2 appear to confer resistance to papilloma development, but have very little effect on carcinoma incidence. If the progression pathway from low risk to high risk papillomas and carcinomas is purely linear (Fig. 4A), then any gene that inhibits papilloma development should have a profound effect on carcinoma incidence. The fact that this is not the case means that divergent pathways must lead from normal cells to either low risk or high risk papillomas, and that only the latter have a substantial probability of tumour progression. Therefore, we conclude that Sprl and Spr2 are important in the control of the development of low risk papillomas, which are unlikely to undergo malignant progression, whereas Spr3 controls both papilloma and carcinoma development (Fig. 4B). An alternative scenario is that the low risk and high risk papillomas could be derived from different populations of cells within the normal mouse epidermis. For example, the high risk tumours could be derived from the more primitive

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stem cells, thought to reside within the hair follicle region of the epidermis, whereas low risk papillomas may be derived from the interfollicular epidermis. By this interpretation, the categorization of a benign tumour as either low risk or high risk does not depend on the initial genetic event that initiates tumour formation, which is a mutation in H-ras in both cases, but rather on the particular target cell in which this mutation takes place. Mutation of the stem cell may generate a papilloma that has a high capacity for malignant progression, whereas cells that are further along the epithelial cell lineage in the interfollicular epidermis may give rise predominantly to low risk papillomas. It should be stressed that low risk papillomas may not be completely incapable of giving rise to malignant carcinomas, but that the number of additional genetic events required for this transition may be substantially higher. Preliminary results using transgenic approaches to target mutant rus to different epidermal cell populations lend support to some of the ideas discussed above. The use of keratin 10 or keratin 1 promoters to direct expression of mutant rus in the interfollicular epidermis (Bailleul et a1 1990, Greenhalgh et a1 1993) gives rise to papillomas that have a low probability of malignant progression. Initial experiments in which the same gene is expressed within the hair follicles indicate that a different category of benign tumour, which has much greater malignant capacity, is produced (K. Brown, W. Lambie, S. Bryson & A. Balmain, unpublished results 1994).

Candidate genes Several interesting candidate genes have been mapped close to these predisposition loci (Figs 3A,B). H-rus, which is mutated in somatic cells

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after DMBA treatment, is a good candidate for Spr2 (Fig.3A). The A - P T transition at codon 61 is found in almost all papillomas initiated using DMBA, and the mutated ras is generally amplified or overexpressed during further promotion and progression (Quintanilla et a1 1986, Bizub et a1 1986, Bremner & Balmain 1990, Bianchi et a1 1990). Signal transduction through the H-ras pathway, therefore, plays an important role at several stages of skin carcinogenesis. A marker at D7Mit12 shows a significant peak for papilloma predisposition when it is calculated after fixing another peak between D7Mit87 and C. A nearby marker (D7Mit14) shows borderline significant linkage (LOD = 2.040) with carcinoma development. H-ras is located within a 2cM region flanked by D7Mit12 and D7Mit14, and it may be responsible for resistance to tumour development at both stages, although the effect is much stronger for papillomas. K-ras, which is mutated and activated in many tumours, is a candidate for the Pas1 gene identified as a resistance locus for chemically induced pulmonary adenomas (Gariboldi et a1 1993). In humans a variable number of tandem repeats region within the H-ras locus represents a risk factor for several common types of cancer (Krontiris et a1 1993). Although there is no clear evidence that H-ras is a predisposing gene in mice, these findings suggest that H-ras is a strong candidate for Spr2. However, about 2.6cM on the telomeric side of D7Mit14 is the gene encoding the cyclin D1 protein, which is important in cell cycle regulation and has been shown to be overexpressed in a proportion of both human and mouse squamous tumours (Lammie et a1 1991, Bianchi et a1 1993). Cyclin D l overexpression is also observed in most advanced papillomas and carcinomas and in some early papillomas (Bianchi et a1 1993). Cyclin D1 must also be regarded as a good candidate for a tumour resistance gene located close to D7Mit14. Zgflr, which encodes the insulin-like growth factor 1 receptor, is located 33cM from the centromere on chromosome 7 (Fig. 3A). Sprl lies within an interval flanked by D7Mit87 and C which are 29 and 44cM, respectively, from the centromere on chromosome 7. This receptor system is extremely important in mitogenic signalling mediated through the ras protein transduction pathway (Coppola et a1 1994), and its level of expression can affect the degree of transformation of cells in culture by SV40 T antigen (Sell et a1 1993). It is noteworthy that a murine susceptibility locus for liver carcinogenesis has also been mapped to this region (Gariboldi et a1 1994). Zgflr, or another candidate in this area, may confer susceptibility in both carcinogenesis systems. H d , which is located within 0.5cM of DSMit1 on chromosome 5 (Fig. 3B), encodes the hepatocellular growth factor; and its receptor, the protooncogene c-met, is amplified in chemically induced skin tumours (Bol et a1 1995). The known involvement of Hgf in increasing cell motility, invasion and angiogenesis (Weidner et a1 1990, Grant et a1 1993, Rosen et a1 1994),

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together with its location at the proximal part of mouse chromosome 5 (Lee et a1 1993), suggests that it is a good candidate for Spr3 implicated in both the promotion and the malignant progression stages of skin tumour development.

Future prospects Several tumour suppressor genes and oncogenes acquire mutations in skin papillomas and carcinomas, but the relationship between these somatic mutations and the predisposition genes mapped here are, at present, unclear. An exciting possibility is that these resistance loci might act as modifiers of the activities of oncogenes or tumour suppressor genes. An interesting precedent can be seen using the Min mouse, which has a truncating APC mutation in the germline, and acts as a model of human familial adenomatous polypopsis coli (Su et a1 1992). These mice develop multiple, small intestinal adenomas, but crosses between Min mice and SWR or AKR strains reduces the probability of developing tumours by a factor of two. This resistance gene has been called a modifier of Min (Moml) and is located on mouse chromosome 4 (Dietrich et a1 1993). In skin carcinogenesis, the predisposition genes could be modifiers of the functions of the genes known to be involved at different stages of tumour development in this system, including H-rus, p53 and Ink4. These possibilities can now be tested by using transgenic mice expressing oncogenes under the control of tissue-specific promoters, or by using mice in which tumour suppressor genes have been deleted by homologous recombination. For example, transgenic mice which express mutant H-rus under the control of keratin gene promoters spontaneously develop skin tumours without carcinogen treatment (Bailleul et a1 1990, Greenhalgh et a1 1993). In addition, carcinogenesis studies on p53 null mice showed a reduced yield of papillomas, but these underwent much more rapid malignant progression to histologically poorly differentiated carcinomas (Kemp et a1 1993b). Crosses between these mice and either M . spretus or congeneic mice with Sprl, Spr2 or Spr3 may enable us to investigate interactions between the target genes in skin carcinogenesis and the modifier effects of M . spretus resistance loci. Indeed, dominant resistance genes might lead to novel strategies for tumour prevention and therapy.

Acknowledgements This work was supported by grants from the Cancer Research Campaign. We are grateful to Chris Kemp for his assistance in the early stage of this work, to Heather Cordell for carrying out the multiple regression analysis and to John Todd for

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References Asada Y, Varnum DS, Frankel WN, Nadeau JH 1994 A mutation in the Ter gene causing increased susceptibility to testicular teratomas maps to mouse chromosome 18. Nat Genet 6:363-368 Bailleul B, Surani MA, White S et a1 1990 Skin hyperkeratosis and papilloma formation in transgenic mice expressing a ras oncogene from a suprabasal keratin promoter. Cell 62:697-708 Bianchi AB, Aldaz CM, Conti CJ 1990 Nonrandom duplication of the chromosome bearing a mutated Ha-ras-1 allele in mouse skin tumors. Proc Natl Acad Sci USA 87:69024906 Bianchi AB, Fischer SM, Robles AI, Rinchik EM, Conti CJ 1993 Overexpression of cyclin D1 in mouse skin carcinogenesis. Oncogene 8:1127-1133 Bizub D, Wood AW, Skalka AM 1986 Mutagenesis of the Ha-rus oncogene in mouse skin tumors induced by polycyclic aromatic hydrocarbons. Proc Natl Acad Sci USA 83:604840 52 Bol D, Beltran L, Zernegar R, DiGiovanni J 1995 Altered expression of hepatocyte growth factor receptor, c-Met, in multistage mouse skin carcinogenesis. Proc 36th Meet Am Assoc Cancer Res, p 17l(abstr 1017) Bos JL 1989 ras oncogenes in human cancer: a review. Cancer Res 49:46824689 Bremner R, Balmain A 1990 Genetic changes in skin tumor progression: correlation between presence of a mutant ras gene and loss of heterozygosity on mouse chromosome 7. Cell 61:407-417 Brown K, Kemp CJ, Stoler PA, Fowlis DJ, Akhurst RJ, Balmain A 1993 Positive and negative control in multistage skin carcinogenesis. Recent Results Cancer Res 128:309-321 Burns PA, Kemp CJ, Gannon JV, Lane DP, Bremner R, Balmain A 1991 Loss of heterozygosity and mutational alterations of the p53 gene in skin tumors of interspecific hybrid mice. Oncogene 6:2363-2369 Coppola D, Ferber A, Miura M et a1 1994 A functional insulin-like growth factor I receptor is required for the mitogenic and transforming activities of the epidermal growth factor receptor. Mol Cell Biol 1445884595 Cornall RJ,Aitman TJ, Hearne CM, Todd JA 1991 The generation of a library of PCRanalyzed microsatellite variants for genetic mapping of the mouse genome. Genomics 10:874-88 1 Demant P 1992 Genetic resolution of susceptibility to cancer-new perspectives. Semin Cancer Biol 159-166 Dietrich W, Hillary K, Lincoln SE et a1 1992 A genetic map of the mouse suitable for typing intraspecific crosses. Genetics 131:423447 Dietrich WF, Lander ES, Smith JS et a1 1993 Genetic identification of Mom-1, a major modifier locus affecting Min-induced intestinal neoplasia in the mouse. Cell 75:631639 DiGiovanni J, Imamoto A, Naito M et a1 1992 Further genetic analyses of skin tumor promoter susceptibility using inbred and recombinant inbred mice. Carcinogenesis 13525-531 Gariboldi M, Manenti G, Canzian F et a1 1993 A major susceptibility locus to murine lung carcinogenesis maps on chromosome 6. Nat Genet 3: 132-136 Gariboldi M, Manenti G, Ganzian F et a1 1994 Chromosome mapping of murine susceptibility loci to liver carcinogenesis. Cancer Res 53:209-211 Grant DS, Kleimman HK, Goldberg ID et a1 1993 Scatter factor induces blood vessel formation in viva Proc Natl Acad Sci USA 90:1937-1941

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Greenblatt MS, Bennett WP, Hollstein M, Harris CC 1994 Mutations in the p53 tumor suppressor gene: clues to cancer etiology and molecular pathogenesis. Cancer Res 54:48 55 4 8 78 Greenhalgh DA, Rothnagel JA, Quintanilla MI et a1 1993 Induction of epidermal hyperplasia, hyperkeratosis, and papillomas in transgenic mice by a targeted v-Ha-rus oncogene. Mol Carcinog 7:99-110 Hussussian CJ, Struewing JP, Goldstein AM et a1 1994 Germline p16 mutations in familial melanoma. Nat Genet 8:15-21 Jacoby RF, Hohman C, Marshall DJ et a1 1994 Genetic analysis of colon cancer susceptibility in mice. Genomics 22:38 1-387 Kamb A, Gruis NA, Weaver-Feldhaus J et a1 1994a A cell cycle regulator potentially involved in genesis of many tumour types. Science 264:43640 Kamb A, Shattuck-Eidens D, Eeles R et a1 1994b Analysis of the p16 gene (CDKN2) as a candidate for the chmnosome 9p melanoma susceptibility locus. Nat Genet 8:22-26 Kemp CJ, Bremner R, Balmain A 1993a A revised map position for the Ha-ras gene on mouse chromosome 7: implications for analysis of genetic alterations in rodent tumors. Mol Carcinog 7: 147-1 50 Kemp CJ, Donehower LA, Bradley A, Balmain A 1993b Reduction of p53 gene dosage does not increase initiation or promotion but enhances malignant progression of chemically induced skin tumors. Cell 7 4 3 13-822 Khazaie K, Schirrmacher V, Lichtner RB 1993 EGF receptor in neoplasia and metastasis. Cancer Metastasis Rev 12:255-274 Knudson AG 1993 Antioncogenes and human cancer. Proc Natl Acad Sci USA 90: 10914-10921 Krontiris TG, Devlin B, Karp DD, Robert NJ, Risch N 1993 An association between the risk of cancer and mutations in the HRASl minisatellite locus. N Engl J Med 329~517-523 Lammie GA, Fantl V, Smith R et a1 1991 DllS287, a putative oncogene on chromosome llq13, is amplified and expressed in squamous cell and mammary carcinomas and linked to bcl-1. Oncogene 6:4394M Lander E, Green P, Abrahamson J et a1 1987 MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1:174-181 Lee CC, Kozak CA, Yamada KM 1993 Structure, genetic mapping, and expression of the mouse Hgf/scatter factor gene. Cell Adhes Commun 1: 101-1 11 Lee G-H, Bennett LM, Carabeo RA, Drinkwater NR 1995 Identification of hepatocarcinogen-resistance genes in DBA/2 mice. Genetics 139:387-395 Love JM, Knight AM, McAleer MA, Todd JA 1990 Towards construction of a high resolution map of the mouse genome using PCR-analysed microsatellites. Nucleic Acids Res 18:41234130 Manenti G, Binelli G, Gariboldi M et a1 1994 Multiple loci affect genetic predisposition to hepatocarcinogenesis in mice. Genomics 23: 118-124 Mock BA, Krall MM, Dosik JK 1993 Genetic mapping of tumor susceptibility genes involved in mouse plasmacytomagenesis. Proc Natl Acad Sci USA 99: 9499-9503 Nagase H, Bryson S, Cordell H, Kemp CJ, Fee F, Balmain A 1995 Cancer predisposition in mice: distinct genetic loci control development of benign and malignant skin tumours. Nat Genet 10:424429

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Naito M, DiGiovanni J 1989 Genetic background and development of skin tumours. In: Conti CJ, Slaga TJ, Klein-Szanto AJP (eds) Carcinogenesis: a comprehensive survey, vol 11: Skin tumours: experimental and clinical aspects. Raven, New York, p 187-212 Paterson A, Lander E, Lincoln S, Hewitt J, Peterson S, Tanksley S 1988 Resolution of quantitative traits into Mendelian factors using a complete RFLP linkage map. Nature 335:721-726 Pet0 J 1980 Predisposition to cancer. In: Cairns J, Lyon JL, Skolnick M (eds) Cancer incidence in defined populations (Banbury report 4). Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY,p 203-213 Ponder BA 1990 Inherited predisposition to cancer. Trends Genet 6:213-218 Quintanilla M, Brown K, Ramsden M, Balmain A 1986 Carcinogen-specific mutation and amplification of Ha-ras during mouse skin carcinogenesis. Nature 322: 78-80 Robert B, Barton P, Minty A et al 1985 Investigation of genetic linkage between myosin and actin genes using an interspecific mouse back-cross. Nature 314 181-183 Rosen EM, Nigam SK, Goldberg ID 1994 Scatter factor and the c-Met receptor: a paradigm for mesenchymal/epithelial interaction. J Cell Biol 127:1783-1787 Sell C, Rubini M, Rubin R, Liu JP, Efstratiadis A, Baserga R 1993 Simian virus 40 large tumor antigen is unable to transform mouse embryonic fibroblasts lacking type 1 insulin-like growth factor receptor. Proc Natl Acad Sci USA 90:11217-11221 Su L-K, Kinzler KW, Vogelstein B et al 1992 Multiple intestinal neoplasia caused by a mutation in the murine homolog of the APC gene. Science 256:668470 Weidner KM, Behrens J, Vanderkerckhove J, Birchmeier W 1990 Scatter factor: molecular characteristics and effect on the invasiveness of epithelial cells. J Cell Biol 111:2097-2 108 Wiseman RW, Cochran C, Dietrich W, Lander ES, Soderkvist P 1994 Allelotyping of butadiene-induced lung and mammary adenocarcinomas of B6C3F1 mice: frequent losses of heterozygosity in regions homologous to human tumor-suppressor genes. Proc Natl Acad Sci USA 91:3759-3763 Yuspa SH 1994 The pathogenesis of squamous cell cancer: lessons learned from studies of skin carcinogenesis-thirty-third G. H. A. Clowes Memorial Award Lecture. Cancer Res 54:1178-1189

DISCUSSION Weiss: For a long time before molecular genetics could be applied, the age of onset pattern of cancers, as well as experimental evidence, led numerous investigators to model tumours as stochastic mutations (for example: Moolgavkar 1991, Weiss 1995, Whittemore & Keller 1978, Weiss & Chakraborty 1990). The idea was that the accumulation of certain combinations of mutations resulted in the development of a tumour. A1 Knudson (1971, 1993) suggested a two-stage model of tumour development, i.e. that one inherits one mutation and then experiences a required second mutation somatically in the same cell to produce the tumour clone. This idea was applied by Knudson to retinoblastoma, which was essentially manifest at birth and in which the picture seemed rather clear.

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Subsequently, to explain the presence of tumours with adult onset in which there were intermediate phenotypes, such as colorectal polyps, Knudson and Moolgavkar expanded the model to include a growth advantage for cells in the intermediate lesion. This provided an increased number of mitoses in which transforming somatic mutations could occur, so that the risk of cancer increased with age. The accumulation of somatic mutations was associated with progression to (and in) the final malignant turnour. Do you know what the somatic mutation rate is in the genes you are looking at? Do your results fit the cell number idea that underlies these statistical models, or is it a wrong idea from the molecular point of view that just happens to fit the statistical data on age of onset? Balmain: Our results do fit the cell number idea, but it does depend on the starting point. Under the microscope, the different classes of benign tumours look similar but the starting point is different. I’m not saying that the ‘low risk‘ benign tumour is completely incapable of becoming malignant, but it may just need additional mutational events in order to become malignant. Our results suggest that, if an initiating mutation occurs within an early stem cell within the lineage, fewer genetic events may be required to become malignant. There has been much discussion about how many genetic events are required to generate a malignant cell. Some of the earlier models (Armitage & Doll 1954, 1957) predict seven or eight genetic events, and we have identified mouse tumours that have between six and eight genetic changes (J. Liddell & A. Balmain, unpublished observations). A human tumour may take 30-50 years to develop but in the mouse everything has to happen within six to 12 months because the mouse then dies, so the whole process occurs within a much shorter time-scale. Weiss: That point was also made by Richard Pet0 (1978). W. Bodmer: This is a well-known observation. Bruce Ames has shown that the incidence of tumours increases sharply with age in rats, even more so than in humans. The mechanism behind this probably involves the overall efficiency of repair processes in a general sense and the evolutionary pressure on longevity, i.e. the longer the organism lives, the more efficient are its repair processes. Balmain: How well founded is that statement? W. Bodmer: It’s not terribly well founded but it’s the necessary answer. If the number of events is multiple, the difference in efficiency for any particular event doesn’t have to be very large. J. Bodmer: In humans a large number of skin cancers are associated with papillomavirus infections, whereas in normal individuals the infection is associated with an HLA antigen. This has been found in many studies, including one in Sardinia and our study on normal individuals from the UK (Cerimele et a1 1988, Kaslow et a1 1990, Glover et a1 1993). Most immunosuppressed patients, if they live long enough after kidney transplantation, develop skin cancer, presumably as a result of the papillomavirus

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infection. Have you looked at the effect of papillomavirus infections in mouse models? Balmain: We carried out a number of studies a few years ago using a series of probes to look for papillomaviruses in these tumours (M. Ramsden & A. Balmain, unpublished results). The idea was to find out whether carcinogens could activate transcription or replication of papillomaviruses, but we didn’t find any evidence for mouse papillomavirus involvement. There are some known rodent papillomaviruses but all the results can be explained by the genetic events within the tumour cells. This is also supported by the transgenic approaches that we’ve used. J. Bodmer: Have you studied immunosuppressed mice? Balmain: Nude mice, which represent the ultimate immunosuppressed mice, are resistant to the effects of twnour promoters (Chambers et a1 1981) and they do not appear to develop spontaneous tumours at high frequency. Bowcock: I agree that there are similarities between mice and humans and that it’s useful to use mice as a model to study human genes, but there are differences between the two species. For example, in the mouse, integration of the mouse mammary tumour virus (MMTV) into the Wnt3 locus predisposes the mouse to mammary twnours, whereas in humans no one has demonstrated that there’s a mammary tumour virus that causes mammary tumours by integrating into the human Wnt3 homologue, INT4 (Roelink et a1 1990). Balmain: That’s true. The MMTV-induced mouse tumours do seem to be a special case which have been valuable in their own right. However, there are chemically induced mouse mammary tumours where genetic changes similar to those in human tumours are induced by chemical mutation without any obvious involvement of MMTV. Some strains of mice are susceptible to tumours induced by MMTV and others are more resistant. Nevertheless, the resistant strains do develop mammary tumours in response to chemical treatment. Many mammary tumours have genetic alterations in oncogenes and tumour suppressor genes, which is similar to the situation in human mammary tumours (Wiseman et a1 1994). Bowcock: Another difference between mice and humans, and their susceptibilities to tumours can be demonstrated by knockout experiments. That is, there are cases where human tumour suppressor genes inserted in their homologous counterparts in mice do not cause the same tumours in mice as they would do in humans. For example, Rb knockout mice are not predisposed to develop retinoblastomas; some heterozygotes display pituitary tumours, and homozygous embryos for the mutation die in Days 14 or 15 of gestation exhibiting neuronal cell death and defective erythropoiesis (Jacks et a1 1992). In humans alterations of Rb result in strong susceptibility to retinoblastomas in children.

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W. Bodmer: Another difference between humans and Mus musculus is that when APC (the adenomatous polypopsis coli gene) is mutated, tumours occur in the small intestine and not in the large intestine (Moser et a1 1990, Su et a1 1992). This contrasts with the tumours in humans. Also, malignant tumours are not formed in the mice, and they usually die of anaemia due to bleeding from intestinal benign tumours. They often have spontaneous mammary tumours, but the development of these is not fully understood. One has to study carefully the development of the tissue, the number of stem cells, the growth patterns and the number of cell divisions, because these may influence the pattern of mutations. For example, in the mouse tumours there is an early and universal loss of the wild-type allele, which is not the case in the human situation. Mutations of p53 in human colon carcinomas are not null mutations, they are specific mutations that are probably initially dominant. Therefore, p53 knockout mice are not a correct model for the human situation. A better model would be to use the mouse equivalent of the human mutation, i.e. against a p53 null heterozygous background. Balmain: Ziegler et a1 (1994) have analysed p53 mutations in human skin tumours, and they have found that many of these mutations are null mutations-there are many mutations causing premature termination as opposed to missense mutations. W. Bodmer: But that’s not the case in most carcinomas in humans, although premature termination codons are found in most osteosarcomas. Sing: What is the biological aetiology of a multifactorial disease in the mouse? In humans multifactorial diseases are not caused by genes or the environment, but by the interaction between the two. Therefore, there must be a major difference between mouse models of common diseases and the biology of common diseases in humans. For instance, the genetic component of disease in a strain of mice is exaggerated because the environment that interacts with the corresponding genotypes in human populations is not represented. Balmain: But we’re using mutagens and promoting agents as environmental agents to induce tumours, although one can argue about how prevalent they are in the human environment. Therefore, I would argue that environmental/ genetic interactions do occur in these mice. Edwardr: Are wild mice more resistant to the development of tumours than laboratory mice? Balmain: No one has looked at the susceptibility of wild mice living in the wild, as opposed to wild mouse strains, such as Mus spretus, in laboratory conditions. Sing: But the distributions of genetic variations and environmental variations in a population of humans is going to be different than it is in a strain of mice.

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Balmain: Yes, I agree. Weiss: In your opinion, when these mice develop tumours, is the initially transformed genotype of the cell the same among different tumours? Or are there various combinations, as there are with other multifactorial diseases, that produce the same end phenotype? Balmain: In this model system, the initiation step is produced by one treatment with DMBA (7,12-dimethyl-1,Zbenzanthracene), which is specific for mutations at codon 61. Therefore, all the mice have the same genetic initiating event, and we assume that the differences in resistance occur postinitiation. Weiss: Is the whole tumour generated by that one mutation? Balmain: No, a single mutation does not make a tumour. The mice need to be exposed to promoting agents. Weiss: So the tumours in these mice could be genetically different, if different genes were mutated? Balmain: Yes. But certain genetic changes, such as mutations in ras and a trisomy of chromosome 7, are found in almost all of these tumours. Chakravarti: There are variations in the genetic backgrounds of each mouse strain. How do you assess that the genes in each strain have a low penetrance? Balmain: Only 20-30% of the genetic variance between strains that develop different numbers of benign tumours is accounted for by the genes for which we have a map location. Heather Cordell from Oxford has carried out multiple regression analysis, and she has shown that there are multiple low penetrance genes, of which we have found only a quarter (Nagase et a1 1995). It is unlikely that we have missed a major locus that accounts for a high proportion of the resistance to the benign tumours, but we may have missed some less important loci. Chakravarti: Have you done any intercrosses to ascertain whether these are truly dominant genes or whether the resistant alleles have a dose effect? Balmain: We are setting up some intercrosses, but we don’t have any results yet. Clark: One important point concerning quantitative trait loci mapping studies is that only genes responsible for the differences between M . musculus and M . spretus can be mapped. Therefore, the two species may differ at genes that are not segregating within either species, and the variation within each species may be caused by segregation at entirely different genes. Is the increased resistance to tumours universal for all M . musculus/M. spretus hybrids? And have you looked at hybrids among the inbred strains of M . musculus? Balmain: We’ve done a limited number of studies on crosses between different M . musculus strains. The skin tumour yield in F1 mice is generally intermediate between the two strains. It has been reported that several loci are involved, but nobody has actually mapped any of these loci (Naito et a1 1991).

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In contrast, in M . spretus tumour development is almost completely suppressed in the F1 hybrids. Clark: How can this be explained in terms of DNA repair? Why shouldn’t heterozygosity for a few genes confer such extraordinary high fidelity of DNA repair? Balmain: I didn’t suggest that DNA repair was involved. W. Boaher: When people are talking about repair, they do not just mean DNA repair. There are other features of tissue repair which deal with other processes that control biological integrity, such as repair of oxidative damage. Moser et a1 (1990) have worked with the Min mutant and found that there is a higher frequency of tumours on certain backgrounds than on others. The predominant background in mice includes a dominant suppressor of the Min mutation, which has been mapped to chromosome 4. We have to remember that inbreeding is just making genes homozygous, and that the highly inbred stains are homozygous for a number of loci which presumably are not homozygous in the strains that Allan Balmain is studying. Freimer: What is known about the effect of mutations in the genes encoding enzymes involved in DNA repair in the mouse? Balmain: The best results have come from studies of knockout mice. A number of genes encoding DNA repair enzymes have now been inactivated by techniques involving homologous recombination. The most recent of these is the Xpc knockout in Allan Bradley’s laboratory (Sands et a1 1995). These mice are a good model for xeroderma pigmentosum because they’re viable and fertile but if they’re treated with U.V.light, they develop a high frequency of skin carcinomas. Therefore, when the models are engineered, they can be shown to have a number of interesting parallels. However, there don’t seem to be any consistent differences between mice and humans when people have looked at differences in DNA repair in vitro. W. Boaher: There’s one well known difference, which is that it’s much easier to transform mouse fibroblasts than human fibroblasts. Balmain: Yes, but I don’t know of any indication that that’s due to differences in repair Bowcock: Have you considered the effect of imprinting on papilloma development in the M . spretuslM. musculus crosses? In both the mouse and humans the gene encoding insulin-like growth factor 2 (IGF2) is maternally imprinted, but loss of imprinting has been reported in Wilms’ tumour and in several other adult malignancies, leading to the hypothesis that biallelic expression of the gene leads to overexpression of the IGF2 peptide and increased mitogenic activity (Steenman et a1 1994). One could hypothesize that aberrant imprinting mechanisms are operating in the mouse crosses. Balmain: We’ve looked fairly carefully at that, but at the moment we have no obvious indication of loss of imprinting or changes in imprinting. We have

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observed that the genetic event which follows the initial ras mutation caused by the mutagen is a duplication of the chromosome that carries the mutant ras allele (Bremner & Balmain 1990). In most cases it is the mother’s chromosome which is duplicated (P. Burns, C. Kemp, F. Fee & A. Balmain, unpublished results 1994). This contrasts with the expected situation if IGF2 was involved because IGF2 is paternally expressed and the maternal allele is silent. There may have been a loss of imprinting followed by duplication, but that’s quite far fetched. It is possible that the duplication of the maternal allele is a strain effect because the maternal allele comes from a sensitive M.musculus strain, and M . spretus provides the paternal allele. There is a preferred duplication of the chromosome that does not carry the strong M . spretus resistance allele. Analysis of the tumours in the backcross mice that have inherited different regions of the M. spretus genome supports this. Weatherall: How is the variation in the resistance to tumours in different strains of mouse explained in evolutionary terms? Is it possible that, although there are many genetic pathways to pre-malignancy and malignancy, there is a limited number of genes involved in the final transforming event to acute malignancy? Balmain: That’s what we have found in practice. The three most important loci that control development of benign tumours in M . spretus only account for about 25% of the variance. We have mapped several other genes, each of which makes a relatively small contribution to the overall resistance (H. Nagase & A. Balmain, unpublished results 1995). For the malignant tumours, about 75% of the variance can be accounted for by three genes, of which one is more important than the other two. W . Bodmer: Cancer is the least heritable human chronic disease. Therefore, although there may be many genes that give rise to mild susceptibilities, estimates of their frequency and effect are questionable. Balmain: My knowledge in this field is limited, but it has been suggested that the impact of these low penetrance susceptibility genes may be strong in cases of human sporadic cancers (Ponder 1990). W. Bodmer: Those results may be exaggerated. One has to be cautious when interpreting the results of these experiments. Clark: It may seem as though these genes have a small effect in humans because we haven’t had the means for manipulating the genetic background like we have in the mouse. It is possible that if the genetics and environment were controlled, the genes with smaller effects would become more apparent. W . Bodmer: There is no evidence for heritability in twin studies from the Danish registry data (Holm et a1 1982). The usual estimate is 20%. This still leaves room for a number of genes. Also, epidemiological evidence can set reasonable limits to the extent of environmental effects, by taking the difference between the highest and lowest incidence of any cancer in different

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countries. These results suggest that differences between populations, such as the low incidence of breast cancer in the Japanese compared to European or American populations, are unlikely to be specifically genetic. One could increase the heritability by, for example, stopping smoking and making dietary changes. Freimer: The environmental effects may be much stronger but they can change rapidly. For example, we cannot tell if people will continue to smoke at the same rate in 100 years time. Therefore, it will be important to consider models that can be tested independently of such effects. W. Bodmer: But I didn’t say that wasn’t the case. We are looking for genes that have a small effect on cancer. My point is that it’s wrong to assume that there’s a major genetic contribution for the majority of cancers. Bafmain: No one is arguing that the environment does not play a major role in determining who develops cancer. However, if a group of people are exposed to cigarette smoke or to mutagens in the environment, not all of them will develop tumours. Therefore, within the population there is an enormous variation in susceptibility to cancer. W. Bodmer: That’s a classic problem. Cancer is a stochastic process and people who smoke but do not get cancer are just lucky. Also, if one takes the polyps that arise in polyposis, which are precursors of colorectal cancers, one cannot tell which ones will develop carcinomas because there’s a large stochastic element. I’m sure that this is also true for skin papillomas. Bafmain:I don’t agree that it’s purely a stochastic process. If certain tumourresistant mouse strains are treated with the same level of environmental carcinogens as sensitive strains, they presumably get the same number of mutations but they do not develop tumours. W. Bodmer: But I’m talking about different papillomas within a given strain. Differences in the response to carcinogens are related to the metabolism of the carcinogen by the cell. The mutagen that you use has to be metabolized, so if the metabolic enzyme is not present, the genetic change will not occur. Balmain: The study of metabolism in the sensitive and resistant strains suggests that metabolism of DMBA does not play a role. The strains are able to metabolize DMBA equally. Clark: One could get a handle on the human epidemiological data by asking whether the heritability is higher among people who smoke. W. Bodmer: We’ve looked extensively for genetic factors that interact with the effect of smoking. Many claims have looked promising, including a search for P450 variation. On the basis of sensitivity to carcinogens, such variation was thought to be correlated with the resistance to lung cancer among smokers. When this was defined at the genetic level by Roland Wolf, that correlation disappeared. One could also imagine that old people who smoke heavily may have some protective genetic factors, but the evidence for t h s is also weak. Another complicating factor is the relationship between the

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development of lung cancer and the number of cigarettes smoked, which is also not clear-cut. Edwarh: I always thought that there was good evidence for the involvement of a large number of alleles with a weak effect. Almost all cancers, including those in childhood, have a substantial sex difference, which is more likely to be due to the effects of the genetic background rather than of specific loci related directly to cancer. If that background can have so marked an effect, it seems reasonable to suppose that other loci could have an effect. This may partly explain the increased predisposition of relatives. W . Bodmer: But childhood cancer may be different. Also, if one counts sex as a genetic factor, then there are many tumours, such as breast tumours, which do occur rarely in males but are obviously much more common in females. The familial concentration of cancers is divided into two groups: clear-cut dominance situations and situations where there may be low penetrance genetic factors that give a modest increased risk. There is also the issue of coincidence. In almost all common cancers there is an increased incidence within a family of about two-fold to three-fold. Genes with a low penetrance, even up to 20%, contribute practically nothing to familial concentration. It is difficult, just by looking at familial concentrations or sib pairs, to distinguish which sib pairs are due the presence of a low penetrance gene and which are the result of coincidence (Bodmer 1982). Weiss: Ranajit Chakraborty and I spent almost 10 years putting together a large database in Laredo, Texas, to look at the familial aggregation of cancer. Ryk Ward was also involved in that study. We found a little evidence for familial breast and ovarian cancer (Buchanan et a1 1985), and we did not find any other site-specific,familial tumours. The population was too small to pick up many childhood tumours. For adult tumours of all types aggregated together, we found a slight, but statistically significant, increase in the risk. These results are consistent with studies in other populations, such as the Utah Mormon study (Weiss et a1 1986). There may be many modifying factors but the age of onset obscures the signal. Scriver: The Danish adoptive family study (Slarensen et a1 1988) examined environmental versus biological causative factors in disease causing death before 50 years of age. The relative risk ratio was 1.19 for the biological factors in cancer, and 5.16 for the environmental factors. For infectious diseases, the relative risk ratio for the biological factor was 5.81, and 0.73 for the environmental factor. Chakravarti: Even in familial cases of breast cancer, there are some individuals in whom the tumour develops later. These are probably phenocopies. Weiss: In the original BRCAZ paper (Miki et a1 1994), many of the breast cancers occurred in non-carriers of the putative mutations.

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W. Bodmer: Age is a limiting factor and age of onset has to be taken into account. There are also some recent data on the incidence of colon cancer in people below the age of 35. These are nearly all due to genetic factors, but as soon as the age is increased to 45, the proportion of tumours due to genetic factors decreases rapidly. Weiss: To my knowledge, multiple endocrine neoplasia 2A ( M E N 2 4 is the only cellularly dominant human tumour gene that’s been identified; all the other ‘inherited’ cancers are cellularly recessive. The standing argument has been that individuals with a cellularly dominant tumour gene for cancer, unlike those with a cellularly dominant gene for other chronic diseases, don’t survive embryogenesis because these genes are also involved in development. Is this a valid argument? W. Bodmer: p53 acts as a dominant allele notwithstanding the fact that eventually there are other mutations in the tumours and the p53 wild-type allele is lost. Chakravarti: I agree, but multiple endocrine neoplasia type I1 is the only inherited tumour syndrome that’s due to a oncogene, i.e. ref.H-ras may also be cellularly dominant. W. Bodmer: But H-ras doesn’t give rise to inherited tumours. Weiss: Are there inherited serious mutations of p53, other than in LiFraumeni families? And even in these families aren’t somatic second hits in p53 required at the cellular level? W . Bodmer: The distribution of p53 mutations is narrower than that which is seen somatically, but it certainly overlaps it. Weiss: This may explain why the heritability is so low. We may have to look at cancer differently because embryos with seriously cellularly dominant mutations can’t survive embryogenesis. W. Bodmer: There are also partial inactivating mutations. For example, mutations in APC early exons produce a milder form of the disease. They are not, presumably, embryonic lethals. Balmain: Many Li-Fraumeni patients have a so-called dominant p53 mutation, but they can survive until their forties and fifties without developing tumours, so it’s not a strong argument that p53 missense mutations are strongly dominant mutations (Li et a1 1988). W . Bodmer: But I’m using the cellular argument for dominance based on the majority of situations where knockouts of p53 are not present. Balmain: A p53 knockout mouse has a similar spectrum of tumours as LiFraumeni patients. If these p53 null mice are crossed with p53 transgenic mice that have a so-called dominant mutation in p53, the rate of tumour development and the tumour spectrum are also similar, i.e. there is no evidence for dominant gain of function in vivo (Harvey et a1 1995). Beighton: We have discussed the dominant inheritance of neoplasia, but can anyone comment on the well-known single gene autosomal dominant

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conditions in which there’s a well-defined increased risk of neoplasia? For instance, people with neurofibromatosus have about a 5% increased chance of developing cancer, most often in the neurofibrosarcoma category (Riccard & Eichner 1986). W. Bodmer: I don’t know enough about those disorders, but the Knudson hypothesis that relates somatic and germline changes suggests a definition based on family studies of tumour suppressor genes. The classic example of this is retinoblastoma, where a single defect in the expression of the gene may be inherited, but a second event is required before the Rb deletion is expressed properly in the tumour (see Bodmer 1982). Weiss: The second somatic mutation theory is really the extension of Knudson’s idea (Knudson 1971, 1993). In other words, it’s not just a single second hit that’s required, but a particular combination of somatic mutations. W . Bodmer: It’s a two-hit situation with respect to the gene you’re looking at, but the tumorigenic effect is not produced until a non-functioning gene is present in a tumour. This may occur by a second mutation, non-disjunction or somatic recombination.

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Kaslow RA, Duquesnoy R, van Raden M et a1 1990 Al, Cw7, B8, DR3 HLA antigen combination associated with rapid decline of T-helper lymphocytes in HIV-1 infection. A report from the multicentre AIDS cohort study. Lancet 335:927-930 Knudson AG 1971 Mutation and cancer: statistical study of retinoblastoma. Proc Natl Acad Sci USA 68:82&823 Knudson AG 1993 Antioncogenes and human cancer. Proc Natl Acad Sci USA 90: 1091&10921 Li FP, Fraumeni JF, Mulvihill JJ et a1 1988 A cancer family syndrome in 24 kindreds. Cancer Res 485358-5362 Miki Y, Swensen J, Shattuck-Eidens D et a1 1994 A strong candidate for the breast and ovarian cancer susceptibility gene BRCAI. Science 266:66-71 Moolgavkar SH 1991 Stochastic models of carcinogenesis. In: Rao CR, Chakraborty R (eds) Handbook of statistics, vol8. Elsevier-North Holland, New York, p 373-393 Moser AR, Pitot HC, Dove WF 1990 A dominant mutation that predisposes to multiple intestinal neoplasia in the mouse. Science 247:322-324 Nagase H, Bryson S, Cordell H, Kemp CJ, Fee F, Balmain A 1995 Cancer predisposition in mice: distinct genetic loci control development of benign and malignant skin tumours. Nat Genet 10:424-429 Naito M, Chenicek KJ, Naito Y, DiGiovanni J 1991 Susceptibility to phorbol ester skin tumor promotion in (C57BL/6 x DBA/2) F1 mice is inherited as an incomplete dominant trait: evidence for multi-locus involvement. Cancer Res 15639-645 Pet0 R 1978 Epidemiology, multistage models and short-term mutagenicity tests. In: Hiatt HH, Watson JD, Winsten JA (eds) Origins of human cancer. Cold Spring Harbor Laboratory Press, New York, p 1403-1414 Ponder BA 1990 Inherited predisposition to cancer. Trends Genet 6:213-218 Riccardi VM, Eichner JE 1986 Neurofibromatosis. Johns Hopkins University Press, Baltimore, MD Roelink H, Wagenaar E, Lopes Da Silva S, Nusse R 1990 Wnt-3, a gene activated by proviral insertion in mouse mammary tumours, is homologous to Int-l/ Wnt-1 and is normally expressed in mouse embryos and adult brain. Proc Natl Acad Sci USA 87:45 194523 Sands AT, Abuin A, Sanchez A, Conti CJ, Bradley A 1995 High susceptibility to ultraviolet-induced carcinogenesis in mice lacking XPC. Nature 377: 162-1 65 Ssrensen TIA, Nielsen GG, Anderson P-K, Teasdale TW 1988 Genetic and environmental iniluences on premature death in adult adoptees. New Engl J Med 318:727-732 Steenman MJC, Rainier S, Dobry CJ, Grundy P, Horon IL, Feinberg AP 1994 Loss of imprinting of IGF2 is linked to reduced expression and abnormal methylation of H19 in Wilm’s tumour. Nat Genet 7:433-439 (erratum: 1994 Nat Genet 8:203) Su L-K, Kinder KW, Vogelstein B et a1 1992 Multiple intestinal neoplasia caused by a mutation in the murine homolog of the APC gene. Science 256:668-670 van Heyningen V 1994 One gene-four syndromes. Nature 367:319-320 Weiss KM 1995 Genetic variation and human disease: principles and evolutionary approaches. Cambridge University Press, Cambridge Weiss KM, Chakraborty R 1990 Multistage models and the age-patterns of cancer: does the statistical analogy imply genetic homology? In: Herrera L (ed) Familial adenomatous polyposis. Wiley, New York, p 79-89 Weiss KM, Chakraborty R, Smouse PE, Buchanan AV, Strong LC 1986 Familial aggregation of cancer in Laredo, Texas, a generally low risk Mexican-American population. Genet Epidemiol 3: 121-143

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Whittemore AS, Keller JB 1978 Quantitative theories of carcinogenesis. SOCIndustr Appl Math Rev 2O:l-30 Wiseman RW, Cochran C, Dietrich W, Lander ES, Soderkvist P 1994 Allelotyping of butadiene-induced lung and mammary adenocarcenomas of B6C3F1 mice: frequent losses of heterozygosity in regions homologous to human tumor-suppressor genes. Proc Natl Acad Sci USA 91:3759-3763 Ziegler A, Jonason AS, Leffell DJ et a1 1994 Sunburn and p53 in the onset of skin cancer. Nature 372:773-776

Population genetics of tumours W. F. Bodmer and Ian Tomlinson Imperial Cancer Research Fund, 44 Lincoln's Inn Fields, London WC2A 3PX, UK

Abstract. The available evidence suggests that cancer is essentially a somatic evolutionary process involving a series of mutations. Each mutation gives some advantage to a selected clone, and expansion then occurs within that selected clone. The advantages are associated with both growth rate and factors leading to independent growth. The aim of this paper is first to give some background information on genetic changes in tumours, using colorectal cancer as an example. We will then introduce a mathematical model that explains many phenomena associated with the development of benign tumours and the long lag periods that are characteristic of the development of human tumours. The model addresses populations of cells and not populations of people. 1996 Variation in the human genome, Wiley, Chichester (Ciba Foundation Symposium 197) p 181-193

Genetic steps in cancer The age-incidence data for the development of colorectal cancer, for example, show a sharp curvilinear relationship. A logarithmic plot turns this into a straight line, showing that the incidence of cancer at age t is proportional to tN-l, where N represents a series of N independent steps. In a logarithmic plot, the slope of the line is N- 1. Thus, if 1 is added to the slope, then the number of steps is obtained. This is based on constant exponential growth, where one mutation replaces another (Armitage & Doll 1954). The value of N is usually estimated as being between six and eight. In common with heritabilities, these are not precise estimates of the aggregate number of key steps. In our view, they have to be taken with caution because the model is too simplistic. Many tumours involve a number of discrete stages; for example, papillomas or polyps as pre-cancerous growths. These, together with both the apparent long periods where nothing happens and selection, have to be taken into account. Some people have argued that, ignoring selection, if six steps are required, then six independent mutations must accumulate in the same cell. If the mutation rate is p, the probability that the six mutations occur together is p6. If the mutation rate is of the order of for example, then the probability that the six mutations occur together becomes horrendously small. The argument, 181

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therefore, is that there has to be a high mutation rate in order to explain the occurrence of cancer. We believe that this argument is incorrect because the calculations ignore selective processes. It is also interesting that classical population genetic models are based largely on assuming a constant population size, and they do not assume a relationship between gene substitution and population growth. Many aspects of ecological theory deal with this point. There is no reason why this also should not be true within a tumour, where there is a high turnover of cells, so that there is essentially no growth of the tumour but there is continuous selection for new variants.

Tumour suppressor genes Familial adenomatous polyposis is a classical, dominantly inherited syndrome. The bowels of affected individuals are carpeted with polyps or pre-cancerous growths. It is not possible to determine which of these will develop into a large adenoma and then into a carcinoma, but all polyps in affected individuals have a mutation in the adenomatous polyposis coli gene (APC) and, therefore, they have been set along that particular road at an early stage. APC has been cloned, and it has been shown that a mutation in APC is one of the earliest genetic changes that takes place in sporadic tumours (Bodmer 1994). Knudson (1971) made an important observation relating to germline and somatic changes more than 20 years ago. He pointed out that if a gene can mutate to give rise to a cancer somatically and there is a germline change in that gene, then this can be the basis for an inherited susceptibility. There are two fundamental points relating to his idea. The first is the relationship between somatic and germline changes and the second is that this relationship is often a two-step process. Thus, when a non-functional version of the gene is inherited, the individual is phenotypically normal, but a second event occurs in the tumour that knocks out the function of the remaining normal gene. This does not suggest that only two steps are required for the development of the cancer but that, with respect to that particular change, two stages are required for an initial major selective advantage. Thus, if such a gene is found in families, it should also be relevant in the corresponding sporadic, non-inherited tumours. This has been shown for APC, where about 80% of all colorectal carcinomas have mutations in APC at early stages (Miyaki et a1 1990, Miyoshi et a1 1992, Cottrell et a1 1992). The vast majority of these become homozygous for the mutated allele. This fits the classical notion that all the mutations are non-functional mutations. In sporadic tumours the second event has to generate an additional advantage over the first mutation, which is in the heterozygote state. There is a classical approach for looking for such genes in tumours by looking for loss of heterozygosity, i.e. by h d i n g only one allele for a marker on the chromosome instead of two (Cavanee et a1 1983). There are two main mechanisms involved: non-disjunction and mitotic recombination.

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Deletions and second mutations can also give rise to those changes but they may be different in nature and less frequent. APC is involved in determining cellhell and cellhell basement membrane contacts (Rubinfeld et a1 1993, Su et a1 1993), suggesting that one of its important functions is to encourage independent, but not necessarily rapid, growth.

Mutation, repair genes and apoptosis There has been a lot of discussion about the types of mutations that occur in genes, such as p53 and the mismatch repair genes, that increase mutation rates. It has been assumed that they are selected because of this reason. However, this is unlikely for the following reasons. There are now numerous data at the level of the DNA sequence for germline and somatic mutations of APC (Nagase et a1 1992, Nagase & Nakamura 1993, Cottrell et a1 1992). These data show, for example, that there is a high relative frequency of changes from codons encoding arginine to a premature termination codon. These changes occur more often than changes from codons encoding glutamine to a premature termination codon probably because of differences in the frequency of methylation at these positions. The spectrum of such mutations is similar in the germline and somatically. CG to TA transversions are associated with chemical mutagenesis. For example, there is a high frequency of such mutations in p53 in lung cancers because cigarette smoke contains hydrocarbon mutagenic carcinogens. In colon carcinomas, although there is a significant excess of these changes relative to others, they make a small contribution (no more than 10%) to the pattern of mutations. This suggests that environmental carcinogens which are mutagens are not important in the development of colon cancer, although one would have thought that mutagenesis would be most important during the initial stages when the early mutations, such as in APC,arise. Therefore, this is an argument against a limiting role for the mutation rate even in the early development of colorectal tumours. Mutations of p53 are present in about 50% of all tumours (Harris 1993). Mutations of genes encoding the mismatch repair enzymes are also interesting. A series of such mutated genes have been identified in the germline which give rise to hereditary non-polyposis cancer (see Dunlop 1995 for review). Those same mutations also occur in sporadic colorectal tumours. About 10-1 5% of colorectal tumours have a mutator phenotype, which means that they have lost both the wild-type genes for one of these particular repair functions. However, we do not yet know to what extent there may be heterozygotes for such a function that have lost the activity of only one of the wild-type genes. As in the case ofp53, it is quite possible that these changes are selected for not because of their secondary effect on mutation rate, but because of a primary effect on the control of programmed cell death, which is an extremely important event. There are points in the cell cycle when the decision

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must be made to grow or die following DNA damage, for example. Mutations in p53 and the genes associated with hereditary non-polyposis colorectal cancer (HNPCC) presumably decrease the probability of cell death. This represents an important part of the selective process. In other words, growth is a balance between division and death. Division, meaning exponential growth, has previously nearly always been considered without taking into account the probability of death. Therefore, these changes, which look as though they are increasing the mutation rate, may actually be selected for because of their effect on cell death. A model for cancer incorporating differentiation and apoptosis

In epithelial tumours, there is often an assumption that there is a stem cell, from which the major cells of a particular structure like a colonic crypt are derived, and that within each crypt one or more stem cells continually regenerate the crypt. Our model (see Fig. 1 and Tomlinson & Bodmer 1995) proposes that there is not only a stem cell, but also an intermediate stage of differentiation when cell turnover is still occurring. This turnover renews the tissue. Differentiation then occurs, followed by apoptosis. At each stage there are a number of cells at different generations. The assumption is that the generation time of different tissues may be different and that the ratios of the generation times take that into account. At each of the two stages there are three alternatives: moving on to the next stage; apoptosis; and renewal of cells.

Stage o f FO differentiation

F1

.--a1 I

F2

--,death

/

I

,,-- p l --,death

~1 ---- 8 2 -------, N2 ---Y -+N3

.‘--a3

Generation time

tO

--,renew-’

-

tl

F3

-- -- -- --

.

death

-- - -

83 --,renew

-’ t2

FIG. 1. The model of cell division, differentiation and death. a1 and fl1 represent rate of apoptosis at first and second stages, respectively. a2 and fl2 represent rate of differentiation at first and second stages, respectively. a3 and 83 represent rate of renewal at first and second stages, respectively. to, t l and t2 are the generation times at the three stages: FO, stem cell; F1 semi-differentiated; F2, differentiated. N, number of cells.

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Nl(G+l) = 2P3Nl(G)tO/tl

+

2a2NO(G)

At equilibrium

N l ( G + l ) = Nl(G) => Nl(G) = 2P3Nl(G)tO/tl

+

2a2NO(G)

=> Nl(G) = 2a2NO(G) (1-(2P3tO/t 1))

FIG. 2. Equations for change in number of semi-differentiated cells. a2, rate of differentiation at fmt stage; 83, rate of renewal at second stage; G , generation number; N, number of cells; t, generation time.

In a stable tissue, at the first stage, the rate of renewal, a3,must be 0.5 because if the tissue is not growing, renewal must be exact for a stable state. If a stable state persists, the rate of renewal at the second stage, &, must be less than 0.5 because exponential growth cannot occur at that stage. The ratio of stem cells to intermediate to fully-differentiated cells can be, for example, of the order of 40 : 400 : 1000 or more. The precise number depends, in the normal state, on the number of cells (No).The mathematical model for this is given in Fig. 2. The equations determine the number of cells in the intermediate state N1. At generation G + 1 the contribution N,(G + 1) from N,(G) is 2p3N1(G) multiplied by to/tl for the generation difference. There is also a contribution of Nl(G+l) = 2P3(1+6/(P2+P3))Nl(G)tO/tl

+

2a2NO(G)

At equilibrium

Nl(G) = 2P3(1+6/(P2+P3))Nl(G)tO/tl

+

2a2NO(G)

=> Nl(G) = 2cr2NO(G)

1-{2P3(tO/tl)(1+6/(Pz+P3))} There is no equilibrium when 2P3(tO/tl)(l+h/(P2+P3)) > 1 => 2p3(tO/tl)

+ 26P3(tO/tl)/(P2+P3)

>1

=> 6 > 11-2B31tO/tl))102+031 2P3(tO/tl)

FIG. 3. Equations for change in number of semi-differentiated cells when rate of apoptosis (81) is reduced by an amount 6. This change is partitioned among the rates of renewal (83) and differentiation (82) relative to their original values. a2, rate of differentiation at first stage; G, generation number; N, number of cells; t, generation time.

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2t12NO(G)from the stem cells, where at is the initial differentiation rate. Equilibrium can exist only if f13 < 0.5(tl/to), i.e. f13 is small enough so that there is no exponential growth of that intermediate stage. What are the effects of genetic changes on these various parameters? What, for example, is the effect of a mutation that decreases the rate of apoptosis (&) on this process? The relevant equations are given in Fig. 3. If f13 is increased by an amount greater than the limit given in Fig. 3, exponential growth occurs at this intermediate stage and the tumour is essentially malignant. However, we are looking for intermediate stages where that does not occur. Thus, if Dl is decreased by 6 below the limit, there is no exponential growth and a new equilibrium is reached. The new equilibrium is equivalent to a benign growth. (Examples of changes according to the model are shown in Figs 4 and 5.) Within that stable state, turnover continues until the next mutation occurs. In this way, if 6 is not too large, one can explain

o f 0

I

I

I

1

8

%

8

2

G FIG. 4. The approach of Nl(G) and N2(G) to equilibrium (based on the model of Fig. 3). Time (G) is shown on the x-axis, and Nl(G) (lower solid line) and N2(G) (upper dotted line) are shown on the y-axis. Values of al, a2 and a3 (rates of apoptosis, differentiation and renewal, respectively, at first stage) are 0.1,0.4 and 0.5, respectively. Values of j 1 , j 2 and 83 (rates of apoptosis, differentiation and renewal, respectively, at second stage) are 0.275, 0.5 and 0.225, respectively. Value of y (death rate of differentiated cells) is 0.2. Values of to, t l and t2 (generation times) are 2, 1 and 0.5, respectively. S in this model (see Fig. 3) is set at 0.05.

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0 0

I

1

I

1

0

0

3

8

m

w

G FIG. 5. The exponential increase in Nl(G) and N2(G). Time (G) is shown on the xaxis, and Nl(G) (lower solid line) and N2(G) (upper dotted line) are shown on the yaxis. Values and definitions of al, a2, a3,fl11f12,f13, y , to, tl, t2 are as given in Fig. 4 . 6 in this model (see Fig. 3) is set at 0.1. When 6 exceeds 0.08056 in this model, exponential growth occurs.

the steps from one benign growth to another with an increase each time in the number of cells. At each stage the probability of malignancy increases as the limit on 6 for exponential growth decreases. This model can, therefore, explain the development of benign tumours, although it does not pursue the details of why some benign tumours may not become malignant. There must always be a large stochastic element as to when the next mutation occurs. The model can also account for long lag phases without the awkwardness of having exponential growth with very low exponents. Similar results are obtained by decreasing the differentiation rate (p2), instead of decreasing the rate of apoptosis. The progression of tumours is often misunderstood because it is assumed there is a definite order in which mutations occur. It is clear, for example, that APC mutations give an early advantage, whereas the ras mutants can probably function at any stage. Mutations in p53 probably arise at late stages in adenomas but before metastasis occurs. The early events, which confer

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independent replication, probably increase the rate of cell death and then it becomes important to select for more mutations that decrease PI. Specific values of mutation rates and population sizes can be incorporated into this model. There may be lo6 stem cells, and 10l2cells weigh about 1 kg (about the size of a lethal tumour); therefore, there is a finite time during which these processes have to occur. This can be incorporated into the model using specific examples of mutations with given rates and given selective effects. This is a model, we believe, that is genetically well founded and can explain many of the features of the development of tumours. It will be interesting to combine the model and its quantitative predictions with further observations on the actual nature of genetic change.

References Armitage P, Doll R 1954 The age distribution of cancer and a multi-stage theory of carcinogenesis. Br J Cancer 8:l-12 Bodmer WF 1994 Cancer genetics. Br Med Bull 50517-526 Cavenee WK, Dryja TP, Phillips RA et a1 1983 Expression of recessive alleles by chromosomal mechanisms in retinoblastoma. Nature 305:779-784 Cottrell S, Bicknell D, Kaklamanis L, Bodmer WF 1992 Molecular analysis of APC mutations in familial adenomatous polyposis and sporadic colon carcinomas. Lancet 340:626-630 Dunlop MG 1995 Molecular genetics of colon cancer. In: Cowell JK (ed) Molecular genetics of cancer. BIOS Scientific, Oxford, p 113-134 Hams CC 1993 p53: at the crossroads of molecular carcinogenesis and risk assessment. Science 262:1980-1981 Knudson AG 1971 Mutation and cancer: statistical study of retinoblastoma. Proc Natl Acad Sci USA 682320-823 Miyaki M, Seki M, Okamoto M et a1 1990 Genetic changes and histopathological types in colorectal tumors from patients with familial adenomatous polyposis. Cancer Res 507166-7173 Miyaki M, Konishi M, Kikuchi-Yanoshita R et a1 1994 Characteristics of somatic mutation of the adenomatous polyposis coli gene in colorectal tumours. Cancer Res 54301 1-3020 Miyoshi Y,Nagase H, Ando H et a1 1992 Somatic mutations of the APC gene in colorectal tumours: mutation cluster region in the APC gene. Hum Mol Genet 1~229-233 Nagase H, Nakamura Y 1993 Mutations of the APC (adenomatous polyposis coli) gene. Hum Mutat 2:425434 Nagase H, Miyoshi Y, Horii A et a1 1992 Screening for germ-line mutations in familial adenomatous polyposis patients: 61 new patients and a summary of 150 unrelated patients. Hum Mutat 1:467473 Rubinfeld B, Souza B, Albert I et a1 1993 Association of the APC gene product with p-catenin. Science 262:1731-1734 Su LK, Vogelstein B, Kinzler KW 1993 Association of the APC tumor suppressor protein with catenins. Science 2 6 2 1734-1737

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Tomlinson IPM, Bodmer WF 1995 Failure of programmed cell death and differentiation as causes of tumors: some simple mathematical models. Proc Natl Acad Sci USA 92: 11130-1 1134

DISCUSSION Chukravarti: I largely agree with Walter Bodmer’s model, but I would like to put it in a more classical population genetics context. Mutations arise constantly in any given population, in this case a population of cells, and selection weeds them out. The incidence of the trait (recessive or dominant), rather the proportion of mutant cells, is determined by the balance of cell death and survival. The idea of a relative growth rate advantage, i.e. independence from growth, versus apoptosis of these cells is analogous. If a relative growth rate advantage is introduced into the model, the equilibrium changes so that a higher number of mutant cells can be maintained. The change in equilibrium can then go in one of two directions: total exponential growth or a stable equilibrium. A relevant paper has recently been published about the behaviour of systems in which different populations have different growth rates and undergo imperfect mixing (Epstein 1995). Normally, a uniform population of cells grows in proportion to their current number (autocatalysis). If there is some biological constraint on total growth, the outcome is one of three: an equilibrium is reached; exponential growth occurs; or the mutant cell population is lost. These outcomes may be severely affected by the initial conditions, i.e. the number of mutated genes already present that predisposes the individual to cancer. In the proposed model.the multiple genetic changes that occur don’t have to be in any given order. Some mutations may occur in an order that is fixed by the biology, but the classical model of changes does not require a defined order. Thus, in a group of ‘mutant’ cells there are many cells that contain almost all of the genetic changes. This implies that in patients with cancer a large proportion of cells may be in the pre-malignant state. This is exemplified by patients with retinoblastoma who have had one eye enucleated because of the finding of a tumour, and then who develop osteosarcomas following radiation in the field due to the second hit being induced by the radiation. W. Bodmer: The ecological carrying capacity models are similar to the relationship between natural selection acting on genetic differences and population growth. You also mentioned regression. In the model, if the sign of 6 is changed, then regression is predicted. Spontaneous remissions are often related to natural killer cell activity or immunological responses, so any of these genetic changes could reverse the situation. If you want to target apoptosis the only therapy is to reverse the sign of 6, but you might not have to turn it very much in order to

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have an effect. Chemotherapy probably works not by killing directly but by inducing apoptosis. Also, a lot of resistance is not due to the multi-drug resistance gene but actually selection for genes that are evading the apoptosis induced by chemotherapy. You also asked how many pre-malignant mutations are present in a cancer. The further along that line you get, the less probable it is that you’ve got such a combination of events. There is a time relationship between the accumulation and the number of events. One would expect a high frequency of single mutants, a more limited number of double mutants and a small proportion of early polyps, although any one polyp still has a low probability of progressing to malignancy. There may also be certain types of dead-end situations where the sequence of changes is such that there’s nothing further that can happen which will give rise to exponential growth. I imagine that Allan Balmain would argue this for the papillomas that he studies which do not progress to malignancy. Chakravarti: Is it true that during progression there are fewer and fewer cells that have more and more hits, but those fewer and fewer cells have a greater risk of eventually becoming malignant? W . Bodmer: I don’t know. The main point is that during progression there are more and more cells that have a mutation. The mutation rate multiplied by the population size gives the probability of getting the change that you want. This is clearly a somatic evolutionary process. Sing: What’s the mapping function between the genome and a l , a2 and q? Does one gene affect multiple parameters or is there a particular set of genes for a particular parameter? W . Bodmer: The simplest situation is that one gene affects one or other of those parameters. This can be complicated by both having an effect on differentiation and apoptosis. Sing: Can different alleles at the same locus affect different parameters? W . Bodmer: You’re getting unnecessarily complicated. Sing: How does your model differ from Brian Goodwin’s modelling of development in his recent book How the leopard changed its spots (Goodwin 1994)?

W. Bodmer: I can’t tell you because I haven’t read the book. However, this model is similar to the one proposed by (McLean & Michie 1995) for aspects of the immune system. Weiss: There are differences in the approaches that people have used to model age pattern. One approach is to estimate the number of steps from a model that looks at incidence patterns. Others have specified the number of steps and then estimated the parameters associated with it. The people who tried to estimate the number of steps tried to estimate how many genes had misfired. Each step was conceptually identified with a gene. Is it possible that multiple genes are necessary to progress to the next stage?

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W . Bodmer: No. I’m arguing against that situation because it leads to the requirement of high mutation rates, which I believe is not true. Eachmutation has to be associated with a selective advantage. We have studied the HLA system and P2 microglobulin. After the first mutation in b2 microglobulin there is a reduced level of b2 microglobulin, but not a total loss. We have also found tumours that are heterozygous for the mutation in b2 microglobulin. If this occurs at a high frequency in the tumour, it must be selected for. There are no steps that require two mutations. That’s notwithstanding the support for the Knudson hypothesis (Knudson 1971) because the first mutation even in a sporadic adenoma gives some growth advantage. In our original paper on the mapping of polyposis, we showed that a significant proportion of the polyps in polyposis patients do not have a further genetic change. We also have recent, dramatic evidence which supports that, by looking at the clonality of polyps. There are single mutational steps, although I’m not specifying the number of steps because the model can fit any number of steps. The critics of Knudson, such as John Cairns and others, misunderstood what he was saying. He said that certain critical stages need two events. He did not say that two steps were sufficient. Weiss: What is the particular significance of those two steps if a series of mutational steps is required? W. Bodmer: A series of mutational steps is required, but the first two steps could be critical, so that the other steps can’t occur without them. There is not a unique sequence of mutations, but the mutation in APC is the earliest mutation we see in colorectal carcinomas. One would expect a gene that is mutated early to be one which, when it’s mutated in the germline, results in an inherited susceptibility. That is less likely later because it doesn’t give the same advantage. Therefore, if 80% of colorectal carcinomas have that mutation, then for those 80% it’s a necessity. The other 20% are interesting because they may have a similar genetic change that leads to escape from the controls governing cell-cell and cell-cell basement membrane interactions. Sing: This model represents the interface between mathematics and molecular biology, which will be a necessary marriage for understanding and predicting interindividual variation in phenotypes that define the common diseases. Bulmuin: I’ve always had an aversion to mathematical models, so can you explain to me what’s the practical advantage of using this model? Sing: Walter Bodmer is working with the dynamics of the process rather than the statics. The model takes into account the time factor, which is not usually taken into account in standard genetic analyses that look only at static phenomena. This model is a closer simulation of reality. Health is a complex adaptive system that is constantly changing from birth to death. W. Bodmer: Models are extremely important, but the question is how relevant are they and what do they tell us? The reason for having a model is to have predicative power, both in terms of rates, as Charles Sing has said, and

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whether the observed system can fit the known components of it. Models set absolute limits to what can happen, but they do have their limitations. For example, mutation rates involve a second-order selective effect, and yet it’s often assumed that the mutation rate is being selected for. Part of the reason for setting up some of these models is to show that is not necessary or even likely. One can fit the observed numbers of steps that you’re talking about by what’s observed without assuming an abnormally high mutation rate. Sing: Models can help us organize the necessary information to predict future events. A few of us here today feel that a complete explanation of aetiology may be beyond our reach. The best we may ever be able to do is to develop an accurate predictor of future health. Balmain: But what exactly can you predict from this model? What experiments can you design from looking at the model? W. Bodmer: At the moment, it’s not a question of designing particular experiments. The power of the model is that we can take the observations that we have-for instance, we have estimates of the number of mutations involved, the mutation rates, the number of stem cells at the start and the time periodand try to fit it all together. Balmain: Can you incorporate the DNA repair mutants into this model? Because inefficient DNA repair would result in an increased mutation rate. W. Bodmer: But two mutations are required to produce inefficient DNA repair, so there must be an advantage for the first mutation. It is unclear, notwithstanding a recent paper on lymphoblastoid cell lines from carriers of the mismatch mutations (Parsons et a1 1995), that heterozygosity for the mismatch mutation does anything, yet that’s got to be selected for. Therefore, there’s no evidence yet that the primary selection occurs within the heterozygote,but it’s the most likely scenario. Templeton: Allan Balmain has presented some empirical evidence that there are multiple pathways to get to the same endpoint (Nagase et a1 1996, this volume). It occurred to me that in these models there is a sequence of mutation accumulation, but one thing that doesn’t seem to be incorporated is the order of this accumulation. You mentioned a model in which there were two mutational hits, could these two hits occur at the end? W. Bodmer: They can’t occur at the end. The APC mutations would not produce an advantage later on. Some mutations occur earlier and others occur later. Templeton: But you don’t know the role of these other mutations. They may accumulate and be selected for earlier, so that the other mutations could occur at different stages. Have you taken this into account in your model? W . Bodmer: No. But one can take that into account with simulation, so that the selective advantages are different at different stages. Chakravarti: We have been talking about cancer, but this model may also be applied to other situations, for example developmental defects. Genes might

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not only have an advantage in promoting growth, but also control overall growth. Thus, relative negative growth can lead to the death of a cell type. I have an interest in the birth defect called Hirschsprung disease. I find it intriguing that the gene encoding the receptor tyrosine kinase Ret can have activating mutations that lead to the development of a tumour, whereas almost all of the other mutations in that gene lead to a loss of function that leads to the loss of enteric ganglia and Hirschsprung disease. W. Bodmer: I have often thought that there is an analogy between congenital malformations and the nature of their genetics, even to the extent that one might do what Knudson suggested for cancer, i.e. to look for the subset that’s clearly inherited in congenital malformations. It is possible that early genetic changes during embryological development could give rise to congenital malformations but not cancers. Our model fits this entirely. There are a few recent papers which show that p53 knockout mice have a neurological defect. Armour: Leaving aside the remarks that have been made about mismatch repair, what general methods might be used to obtain estimates for those mutation rates that might have important consequences for tumour evolution in general? W. Bodmer: We have some results for germline mutations of the APC dominant gene (W. F. Bodmer, S. Cottrell & A. Rowan, unpublished observations), but we don’t have the somatic equivalents. There are estimates for example, of mutation rates in cell lines, but the trouble is that these cell lines are already transformed. The estimates of the mutation rate are also poor in normal cells. The actual rate must be much less than the observed rate, so one can at least set upper limits of the mutation rates from looking at cell lines. Looking at normal lymphoblastoid cell lines, which are close to normal although they are transformed, may be informative. PCR-based techniques may be used to look for single base pair changes. However, my main point is that if these rates of genetic change are not limiting at the beginning of tumour development, they’re certainly not going to be limiting later on, when there are many more cells.

References Epstein IR 1995 The consequences of imperfect mixing in autocatalytic chemical and biological systems. Nature 374:321-327 Goodwin B 1994 How the leopard changed its spots. Scribner, New York Knudson AG 1971 Mutation and cancer: statistical study of retinoblastoma. Proc Natl Acad Sci USA 6k820-823 McLean AR, Michie CA 1995 In vivo estimates of division and death rates of human T lymphocytes. Proc Natl Acad Sci USA 92:3707-3711 Nagase H, Bryson S, Fee F, Balmain A 1996 Multigenic control of skin tumour development in mice. In: variation in the human genome, Wiley, Chichester (Ciba Found Symp 197) p 156180 Parsons R, Li G-M, Longley M et a1 1995 Mismatch repair deficiency in phenotypically normal human cells. Science 268:738-740

Genetic factors that contribute to interindividual variations in plasma low density lipoprotein-cholesterol levels1 Jonathan Cohen, Allan Gaw**, Robert I. Barnes?, Katherine T. Landschulz* and Helen H. Hobbs* Center for Human Nutrition, *Departments of Internal Medicine and Molecular Genetics, and -/'Department of Pediatrics and Eugene McDermott Center, University of Texas, Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75235, USA

Abstract. The interplay of multiple genes and environmental factors generates interindividual variation in plasma low density lipoprotein-cholesterol (LDL-C) concentrations. As a result, it has been difficult to identify individual genes that contribute to variation in plasma LDL-C levels using classical linkage analysis. We have exploited a genetic defect in the gene encoding the LDL receptor that is associated with a dramatically elevated plasma LDLC level to unmask an allele at another locus that lowers plasma LDL-C levels. The existence of such an allele was implied by the analysis of a human pedigree with familial hypercholesterolaemia in which a third of the familial hypercholesterolaemia heterozygotes had normal levels of LDL-C. To develop an animal model of this LDL-C lowering effect and to identify genes that modify the plasma LDL-C level, we crossed LDL receptor-deficient mice with other strains of mice.

1996 Variation in the human genome. Wiley, Chichester (Ciba Foundation Symposium 197) p 194-210

Both genetic and environmental factors contribute to the development of coronary artery disease in humans. Family and twin studies have revealed the importance of genetic factors in the development of coronary atherosclerosis, especially in individuals who develop symptoms prior to age 60 (Marenberg et a1 1994). Several lines of evidence suggest that elevated plasma concentrations of low density lipoprotein (LDL), the major vehicle for cholesterol transport in 'This paper was presented by Helen H. Hobbs. 2Presentaddress: Department of Pathological Biochemistry, Royal Infirmary, Glasgow, UK. 194

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human plasma, contribute to coronary artery disease. In numerous crosssectional and prospective epidemiological studies, elevated plasma levels of LDL-cholesterol (LDL-C) were associated with an increase in coronary atherosclerosis (for review, see Grundy et a1 1993). Recent studies indicate that pharmacological reduction of plasma LDL-C levels is associated with a decrease in cardiac events and total mortality, lending further support to the pathophysiological role of LDL-C in atherogenesis (Scandinavian Simvastatin Survival Study Group 1994). In familial hypercholesterolaemia, an autosomal dominant disorder due to a mutation in the gene encoding the LDL receptor (LDLR), elevated plasma levels of LDL co-segregate with premature coronary atherosclerosis (Goldstein et a1 1995). The LDLR is a cell surface protein found on most cells of the body that binds and internalizes lipoproteins by receptor-mediated endocytosis, thus transporting cholesterol from the blood into cells. The association between decreased LDLR function, increased plasma LDL-C concentrations and atherosclerosis has also been shown in other mammalian species. Rabbits and genetically-engineered mice that have mutations in LDLR have elevated plasma LDL-C concentrations associated with severe and generalized atherosclerosis (Ishibashi et a1 1993, 1994, Goldstein et a1 1983). LDL particles are cholesteryl ester rich, and they transport about 70% of the cholesterol in the plasma. Each particle contains only a single large apolipoprotein, apolipoprotein B- 100 (apoB-100). ApoB- 100 is required for lipoprotein synthesisand also serves as a ligand for the LDLR. LDL is formed in the circulation from lipoproteins of lower density. Very low density lipoprotein (VLDL) particles are secreted from the liver and undergo lipolysisat the vascular endothelial surfaces to form intermediate density lipoprotein (IDL). Approximately 50% of the IDL formed is cleared by LDLRs in the liver, and the remainder is converted in the circulation to LDL, about 70% of which is also cleared via the liver by LDLRs (Packard et a1 1985, Demant et a1 1991). The LDLR has two ligands: apoB-100 which is found in VLDL, IDL and LDL; and apoE, a much smaller apolipoprotein present in multiple copies on VLDL and IDL, as well as on a subset of high density lipoprotein (HDL) particles. ApoE has a higher affinity than apoB-100 for the LDLR and serves as the ligand by which VLDL and IDL are removed from the circulation (Innerarity & Mahley 1978). The plasma concentration of LDL has a normal distribution in the population. Family and twin studies suggest that about 50% of the interindividual variability in plasma LDL-C levels is genetic in origin (Heller et a1 1993). Known genetic defects account for only a small fraction of individuals with either very high, or very low, plasma LDL-C levels. In addition to familial hypercholesterolaemia, there is a second autosomal dominant disorder that is associated with markedly elevated levels of plasma LDL-C (Innerarity et a1 1990). This disorder, called familial defective apoB100, is due to a missense mutation in the LDLR-binding region of apoB-100.

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Familial hypercholesterolaemia and familial defective apoB- 100 are phenocopies of each other, and they have a frequency in most human populations of about 1/500 and 1/1000, respectively. Thus, they comprise only a small percentage of all individuals with very high plasma LDL-C levels. Similarly, known genetic defects account for only a small subset of the individuals with very low plasma LDL-C levels (i.e. less than the 5th percentile). Approximately 0.2% of the population has a monogenic autosomal dominant disorder called familial hypobetalipoproteinaemia, which is caused by mutations in the gene encoding apoB (APOB) that interfere with its synthesis (Linton et a1 1993). A rare autosomal recessive form of hypobetalipoproteinaemia termed abetalipoproteinaemia results from mutations in the microsoma1 triglyceride transfer protein, a protein required for the proper assembly of apoB-containing lipoproteins in the endoplasmic reticulum (Sharp et a1 1993). Genetic studies in families have failed to identify sequence differences in either LDLR or APOB that contribute to the interindividual variation in plasma LDL-C levels in the normal population. J. Cohen & R. Guerra (unpublished data 1995) have analysed the segregation of LDLR and APOB alleles in 100 unrelated Caucasian families. They compared the plasma LDL-C levels of sibling pairs who inherited none, one or both parental alleles in common and found no significant differences in the three groups. The only gene that has been convincingly and repeatedly demonstrated to contribute measurably to the plasma LDL-C distribution in the normal population is the gene encoding apoE (Boerwinkle & Utermann 1988, Xhignesse et a1 1991). APOE has three common alleles in the population: ~ 2 , ~3 and ~ 4 These . alleles encode three different isoforms-apoE2, apoE3 and apoE4-that differ at two amino acid positions. The three different alleles are associated with subtle, but significant, differences in the plasma concentrations of LDL-C (Boerwinkle & Utermann 1988, Xhignesse et a1 1991). The ~3 allele is the most common and has a frequency of about 75% in the population. ApoE2, which has a lower affinity for LDLR than apoE3, is associated with LDL-C levels that are about 10% lower than apoE3. Conversely, apoE4, which binds to the LDLR with equal or greater affinity than apoE3, is associated with slightly higher plasma LDL-C levels. In multiple studies using a variety of methods, the apoE polymorphism has been shown to be responsible for 5-10% of the interindividual variation in plasma LDL-C levels (Sing & Davignon 1985). Efforts to identify other genes that contribute to the plasma concentrations of LDL-C have been complicated by the fact that multiple non-genetic factors-including physiological, hormonal, pharmacological and dietary factors-interact singly and/or together with genetic factors to modify the plasma LDL-C level. The multifactorial nature of this trait has complicated efforts to identify the individual genes that are important.

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FIG. 1. Pedigree of the Puerto Rican family (the so-called P. family). The proband is designated by an arrow. Family members who are familial hypercholesterolaemia heterozygotes are denoted by a half-fdled symbol. Circles, females; squares, males; ND, not determined; *, family members who are familial hypercholesterolaemia heterozygotes and who have normal plasma low density lipoprotein-cholesterol levels. (Figure adapted from Hobbs et a1 1989.)

The approach we have taken to identify genes that contribute to the interindividual variations in plasma levels has been to examine the segregation of a gene that modifies the phenotype caused by a defect in LDLR. Family with familial hypercholesterolaemia associated with normocholesterolaemia Previously we described a Puerto %can family (the so-called P. family) in which several individuals yith heterozygous familial hypercholesterolaemia had a normal level of LDL-C (Fig. 1; Hobbs et a1 1989). This family was brought to medical attention when the proband’s father died suddenly of a heart attack in his thirties while playing baseball. At autopsy he was found to have severe three-vessel coronary artery disease. Appropriately, his family was screened for hypercholesterolaemia. One of his sons, who was four years old, had a plasma cholesterol level of 800 mg/dl and cutaneous xanthomas, which are characteristic of familial hypercholesterolaemia. Interestingly, the mother of this child, an obligate familial hypercholesterolaemia heterozygote, had a normal plasma LDL-C concentration when compared to age-matched and sexmatched controls. The LDLR missense mutation results in a processing-defective phenotype Biosynthetic studies of LDLR were performed on cultured skin fibroblasts from the proband and his mother. In normal cells, LDLR is synthesized in the endoplasmic reticulum where N-linked and nascent 0-linked sugars are added

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(Goldstein et a1 1995). At this stage, LDLR has an apparent molecular mass of 120kDa as determined by migration on SDS polyacrylamide gels. After 30 min, LDLR is transported to the Golgi complex where the O-linked sugars undergo maturation. Coincident with these modifications, the apparent molecular mass of LDLR increases from 120 to 160kDa. Within about 15min, LDLR leaves the Golgi complex, is transported to the cell surface and clusters in clathrin-coated pits where it binds lipoproteins. LDLR and its ligand are internalized together and dissociate in the low pH environment of the endosome; LDLR is recycled to the cell surface and the cholesterol is transported into the cytoplasm. Pulse-chase biosynthetic experiments using cultured fibroblasts from the proband and a normal individual revealed the same amount of the 120kDa precursor protein after a 2 h pulse (Hobbs et a1 1989). In the normal fibroblasts, all the LDLR was in the mature 160 kDa form after a 2 h chase, but in the fibroblasts from the proband only 10% of the LDLR had the expected 40 kDa increase in apparent molecular mass. Analysis of the mother’s fibroblasts revealed that she was heterozygous for this same defect: at the 2 h timepoint, about 50% of her LDLR was still in the precursor form and the remaining 50% had increased in size to the expected 160 kDa form. This mutation is typical of the so-called class 2 LDLR defect, which is the largest phenotypic class of mutations responsible for familial hypercholesterolaemia (Hobbs et a1 1992). The mutations responsible for these transportdeficielit alleles presumably interfere with the proper folding of the LDLR in the endoplasmic reticulum. As a consequence, most of the LDLR synthesized is degraded in the endoplasmic reticulum rather than transported to the Golgi complex and, therefore, fails to reach to the cell surface. Similar mutations have now been found in several other genes, and they are a frequent cause of human genetic diseases (Amara et a1 1992). Molecular analysis of the LDLR alleles in the proband revealed that he was homozygous for a missense mutation in the fourth ligand binding repeat of LDLR (Fig. 2). The ligand binding domain is one of six functional domains in LDLR, and it resides at the N-terminus. This domain is composed of seven approximately 40 amino acid cysteine-rich repeats. Each of the seven repeats is encoded by a different exon (exons 249, except for repeat numbers 3 , 4 and 5 which are all encoded by a single large exon (exon 4). The mutation is in a CpG dimer and changes a cytosine to a thymine and, thus, a serine residue to leucine in codon 156. Serls6is part of a highly conserved amino acid triplet found near the C-terminus of each ligand binding repeat. This triplet, Ser-Asp-Leu, is required for binding of apoB-100 to the LDLR (Esser et a1 1988). Functional analysis of the mutant LDLR protein revealed that it was unable to bind and internalize LDL via apoB-100 (Hobbs et a1 1989). However, mutant LDLRs that did reach the cell surface were able to bind apoEcontaining lipoproteins such as P-VLDL. Extensive mutagenesis studies have

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FIG. 2. Location of the missense mutation in the gene encoding the low density lipoprotein receptor in the Puerto %can family (the so-called P. family). A schematic representation of the receptor is given and the location of the missense mutation in the P. family is indicated. EGF, epidermal growth factor.

demonstrated that mutations in conserved residues of each ligand-binding repeat have the same functional sequelae as a deletion of the repeat (Esser et a1 1988). Only ligand-binding repeat 5 is required for apoE binding, whereas repeats 3-7 (plus the first cysteine-rich repeat, the A repeat, in the epidermal growth factor precursor homology domain) are necessary for the proper binding of LDL to LDLR. The low density lipoprotein-lowering trait segregates as an autosomal dominant allele The mother of the proband was found to be heterozygous for this same molecular defect. She was part of a sibship of nine and examination of her eight siblings and their offspring revealed five additional individuals who were heterozygous for the mutant LDLR allele and yet had normal plasma levels of LDL-C. Analysis of the extended pedigree revealed that all of the familial hypercholesterolaemia heterozygotes who had a normal LDL-C level were offspring of an individual with the same genotype and phenotype (Fig. 1). Additionally, all of the offspring of the hypercholesterolaemic familial hypercholesterolaemia heterozygotes who inherited the defective LDLR allele also had elevated plasma LDL-C levels. Based on this analysis, it was proposed that an autosomal dominant allele segregatingin this family lowered the plasma LDL-C levels of individuals with an LDLR mutation (Hobbs et a1 1989). To probe the mechanism responsible for the lower levels of LDL-C in some of the individuals with familial hypercholesterolaemia,we performed metabolic studies in selected family members in collaboration with Gloria Vega & Scott Grundy (Vega et a1 1991). Autologous LDL was radiolabelled and injected into four familial hypercholesterolaemia family members with an elevated LDL-C level, four familial hypercholesterolaemia family members with a normal or low LDL-C level, four non-familial hypercholesterolaemia relatives and the

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proband. Blood was collected at serial time points after the injection and the amount of radioactive apo-B100 remaining in the plasma was measured. The fractional catabolic rate (FCR) and the production rate for LDL-apoB were calculated as described (Vega et a1 1991). The four normal individuals had a mean LDL-apoB FCR of 0.48 pools/day. As expected, the familial hypercholesterolaemia homozygote had a very low FCR for LDL-apoB (0.15 pools/ day), confirming that the mutant LDLR allele, which was unable to bind LDL in cultured cells, also did not bind LDL in v i v a The normocholesterolaemic and hypercholesterolaemic familial hypercholesterolaemia heterozygotes had lower mean LDL-apoB FCRs (0.39 and 0.37 pools/day, respectively) than agematched and sex-matched normal family members (0.48 pools/day). What distinguished the familial hypercholesterolaemia heterozygotes with an elevated LDL-C from those with a normal LDL-C was the mean production rate of LDL, which was significantly lower in the normolipaemic familial hypercholesterolaemia heterozygotes (17 mg/kg per day versus 8.7 mg/kg per day). Multiple candidate genes ruled out by linkage analysis as being the site of the low density lipoprotein-lowering allele

Based on these results, we focused on genes known to be involved in the LDL synthetic pathway as candidate genes for the LDL-lowering effect. Mutations in the apoB-100 gene are found in approximately one in 500 individuals, and they are associated with plasma LDL-C levels that are less than 50% of normal (Linton et a1 1993). Therefore, this was the most likely candidate gene. This gene was ruled out by examining the segregation of a highly informative minisatellite sequence located 3’ to the apoB-100 gene (Hobbs et a1 1989). In addition, linkage analysis using at least one informative intragenic singlestrand conformation polymorphism and two flanking microsatellite polymorphisms located within 5cM of the gene (Genome Database 1995) was performed for 12 other candidate genes. None of the alleles that encode those proteins (normal LDLR, apoB, apoE, apoCII, apoAI, apoCIII, apoAIV, 3-hydroxy-3-methylglutarylcoenzyme A [HMG-CoA] reductase, acyl coenzyme A-cholesterol acyltransferase [ACAT], cholesterol 7a-hydroxylase, VLDL receptor, LDLR-related protein, lipoprotein lipase, hepatic lipase, cholesteryl ester transfer protein and ileal bile acid transporter) co-segregated with the LDL-lowering trait. In order to map the location of the gene of interest, we obtained additional informative meioses. Two approaches were taken. First, the families of both maternal grandparents were extended, resulting in the identification of only a single additional informative meiotic event. Second, a search was made for other unrelated individuals with familial hypercholesterolaemia who had a normal level of LDL-C. However, this was difficult because an LDLR mutation

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would not be suspected in individuals with normal plasma LDL-C levels unless they had a child with homozygous familial hypercholesterolaemia, as was the case in the P. family. Simulation of the Puerto Rican family using LDLR-1- mice We turned to the mouse as an animal model in which to simulate the LDLlowering effect seen in the P. family. LDLR has been successfully inactivated by homologous recombination in embryonic stem cells from the 129/Sv strain in mice (Ishibashi et a1 1994), and a line of 129/Sv mice homozygous for the inactivated mouse LDLR allele (LDLR-I-) has been developed and maintained. In normal mice the plasma level of LDL-C is only about 16mg/ dl, in contrast to humans where the mean plasma level is 130 mg/dl. Mice that are heterozygous or homozygous for the mutant allele have an LDL-C that is about twofold or eightfold that of normal mice, respectively (top panel, Fig. 3). The plasma high density lipoprotein-cholesterol (HDL-C) levels in these genetically altered mice is either unchanged or increased when compared to controls. There are important physiological differences between the metabolism of lipoproteins in mice when compared to humans. In mice over 95% of the cholesterol is transported as part of HDL, whereas in humans a similar proportion of plasma cholesterol is in the LDL fraction. This is at least partly due to the fact that mice do not have cholesteryl ester transfer protein, an enzyme that transfers cholesterol from HDL to apoB-containing lipoproteins. The LDLR-1- mice were crossed with eight different strains of miincluding C3H, CBA, C57BL/6, SJL, AKR, BALB/c, FVB and DBA-to examine interstrain differences in the effect of LDLR deficiency on plasma LDL-C concentrations. The plasma cholesterol levels from at least 20 F1 offspring, who were L D L R + / - and heterozygous for 129/Sv alleles at every other locus, were analysed and compared to age-matched and sex-matched 129/Sv LDLR+/- mice. There were significant strain differences in the total cholesterol of these F1 mice. However, more detailed analysis of the cholesterol content of the different lipoproteins fractions revealed that some of these differences were due to variations in plasma HDL-C, rather than LDL-C concentration. As the densities of the different classes of mouse lipoproteins differ from those of humans, standard density fractionation methods could not be used to quantitate the lipid content of each fraction (Ishibashi et a1 1994). Therefore, plasma lipoproteins were analysed by size-separation using fast protein liquid chromatography (FPLC) to compare the lipoprotein distribution of plasma cholesterol (de Silva et a1 1994). The lipoprotein profiles of the L D L R + / - F1 mice from two interstrain crosses are shown in Fig. 3 (middle and lower panels) compared with the parental wild type and 129/Sv LDLR+/mice.

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FIG. 4. Fast protein liquid chromatography lipoprotein profiles from 129/Sv LDLR-I- x (BALB/c x 129/Sv) N2 backcross mice. Lipoprotein profile of two male LDLR-I- N2 mice, one with a relatively low total cholesterol (200mg/dl) (open circles) and one with a relatively high total cholesterol (295 mg/dl) (closed circles). Lipoprotein profile from plasma pooled from four male 129/Sv LDLR-I- mice also are given (closed squares). HDL, high density lipoprotein; LDLR, low density lipoprotein receptor; VLDL, very low density lipoprotein.

In the (C57BL/6 x 129/Sv) LDLR+I- mice, there was a significant decrease in total plasma cholesterol compared to 129/Sv LDLR+I- mice (157 versus 187 mg/dl) but this difference was due to differences in the HDL-C, rather than the LDL-C (middle panel, Fig. 3). Incontrast, the (BALB/c x 129/Sv) L D L R + / F1 mice had plasma LDL-C levels that were similar to the wild type BALB/c mice (lower panel, Fig. 3).

FIG. 3. Fast protein liquid chromatography (FPLC) profiles of plasma lipoproteins from L D L R t / + ,LDLR'I- and LDLR-I- mice. Plasma from two to three mice in each group was pooled and subjected to Superose 6 gel filtration on FPLC (Ishibashi et a1 1993). The cholesterol content of each fraction was assayed spectrofluorimetrically. Upper panel: lipoprotein profile from 129/Sv LDLR+I+ mice (open circles); 129/Sv LDLR+I- mice (closed circles); and 129/Sv LDLR-1- mice (closed squares). Middle panel: comparison of plasma lipoproteins from C57BL/6 LDLR+I+ mice (open circles); 129/Sv LDLR+I- mice (closed squares); and (C57BL/6x 129/Sv) LDLRtI- F1 mice (closed circles). Lower panel: comparisonof plasma lipoproteins from BALB/c LDLR+l+ mice (open circles); 129/Sv LDLR+I- mice (closed squares); and (BALB/c x 129/Sv) LDLR+I- F1 mice (closed circles). HDL, high density lipoprotein; LDLR, low density lipoprotein receptor; VLDL, very low density lipoprotein.

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To determine if the LDL-lowering effect seen with BALB/c was due to the effect of one major gene or to several genes, we employed a strategy similar,to that used by Dietrich et a1 (1993). The (129/Sv x BALB/c) LDLR+I- F1 mice were backcrossed to 129/Sv LDLR-I- mice, and the LDL-C and HDL-C levels were genotyped (Gaw et a1 1995). Examples of the LDLR-1- offspring with ‘low’ and ‘high’ plasma LDL-C levels are shown in Fig. 4. One mouse had an LDL-C level that was identical to the 129/Sv LDLR-1- mice (closed circles), whereas another mouse had a plasma LDL-C level that was much lower (open circles). Studies are in progress to measure the plasma LDL-C, HDL-C and mouse apoB-100 levels in other LDLR-1- N2 offspring. The distribution of plasma levels of LDL-C will be analysed in these offspring and used to estimate the number of genes responsible for the observed intermouse differences in plasma LDL-C concentrations (Wright 1968). If this analysis suggests that a small number of genes contribute to the differences in plasma LDL-C levels, the mouse genome will be screened for genomic regions that cosegregate with different plasma levels of LDL-C. Co-segregation of non- 129/Sv genetic material with lower LDL-C levels will allow identification of a so-called quantitative trait locus (Lander & Botstein 1989). It is hoped that this strategy will allow us to uncover an autosomal dominant gene (or genes) that impacts importantly on the plasma LDL-C levels, either in the absence or presence of an LDLR defect. Acknowledgements We wish to thank Tommy Hyatt, Kathy Schueler, Melissa Christiansen, Carla Leffert and Sijing Niu for their excellent technical assistance. We wish to thank Michael Brown and Joseph Goldstein for helpful discussion. This work was supported by the National Institutes of Health (HL 20948) and the Perot Family Foundation. A. G. was supported by the British Heart Foundation. H. H. H. is an Established Investigator for the American Heart Association.

References Amara JF, Cheng SH, Smith AE 1992 Intracellular protein trafficking defects in human disease. Trends Cell Biol2:145-149 Boerwinkle E, Utermann G 1988 Simultaneous effects of the apolipoprotein E polymorphism on apolipoprotein E, apolipoprotein B, and cholesterol metabolism. Am J Hum Genet 42:104-112 de Silva HV, Mis-Oliva J, Taylor JM, Mahley RW 1994 Identification of apolipoprotein B- 100 low density lipoproteins, apolipoprotein B-48 remnants, and apolipoprotein E-rich high density lipoproteins in the mouse. J Lipid Res 351297-1310 Demant T, Bedford D, Packard CJ, Shepherd J 1991 Influence of apolipoprotein E polymorphism on apolipoprotein B100 metabolism in normolipaemic subjects. J Clin Invest 88:1490-1501 Dietrich WF, Lander ES, Smith JS et a1 1993 Genetic identification of Mom-1, a major modifier locus affectingmin-induced intestinal neoplasia in the mouse. Cell 75:63 1-639

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Esser V, Limbird LE, Brown MS, Goldstein JL, Russell DW 1988 Mutational analysis of the ligand binding domain of the low density lipoprotein receptor. J Biol Chem 263: 13282-1 3290 Gaw A, Mancini FP, Ishibashi S 1995 Rapid genotyping of low density lipoprotein receptor knock-out mice using a polymerase chain reaction technique. Lab Anim 29: 1-3 Genome Database 1995 The Human Genome Data Base Project, Johns Hopkins University, Baltimore, MD. World-Wide Web < URLhttp://gdwww.gdbwww. gdb .org/gdbdoc/ropq .html> Goldstein JL, Kita T, Brown MS 1983 Defective lipoprotein receptors and atherosclerosis. Lessons from an animal counterpart of familial hypercholesterolemia. N Engl J Med 309:288-296 Goldstein JL, Hobbs HH, Brown MS 1995 Familial hypercholesterolemia. In: Scriver CR, Beaudet AL, Sly WS, Valle D (eds) The metabolic and molecular bases of inherited disease, 7th edn. McGraw-Hill, Maidenhead, p 1981-2030 Grundy SM, Bilheimer D, Chait A et a1 1993 Summary of the 2nd report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel 11). JAMA 269:3015-3023 Heller DA, Faire U, Pedersen N, Dahlin G, McCleam G E 1993 Genetic and environmental influences on serum lipid levels in twins. N Engl J Med 328: 1150-1 156 Hobbs HH, Leitersdorf E, Leffert CC, Cryer DR, Brown MS, Goldstein J L 1989 Evidence for a dominant gene that suppresses hypercholesterolemia in a family with defective low density lipoprotein receptors. J Clin Invest 8 4 6 5 M 6 4 Hobbs HH, Brown MS, Goldstein JL 1992 Molecular genetics of the LDL receptor gene in familial hypercholesterolemia. Hum Mutat 1:445466 Innerarity TL, Mahley RW 1978 Enhanced binding by cultured human fibroblasts of apo-E-containing lipoproteins as compared with low density lipoproteins. Biochemistry 17:1440-1447 Innerarity TL, Mahley RW, Weisgraber KH et a1 1990 Familial defective apolipoprotein B-100 a mutation of apolipoprotein B that causes hypercholesterolemia. J Lipid Res 31:1337-1349 Ishibashi S, Brown MS, Goldstein JL, Gerard RD, Hammer RE, Herz J 1993 Hypercholesterolemia in low density lipoprotein receptor knockout mice and its reversal by adenovirus-mediated gene delivery. J Clin Invest 92383493 Ishibashi S, Goldstein JL, Brown MS, Herz J, Bums DK 1994 Massive xanthomatosis and atherosclerosis in cholesterol-fed low density lipoprotein receptor-negative mice. J Clin Invest 93:1885-1893 Lander ES, Botstein D 1989 Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121:185-199 Linton MF, Farese RV Jr, Young SG 1993 Familial hypobetalipoproteinemia.J Lipid Res 34521-541 Marenberg ME, Risch N, Berkman LF, Floderus B, Faire U 1994Genetic susceptibility to death from coronary heart disease in a study of twins. N Engl J Med 330:1041-1046 Packard CJ, Boag DE, Clegg R, Bedford D, Shepherd J 1985 Effects of 1,2cyclohexanedione modification on the metabolism of very low density lipoprotein apolipoprotein B: potential role of receptors in intermediate density lipoprotein catabolism. J Lipid Res 26: 1058-1067 Scandinavian Simvastatin Survival Study Group 1994 Randomized trial of cholesterol lowering in 4444 patients with coronary heart disease: the Scandinavian Simvastatin Survival Study (4s). Lancet 344:1383-1389

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Sing CF, Davignon J 1985 Role of the apolipoprotein E polymorphism in determining normal plasma lipid and lipoprotein variation. Am J Hum Genet 37:268-285 Sharp D, Blinderman L, Combs KA et a1 1993 Cloning and gene defects in microsomal triglyceride transfer protein associated with abetalipoproteinaemia.Nature 36565-68 Vega GL, Hobbs HH, Grundy SM 1991 Low density lipoprotein kinetics in a family having defective low density lipoprotein receptors in which hypercholesterolemia is suppressed. Arterioscler Thromb 11578-585 Wright S 1968 Evolution and the genetics of populations, vol 1. Genetic and Biometric Foundation, University of Chicago Press, Chicago, IL Xhignesse M,Lussier-Cacan S, Sing CF, Kessling AM, Davignon J 1991 Influences of common variants of apolipoprotein E on measures of lipid metabolism in a sample selected for health. Arterioscler Thromb 11:110&1110

DISCUSSION

Zechner: There is no doubt that the quantitative trait loci mapping approach will be extremely useful to characterize relevant genes in polygenic disorders. However, using a mouse model to study common human diseases has certain limitations because of the differences between mice and humans (Lusis 1993, Chapman 1986). For example, there are remarkable differences in the metabolisms of low density lipoprotein (LDL) and high density lipoprotein (HDL). Mice have lower LDL plasma concentrations than humans because of differences in the metabolism of triglyceride-rich lipoproteins. The lipolytic cascade, as well as receptor-mediated lipoprotein uptake, may be quite different. Can you comment on the differences in lipoprotein metabolism between mice and humans and their relevance for your studies? Hobbs: One of the big differences between mice and humans is that humans have cholesteryl ester transfer protein (CETP), which transfers cholesterol from HDL particles to LDL particles. Mice do not have this protein, which is one of the reasons why they have more HDL than LDL. Another difference is that the enzyme hepatic lipase circulates in the plasma of mice, but not in humans. The phenotype of the LDL receptor knockout (LDLR-I-) mice is quite similar to that seen in humans with familial hypercholesterolaemia. The LDLR-I- mice have a dramatic increase in their plasma LDL-cholesterol (LDL-C) levels, and they develop arterial atherosclerotic lesions when challenged with a high fat diet. Therefore, it is reasonable to use these mice as an animal model of familial hypercholesterolaemia.We recognize that there are limitations using mice as a model to look for new genes involved in controlling plasma levels of LDL-C. Genes found to modify plasma LDL-C levels in mice may not do so in humans. Zechner: Why are some strains of mice, such as C57BL6/JYsusceptible to atherosclerosis, and others, such as C3H/HeJ, resistant (Roberts & Thompson 1976, Qiao et a1 1995)?

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Hobbs: This has been studied in detail but to date the underlying mechanisms are not clear. The differences in susceptibility appear to be due to the effect of more than one gene. Zechner: Was one of your aims to cross your mice with inbred mouse lines that are susceptible or resistant to the development of atherosclerosis? This might allow the mapping of genes that are involved in the regulation of plasma LDL concentrations. They might co-localize with the atherosclerosis genes that have already been mapped (Paigen et a1 1987, 1989, Stewart-Phillips et a1 1989). Hobbs: Plasma levels of LDL-C and atherosclerosis susceptibility may or may not be related in mice. In these studies I’m not examining atherosclerosis, I’m looking at plasma LDL-C concentrations. Zechner: Mice that are susceptible to atherosclerosis also have diet-induced increases in LDL-C concentration (Ishida et a1 1991). Hobbs: The C57BL6/J mouse strain, which is the strain that we used initially in our cross with the LDLR-1- mice, has a lower plasma cholesterol level. When C57BL6/J mice are put on a high fat, atherogenic diet, their HDL-C level decreases dramatically. Beverly Paigen and others have been trying for years to map the locus that’s responsible for the decrease in HDL-C, which correlates with the propensity to develop atherosclerosis, but to date no gene has been identified. Initially, it was thought that one gene, the so-called Athl gene, was responsible. However, it is now apparent that there are a series of genes that contribute to the trait. Chakravarti: Is the gene homologous to that which encodes human CETP present in the mouse? Hobbs: People have tried to find sequences that resemble CETP in the mouse, but to my knowledge no one has found any. Zechner: Familial hypercholesterolaemia probably doesn’t contribute to the large variation of LDL-C concentrations in the population. Is that because the mutations are rare or because milder mutations that do not have the severe phenotype of LDLR deficiency have not yet been identified? Hobbs: We’ve looked at the segregation of the LDLR in 25 families with polygenic hypercholesterolaemia and performed sib pair analysis in 150 normal families to see whether variations in the LDLR contribute to differences in plasma cholesterol levels. We did not find any effect of the LDLR locus on plasma LDL-C levels in either group. When we did sib pair analyses in the families, we did not find any association between the per cent concordance for receptor alleles and the plasma LDL-C levels. We obtained the same result for APOB. This does not preclude there being some individuals who have moderately high plasma LDL-C levels due to ‘mild’ mutations in LDLR. However, it is unlikely that LDLR is the major gene responsible for the observed differences in LDL-C levels in the population. Weiss: Were the normal families chosen at random?

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Hobbs: No. The minimum requirement for entry was families with three offspring of the same two parents. Moderate hypercholesterolaemia clusters in families, but there is no evidence that LDLR segregates with hypercholesterolaemia in those families. Edwards: If a large number of genes that have weak effects are involved, or genes that have a strong effect with alleles divided between those conferring protection and disposition, then the linkage studies in regular use in sib pair analysis will not detect them. Hobbs: We did haplotype analysis on LDLR, initially on a total of about 25 families. We looked at whether different haplotypes were associated with different plasma levels of LDL. The sample size was small, but we didn’t find any association (E. Boerwinkle & H. Hobbs, unpublished data). Scriver: Gavish et a1 (1989) used monoclonal antibodies to identify polymorphisms in apoB. They thought that these polymorphisms may contribute to variations in LDL-C levels. Have there been any further developments on this aspect? Hobbs: They looked at whether variations in apoB contribute to differences in the concentration of plasma LDL-C by using an antibody that detects a polymorphic sequence in apoB. They used this antibody to determine the relative concentration of the two different isoforms in the plasma of heterozygous individuals. They hypothesized that if the ratio was not 50 : 50, then one of the APOB alleles has a mutation whose effect is either to slow down the removal of apoB-containing lipoprotein from the circulation, or that interferes with its synthesis. They predicted that one in 10 APOB alleles was associated with such a difference. However, others have not been able to replicate these studies. Therefore, this assay, although it first appeared to be very powerful, has not been used successfully to identify any alleles at the APOB locus that are responsible for differences in LDL-C levels. Scriver: Have you looked at the process that resets the serum cholesterol level in humans after birth? Hobbs: The increase in plasma LDL-C level that occurs with ageing in Western populations appears not to occur in the Chinese population. Thus, Chinese adults have lower plasma LDL levels than adults from Western countries. The reason for these differences are suspected to be due to population differences in dietary fat intake. Dietary fat down-regulates LDLR, and this may be responsible for the increase in plasma LDL-C in Western societies. Scriver: Why don’t cholesterol levels decrease in adults, if the environmental effect is the cause of the increase in infancy? Why is the threshold fixed in these human populations at a new higher level? Hobbs: There may be a lack of plasticity in the system at later ages. Templeton: I would like to comment on the importance of the APOB locus and suggest that it depends on the phenotype being studied. Friedlander et a1 (1995) did a cladistic analysis of the APOB locus. Their results support yours,

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in that they found that the locus did not have a large effect on the average level of LDL-C. But they also found that genetic variation at the APOB locus could explain about 75% of the variance in the amount of reduction in LDL-C levels in a population of young males placed on a low saturated fat diet. Therefore, something that looks trivial by one phenotypic measure may be important if one looks at another phenotypic measure. Hobbs: But these results were based on a small sample and the magnitude of the change that could be accounted for by variation in apoB was modest. Diet studies are difficult. Katan et a1 (1986)challenged people with a high fat diet twice and examined the effect on plasma LDL-C levels. Some individuals responded differentlyto the diet on the two occasions,demonstrating the irreproducibility of the effects of short-term changes in fat intake on plasma LDL-C levels. Thus, diet-response studies are difficult to interpret. We would like to know why some people can consume a high fat diet without increasing their levels of cholesterol, whereas others cannot, but these are difficult studies to perform in humans. Zechner: Have you considered proteins or enzymes that are involved in the intracellular assembly of lipoproteins, such as the microsomal triglyceride transfer protein? Hobbs: We have not looked at the gene encoding microsomal triglyceride transfer protein, although it has been cloned (Sharp et a1 1993). We’ve looked at two other enzymes involved in cholesterol synthesis and its storage in cells, i.e. HMG-CoA (3-hydroxy-3-methylglutaryl coenzyme A) reductase and ACAT (acyl coenzyme A-cholesterol acyltransferase) and we found that neither of these segregate with the lower plasma LDL-C level. Zechner: Defects in microsomal triglyceride transfer protein cause abetalipoproteinaemia (Sharp et a1 1993), and they may also be involved in familial combined hyperlipidaemia because of the protein’s role in the assembly of triglyceride-rich lipoproteins. Very low density lipoproteins (VLDLs) represent precursors of LDLs, so microsomal triglyceride transfer protein might be a good candidate to consider. Hobbs: This protein is essential for the assembly of the lipoprotein particle. Abetalipoproteinaemia is a rare recessive disorder that is caused by mutations in the gene encoding this protein. Individuals with this disorder cannot secrete apoB-containing lipoproteins from the liver. However, individuals heterozygous for mutations in microsomal triglyceride transfer protein have normal plasma LDL-C levels, so it is unlikely to be responsible for the LDL-lowering effect that is observed in the Puerto Rican family (the P. family).

References Chapman MJ 1986 Comparative analysis of mammalian plasma lipoproteins. Methods Enzymol 128:7&143

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Friedlander Y, Berry EM, Eisenberg S, Stein Y,Leitersdorft E 1995 Plasmid lipids and lipoproteins response to a dietary challenge: analysis of four candidate genes. Clin Genet 47: 1-12 Gavish D, Brinton EA, Breslow J L 1989 Heritable allele-specific differences in amounts of apoB and low-density lipoproteins in plasma. Science 244:72-76 Ishida BY, Blanche BJ, Nichols AV, Yashar M, Paigen B 1991 Effects of atherogenic diet consumption on lipoproteins in mouse strains C57BL/6 and C3H. J Lipid Res 32~559-568 Katan MB, Beynen AC, de Vries JHM, Nobels A 1986 Existence of consistent hypoand hyperresponders to dietary cholesterol in man. Am J Epidemiol 123:221-234 Lusis AJ 1993 The mouse model for atherosclerosis. Trends Cardiovasc Med 3: 135-143 Paigen B, Mitchell D, Reue K et a1 1987 Ath-1, a gene determining atherosclerosis susceptibility and high density lipoprotein levels in mice. Proc Natl Acad Sci USA 843763-3767 Paigen B, Nesbitt MN, Mitchell D, Albee D, LeBoeuf RC 1989 Ath-2, a second gene determining atherosclerosis susceptibility and high density lipoprotein levels in mice. Genetics 122:163-168 Qiao J-H, Xi PZ-Z, Fishbein MC et a1 1995 Pathology of atheromatous lesions in inbred and genetically engineered mice. Arterioscler Thromb 14:1480-1497 Roberts A, Thompson JS 1976 Inbred mice and their hybrids as an animal model in atherosclerosis research. Adv Exp Med Biol 67:3 13-327 Sharp D, Blinderman L, Combs KA et a1 1993 Cloning and gene defects in microsomal triglyceride transfer protein associated with abetalipoproteinaemia. Nature 365:6549 Stewart-Phillips JL, Lough J, Skamene E 1989 Ath-3, a new gene for atherosclerosis in the mouse. Clin Invest Med 12:121-126

Genetic architecture of common muItifactorial diseases Charles F. Sing, Martha 6. Haviland and Sharon L. Reilly Department of Human Genetics, School of Medicine, University of Michigan, Medical Sciences II M4708, Ann Arbor, MI 48109-0618, USA

Abstract. The purpose of this paper is to foster a dialogue among those interested in the genetic analysis of common chronic diseases. The need for an alternative to

the Cartesian-Mendelian research strategy is discussed. A biological model that considers an individual’s health to be an emergent property of a complex adaptive system is presented. A review of the contribution of the gene coding for apolipoprotein E to the genetic architecture of measures of lipid metabolism documents that an individual’s coronary artery health has all the features expected of a complex adaptive system. Suggestions for enhancing our ability to use genetic information to predict onset, progression and seventy of disease are offered. 1996 Variation in the human genome, Wiley, Chichester (Ciba Foundation Symposium 197) p211-232

Common chronic multifactorial diseases are responsible for the greatest demand on medical services (Baird 1990, Baird et a1 1988). They also make the largest contribution to loss of human productivity in westernized societies (American Heart Association 1994). Deviations from health attributable to coronary artery disease, cancer, diabetes and the psychiatric disorders typically aggregate in families, but they do not segregate as Mendelian single gene disorders. The distribution of disease among individuals, families and populations is a direct consequence of the distribution of interactions between the effects of many susceptibility genes and many environmental exposures (Davignon et a1 1983, Sing & Moll 1990, Strohman 1993, Zerba & Sing 1993). The genetic analysis of these multifactorial diseases presents the most difficult research challenge facing human geneticists today. In this paper we question the application of the traditional research paradigm developed for Mendelian single gene disorders to the common multifactorial diseases. We consider why geneticists should pay greater attention to the biological reality of disease causation when designing and analysing studies of the genetic architecture of the traits that define a 211

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multifactorial disease. We present a model for the biological relationships between genome variation and interindividual variation in onset, progression and severity of disease that is motivated by studies of quantitative trait variation in experimental organisms (Lewontin 1992, Tanksley 1993, Weir et a1 1988), research on quantitative biochemical and physiological agents involved in human disease (Sing et a1 1988), and by recent developments in the study of complex adaptive systems (Cohen & Stewart 1994, Gell-Mann 1994, Salthe 1993, Waldrop 1992). To validate this model for coronary artery disease, we review studies of the association between common allelic variations in the gene coding for apolipoprotein (apo) E and measures of lipid metabolism. We conclude with suggestions for gaining new insights about the genetic architecture of a common multifactorial disease. The research problem We consider research on coronary artery disease to involve the same problems encountered in the study of most common chronic diseases. Each case of coronary artery disease has a complex multifactorial aetiology. Neither genetic nor environmental agents acting separately cause disease. Full knowledge about an individual's genome type cannot predict with certainty the onset, progression or severity of disease. Disease develops as a consequence of interactions between the 'initial' conditions, coded in a genome type, and exposures over time and space to variations in environmental agents indexed by the individual, the family and the population (Zerba & Sing 1993). Chance, as well as choice, plays a significant role in determining the environmental alternatives that an individual, family or population experiences. The importance of exposures to environmental agents emphasizes the reality that genome type is necessary but not sufficient for the development of disease. The genome is not an isolated source of fixed, one-way information but is constantly being shaped, changed and transposed, and is generally responsive to epigenetic networks of cellular dimensions. These networks influence DNA methylation and repair, and they serve to organize co-ordinated responses to heat shock, oxygen deprivation and other enGronmenta1 changes. Predicting multifactorial disease outcomes without consideration of epigenetic networks is increasingly seen as naive. Most research on common multifactorial diseases does not consider the role of epigenesis in determining interindividual variation in disease risk (Strohman 1994). The genetic studies of cancer (Bodmer & Tomlinson 1996, this volume) and of AIDS (Templeton 1996, this volume) are exceptions. There are probably more than 100 genes that have functional allelic variations, each of which have the potential to contribute to determining an individual's susceptibility to coronary artery disease. Because at least 50% of the population at large has an elevated risk, and because multigene genotypes

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will have a multinomial distribution in the population at large, different combinations of susceptibility genes will be involved in determining disease risk in different individuals. It is simply not mathematically possible for every individual who has, or will develop, disease to have the same genotype for all of the susceptibility genes. Each subset of diseased individuals, of which there are many, will have a particular subset of segregating genes that determines susceptibility. Few genetic studies of common multifactorial diseases recognize this truth. For the purpose of prediction of disease it is particularly relevant to recognize that variation in disease susceptibility among individuals in the population at large is a consequence of the intersection of the distribution of genotypes with the distribution of past environmental exposures and future environmental trajectories (Fig. 1). Each genotype has many potential environmental trajectories. Uncertainty in prediction using genotype information arises because each genotype has a range (norm) of reactions determined by its response to variations in environmental exposures. For instance, many of the individuals who have a genotype that is found in those with disease will remain healthy because of the compensatory effects of a different environmental history on the same initial conditions. The converse will also be true. Individuals who do not have a high risk genotype may develop disease because of an adverse environmental history. Furthermore, as the number of interacting causal factors increases, fewer and fewer cases of disease will be associated with a specific aetiology, i.e. interaction between a particular genotype and a particular trajectory of environmental exposures. Again, few genetic studies of common multifactorial diseases recognize this reality. It is not possible to measure directly the aetiological interactions that are responsible for the distribution of disease susceptibilities+each individual would need to be destroyed in order to obtain the necessary data. Accurate and efficient predictions of who will become diseased based on genetic information depend on knowledge about genetic architecture. Genetic architecture is defined by statistical measures of, i.e. shadows of (Schrodinger 1944), the distribution of the aetiologies responsible for the distribution of disease susceptibilities in the population at large. These measures include the number of genes involved, the number of functional allelic variations in each of these genes, the relative frequency of each of these alleles in the population at large and the influence of functional allelic variations on the prediction of measures of onset, progression and severity of disease. Genetic architecture differs from genetic structure in that it involves the relationships between genetic and phenotypic variations rather than only the relationships among genetic variations. Studies of the genetic architecture of the quantitative biochemical, physiological and structural measures of onset, progression and severity that define the continuum of health and disease are central to understanding the genetic architecture of disease (Sing et a1 1988, 1992).

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FIG. 1. The influence of susceptibility genotype (initial conditions represented by A or B), environmental history and future environmental trajectories determined by a genotype’s norm of reaction on the prediction of health or disease.

It is acknowledged by most researchers that information about genetic architecture is useful in the identification of presymptomatic individuals at risk of developing a common multifactorial disease, and also in predicting progression and severity for those with disease. There is not universal accord, however, on the extent to which information about the complexity of the aetiologies of a common multifactorial disease such as coronary artery disease should be included in defining genetic architecture. The a priori importance assigned by researchers of the role that knowledge about the complexity of the aetiologies of disease can play in developing genetic predictors is reflected by the research strategies that they employ.

Two different research strategies Two different strategies for understanding genetic architecture are currently being taken: for emphasis we will call them the simple and the complex. The former focuses exclusively on the independent contribution of each gene, whereas the latter acknowledges a role for statistical interactions among the effects of causal agents in the prediction of disease. We next review the rationale for embracing either of these two strategies.

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Disease is a ‘simple’ consequence of variations in independent causal agents

The widely held belief that disease is simply caused by variation in biologically independent agents follows from the medical successes that have been achieved for the infectious diseases where a Cartesian relationship between cause and effect has served as a close model of reality (Golub 1994). Our culture encourages a research approach that seeks to identify ‘the’ cause of disease. The industrialization of medicine has dictated a need to search for particular agents that ‘control’ the health of individuals and populations. It is taken to be axiomatic that knowledge about the nature of each causal agent can provide the power to prevent, or alter the course of, disease. This follows from the belief that each case of disease is primarily a consequence of alterations in a single gene or environmental agent. For each individual, the role of the relationships among, or organization of, multiple causal agents in determining disease aetiology is considered to be minor. This BCNDC (BaconianCartesian-Newtonian-Darwinian-Comtean) research paradigm (Salthe 1993) depends critically on three major simplifying assumptions: (1) one can isolate the agents that are involved without altering their nature; (2) the agents that are involved do not change as a consequence of being measured; (3) interference with an agent does not alter the behaviours of the other agents influencing the phenomenon of interest.

The primary focus of the BCNDC research strategy for understanding genetic architecture is on the nature of each genetic agent and its separate role in prediction of disease. Studies designed to identify new susceptibility genes (see Lander & Schork 1994, Risch & Zhang 1995) do not take into account the role of interactions among causal agents in determining the distribution of disease aetiologies in the population at large. Repeated applications of a single locus analysis to unravel the genetic architecture is the rule rather than the exception in research on common multifactorial diseases. The number of type 2 errors associated with this strategy will be directly related to the number of susceptibility genes that are not contributing to prediction in an independent additive fashion only (Eaves 1994). The genetic architecture of diseases such as coronary artery disease is expected to involve only a small number of genes with large, independent, average allelic effects and a large number of genes with small average allelic effects (Sing & Reilly 1993, Weiss 1995). Alleles with large phenotypic effects are expected to be rare in the population at large. Figure 2A presents the expected distribution of the size of the average, marginal, allelic effects of disease susceptibility genes. The marginal effect is a statistical measure of the impact of a particular allele averaged over all of the phenotypes which are

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FIG. 2. (A) The relationship between the expected frequency distribution of disease susceptibility alleles and their expected marginal phenotypic effects. (B) The relationship between context dependency and expected marginal phenotypicqeffect.

associated with genotypes that include that allele. It is apparent to a few that focusing on the single genes with large marginal allelic effects cannot produce the promised medical successes (Sing et a1 1994, Strohman 1993). As Morton (1993) has emphasized, the genetic architecture of the continuously distributed phenotypes of health will not be revealed by the BCNDC model embraced by molecular reductionists (such as Collins 1995). The majority of the prevalent cases of disease are likely to be a consequence of context-dependent effects of allelic variations (see Fig. 2B). Variations in most causal agents, genetic and environmental, will have small average effects in the population at large; however, in a particular subset of individuals representing a particular subset of aetiologies in which some other necessary genetic or environmental agent is present, an agent may have a relatively large effect. Focusing our research efforts on genes with large average allelic effects in the population at large impedes efforts to understand and/or predict the largest fraction of the prevalent cases of coronary artery disease. Currently, only a small fraction of the prevalent cases of coronary artery disease are explained by genes that have become established risk factors because of their independent effects (Davignon 1993, Hobbs et a1 1990). Furthermore, if we don’t accept, and study, the possibility that genes which have small average effects can contribute to the prediction of disease through interaction effects, it will not be possible to address the issue of whether information about these genes is useful

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for prediction. We may miss a major portion of the genetic architecture by assuming that only alleles with large average effects are important for prediction. As Morin (1992) implores in a call for a paradigm shift, the very way we think about the problem prevents us from knowing. Cohen & Rice (1995) call to attention the conundrum we all face, “The problem that afflicts all sciences is the fact that once you have defined the kind of answers that you expect to get, it is very difficult to know what you are missing.” This certainly appears to be true in the application of the simple BCNDC paradigm to the prediction of common multifactorial diseases such as coronary artery disease. Health is a consequence of the ‘complex’ organization of causal agents

Where might we start in developing an alternative to the simple BCNDC approach for studying the genetic architecture of common diseases? First, we must recognize that the study of the genetic architecture of disease cannot be separated from the study of the genetic architecture of normal variation in healthy individuals. Health and disease are defined by the same continuum of biological traits. The same network of interacting causal agents that influences the so-called normal range of interindividual variability also influences the development of disease. Individuals with disease are just in a different part of the state space, defined by variation in the causal agents, than are individuals who are healthy. Inferences about genetic architecture from studies that focus primarily on individuals who are diseased cannot provide unbiased information for prediction of disease risk. Knowledge about the distribution of aetiologies associated with disease among the healthy is essential for developing realistic statistical models for prediction of disease in the population at large. Second, we must recognize that the biological relationships between the causal state space and health are non-linear (Goodwin 1994). Disease is a consequence of an individual’s homeostatic mechanisms not compensating for disturbances in levels of, and the relationships among, the casual agents. Changes in the level of one causal agent may influence disease risk by altering the relationship(s) among other causal agents. We expect the sizes of the marginal effects of variations in a causal agent to be inversely related to the dependency of its effect on the context defined by relationships with other agents (Fig. 2B). Geneticists, in particular, must be aware that the context in which a molecular process takes place deserves as much study as the biochemical content of the process (Cohen & Rice 1995, Lewontin 1992). We should be asking, what fraction of the genetic component of disease risk is attributable to the impact of genetic variation on relationships between causal agents? It follows that studies of genetic architecture must accommodate this question. Third, we must recognize that the genetic architecture of health is expected to be population specific. Few populations will have the same relative

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frequency distributions of genetic variations (this volume: Scriver 1996, Weatherall 1996). Differences among populations in the relative frequency of a susceptibility genotype or an environmental exposure will contribute to differences in the utility of a susceptibility genotype for prediction of disease. Even for those rare instances when the relative genotype frequencies are the same across populations, the contribution of genetic variation to prediction could still be different because its influence on variation in disease susceptibility may depend on a particular environmental exposure (such as smoking or exposure to toxic chemicals) whose relative frequency varies among populations. Then the utility of genetic variation for prediction in each population will depend on the relative frequency of the environmental exposure in that population. Fourth, we must recognize that our studies of genetic architecture can be guided by an understanding of the complexity of the aetiologies of health. We concur with Anderson (1994) that we must turn to nature to inform us about the type of model that should be used to describe nature. Even though it may never be possible to know everything about aetiologies (Barrow 1991), the study of genetic architecture can be guided by our current knowledge, albeit incomplete. Recognition of complexity in the organization of causal agents can foster synergy between efforts to predict disease and efforts to understand the aetiology of disease. For example, a particular gene may be a candidate for prediction because its product is involved in the metabolism of biochemical and/or physiological agents involved in the aetiologies of disease. Also, aetiological relationships between casual agents may suggest genetic studies of trait relationships and their role in prediction of disease. On the other hand, studies of genetic architecture can provide insights about the dimensions of aetiology. New genes may be discovered as a consequence of exploring associations with anonymous DNA markers. Also, new hypotheses about the aetiological role of known genes may be proposed as a consequence of statistical analyses of genotype-phenotype relationships. Embracing a more realistic biological model that incorporates the complexity of the organization of causal agents in determining health will enhance our efforts to answer the three cardinal genetic questions about disease (Sing et a1 1995): (1) where are the susceptibility genes located; (2) what are the functional DNA sequencevariations in these genes; and (3) what are the statistical (for prediction) and biological (for aetiology) relationships between genome type variation and variation in onset, progression and severity. An unwillingness to adapt a realistic biological model for health when designing and analysing studies of disease may be the greatest deterrent to answering these questions without prejudice.

A realistic biological model for the aetiologies of coronary artery disease Many years ago, McGill et a1 (1963) emphasized that atherosclerosis is a dynamic process that begins in the second decade and progresses in

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asymptomatic individuals for several decades before clinical symptoms appear. Molecular, biochemical, physiological and clinical studies over the past 32 years have not only exposed a plethora of agents that are involved but have also reinforced McGill’s description of the disease process (Beam 1992, Berg et a1 1990, Ciba Foundation 1987, Davignon et a1 1983, Sing & Hanis 1993, Sing & Skolnick 1979, Williams & Tabas 1995). It is becoming apparent that the biology of an individual’s coronary artery health has all the salient features of a complex adaptive system (Gell-Mann 1994, Goodwin 1994, Salthe 1985, 1993, Waldrop 1992). We have presented details of these features elsewhere (Sing & illy 1993). These features (visualized in Fig. 3) are summarized briefly below.

(2)

(3)

(4)

(5)

Many agents are involved in determining an individual’s coronary artery health including the intermediate quantitative biochemical and physiological traits that connect the primary genetic and environmental causal agents to clinical endpoints. Agents are organized into a hierarchy with the genome in the basement, intermediate traits at intermediate levels, clinical endpoints on the top floors and environmental agents a t all levels. Agents are functionally organized into a network of subsystems. The agents in each subsystem act in a coherent fashion. There are strong physiological relationships acting between agents in the same subsystem (e.g. lipid metabolism) and weak physiological relationships acting between agents in different subsystems (e.g. lipid metabolism and blood pressure regulation). The property of coherence facilitates partial decomposability (Simon 1962) of the aetiologies of coronary heart disease into particular subsystems, which encourages separate laboratories to study only separate subsystems. Coronary artery health is an emergent property of the complex adaptive response of the molecular, biochemical and physiological hierarchy to environmental inputs indexed by age and ecological context. Age of onset, rate of progression and severity are emergent properties not predicted with certainty from knowledge about an individual’s initial genome type or environmental history. These emergent properties make the whole more than the sum of the parts, and they contribute to the labels that are attached to each part (Morin 1992). The relationship between the initial genetic conditions coded in the genome type and environmental agents, which determines an individual’s health, is dynamic in time and space. The influences of particular genetic and environmental agents on the organization of the network of subsystems change throughout the life cycle. Variation in risk of disease is explained in part by interindividual variation in epigenetic patterns (Strohman 1994).

FIG. 3. A graphical visualization of a model for human health.

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As Gell-Mann discusses (1994), the emergent behaviours of all complex adaptive systems are non-linear, dynamic functions of the mixture of invariant features (order), context-dependent features (interactions between agents) and chance processes (disorder). Health is an emergent property of the organization and balance of the contributions of these basic features. A mixture of order and disorder creates an ‘edge of chaos’ dynamic that is characteristic of health. Deviations from health can be caused by either too much order or too much disorder in the organization of the primary effects of causal agents. Both order and disorder may be context dependent. Genetic variation may play a role in determining the mix of invariant features, context-dependent features and chance processes that contribute to interindividual variation in risk of disease. This is not an unfamiliar concept for those working in development (Goodwin 1994) or evolutiorlary biology (Salthe 1993). Until a unifying theory for the analysis of complexity emerges, we must apply the reductionist, linear statistical strategy to document the complexity of the organization of the genotype-phenotype relationships in addition to revealing invariant features of the causal agents. We next present an example of interpreting linear measures of genetic architecture from a complex adaptive systems viewpoint that illustrates how contextual thinking can make reductionism more effective (Cohen & Rice 1995). Lessons about the complexity of genetic architecture from studies of APOE The gene encoding the apoE molecule, APOE, is the only susceptibility gene which has been studied in samples from a broad range of populations ascertained without regard to health (Davignon et a1 1988, Gerdes et a1 1992). ApoE is involved in the metabolism of the major plasma lipoprotein particles that have been established as measures of risk of coronary artery disease (Davignon 1993, Mahley 1988). Studies of the three common alleles ( ~ 2~3 , and ~ 4 that ) code for three isoforms of apoE illustrate how genetic variation can influence features expected of a complex adaptive system: invariance (order); context dependency (interaction); and chance (disorder). Invariant features

Nearly all studies of the role of APOE in determining the genetic architecture of the risk of coronary artery disease have sought to estimate the invariant features of the gene. Studies over the past 10 years have established that the three common alleles are present in representative samples of over 50 populations (Davignon et a1 1988, Gerdes et a1 1992). In every case the relative frequency of the ~3 allele is the most common, ranging from 0.70 to 0.85. In all but a few cases the ~2 allele is associated with elevated levels of plasma apoE and triglycerides, and the ~4 allele is associated with lower than

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FIG. 4. Selected examples of the contribution of the APOE polymorphism to invariant features (A), context-dependent features (B) and chance processes (C). (A) The relative frequency and average effect of the three common APOE alleles ( ~ 2 ,~3 and ~ 4 in ) five populations (Quebec, Canada (women): Xhignesse et a1 1991; Nancy, France: Boerwinkle et a1 1987; Munster, Germany: Menzel et a1 1993; Helsinki, Finland: Ehnholm et a1 1986; Rochester, MN, USA (women): Kaprio et a1 1991). (B) The APOE genotype influence on the high density lipoprotein-cholesterol (HDL-C) regression on waist-to-hip ratio in men (N = 247) from Rochester, MN, USA (Reilly et a1 1992). (C) The 95% confidence ellipses for the bivariate distribution of total cholesterol and apoB in women (N = 260) from Rochester, MN, USA (Reilly et a1 1994).

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average levels of these traits. Also in the majority of studies, ~2 is associated with lower levels of total plasma cholesterol and 84 is associated with elevated levels. Figure 4A presents representative studies that illustrate the invariant features of the relative allele frequencies of the APOE polymorphism and their associations with total plasma cholesterol. Context-dependent features

A small number of studies have asked whether the effects of the APOE genotypes on lipid metabolism are consistent among strata within populations. Kaprio et a1 (1991) and Xhignesse et a1 (1991) established that the effects of APOE are gender specific, and are dependent on hormone use in females. Pederson & Berg (1989) reported that the effectsof APOE are not independent of variation in the gene coding for the low density lipoprotein receptor (LDLR). C. F. Sing & R. E. Ferrell (unpublished results 1995) found that the APOE allelic effects on plasma apoE levels were dependent on the APOH genotype. Lehtinen et a1 (1995) found that the effects of the APOE genotypes on plasma lipid levels is dependent on dietary fat intake. K. E. Zerba, R. E. Ferrell & C. F. Sing (unpublished results 1995) found that the fraction of variance in plasma apoE that is associated with APOE genotypic variation is a function of gender and age. Research to establish an invariant marginal association between variation in high density lipoprotein cholesterol (HDL-C) levels and allelic variation in APOE have been negative. In a study designed to estimate and test for context-dependent effects of APOE, Reilly et a1 (1992) found significant associations in males between the common APOE genotypes and plasma HDL-C levels only in the tails of the distribution of waist-to-hip ratio (Fig. 4B). Individuals with the ~ 3 / 2genotype are expected to have the greatest risk of coronary artery disease (because of low HDL-C) at low waistto-hip ratios but have the smallest risk at high waist-to-hip ratios. Chance processes

Chance processes are broadly defined here as being responsible for unexplained variations at a particular level of inference (e.g. among individuals or within an individual over time and space). In general, what appears to us as chance variation at one level is often associated with unmeasured, or unmeasurable, causal relationships at a lower level. ‘Pure chance’ includes both random stochastic effects and the effects of causal relationships that are inaccessible to our measurement or observation and are, therefore, unexplainable. Few studies of common multifactorial diseases ask whether residual unexplained phenotypic variation among individuals with the same genotype varies among genotypes. Such variation may be attributable to both ‘pure chance’ and currently unexplained context-dependent genotype effects. Reilly et a1 (1991,

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1992)reported that the intragenotypic variance among individualsis significantly greater for particular measures of lipid metabolism in those with the ~ 3 / and 2 ~4/3 genotypes compared to those with the ~ 3 / genotype. 5 Significant variation among the common APOE genotypes in the bivariate dispersion of measures of plasma apoB and total cholesterol is illustrated in Fig. 4C. These analyses document that particular genotypes predict a larger fraction of individuals at high risk because of greater variation in residual unexplained variations. Reilly et a1 (1992) found that in particular cases these genotype differences are explained by heterogeneity in the relationships of plasma lipid traits with age, body size and/or smoking. But, the largest fraction of intragenotype variability remains unexplained. These unexplained genotype differences may be attributable to gene-gene interaction, genotype-environment interaction, linkage disequilibrium, or a correlation between genotypic and environmental variations. Furthermore, studies in plants and animals which document that phenotypic plasticity is under genetic control (Bradshaw 1965, Lerner 1954, Nee1 1958) suggest that a fraction of this variation may be attributable to intraindividual variation in response to environmental variations across time and space. Studies of the APOE polymorphism support the proposition that the initial conditions coded in the genome type influence invariant features at the population level (Fig. 4A), context-dependent features at the subpopulation level (Fig. 4B) and chance variation at the individual level (Fig. 4C). Statistical analyses of invariant and context-dependent features focus on trait levels, whereas the analysis of chance processes focuses on trait variances and covariances. We expect that such analyses of every disease susceptibility gene will reveal contributions to all of these features because order and disorder are essential components of the organization of complexity. “To have organization there must be interaction; to have interaction, there must be encounters; to have encounters there must be disorder (agitation, turbulence).” (Morin 1992).

The future More data on genetic and environmental agents will be collected. The revealed potential for biological complexity of the aetiologies of health will be enormous. Given this large number of complex aetiologies, the key question for studying genetic architecture will be how many models (combinations of genetic and environmental agents), and not which model, are necessary to predict interindividual variation in the onset, progression and severity of disease? The first step for studies of genetic architecture will be to estimate the joint distribution of genetic and environmental causal agents. The second step will be to establish which combinations of genetic and environmental causal agents are responsible for determining variation in risk of disease in particular subsets of individuals, families or populations. Fig. 5 illustrates the model space for the problem at hand.

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n= Healthy ==

CAD

Frequency

FIG. 5 . A schematic of the relationship between the joint distribution of genetic and environmental agents and the distribution of health and coronary artery disease (CAD). The i subscript indexes an individual’s genotype and the j subscript indexes an individual’s environmental history.

A mix of research strategies will be used to study this model space. The BCNDC strategy can be successful for the small fraction of disease cases that are due to susceptibility genes which have large marginal effects. For the majority of disease cases, studies that include information about relationships between causal agents and disease and that consider the dynamics of these relationships in time and space are expected to lead to more accurate predictors of disease than studies that employ the BCNDC approach. The complexity of the aetiologies of health begs the question, what is the cost to medicine ascribable to ignoring the largest fraction of genes that are involved in contextdependent and chance features of the onset, progression and severity of disease?

Summary Accepting the complexity of the aetiologies of health should change the way we design and interpret studies of the multifactorial diseases. The organization of causal agents should be considered when defining the questions, designing the sampling schemes, measuring the organism and carrying out the statistical reduction of data. Success will depend on the availability of scientists trained to cope with the plethora of data that is at hand as a consequence of the last 20 years of data collection. As important will be the invention of a ‘Darwinianlike’ theory that will facilitate bringing together large amounts of genetic information for prediction of the onset, progression and severity of common diseases having a complex multifactorial aetiology. The writings of Cohen & Stewart (1994), Goodwin (1994), Kauffman (1993), Morin (1992) and Salthe (1993) define clearly the nature of the challenge that we face.

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Acknowledgements We thank David Sing for his imaginative assistance with the graphics presented here and Debbie Theodore for her meticulous help in the manuscript preparation. We are grateful to Eric Boerwinkle, Andy Clark, Jim Neel, Jack Schull, Jan Stengard, Richard Strohman, Ken Weiss and Kim Zerba for their critical assessments of this manuscript. This work was supported in part by a grant from the National Institutes of Health (R01 HL39107).

References American Heart Association 1994 Heart and stroke facts: 1994 statistical supplement. American Heart Association National Center, Dallas, TX Anderson PW 1994 The eightfold way to the theory of complexity: a prologue. In: Cowan G, Pines D, Meltzer D (eds) Complexity: metaphors, models, and reality. Santa Fe Institute studies of complexity, vol 19. Addison Wesley, Reading, MA Baird PA 1990 Genetics and health care: a paradigm shift. Perspect Biol Med 33: 203-2 13 Baird PA, Anderson TW,Newcombe HB, Lowry RB 1988 Genetic disorders in children and young adults: a population study. Am J Hum Genet 42:677493 Barrow J D 1991 Theories of everything: the quest for ultimate explanation. Oxford University Press, New York Bearn AG (ed) 1992 Genetics of coronary heart disease. Nordahls Trykkeri, Oslo Berg K, Retterstol N, Refsum S (eds) 1990 From phenotype to gene in common disorders. Munksgaard, Copenhagen Bodmer WF, Tomlinson I 1996 Population genetics of tumours. In: Variation in the human genome. Wiley, Chichester (Ciba Found Symp 197) p 181-193 Boerwinkle E, Visvikis S, Welsh D, Steinmetz J, Hanash SM, Sing CF 1987 The use of measured genotype information in the analysis of quantitative phenotypes in man. 11. The role of the apolipoprotein E polymorphism in determining levels, variability, and covariability of cholesterol, betalipoprotein, and triglycerides in a sample of unrelated individuals. Am J Med Genet 27:567-582 Bradshaw AD 1965 Evolutionary significance of phenotypic plasticity in plants. Adv Genet 13:115-1 55 Ciba Foundation 1987 Molecular approaches to human polygenic disease. Wiley, Chichester (Ciba Foundation Symposium 130) Cohen J, Rice SH 1995 Where do biochemical pathways lead? In: Collado-Vides J, Magasanik B, Smith T F (eds) Integrative approaches to molecular biology. MIT Press, Cambridge, MA Cohen J, Stewart I 1994 The collapse of chaos. Viking, New York Collins FS 1995 Positional cloning moves from perditional to traditional. Nat Genet 9:347-350 Davignon J 1993 Apolipoprotein E polymorphism and atherosclerosis. In: Born GVR, Schwartz CJ (eds) New horizons in coronary heart disease. Current Science, London, p 1-21 Davignon J, DuFour R, Cantin M 1983 Atherosclerosis and hypertension. In: Genest J, Kuchel 0, Hamet P,Cantin M (eds) Hypertension, physiopathology and treatment, 2nd edn. McGraw Hill, New York, p 810-852 Davignon J, Gregg RE, Sing C F 1988 Apolipoprotein E polymorphism and atherosclerosis. Arteriosclerosis 8: 1-2 1

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Eaves W 1994 Effect of genetic architecture on the power of human linkage studies to resolve the contribution of quantitative trait loci. Heredity 72:175-192 Ehnholm C, Lukka M, Kuusi T, Nikkila E, Utermann G 1986 Apolipoprotein polymorphism in the Finnish population: gene frequencies and relation to lipoprotein concentrations. J Lipid Res 27:227-235 Gell-Mann M 1994 The quark and the jaguar: adventures in the simple and the complex. Freeman, New York Gerdes LU, Klausen IC, Sihm I, Faergeman 0 1992 Apolipoprotein E polymorphism in a Danish population compared to findings in 45 other study populations around the world. Genet Epidemiol 9:155-167 Golub ES 1994 The limits of medicine: how science shapes our hope for the cure. Times Books, New York Goodwin B 1994 How the leopard changed its spots. Scribner, New York Hobbs HH, Russell DW, Brown MS, Goldstein JL 1990 The LDL receptor locus in familial hypercholesterolemia: mutational analysis of a membrane protein. Annu Rev Genet 24 133-170 Kaprio J, Ferrell RE, Kottke BA, Kamboh MI, Sing CF 1991 Effects of polymorphisms in apolipoproteins E, A-IV, and H on quantitative traits related to risk for cardiovascular disease. Arterioscler Thromb 11: 1330-1348 Kauffman S 1993 The origins of order. Oxford University Press, New York Lander ES,Schork NJ 1994 Genetic dissection of complex traits. Science 265:2037-2048 Lehtinen S, Lehtimaki T, Sisto T et a1 1995 Apolipoprotein E polymorphism, serum lipids, myocardial infarction and severity of angiographically verified coronary artery disease in men and women. Atherosclerosis 11483-91 Lerner IM 1954 Genetic homeostasis. John Wiley, New York Lewontin RC 1992 Genotype and phenotype. In: Keller EF, Lloyd EA (eds) Keywords in evolutionary biology. Harvard University Press, Cambridge, MA Mahley RW 1988 Apolipoprotein E cholesterol transport protein with expanding role in cell biology. Science 240:622-630 McGill HC Jr, Geer JC, Strong JP 1963 Natural history of human atherosclerotic lesions. In: Sandler M, Bourne GH (eds) Atherosclerosis and its origin. Academic Press, New York, p 3945 Menzel HJ, Kladetsky RG, Assman G 1983 Apolipoprotein E polymorphism and coronary artery disease. Arteriosclerosis 3:3 10-322 Morin E 1992 Method: towards a study of humankind, vol 1: American university studies. Peter Lang, New York Morton NE 1993 Genetic epidemiology. Annu Rev Genet 27523-538 Nee1 JV 1958 A study of major congenital defects in Japanese infants. Am J Hum Genet 10:39845 Pedersen JC, Berg K 1989 Interaction between low density lipoprotein receptor (LDLR) and apolipoprotein E (apoE) alleles contributes to normal variation in lipid level. Clin Genet 35:331-337 Reilly SL, Ferrell RE, Kottke BA, Kamboh MI, Sing C F 1991 The gender-specific apolipoprotein E genotype iduence on the distribution of lipids and apolipoproteins in the population of Rochester, MN. I. Pleiotropic effects on means and variances. Am J Hum Genet 49:1155-1166 Reilly SL, Ferrell RE, Kottke BA, Sing C F 1992 The gender-specific apolipoprotein E genotype iduence on the distribution of lipids and apolipoproteins in the population of Rochester, MN. 11. Regression relationships with concomitants. Am J Hum Genet 51:1311-1324

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Reilly SL, Ferrell RE, Sing CF 1994 The gender-specific apolipoprotein E genotype influence on the distribution of plasma lipids and apolipoproteins in the population of Rochester, MN. 111. Correlations and covariances. Am J Hum Genet 55:lOOl-1018 Risch N, Zhang H 1995 Extreme discordant sib pairs for mapping quantitative trait loci in humans. Science 268:15841589 Salthe SN 1985 Evolving hierarchical systems. Columbia University Press, New York Salthe SN 1993 Development and evolution: complexity and change in biology. MIT Press, Cambridge, MA Schrodinger E 1944 What is life? Cambridge University Press, Cambridge, MA Scriver CR, Byck S, Prevost L, Hoang L, PAH Mutation Analysis Consortium 1996 The phenylalanine hydroxylase locus: a marker for the history of phenylketonuria and human genetic diversity. In: Variation in the human genome. Wiley, Chichester (Ciba Found Symp 197) p 73-96 Simon HA 1962 The architecture of complexity. Proc Am Phil SOC106:467482 Sing CF, Hanis CL (eds) 1993 Genetics of cellular, individual, family, and population variability. Oxford University Press, New York Sing CF, Moll PP 1990 Genetics of atherosclerosis. Annu Rev Genet 24:171-187 Sing CF, Reilly SL 1993 Genetics of common diseases that aggregate but do not segregate in families. In: Sing CF, Hanis CL (eds) Genetics of cellular, individual, family, and population variability. Oxford University Press, New York, p 140-161 Sing CF, Skolnick M (eds) 1979 Genetic analysis of common diseases: applications to predictive factors in coronary disease. Progress in clinical and biological research, vol 32. Alan R. Liss, New York Sing CF, Boerwinkle E, Moll PP, Templeton AR 1988 Characterization of genes affecting quantitative traits in humans. In: Weir BS, Goodman MM, Eisen El, Namkoong G (eds) Proceedings of the second international conference on quantitative genetics. Sinauer, Sunderland, MA, p 250-269 Sing CF, Zerba KE, Haviland MB 1992 Genetic architecture of interindividual variation in plasma cholesterol. In: Bearn AG (ed) Genetics of coronary heart disease. Institute of Medical Genetics, University of Oslo, Oslo, p 143-162 Sing CF, Zerba KE, Reilly SL 1994 Traversing the biological complexity in the hierarchy between genome and CAD endpoints in the population at large. Clin Genet 46:6-14 Sing CF, Haviland MB, Templeton AR, Reilly SL 1995 Alternative genetic strategies for predicting risk of atherosclerosis. In: Woodford FP, Davignon J, Sniderman AD (eds) Atherosclerosis, vol 10. Excerpta Medica International Congress Series, Elsevier Science, Amsterdam, p 638644 Strohman RC 1993 Ancient genomes, wise bodies, unhealthy people: limits of a genetic paradigm in biology and medicine. Persp Biol Med 37: 112-145 Strohman RC 1994 Epigenesis: the missing beat in biotechnology? Biotechnology 12:156164 Tanksley SD 1993 Mapping polygenes. Annu Rev Genet 27:205-233 Templeton AR 1996 Cladistic approaches to identifying determinants of variability in multifactorial phenotypes and the evolutionary significanceof variation in the human genome. In: Variation in the human genome. Wiley, Chichester (Ciba Found Symp 197) p 259-283 Waldrop MM 1992 Complexity: the emerging science at the edge of order and chaos. Simon & Schuster, New York Weatherall D 1996 The genetics of common diseases: the implications of population variability. In: Variation in the human genome. Wiley, Chichester (Ciba Found Symp 197) p 300-31 1

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Weir BS, Goodman MM, Eisen EJ, Namkoong G (eds) 1988 Proceedings of the second international conference on quantitative genetics. Sinauer, Sunderland, MA Weiss KM 1995 Genetic variation and human disease. Principles and evolutionary approaches. Cambridge University Press, Cambridge Williams KJ, Tabas I 1995 The response to retention hypothesis of early atherogenesis. Arterioscler Thromb Vasc Biol 15:551-561 Xhignesse M, Lussier-Cacan S, Sing CF, Kessling AM, Davignon J 1991 Influences of common variants of apolipoprotein E on measures of lipid metabolism in a sample selected for health. Arterioscler Thromb 11:1100-1 110 Zerba KE, Sing CF 1993 The role of genome type-environment interaction and time in understanding the impact of genetic polymorphisms on lipid metabolism. Curr Opin Lipidol 4 152-1 62

DISCUSSION Weiss: Newton Morton has often said that there may be multiple alleles that produce a weak effect, but we should concentrate on those rare alleles that have strong effects. Certainly, from a reductionist point of view, it’s methodologically straightforward to find such alleles. However, although alleles that have smaller effects may be difficult to identify, they’re the ones that cumulatively have the most impact on public health. Have you thought of a way to approach these complex problems from a public health point of view? Sing: I don’t believe that the situation is quite as complex as my presentation may have indicated. There are probably only a few genes that have large average allelic effects on heart disease. The gene that codes the low density lipoprotein receptor is an example. Weiss: If the situation is this complex, how can enough well-characterized subsets be ascertained to do the kind of study that needs to be done? Sing: That’s a conundrum which depends on the number of models that can be applied. We have used genetic algorithms to estimate out how many models exist in a sample of 283 pedigrees from Rochester, and we found there were 12 models (Congdon et a1 1993). W. Boaher: There’s a danger of giving in to complexity. Complexity is important when interactions are operating, but how often are interactions sufficiently important so that they need to be taken into account? I suspect the answer is not as often as one might think. One can make things too complicated: genes interact with the environment, but the environment is always there. There are probably not many situations where even two genes interact. Most of the heterogeneity is probably due to the observation that an apparent phenotype is defined by different genetic groups. I’m always amazed, given that R. A. Fisher taught everyone to measure interactions and to do randomization, that clinical trials in humans nearly always just test one thing at a time. This is inefficient and

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it does not test for interactions. Trials should be more complex so that more information is obtained from the same number of patients. Sing: There is a statistical problem with observational studies of this nature, where the size of effect is directly related to its relative frequency. W. Boaher: That’s why one does sib pair studies to look only at the affected. Sing: Steve Humphries has reported that the association between marker variability in a gene coding for fibrinogen and the level of fibrinogen is only significant in smokers (Green et a1 1993). This association would have been missed if the smoking stratification was ignored. Freimer: The example of smoking and fibrinogen represents the exception that proves the rule because smoking is one of the few environmental factors that we know anything about. One of the reasons why the reductionist approach has taken hold is that it’s difficult to identify the relevant environmental factors. For example, behaviour has been studied for hundreds of years but there isn’t a single well-documented environmental risk factor known to be primarily responsible for major behavioural phenotypes, such as psychiatric disorders. Chakravarti: We don’t know what the level of interactions are, but it is possible that when we look at the effects of a single gene, because we’re detecting marginal effects, we may miss the effect of the gene. There are some designs that we can use to tease this out. For example, in the study of the inheritance of breast cancer some sampled subsets, such as early-onset families, can reduce the amount of variability. For most common diseases, these subsets can be defined, based on what is already known. Male and female subsets may be an obvious division, given what we know about risk factors. This is also the case in mouse models because, in any given mouse only a small subset of those factors are necessary. Therefore, we should concentrate on taking a particular sample and breaking it down to generate more homogeneous groups. Then we can go back and study the interactions. Sing: If one doesn’t know the stratifications beforehand, one has to do random sampling. We can at least compare males and females, smokers and non-smokers, and other obvious strata that may be indices of environmental variations. Chakravarti: I can think of ways in which, at least at the genotypic level as assessed by genetic markers, one can identify individuals who are more genetically similar than others. Thus, one could design comparisons between sib pairs who are more identical, rather than looking at all affected sib pairs. Sing: Neil Risch (1995) just did a theoretical study, where he turned the whole situation around. He believes that we may be able to find these genes with a small effect by looking at discordant pairs rather than at concordant pairs and, rather than by regressing phenotype difference on identity, by regressing identity on phenotypic differences. Chakraborty: It is dangerous to be a reductionist. However, if we stratify the results, we also have to deal with the fundamental statistical problem of multiple

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testing, i.e. that the sample size becomes very large. We may have to sample everyone to get a realistic approach of the total complexity that is involved. W. Bodmer: It may be dangerous to be a reductionist, but it is much more dangerous not to be. Templeton: No one here is against reductionism, but we have to use reductionism as a stepping stone to study higher-order effects. For example, Sharon Reilly (Sing et a1 1995) has studied the relationships among cholesterol levels, APOE genotypes and the risk for coronary artery disease. For both cholesterol level and APOE genotype considered separately, she found about a fourfold to fivefold range in relative risk. She then looked at it from an interaction point of view, and by studying the joint effects of these two predicators of coronary artery disease and not just their marginal effects, she found about a 100-fold range in relative risk. Therefore, there is more information to be gained by going beyond the marginal effects and by looking at context dependency. W. Bodmer: Fisherrtaught us how to find interactions. Too often clinical studies are performed that don’t look for interactions. Looking for interactions properly is a reductionist approach. Harper: There has been much discussion, from those promoting the genome project, about the practical advances that will result from mapping the genome, such as the development of genetic tests that will determine people’s susceptibilities to common conditions. This view is also being promoted by the researchers in each particular field. But, my feeling is that this is a long way away in terms of regular clinical practice. There is also some tension between people who are impatient for widespread testing and others who think that the situation is exceptionally complex so that testing should not yet be applied. Weiss: This brings to mind the phrase ‘stimulate the phagocytes’, from George Bernard Shaw’s play The Doctor’s Dilemma. This was one of several panaceas being advocated by the physicians in the play (and a line memorably delivered by Robert Morley in the film). In his long, typically cynical preface to that play, Shaw noted the demand by the general public for instant cures for everything, which reinforced the doctors’ natural vested interests in promoting simple hopes to the public. We see the same thing today, reinforced by the additional pressures on scientists for publication and research funding, and the needs of the news media for material. This seems to lead to a positive feedback system putting pressure on the system to make unrealistic promises. I would like to raise another somewhat related issue we may have to consider. This is exemplified by the claim last year that a gene ‘for’ dyslexia had been mapped (Cardon et a1 1994). What had been found was a region associated with a marked effect on reading disability. But dyslexia is a complex trait, and it is likely that most genetic variation that affects reading disability will have much lesser effects. When, as seems inevitable, we are able to identify specific variants, for example ones that may change reading level by one

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standard deviation from the norm, then the parents of those children will demand something be done to alleviate the problem for those below the mean, and to nurture the performance of those above. This also seems likely to occur in other behavioural contexts. For example, there may be claims of variation in criminality or aggression. The scientific community will have to find some way of responding to these demands, as they proliferate to an ever increasing fraction of our populations. W .Eodmer: But we have to solve problems as best as we can with an analytical approach. I cannot question the value of the genome project. Much of the work that we are already doing could not be done without this information. Sing: I wouldn’t criticize the value of the genome project. I agree that we must build on the information that we obtain from it. Weiss: I wasn’t criticizing it either. I was trying to address the problems of societal pressure that scientists will have to deal with. Harper: I agree that the results from the genome project will be valuable, but my fear is that the project could be discredited by commercial or investigatorbiased pressures. There is a danger of hasty and inappropriate applications. At a meeting organized by the National Health Service Research and Development committee (March 1995, London, UK) on genetic factors in common diseases, we heard pleas from several researchers that something must be done now in terms of applying the findings. The subject is immensely complex, and unless we study this complexity before people rush in to clinical applications, we’re in danger of discrediting the whole field and probably doing more harm than good. I speak here as a clinician as well as a geneticist. W . Eodmer: That may be true, but we should not assume that commerce is necessarily evil.

References Cardon LR, Smith SD, Fulker DW, Kimberling WJ, Pennington BF, DeFries JC 1994 Quantitative trait locus for reading disability on chromosome 6. Science 266:276-279 Congdon CB, Sing CF, Reilly SL 1993 Genetic algorithms for identifying combinations of genes and other risk-factors associated with coronary artery disease. Thirteenth joint conference on artificial intelligence: AI and the genome workshop. Morgan Kaufmann, Palo Alto, CA, p 107-1 17 Green F, Hamsten A, Blomback M, Humphries S 1993 The role of b-fibrinogen phenotype in determining plasma fibrinogen levels in young survivors of myocardial infarction and healthy controls from Sweden. Throm Haemostasis 70:9 15-920 Risch N, Zhang H 1995 Extreme discordant sib pairs for mapping quantitative trait loci in humans. Science 268:1584-1589 Sing CF, Haviland MB, Templeton AR, Reilly SL 1995 Alternative genetic strategies for predicting risk of atherosclerosis. In: Woodford FP, Davignon J, Sniderman AD (eds) Atherosclerosis, vol 10. Excerpta Medica International Congress Series, Elsevier Science, Amsterdam, p 638-644

World distribution of HLA alleles and implications for disease Julia Bodrner Imperial Cancer Research Fund, 44 Lincoln’s Inn Fields. London WC2A 3PX, UK

Abstract. The high level of polymorphism of the HLA system makes it useful for the study of diversity in different populations. However, its relationships with disease susceptibility, resistance and progression impose selective pressure on population frequencies that must be taken into consideration. These relationships allow useful studies on the genetic susceptibility to disease, which can be studied further and may provide us with another tool with which to examine the history of populations.

1996 Variation in the human genome. Wiley, Chichester (Ciba Foundation Symposium 197) p 233-258

World distribution of mortality rates from disease Death rates from disease vary greatly in frequency in different parts of the world. A small sample of world mortality rates in a few selected countries (World Health Organization 1993)for infectious and parasitic disease, cancer and heart disease is shown in Fig. 1. Cancer deaths range from 83 per 1000 in Mauritius to 280 per 1000 in Italy, and deaths from heart disease range from 174per 1000in Mexico to 346 per 1000 in the USA. The mortality rates for infectious and parasitic disease are puzzling. The mortality rates in Brazil and Mexico are 37 and 59 per 1000, respectively; but why do the USA and France have rates of 13 per 1000, and the UK and Italy have rates of only 4 per lOOO? Apart from the major environmental factors affecting these rates, the obvious area of interest is the genetic constitution of people in different parts of the world.

The HLA system Organization

The HLA system is useful for studying population differences, and it is related to both disease susceptibility and progression (and in a few cases also to disease 233

FIG. 1. Risk (per 1000) of dying from infectious and parasitic diseases, cancer and heart disease in 10 different countries. Also shown is the life expectancy in each country at one year of age. Data obtained from World Health Organization (1993).

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resistance). It consists of a large number of genes on the short arm of chromosome 6 (Bodmer 1995). The HLA, H-2 and other histocompatibility systems were discovered by analogy with the human erythrocyte blood groups whilst searching for polymorphic antigens to match for transplantation. Subsequently, they have been shown to have a key role in immune response. More and more genes are being found in the HLA region, especially between the Class I and Class I1 clusters. The entire region comprises about 3.5 million base pairs, extending from somewhere between the collagen gene for type IIAII and HLA-DPB2 to the end of the Class I region, so far defined by HLA-F. This size estimate, now made precise by genomic analysis using yeast artificial chromosomes, has remained remarkably stable over a period of nearly 25 years. The overall genomic organization in humans and mice is extraordinarily similar, but there is polymorphism in the HLA region for the number of genes and their arrangement around HLA-DRB2 and the gene cluster encoding the C4 complement. There are three major categories of genes in the HLA region: (1) the HLA Class I genes (which include HLA-A, HLA-B, HLA-C and several other genes), the Class I1 genes (which also include HLA-DMA, HLADMB and HLA-DNA) and various related pseudogenes; (2) other genes with immune-related functions. The first of these to be found were the complement genes encoding C4, C2 and Bf, followed by the genes encoding TNF (tumour necrosis factor)-a and TNF-fi in the region between the Class I and Class I1 clusters. Recently, the analysis of the Class I1 region has led to the discovery of the transporters in antigen processing 1 and 2 (TAPI/2) genes and the genes encoding the proteasome components LMP2 and LMP7. The discovery of a small cluster of genes encoding Hsp70 in the middle of the HLA region is intriguing, although their apparent specificity for the testis suggests that they are unlikely to be involved in the immune response. The Class I1 equivalents of the transporters have not yet been identified clearly; (3) many other genes with functions unrelated to the immune system. These include, for example, the genes encoding 2 1-hydroxylase, a tenascin-related molecule and valyl-t-RNA synthetase, and a number of other genes whose function has yet to be determined. The strong homology between the mouse and human maps implies a common overall arrangement, at least in mammals, apart from minor perturbations due to local expansion and contraction of duplicated genes. It is possible that this overall organization reflects the collection of genes that surrounded the primitive precursor of the histoglobulin genes formed from the immunoglobulin and peptide-binding domain fusion. The various duplications followed by divergence are then presumed to have occurred within and around

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this collection of genes, leading to the present organization. Duplicates could easily have been spread locally by, for example, association with an intrachromosomal inversion giving rise to the gap between the Class I and the Class I1 genes. It is notable that there are relatively few, if any, nonimmune function genes within the Class I1 region. There is also a suggestion of a further major rearrangement which separated HLA-A and the associated cluster of Class I genes from HLA-B and HLA-C. It remains to be seen whether the gap between HLA-A and HLA-B/HLA-Cwill contain as divergent a set of genes with respect to function as does the gap between HLA-B/HLA-C and the Class I1 region. There is no reason why the duplicated genes, which led to the evolution of the HLA Class I and Class I1 genes, should have been separated during evolution. The separation would have required specific selection, either for a translocation or a transposed gene. The duplicated genes have presumably ‘coadapted’ following their divergence, and favourable combinations have no doubt been held together from time to time by interactive selection, resulting in linkage disequilibrium. It is likely, as suggested originally for the complement genes, that some of the immune function genes which are not HLA Class I or Class I1 genes could have been introduced into the HLA region by selection. Thus, selection favouring a particular combination of one of these genes, for example a transporter and a particular pre-existing HLA region gene, would favour, through linkage disequilibrium, a transposition which brought the two together. Once this combination had swept through a population, the reorganization would be established and there would be no need for residual selective interaction between the two genes to maintain the new arrangement. The basic function of Class I1 molecules is to present peptides, derived from exogenous proteins, to T cell receptors (TCRs) on CD4+ cells. The Class I1 molecules are expressed constitutively at a high level on specialized antigenpresenting cells, especially dendritic cells, as well as on B lymphocytes. Their function on the latter cells is presumably to present peptides, from proteins recognized by the specific antibody on the surface of the B lymphocytes, in order to promote the interaction between TCRs and B lymphocytes that is required for the stimulation of antibody production. The basic function of Class I molecules is to present peptides derived from the processing of endogenous proteins to the TCR on cytotoxic CD8+ T cells (Townsend & Bodmer 1989). This process ensures that viruses cannot hide within a cell because they should always leave a signature of processed peptides, derived from their expressed genes held on the surface by HLA Class I molecules ready to be recognized by specificcytotoxicT cells. The power of this recognition system is the reason why viruses have evolved a wide variety of mechanisms to escape from the immune response. These mechanisms involve a range of devices for down-regulating the expression of surface HLA Class I molecules.

HLA alleles

237

TABLE 1 Numbers of alleles at different loci within the HLA region Locus

HLA-A HLA-B HLA-C HLA-E HLA-F HLA-G HLA-H HLA-J HLA-K HLA-L HLA-DRA HLA-DRBl HLA-DRB2 HLA-DRB3 HLA-DRB4 HLA-DRBS HLA-DRB6 HLA-DRB7 HLA-DRB8 HLA-DRB9 HLA-DQA 1 HLA-DQB1 HLA-DQA2 HLA-DQB2 HLA-DQB3 HLA-DOB HLA-DMA HLA-DMB HLA-DNA HLA-DPAl HLA-DPBl HLA-DPA2 HLA-DPB2 TAP1 TAP2 LMP2 LMP7 ND,not determined.

Polymorphic expressed

Non-polymorphic expressed

Not expressed 1

60 125 36 4 ND

5 ND ND ND ND 2 132 1

5 4

6

16 25 ND ND ND 4 4 8 63

ND ND 5 4 ND ND

238

J. Bodrner

Polymorphism One of the valuable features of the HLA genes for the purposes of population distribution is the extremely high level of polymorphism. Table 1 shows the number of loci and alleles currently identified for the HLA system (Bodmer et a1 1995). Gene frequencies of these alleles vary greatly between populations as is shown in Tables 2a and 2b for a small subset of the large number of HLA alleles tested in the 11th International Histocompatibility Workshop (Juji et a1 1992). For example, HLA-A2 is present in all 87 populations tested, except New Guinea, with an average gene frequency of around 25%, whereas B70 is not found at all in 30 populations, has an average frequency of 1.7% and reaches frequencies of 19% and 17% in South African Blacks and Khoi, respectively. HLA haplotypes

Further features of the system are that one inherits all one’s HLA alleles together (one set from each parent as a haplotype) and that certain combinations of alleles from different loci are held together in linkage disequilibrium (as discussed above). The strength of this disequilibrium for a combination of alleles is measured by the observed frequency of the haplotype compared with the expected frequency, based on the frequency of the individual alleles. For clinical purposes, an HLA typing would consist of alleles at the HLA-A, HLA-B, HLA-C, HLA-DRB1, HLA-DRB3, HLADRB4, HLA-DRBS, HLA-DQAl, HLA-DQB1 and HLA-DPB1 loci, with occasionally TAP1 and TAP2 alleles. The number of possible haplotypes that could be generated by all possible combinations of these alleles is astronomical. A calculation done on fewer alleles from three loci only several years ago showed that it was possible to generate over lo9 possible haplotypes. Happily, both for clinicians and anthropologists, only a subset of these possible haplotypes are found to occur with any frequency and they show a high level of regional specificity. HLA haplotype distribution

Examples of the distribution of three locus haplotypes are given in Tables 3a and 3b. Table 3a shows a small selection of serologically determined haplotypes from the HLA-A, HLA-B and HLA-DRBl loci. The European haplotype Al,B8,DR3 is a marker for Europeans both resident in Europe and wherever they have settled, e.g. USA, Canada and Australia. The following two haplotypes are found only in a subset of Europeans. The A30,B18,DR3 haplotype is a clear marker for Basques and Sardinians and is also found in Gypsies. The A2,B46,DR9 haplotype is particularly interesting because it is

1 0 4 25 40 15 1 1 0

0 14 27 32 3 4 4 3

0 1-5 6-10 11-20 21-30 3 1-40 41-50 51-75 76-100

5 60 20 2 0 0 0 0 0

A26

A1 4 31 23 27 2 0 0 0 0

A3 4 33 25 25 0 0 0 0 0 8 29 29 12 6 2 1 0 0

All 9 69 5 2 2 1 1 0 0

10 60 6 9 2

0

0 0 0

A31

A30 11 53 17 6 0 0 0 0 0

A33 16 52 17 2 0 0 0 0 0

A68

19 57 10 1 0 0 0 0 0

A29

A32 23 56 8 0 0 0 0 0 0

A23 21 51 8 7 0 0 0 0 0

'Bodmer et a1 (1973).

Population estimate (%) 16.1 24.6 4.4 7.5 8.2 8.6 4.0 4.0 4.4 3.0 2.3 2.7 2.4 Range (YO) 1-64 0-51 0-13 0-20 0-29 0-42 0-25 0-47 0-19 0-13 0-11 0-17 0-11 Wahlund's variancea 0.1334 0.0250 0.0162 0.0394 0.0478 0.0762 0.0564 0.0693 0.0355 0.0328 0.0210 0.0487 0.0221

0

A2

A24

% Positive

TABLE 2a IUA-A antigen frequency distributions in 87 populations

("/I

5.4

2 1 0 0 0

8

4 58 14

3.1

12 52 18 5 0 0 0 0 0

B62 B13

3.0

11 62 13 1 0 0 0 0 0

B27

3.2

11 57 14 4 0 1 0 0 0

B58

4.4

13 43 23 8 1 0 0 0 0 0

B8

4.3

15 48 11 1 2 0 0 0 0

B60

2.9

16 63 3 5 0 0 0 0 0

B39

2.2

17 63 7 0 0 0 0 0 0

B57

3.0

16 58 11 2 0 0 0 0 0

B52

3.7

18 50 16 2 1 0 0 0 0

B18

4.0

16 56 9 4 2 0 0 0 0

B6l

2.0

21 60 5 0 1 0 0 0 0

B55

1.1

21 63 3 0 0 0 0 0 0

B63

1.7

30 47 6 3 1 0 0 0 0

870

~

~~

'Bodmer et a1 (1973).

~

Range (%) 0-38 0-24 0-20 0-18 0-34 0-19 0-11 0-36 0-16 0-29 0-17 0-8 0-11 0-27 0-26 &22 0-8 0-22 Wahlund's 0.0320 0.0239 0.0203 0.0160 0.0397 0.0309 0.0158 0.0571 0.0339 0.0535 0.0333 0.0126 0.0305 0.0372 0.0577 0.0184 0.0110 0.0553 variancea

6.4

8.4

Population estimate

6.4

5 35 41 6 0 0 0 0 0

3 6 4 40 31 33 27 30 31 2 3 1 7 1 2 1 1 0 2 0 0 0 0 0 0 0 0 0 0 0

0 1-5 610 11-20 21-30 3140 41-50 51-75 76100

7.6

B51

B7

B44

B35

% Positive

TABLE 2b HLA-B antigen frequency distributions in 87 populations

HLA alleles

241

found only in people of Chinese origin. This haplotype is also relevant in disease susceptibility in the Chinese population. The one haplotype clearly associated with African Blacks (A30,B42,DR3) contains the antigen HLA-B42, which is found only in Black populations. There are many other haplotypes that identify subgroups of people (Juji et a1 1992). The haplotypes shown in Table 3b were identified using DNA techniques that define alleles more precisely than is possible with serology (serology often identifies one antigen which is actually a series of closely related but slightly different antigens, as defined at the DNA level). It is therefore somewhat surprising that it is more difficult to identify population-specific haplotypes using DNA techniques than with the less precise serological method. The first haplotype, DRB1*04,DQA1*03,DQB1*0302, is found in groups as far apart as the Tlight (North American Indians), Khoi, Japanese and Danes. However, even this level of resolution masks, for example, 22 variants of HLA-DRB1*04; although it is clear that at a broad level the shape of the haplotype is distributed widely. Of possibly more interest to the anthropologist are some of the more narrowly distributed haplotypes, such as DRB1*03,DQA1*03,DQB1*0201, found only in San (Bushmen), Khoi and southern African Blacks (and not in Blacks elsewhere) and DRB1*12,DQAl*0101,DQB1*0501, found in Blacks in northern and southern Africa and in South America, but not in San and Khoi.

HLA and disease The HLA system is a polymorphic genetic system, with a variable world distribution, that controls immune response and therefore could be expected to have a large influence on disease susceptibility. The first analysis of HLArelated susceptibility to disease was carried out by Amiel (1967), who reported an association between Hodgkin’s disease and a small increase of a particular HLA-B locus allele. The number of diseases that have been analysed for HLA-related susceptibility since then is enormous. For example, Tiwari & Terasaki (1985) published a catalogue of these studies, a brief summary of which is shown in Table 4. This table does not attempt to show all of the most significant associations, but shows the range of categories of disease in which HLA has an influence. Many of these associations have been strengthened and clarified by more recent studies. In particular, the effect of HLA type in papilloma-related cancers, such as cancers of the skin and cervix, has been seen. The relationship with infectious diseases is relatively complicated. In many diseases an HLA association is found with the progression of the disease, such as in leprosy and AIDS, rather than with the initiation of the disease. This may be expected from twin studies, which show that monozygous twins are no more concordant in contracting many infectious diseases than are dizygotic twins.

Hungary Yugoslavia Sweden Norway Australia Slovakia Austria USA Germany Denmark Rumania Canada Ashkenazi Jew Britain IMY Czechoslovakia Belgium Basque Poland Sardinia Spanish Gypsy Iyers

Ethnic Group

13 99 254 393 153 91 224 28 135 590 123 65 61 52 100 15 14

64

99 100 99 41

Population number

6 6 5 5 5 4 4 4 4 3 3 3 3

I I

9 8 8

AI, B8, DM, HF 5 5 4 5 5 4 3 3 3 3 3 2 2 2 2 3 2 2

LD

2 2

3

3

2

3

8

3

4

3

3

1

5 2 13

2

3

1

6

3

(continued)

A2, A30, -42, A30, A3, A33, B 7, €344. B18, B44, B46, B42, DRIS, DR4, DR1, DR7, DR9, DR1, HF LD HF LD HF LD HF LD HF LD HF LD

TABLE 3a HLA-A, HLA-B and HLA-Chaplotype frequencies by population (per 1OOO)

1 1 3 2 2 5 2 1 1

4 3 9 8 8 5 5 3 2

29

3

3 2 2 2

60

4 3 3 2

103

LD

2

2 1 1

A.2, A30, A2, A30, -43, A33, B7, B44, B18, B44, B46, B42, DR15, DR4, DR3, DR7. DR9, DR3, HF LD HF LD HF LD HF L D HF L D HF L D

4 4 4

50 255 267 71 82 42 71 119 118 74 69 52

Population number

AI, B8, D*, HF

HF, haplotype frequency; LD, linkage disequilibrium.

Nepal Thai Korea Thai Chinese Singapore Chinese Taiwan Miao Vietnam China Buyi Han South South American Black Caribbean Southern African Black Northern African Black

Ethnic Group

TABLE 3a (cont.)

Sardinia Romania South American Black Japan (Wajin) USA Southern African Black Buyi Italy North American Black Algeria Greece North American Indian Inuit GYPSY Highlanders (Papua New Guinea) Zuni Thai-Chinese

50 51

79 55 106 360 71 103 203 60 168 237 92 75 42 515 323 89 72 510 132 23 73 45 42 77 197

San (Bushman) Denmark Khoi Germany Singapore Korea Canada Tlingit Spain

France

Pop no

Ethnic group

30.2 13.7 13.7 10.8 8.8 8.6 7.5 6.9 6.5 5.6 5.0 4.7 3.6 3.5 3.4 2.8 2.8 2.6 2.3 2.2 2.1 25.3

DRBI'OI DQAI.03 DQBI'0302 HF

6.0 3.7 3.5 3.2 4.7 3.3 4.0 3.5 3.5 2.7 1.5 2.2 2.4 2.2 2.1 1.9 1.8 1.6 5.2

5.8 5.5

2.9

LD

2.9 4.7 6.8 2.4 4.7 1.7

5.8 7.9 23.9 5.4 7.5 2.8 6.2 3.5 5.1 7.6 11.7 2.1 4.5 6.1 6.0 2.7 2.8 4.8 5.2 1.4 2.6 3.4 3.8

5.8

6.5 4.2 5.0 3.9 2.0 4.6

LD

11.6 6.2 9.2 5.8 2.8 10.0

DRBI.03 DQAPOMI DQBIWOI HF

9.5

4.8 5.0 11.5 3.8 2.2

9.1 7.5 4.7 3.0 3.7

5.3 7.7 4.5

2.1 1.8

4.5 2.7

13.7 7.3 4.3 2.3 4.9

9.6 2.9 7.0 7.6

8.5

DRBI.07 DQAPO20I DQBPOmI HF

3.2 3.1

6.7 6.3 6.1 4.5 2.3 2.5 2.4

4.7 4.8

7.0 6.6 11.3

7.4 4.4 5.7

LD

12.6 6.3 7.8

DRBIWI DQAPOIOI DQBl*OSOI HF

TABLE 3b HLA-DR, HLA-DQA and HLA-DBQ frequencies by population (per 1OOO)

3.7

3.4 4.4

3.7 1.7

5.3 4.8 2.5 1.3 3.5

5.2 2.5 5.3 4.1

3.5

LD

5.7 4.9

7.2

3.2 2.9

5.1

2.7

2.7 3.4

4.4

3.8

LD

6.9

DRBI'09 DQAI.03 DQBIW303 HF

2.9

2.8

4.9

5.8 7.0

DRBIW DQAI.0103 DQBI'MOI HF

1.8

2.3

2.8

4.6 4.8

LD

09

3

hl

2

:

40 31 27 30 32 59 30 28 24 15 6 32 9 19 10

Rheumatology and joint disease

Dermatology

Neurology

Endocrinology

Gastroenterology Opthalmology Malignancy

Allergy Urogenital disease Cardiovascular Connective tissue disease

Pulmonary disease Infectious disease Psychiatric Haematologic Complement and immune deficiencies

Data from Tiwari & Terasaki (1985). *Also found but value not given.

52

Disease category

Relative risk HLA alleles Caucasoid

rheumatoid arthritis DR4 3.8 ankylosing spondylitis B27 69.10 psoriasis Cw6 7.5 Behwt’s disease B5 3.8 narcolepsy DR2/DQl 129.8 multiple sclerosis DR2 2.7 insulin-dependent DR3 3.3 diabetes mellitus DR4 3.6 chronic active hepatitis DR3 2.8 uveitis B27 8.2 nasopharyngeal A2/B46/DR9 carcinoma asbestosis B27 1.56 DR3 13.35 herpes mitralism B35 1.01 systemic lupus DR3 2.7 erythematosus Goodpasture’s syndrome DR2 13.80 mononucleosis B35 1.69 Alzheimer’s disease B15 1.74 pernicious anaemia B7/DR2 >2 many DR3

No. of diseases studied Example of disease

TABLE 4 HLA-related disease susceptibility in ditferent types of disease

4.9

4.8

2.5 209.9 8.5 4.5 358.1

Oriental

4.32

3.2 6.7

*

5.4 54.40

Black

HLA alleles

247

HLA and disease associations are constant in many populations For several of these diseases, the association with a particular antigen is constant in different ethnic groups (a small subset of these is shown in Table 5). This indicates that the susceptibility gene is actually the HLA allele identified or a gene in such close linkage disequilibrium with the HLA allele that it is common to all populations. Of particular interest is the fact that in narcolepsy, 100% of patients carry the HLA-DRZassociated HLA-DQ allele (Mignot et a1 1993). This is the only disease showing 100% concordance with an HLA allele, although the frequency of HLA-B27 in ankylosing spondylitis comes very close to this. Ankylosing spondylitis is always found in association with HLA-B27 (Table 5 ) in spite of the fact that the frequency of HLA-B27 varies from around 20% in Inuit, Tibet and Norway to zero in Columbia, several African Black groups, Algeria and Morocco. Does this mean that ankylosing spondylitis is absent in those populations that do not have HLA-B27? This is clearly a verifiable fact but it has been shown, for instance in several studies in Japan where the frequency of HLA-B27 is only 0.85, that HLA-B27 gives a relative risk in one of the studies as high as 1003. This must be difficult to estimate because HLAB27 was not found in any of the 181 controls tested.

HLA and disease associations are different in different populations There are also diseases which have a different HLA allele association in different populations, for example psoriasis and systemic lupus erythematosus. In these cases the disease gene is not in such a close linkage disequilibrium with the HLA allele and certainly would appear not to be the HLA allele under study. This situation has often arisen in early disease studies because of different linkage disequilibria in different populations, but it has often been resolved when an allele at another locus, in linkage disequilibrium with all the previously determined alleles in different populations, has been identified. In Chinese populations the haplotype A2,B46,DR9 is found in increased frequency in patients with nasopharyngeal carcinoma. It is of interest that this haplotype is only found in Chinese and is not found in patients with nasopharyngeal carcinoma in other parts of the world.

HLA and disease resistance A more difficult relationship between HLA alleles and disease is HLAassociated resistance to disease. It would be expected that, in view of past major epidemics, the HLA allele frequency distribution would in some way represent the survivors and therefore represent those HLA alleles conferring resistance. This is a problem, however, because many alleles that may confer

248

J. Bodrner

TABLE 5 Significant KLA and disease associations in different ethnic groups Number of Disease

HLA

Rheumatoid arthritis

DR4 DR4 DR4 Ankylosing spondylitis B27 B27 B27 Insulin-dependent diabetes DR3 mellitus DR4 DR3 DR4 DR3 DR4 Narcolepsy DR2 DR2 Behpt’s disease B5 B5 Pemphigus vulgaris A26, B8, DR4

Race

studies

Caucasoid 17 Oriental 5 Black 3 Caucasoid 40 Oriental 7 Black 2 Caucasoid 13 Caucasoid 12 Oriental 4 Oriental 4 Black 3 Black 3 Caucasoid 2 Oriental 1 Caucasoid 6 Oriental 6 Caucasoid/Jewish 8

Relative risk

3.8 2.8 5.4 69.1 207.9 54.4 3.3 3.6 4.8 2.6 3.2 6.7 129.8 358.1 3.8 4.5 4.8, 4.6, 14.6

Data from Tiwari & Terasaki (1985).

resistance to one disease may also be associated with greater susceptibility to another disease. For example, HLA-DR2 and the associated HLA-DQB allele (the latter of which lacks aspartic acid at position 57) have been shown to be associated with resistance to insulin-dependent diabetes mellitus. HLA-DR2 is also a susceptibility gene for narcolepsy and multiple sclerosis. On the other hand, HLA-DR3, which is presently positively associated with many autoimmune diseases and occurs at high frequency in many populations, may have been associated with disease resistance in the past. Interaction of disease and HLA allele frequency distribution A restricted set of HLA alleles was found in American Indian populations, for example the Ticuna Indians, (Lawrence et a1 1980) which led to speculation that a bottleneck effect had occurred, the restriction being the result of severe reductions in population size following infections introduced in the 15th century from Europe. More recent studies on American Indians using DNA techniques have found that although the range of broadly defined antigens is small, within that range the number of DNA variants is large. Some of these

HLA alleles

249

variants are not found in other populations. It has been suggested that a faster rate of mutation took place than in other populations, presumably to compensate for the loss in variation previously incurred (Belich et a1 1992). This is an intriguing hypothesis, and when other populations have been studied at the DNA level as intensively as the American Indians, it will be possible to see whether the level of mutation is unusual or whether the new alleles identified in the American Indians are also found in other populations. A more likely explanation of this (Bodmer 1991) is based on the observation that differential selection after the separation of major population groups can give rise to marked divergences between populations, provided that there is a significant rate of production of new alleles by recombination or gene conversion. Thus, modest selective values could give rise to marked changes in gene frequency distributions within a few hundred generations and certainly within the 40 to 50 thousand years since the colonization of the Americas by the American Indians. The anthropology study of the 12th International Histocompatibility Workshop, in which the HLA alleles of over 150 populations will be tested using DNA techniques, will provide results to help answer this.

Absence of HLA allele specificity in a population It is difficult to infer the relationships of HLA alleles and disease in the past and it would certainly be dangerous to conclude that the absence of an allele in a population, such as the HLA-DR3 antigen in the Japanese or the HLA-A2 antigen in natives of Papua New Guinea, means that it was lost through disease. It may, however, be possible to look at what is happening at present with a new disease. HIV has been only widely identified in the last 15 years. Early studies indicated increased seropositivity with HLA-DR5 and rapid disease progression of individuals infected with HIV has been clearly associated with HLA-DR3, and in particular the A1,B8,DR3 haplotype (Donald et a1 1992). The nature of this disease, which affects T cells, makes it a clear subject for HLA allele selection pressure. A group of commercial sex workers in Kenya who have remained HIV negative in spite of repeated exposure have been identified (Willerford et a1 1993). A study is currently underway to see whether they show any disturbance in the frequency of HLA antigens.

HLA restriction HLA-A2 has a widespread frequency worldwide, more than any other single antigen, although it is perhaps unfair to call it a single antigen as there are at least 17 HLA-A2 variants at the DNA level. The common feature of all these variants, however, is a glycine residue at position 62 in the a1 domain (Parham

9.72 37.50

72

47.58 38.81 42.42 42.72 49.44 60.17 44.71 53.49

4.85 27.18

31.55 24.75 19.87 1.32 4.87 1.69 4.71 2.33

A2

103

393 590 297 1058 267 118 255 129

A1

6.94

7.77

14.76 17.29 12.79 58.03 40.07 36.44 29.41 0.78

A24

A30 7.12 4.07 4.38 0.00 1.12 0.00 1.18 2.33

A68

2.91 25.24 20.39

35.09 5.85 4.07 7.63 5.05 9.09 14.27 0.76 28.09 8.61 10.17 4.24 27.45 1.96 16.28 0.78

A33

1.39 27.78 22.22 19.44

7.77

3.82 7.12 4.04 15.50 7.49 3.39 3.14 62.79

A31

HLA-A locus

'No. neg and % neg indicate those people who possess none of the listed antigens.

German Italian Spanish Japanese Korean Chinese Thai Native Brazilians South African Blacks Western Africans

Population

Sample size

125.9

96.1

115.8 103.7 97.6 132.6 139.7 116.9 112.5 138.8

A-loms frequency B44

0.97 38.83

1.78 0.51 0.67 3.12 3.75 1.69 0.00 0.78

B70

5.56 26.39 12.50

13.59

16.03 27.12 14.48 15.60 13.11 7.63 5.10 38.76

835

HLA-B locus

18.32 16.44 27.61 13.71 18.73 1.69 10.59 0.00

TABLE 6 Frequency of 10 most common HLA-A and HLA-B alleles in 10 populations

169.4

149.5

151.9 147.8 140.4 165.0 175.3 127.1 128.2 178.3

Total frequency

4

12

29 68 52 40 10 14 42 9

6

12

7 12 18 4 4 12 16 7

No. % nega nega

HLA alleles

25 1

et a1 1995). In at least two studies, the HLA-A2 antigen was found to be significantly increased in Greek and Italian haemophiliacs who were HIV positive (Papasteriades et a1 1993, Fabio et a1 1990). Many studies looking at restriction with respect to HIV peptides have found HLA-A2 to be a common restriction element. For example, Tsomides et a1 (1994) found that each of two HLA-AZrestricted CD8+ cytotoxic T lymphocytes specific for HIVl gag or reverse transcriptase recognized a single naturally processed HIVl peptide in trifluoroacetic acid extracts of infected cells, i.e. gag 77-85 (SLYNTVATL) or reverse transcriptase 476-484 (ILKEPVHGV). Both processed peptides match the synthetic peptides that are optimally active in cytotoxicity assays, and they have the consensus motif described for HLA-AZassociated peptides. Incidentally, HLA-A2 restriction has been seen for many other viral peptides, such as influenza (Tussey et a1 1994) and hepatitis (Madden et a1 1993). There are, of course, other HLA alleles that are involved in viral peptide restriction but HLA-A2 seems much more prevalent than others. Is this because HLA-A2 is a common antigen worldwide or because there has been a selective bias in the HLA types that have been studied? And what of the rare populations in which HLA-A2 is absent, such as in natives of Papua New Guinea? Are they likely to be at an advantage or a disadvantage, and are they genuinely the only population that has lost HLA-A2? HLA and disease in the future Should we look at the distribution of HLA-A2 and its relationship with HIV or, indeed, at any other HLA antigen whose present frequency has been determined? Also, should we look at HLA alleles in other human viruses that seem to have jumped the species barrier and have arisen from Africa within the last 25 years? HLA and vaccines One final point concerning HLA and disease relates to vaccines. It has been shown in chimpanzees that protection against disease is unrelated to: total antibody titres to human cells used in the production of the vaccine; HLA antibodies; or virus neutralizing activity. However, analysis of the serotype of each animal revealed that all animals protected against cell-associated virus challenge were those which had been vaccinated against simian immunodeficiency virus and which shared a particular major histocompatibility complex class I allele (Mamu-A26) with the donor of the infected cells (Heeney et a1 1994). This approach is being used by many in the search for an effective vaccine against HIV. In order to produce a universal vaccine, researchers attempted to

252

J. Bodmer

find the 10 most common HLA alleles, at least one of which would be found in every person or at least a large percentage of the world’s population. Table 6 shows a preliminary attempt to identify these specificities. The data was obtained from the 1lth Histocompatibility Workshop. Having selected 10 antigens on the basis of being present in the majority of people in the chosen populations, we extracted all the people from each population who carried at least one of the antigens. The last two columns of Table 6 represent the numbers and per cent of people who do not carry any of the antigens listed and so would probably not be protected by the vaccine. This reached an unfortunate total of 18% in Spain, and was down to a possibly acceptable level of 4% in Japan and Korea. It is almost certainly possible to improve this coverage by a more carefully chosen set of alleles, but it may not be possible to limit the selection to 10 alleles. A better approach would be to look at particular epitopes carried by the different alleles and select a set that would cover the entire global population. Acknowledgement The author is grateful for helpful advice and discussion with Walter Bodmer.

References Amiel J L 1967 Study of the leucocyte phenotypes in Hodgkin’s disease. Munksgaard, Copenhagen Belich MP, Madrigal JA, Hildebrand WH et a1 1992 Unusual HLA-B alleles in two tribes of Brazilian Indians. Nature 357:326-329 Bodmer JG, Marsh SGE, Albert ED et a1 1995 Nomenclature for factors of the HLA system, 1995. Tissue Antigens 46:l-18 Bodmer WF 1991 HLA 1991. Oxford University Press, Oxford, p7-16 Bodmer WF 1995 Evolution and function of the HLA region. Cancer Surv 225-16 Bodmer WF, Cann H, Piazza A 1973 Differential genetic variability among polymorphisms as an indicator of natural selection. In: Dausset J, Colombani J (eds) Report of an International Workshop and Conference Colloque de I’Instit National de la Santd et de la Recherie Medicale, Evian, France. Munksgaard, Copenhagen, p 753-767 Donald JA, Rudman K, Cooper DW et a1 1992 Progression of HIV-related disease is associated with HLA DQ and DR alleles defined by restriction fragment length polymorphisms. Tissue Antigens 39:241-248 Fabio G, Smeraldi RS, Gringeri A, Marchini M, Bonara P, Mannucci PM 1990 Susceptibility to HIV infection and AIDS in Italian haemophiliacs is HLA associated. Br J Haematol 75531-536 Heeney JL, van Els C, de Vries P et a1 1994 Major histocompatibility complex class Iassociated vaccine protection from simian immunodeficiencyvirus-infected peripheral blood cells. J EXP Med 180:769-774 Juji T, Akaza T, Tokunaga K, Miyoshi H, Kashiwase K 1992 The serology studies of the Eleventh International Histocompatibility Workshop: an overview. Oxford University Press, Oxford

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Lawrence DN, Bodmer JG, Bodmer WF 1980 Distribution of HLA antigens in Ticuna Indians of Brazil: results of typing a leprosy-affected family. Tissue Antigens 16:152-160 Madden DR, Garboczi DN, Wiley DC 1993 The antigenic identity of peptide-MHC complexes: a comparison of the conformations of five viral peptides presented by HLA-A2. Cell 75:693-708 Mignot E, Lin X,Hesla PE, Dement WC, Guilleminault C, Grumet FC 1993 A novel HLA DR17,DQl (DQA1-0102/DQB1-0602 positive) haplotype predisposing to narcolepsy in Caucasians. Sleep 16:764-765 Papasteriades C, Economidou J, Pappas H et a1 1993 Association between HLA antigens and progression of HIV infection in Greek haemophiliacs. Dis Markers 11:131-137 Parham P, Adams EJ, Arnett KL 1995 The origins of HLA polymorphism. Immunol Rev 143:141-180 Tiwari JL, Terasaki PI 1985 HLA and disease associations. Springer-Verlag, New York Townsend A, Bodmer HC 1989 Antigen recognition by class-I-restricted T lymphocytes. Annu Rev Immunol 7:601424 TsomidesTJ, Aldovini A, Johnson RP, Walker BD, Young RA, Eisen HN 1994Naturally processed viral peptides recognized by cytotoxic T lymphocytes on cells chronically infected by human immunodeficiencyvirus type 1. J Exp Med 180:1283-1293 Tussey LG, Matsui M, Rowland Jones S, Warburton R, Frelinger JA, McMichael A 1994 Analysis of mutant HLA-A2 molecules: differential effects on peptide binding and CTL recognition. J Immunol 152:1213-1221 World Health Organization 1993 1992 World health statistics annual. World Health Organization, Geneva Willerford DM, Bwayo JJ, Hensel M et a1 1993 Human immunodeficiency virus infection among high-risk seronegative prostitutes in Nairobi. J Infect Dis 167:1414-1417

DISCUSSION Kidd: I would like to comment on the first part of your presentation, i.e. that the HLA system represents a human paradigmatic haplotype system. The HLA system has been used for a long time to look at population relationships and the distributions of shared haplotypes. The field is now at a molecular level so that a larger number of haplotypes can be studied and titrated to different levels of differentiation. The HLA system is polymorphic and spans a large molecular region, suggesting that it is involved in local differentiation. There doesn’t seem to be a large number of haplotypes which have a broad distribution, they seem to be more locally confined. My other comment is related to the results you presented on the A2 haplotypes, which showed that one antigen exists on a haplotype with a large number of variants. Have you thought about how you might draw the phylogeny of those alleles? Some of the individual amino acid changes were shared among several haplotypes but others seemed to be present in only one or two.

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J. Bodmer: J. Moses, S . Tonks and myself are just about to do A2 subtyping to look at the frequency of the different alleles in the world. Some of the alleles are more similar to each other than to others, so the question is why do they all have the A2 haplotype? The differences between these alleles are much less than with other HLA locus alleles, so on that basis they do fit a conglomerate. They also share the same structure, which is not the case for many alleles. There is a continuum at the B locus, the DRBl locus and the DP locus such that there are only minor differences of the order of a few amino acids between the DR13 and DR14 loci, for example. Kidd: Do all of the molecularly defined alleles share the constellation of amino acids that give rise to the epitope called A2? J . Bodmer: Yes, and there is one common residue in the sequence that no other epitope has. Templeton: What is that residue? J. Bodmer: Residue 67. Some of the A2 alleles share the same sequence for exons 2 and 3, but differ in exon 4. This situation is complex but not as complex as for some of the B locus alleles, for example B15. We sometimes wonder if we will ever reach the end of these variants and what it all means. We would like to be able to identify which epitopes cause serious rejection problems. This is a serious problem for bone marrow transplants. Kidd: Did Greek and Italian haemophiliacs who developed AIDS have a higher or lower frequency of A2? J. Bodmer: Higher. Kidd: This suggests that patients with A2 are more susceptible to HIV. J. Bodmer: Yes. Odd comments about disease progression don’t seem to fit together. For example, the Al,B8,DR3 haplotype has been shown in several studies to lead to a more rapid progression of AIDS (Cameron et a1 1990). However, this study also reported that the frequency of the A2 allele was higher in Italy and Greece. Freimer: What is the frequency of the A2 allele among the sex workers in Africa? J. B o h e r : It is unclear whether A2 is involved. Andrew McMichael has suggested that this may be involved with resistance to the HIV virus in African sex workers (personal communication). Freimer: In that population is A2 the most common allele? J. Bodmer: It’s reasonably common. Freimer: If these sex workers have an allele that is uncommon in their population and they’re mixing with people who have different alleles, is it possible that they’re protected not by the allele itself but by the difference in HLA type? J. Bodmer: It is interesting that these people remain HIV negative, but we don’t know why. There was an earlier study that assigned another HLA antigen to this group but this subsequently turned out to be incorrect (A. McMichael,

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personal communication). Therefore, it may have nothing to do with HLA. This is currently being tested. Bertranpetit: All of this work relies on theoretical haplotypes. It would be preferable if you were studying real haplotypes. W. Bodmer: But these are bona fide estimates of haplotype frequency. They take into account all combinations and they are just as valid as genetic analysis. Edwards: Estimation of haplotype frequency is necessarily less efficient than counting, which needs parent-child pairs. The inefficiency increases with the number of loci. J. Bodmer: These models can be tested by applying them to families with a known pattern of segregation and to unrelated family members, and seeing if the results match. Bertranpetit: In the association studies of HLA and HIV infection, the results show that there are moderate relative risks, although significantly higher than one. Beyond the‘ social context of AIDS, do you think the associations with HLA are important and are telling us something of biological interest? J. Bodmer: We have found that the Al,BS,DR3 haplotype is present in many patients infected with HIV, and these patients seem to develop AIDS more rapidly (Cameron et a1 1990). This haplotype is associated with a higher level of autoimmunity but it’s not totally clear whether this has anything to do with AIDS. Chakraborty: We seem to be finding that every disease is associated with HLA. I’m not misinterpreting the role of HLA as the genetic factor, but how many of these associations really make biological sense? J. Bodmer: They all make sense because we are now understanding more about how the system works, in terms of the presentation of viral peptides to T cells. W. Bodmer: Not every disease is associated with the HLA system. For example, cancer is not really associated with HLA, and all the blood group associations only have a relative risk of about 1.3. Hodgkin’s disease has a relative risk of about 1.5. In contrast, many HLA associations involve relative risks in the order of 5-100. There is, of course, an absolute requirement for judging the significance of these sorts of results. For autoimmune diseases and the diseases Julia Bodmer has mentioned, the association is unequivocal. Chakraborty: The HLA research community is one of the most wellorganized group of workers. However, by asking one or two additional questions, you may be able to obtain more information from your studies. For example, if we knew how many different associations have been tested, rather than just the significant ones that have been reported, then we would be assured that we were not just looking at results that are significant by chance alone. W. Bodmer: Julia Bodmer and myself have been involved in HLA workshops for the last 31 years. Each of these workshops has had a properly

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organized study of HLA and disease associations. The challenge is now not to go on finding new associations, but to pick out the details of what HLA type goes with which disease and find the functional basis for it. Beighton: You made the point about the importance of studying anthropological data, and the example you gave was the Venezuelan population. You spoke of the Black population in South Africa and the Xhosa. The Black population comprises about 30 million individuals, and is composed of three or four major groups, one of which is the Xhosa, who total about 10 million. J. Bodmer: The people who work on this in South Africa know more about anthropology than myself. The San (Bushmen) are quite distinct from the people who migrated south such as the Zulu. Beighton: The Xhosa are one of the major Black linguistic groups. They are an indigenous African people, as are the Zulu and the Tswana. The San total about 100000 in number and they live mainly in Botswana, in the Kalahari region. They do not form a subset of the African Black population. The Khoi are anthropologically similar to the San. They also are not a subset of the Black population, and they total about 20 000 in number at present. J . Bodmer: The Khoi form a distinct group from the Xhosa. They are not part of the Black population but they do share some haplotypes with them. Beighton: This may have something to do with the historical movement of these populations within Africa. Bertranpetit: Is there a correlation between HLA allele frequencies (or haplotype frequencies) and the prevalence for certain diseases at a geographical level? J. Bodmer: In Japan the prevalence of ankylosing spondylitis is associated with a low frequency of B27 (Tiwari & Terasaki 1985). Only a small proportion of people carrying a particular HLA allele actually get the associated disease, so it would be difficult to correlate disease frequency with allele frequency in a population. Ward: The HLA system represents a good way to look for heterogeneity in terms of relative risk, and B27 is one of the more interesting haplotypes because it has such a well-defined relative risk for ankylosing spondylitis. In those populations in which the frequency of B27 is high, there is a marked level of heterogeneity in terms of the association of ankylosing spondylitis. By following this up, we may be able to learn something about the environmental agents involved in the pathogenesis. J. Bodmer: B27 is also present in patients with uveitis and Reiter’s disease (Tiwari & Terasaki 1985). I don’t know what the common element could be, apart from a common infectious agent. W . Bodmer: Even when ankylosing spondylitis is relatively common, only a few per cent of B27 individuals develop ankylosing spondylitis. Therefore, there’s either a large environmental effect, which is not completely understood, or another significant genetic effect. The latter is extremely unlikely.

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Kidd: In the small data set with ankylosing spondylitis, we found that the B27 relatives of a proband had a twofold higher risk of developing ankylosing spondylitis than B27 individuals in the general population (Kidd et a1 1977). One could argue that a common environment was responsible because they were relatives, or that they shared certain genes that contributed to the elevated risk. Edwards: I cannot see a way of clarifying this. The investigators can make any sex ratio equal if they keep asking people if they are sure they don’t get backache. Stooping, hobbling women are rare, but this is the presentation in males. Whether arthritis-which we would all eventually get if we lived long enough-is more severe or more common in either sex is a theological question. J. Bodmer: I have studied sex differences with Hiliary Hill, and we found that men are diagnosed as having ankylosing spondylitis within three or four years of first developing the symptoms, but for women it takes up to 11 years because women are not expected to get ankylosing spondylitis and they have milder symptoms (Hill et a1 1976). Therefore, women with ankylosing spondylitis may have been misdiagnosed as just having lower back pain. W. Bodmer: Virtually all autoimmune diseases show sex differences in severity and incidence. It’s quite clear that there’s a major effect of sex on the immune response. Edwards: But ankylosing spondylitis is the only one in which males have an increased severity. Kidd: In the Base1 study in the 1970s (Kidd et a1 1977) all of the women in the ascertainment pool were X-rayed and there was a roughly equal frequency of arthritic problems in the lower spine in B27 men and B27 women. There was an increased severity in males, but the problems were present in the females. Edwarh: But it’s the same underlying problem of whether other genes are producing an effect or if the genetic background is producing an effect on manifestation. Kidd: The fundamental metabolic differences between the sexes may also be responsible. Bowcock: There is also a clear HLA association in psoriasis (Tiilikainen et a1 1980), although there are no sex differences. Males and females have an equal 20-fold higher risk, if they have an HLA-Cw6 allele. However, only 5-10% of patients that have the HLA-Cw6 allele develop psoriasis (Barker 1991), so other factors are involved. One predisposing factor seems to be infection with Streptococcus, but the relationship between this and HLA-Cw6 is not known. Freimer: Most of the strong associations seem to make sense in terms of immune responses, but how can you explain the association with narcolepsy? J. Bodmer: Narcolepsy is associated with DR2 in Caucasoid populations, but not in Black populations (Tiwari & Terasaki 1985). The DQ associated with DR2 also seems to be present in Caucasoids. The mechanism is unknown.

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Freimer: Linkage disequilibrium between DR2 and a tightly linked narcolepsy susceptibility gene may be involved. W. Bodmer: The association between HLA and psoriasis is strong. There are only a few other genes in that part of the class HLA I1 region, but in the HLA class I region there’s still a lot of room for more genes. For instance, the psoriasis and haemochromotosis associations can be explained by linkage disequilibrium with variations in a perhaps as yet unidentified gene. It’s difficult to do that for the narcolepsy association. The people working on this should look at it from the other way around and look for immune mechanisms in narcolepsy.

References Barker JNWN 1991 The pathophysiology of psoriasis. Lancet 338:227-230 Cameron PU, Mallal SA, French MA, Dawkins RL 1990 Major histocompatibility complex genes influence the outcome of HIV infection. Ancestral haplotypes with C4 null alleles explain diverse HLA associations. Hum Immunol 29:282-295 Hill HFH, Hill AGS, Bodmer JG 1976 Clinical diagnosis of ankylosing spondylitis in women and relation to presence of HLA-B27. Ann Rheum Dis 35:267-270 Kidd KK, Bernoco D, Carbonara AO, Daneo V, Steiger U, Ceppellini R 1977 Genetic analysis of HLA-associated diseases: the ‘illness susceptible’ gene frequency and sex ratio in ankylosing spondylitis. In: Dausset J, Svejgaard A (eds) HLA and disease. Munksgaard, Copenhagen, p 72-80 Tiilikainen A, Lassus A, Karvonen J, Vartiainen P,Julin M 1980 Psoriasis and HLACw6. Br J Dermatol 102179-184 Tiwari JL, Terasaki PI 1985 HLA and disease associations. Springer-Verlag, New York

Cladistic approaches to identifying determinants of variability in multifactorial phenotypes and tt e evolutionary significance of var ation in the human genome Alan R. Templeton Department of Biology, Washington University, St. Louis, MO 63130-4899, USA

Abstract. Genetic surveys based on detailed restriction site mapping o r DNA sequencing allow one to identify many different classes of mutational change at the molecular level and to estimate the evolutionary history of the genetic variation (a haplotype tree). These two sources of information can be combined in a powerful fashion to test hypotheses about the evolutionary significance of genetic variation and to identify mutations that are associated with diseases. Hypotheses about selection on various classes of genetic variation can be tested by examining the distribution patterns of different mutational classes upon the haplotype tree. The power of this procedure can be enhanced if it is coupled with comparative data from other, closely related species. With respect to disease associations, all mutations that affect phenotypic variation in a population occurred at some point in the evolutionary history of the region of the gene containing the mutations. Even if this evolutionary history is estimated from mutations other than those causing phenotypic effects, the phenotypically important mutations are imbedded in this same evolutionary history. Hence, whole branches (clades) of the haplotype tree should display homogeneous phenotypic effects and this fact is utilized to search for phenotypic associations of haplotypes by using nested clades in a haplotype tree. This procedure has more power than alternatives that do not use evolutionary history, and it avoids several statistical and interpretative problems associated with single-marker analyses. All of these methods could be used more extensively if more human genetic surveys concentrated on greater genetic resolution in small DNA regions and included non-human apes.

1996 Variation in the human genome. Wiley, Chichester (Ciba Foundation Symposium 197) p 259-283

The first applications of molecular techniques to population genetic surveys revealed large amounts of genetic variation in coding sequences (Lewontin & Hubby 1966) that set off a prolonged and often heated debate over the 259

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phenotypic and selective significance of this variation. The issue of the significance of genetic variation in natural populations, including humans, still remains largely unresolved, prompting Lewontin (1991) to wonder, with a 25year perspective, whether the application of protein electrophoresis was a milestone or a millstone in evolutionary genetics. Modem molecular techniques have revealed even more variation, thereby accentuating both the opportunities and the difficulties of interpreting the significance of genetic variation beyond the level of protein electrophoresis. However, it would be a serious mistake to regard the molecular techniques as providing only a quantitatively higher resolution than that provided by protein electrophoresis. Instead, the molecular genetic survey techniques provide qualitatively different information, which can help transform the millstone into a milestone. In particular, there are two new qualitative advantages of using molecular genetic survey techniques. First, protein electrophoresis could only detect a subset of amino acid replacement mutations in protein coding sequences, whereas molecular techniques allow the analysis of the entire genome. Consequently, mutations that can have profound phenotypic and clinical impacts, but are not amino acid replacements (Berry et a1 1992, Orkin & Kazazian 1984), can be detected and studied directly. The second qualitative advantage of using molecular genetic survey techniques is that they provide genealogical information about the nature of genetic variation. A genetic survey using protein electrophoresis reveals the number of alleles and their frequencies, whereas restriction site mapping or DNA sequencing allows the estimation of evolutionary relationships among alleles in the form of an allele or haplotype tree (Templeton et a1 1992).Hence, a survey of current genetic variation can also reveal a temporal dimension for that variation. These two advantages are not independent. Rather, they can and should be integrated into a single analytical framework for studying the evolutionary and phenotypic significance of genetic variation in the human genome. The utility of this mutational/genealogical approach will be illustrated by several worked examples using both human and non-human data. Non-human data must be used because current strategies for studying human genetic variation often do not lead to data sets for which some of these new analytical approaches can be used. Hence, it will be argued that important strategies for understanding the significance of genetic variation are being underutilized or even totally ignored in human genetics, and that some altered data-gathering strategies should be implemented that could greatly enhance the value of surveys on human genetic Variation.

General methods In all the examples given in this paper, genetic surveys are performed using either restriction site mapping or DNA sequencing to identify distinct

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haplotypes (alleles) in a small DNA region and their respective frequencies within the surveyed population. An evolutionary tree is then estimated from the haplotype data using the algorithm of Templeton et a1 (1992). This algorithm first assesses the data for evidence of recombination. If recombination is rare relative to mutational changes, then the handful (if any) of recombinant haplotypes is first identified and then excluded from the subsequent estimation of the evolutionary history of the remaining haplotypes. If recombination is more frequent in the surveyed DNA region but still much less common than mutational changes, the DNA region can be subdivided into smaller regions such that recombination within each subregion is very rare to non-existent. In those cases, separate evolutionary histories are estimated for the haplotypes defined by the genetic variation found only in a particular subregion. If recombination is common relative to mutational events, then the evolutionary history cannot be reconstructed in a reliable fashion, and the techniques outlined in this paper are inapplicable. Given that recombination is sufficiently rare in the DNA region (or subregions) being surveyed, the algorithm of Templeton et a1 (1992) next assesses the limits of parsimony and constructs a 95% plausible set of unrooted haplotype trees. These unrooted trees or networks indicate the specific mutational events that occurred in evolution which are needed to interrelate the present-day haplotypes to one another. In some cases, more than one sequence of mutational changes are likely between two or more haplotypes. The 95% plausible set includes all linkages among haplotypes until the combined probability of all plausible linkages exceeds 0.95. Hence, this algorithm not only yields an estimator of the evolutionary tree of haplotypes, but also yields a statistical confidence set of plausible estimators. Uncertainty about the exact nature of the evolutionary tree of haplotypes is explicitly incorporated into all analyses. Because the above tree estimation algorithm specifies the exact mutational changes that occur on each segment of the unrooted haplotype networks, the algorithm always produces a mapping of mutational types upon the evolutionary tree of genetic variation. These mappings will serve as the data in some of the analyses to be presented below. In other examples, the evolutionary tree of genetic variation is used to define a nested statistical design for the analysis of phenotypic data. This nested design is defined from the haplotype tree by using the nesting rules given in Templeton et a1 (1987) and Templeton & Sing (1993).

Evolutionary significance of genetic variation and the structure of haplotype trees One potential source of information about the evolutionary significance of genetic variation lies within the very shape and structure of the haplotype tree itself. Results from coalescent theory, the population genetic theory of the

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genealogical structure of current samples of genes, indicate that natural selection on genetic variation will bias the nature of the haplotype tree in predictable fashions under various selective regimes (Golding 1987, 1992, Kaplan et a1 1991, Takahata & Nei 1990). Sometimes, even a simple qualitative observation is sufficient to indicate selective importance. For example, the selective importance of Tay-Sachs disease in Ashkenazi Jews has long been controversial. However, recent molecular genetic surveys revealed a high frequency of at least two different Tay-Sachs alleles of independent evolutionary origin in Ashkenazi populations, thereby making it more likely that carriers of the Tay-Sachs disease did have a fitness advantage, presumably from increased resistance to tuberculosis, which has had a particularly large impact on eastern European ghetto populations in the last few centuries (OBrien 1991). Another example of the phenotypic importance inferred from the qualitative nature of a haplotype tree is provided by the work of McNearney et a1 (1995) on sequence variation in the negative factor gene (nej) and long terminal repeat (LTR) loci of the human immunodeficiency virus HIV 1, as surveyed longitudinally during the course of disease progression in four HIVl-infected subjects. Although the precise role of the nef locus is not known, it had been hoped that an attenuated live vaccine could be produced by engineering an HIVl virus that was defective for nef. The phenotypic importance of nef once an HIVl infection is established can be studied by genealogical studies as the virus evolves within a patient during the course of an infection. Accordingly, McNearney et a1 (1995) constructed haplotype trees for the sequence variation found in the nefand nearby (and partially overlapping) LTR gene regions in four subjects. One qualitative feature of the resulting haplotype trees was indicative of the in vivo functional importance of nefand casts serious concern over using a live vaccine with a nef deletion: in three out of four patients, spontaneous deletions of nef occurred after infection. Moreover, lineages with a nef deletion persisted as evolving, replicating and diversifying lineages within the patients. Because these deletion lineages always coexisted with lineages containing nef, it is possible that the deletion lineages can only persist and replicate in the presence of nondeletion lineages. Nevertheless, it is clear that viruses lacking a functional nef gene can and do replicate in vivo, and are able to persist throughout all stages of the disease. This observation alone gives pause to the nef-deleted, attenuated live vaccine strategy. However, more sophisticated, quantitative studies of evolutionary and phenotypic significance are possible if the haplotype tree is well resolved and many mutations of various categories have been included in the genetic survey. McNearney et a1 (1995) have performed a detailed analysis of how mutational changes overlay the intrasubject haplotype trees. Many small, in-frame deletions and insertions, as well as non-synonymous nucleotide substitutions

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that were predicted to alter nef or LTR function based upon biochemical models, occurred during the course of the evolution of the nefLTR gene region. For example, Fig. 1 portrays the estimated haplotype tree for the HIV 1 sequences taken from one of the subjects. The distribution of mutations potentially affecting Nef function and the LTR (involved in transcription termination during viral replication) is also shown in Fig. 1 in the clades (branches) of this haplotype tree that retain the nefregion. By using the ability of sequence data to determine all classes of mutations, coupled with the models of biochemical function of proteins and transcription regulation, it is possible to classify mutations into four categories: potentially functional (disruptive) mutations in n e j other mutations in nef that should have little or no phenotypic effect; potentially functional (disruptive) mutations in LTR; and other mutations in LTR that should have little or no phenotypic effect. If selection is maintaining the functional properties of the nef and LTR regions, those mutations that potentially disrupt function should be selected against and therefore be found preferentially on shorter branches in the haplotype tree; that is, branches bearing a deleterious mutation should either replicate less or persist for shorter periods of time, leading to fewer accumulated nonfunctional mutations. However, if the function of the gene region is no longer needed and all the mutations are now neutral, then potentially functional mutations should be distributed across branches in a fashion that reflects the overall branch length distribution. Because of sample size considerations, branch lengths were divided into two categories: less than or equal to three mutational steps, and greater than three mutational steps. Table 1 gives the resulting x2 tables for testing the null hypothesis that potentially functional mutational categories for both the nef and LTR regions are randomly distributed across these branch length categories (details are in McNearney et a1 1995). As can be seen, the null hypothesis is not rejected for nef, but it is strongly rejected at the 0.5% level for LTR. Hence, the LTR region is under stabilizing selection throughout the course of HIV 1 infection to maintain its original function (transcription termination), but the hypothesis of neutrality of the nefregion cannot be rejected during HIV 1 infection even for functional mutations. The conclusion of neutrality of nefgiven an infection indicates that the nef function is not under strong stabilizing selection once the virus has become established in the subject, and this in turn recommends strongly against using nef-depleted attenuated live viruses as vaccines against HIV 1 infection. The above example illustrates how information about various classes of mutational types can be combined with gene genealogical data to test hypotheses about selective significance with simple and straightforward statistical procedures. Although simple, such tests are generally not applicable to human DNA sequence data. It is necessary to either sequence or create a thorough, detailed restriction site map of the region (e.g. Golding 1987). This

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I1 Mutational Change

0 Potentially Biochemically Important Mutations in the LTR

A

DPotentially Biochemically Important Mutations In nef

FIG. 1. Estimated haplotype tree for a subject infected with HIV1. The numbers in boxes refer to the clone number designations given in McNearney et a1 (1995). The dashed lines occur a t the ends of segments whose exact lengths are uncertain because of probable deviations from parsimony. Because of these deviations, there is some uncertainty as to how these segments connect to adjoining segments, with the probable limits of uncertainty being shown by the points of attachments of the dashed lines to the segments. The dashed lines that create closed loops within the cladogram represent areas of ambiguity where several equally parsimonious alternatives exist. Boxes in the haplotype tree indicate large deletions in the negative factor gene (nej), with brackets indicating the places in the tree on either side of the deletion where the deletion could have occurred in the tree. The evolutionary location of potentially important mutations in the long terminal repeat (LTR) and nef loci are also indicated on this diagram. The time periods of the isolation of the clone from the subject are indicated by the letters on the right side of the figure. A, earliest time after seroconversion; B, intermediate time after seroconversion; C, latest time after seroconversion.

would reveal many different polymorphic mutations of various classes and would produce a highly resolved haplotype tree that provided detailed topological and branch length information. Unfortunately, much of the effort driving the Human Genome Project is directed towards creating a map that will allow linkage studies. Consequently, there is a strong tendency to find only a handful of polymorphic markers, which are necessary for providing a mapping site, in a particular region and then to move onto another region. For the mutational/genealogical strategy to work, it is necessary to study a small DNA region in great genetic detail. Although this is commonly done in other

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TABLE 1 x2 goodness of fit tests for the null hypotheses that the occurrence of a mutation of a specified type has no impact on branch length in the intrasubject HIVl haplotype tree LTR all"

Number on branch lengths < 3 Observed 12 frequency Expected 6.03 frequency x2 valueC 8.15 (P= 0.0043)

Nef all Number on branch lengths > 3

Number on branch lengths 3 8

10.89

2.80 (P = 0.0943)

PAllpotentially functional and insertion/deletion mutations in the LTR region. bAll potentially functional and in-frame insertion/deletion mutations in the ne/ region. 'All xz square tests have one degree of freedom. More details are given in McNearney et al(l995).

organisms (e.g. HIV 1 as shown above, and Drosophila melanogaster as shown in Golding 1987), this is not a common practice in human genetics. Consequently, this simple yet powerful method of assessing evolutionary significance will not play an important role in the study of human genetic variation until the genetic survey strategies for studying humans are radically altered. Interspecific and intraspecific patterns of genetic variation In the statistical methodology outlined above, different classes of mutations are contrasted with respect to their distribution within an intraspecific haplotype tree. However, the genealogical history of a gene or DNA region extends beyond the boundaries of a species. Thus, it is also possible to characterize how different mutational classes are distributed within the intraspecific versus interspecific portion of a haplotype tree when samples from more than one species are surveyed (see Fig. 2). In common with the methods given in the previous section, sometimes the mere observation of qualitative features of the interspecific versus intraspecific aspects of the haplotype tree can be powerful indicators of natural selection and phenotypic significance. For example, the observation that different allelic lineages at the major histocompatability complex (MHC) locus have remained polymorphic within the human lineage all the way back until humans shared a common ancestor with Old World monkeys is strong evidence that some sort of polymorphic-maintaining selection has occurred at this locus within humans (Satta et a1 1994, Slade &

Templeton

266 SPECIES 1

SPECIES 2 -CURRENT

VARIATION -

POLYMORPHIC MUTATIONS

FIXED MUTATIONS

FIG. 2. Hypothetical interspecific/intraspecifichaplotype tree.

McCallum 1992, Zhu et a1 1991). However, more subtle selective effects can also be detected by simple quantitative analyses when the intraspecific versus interspecific distributions of mutations are contrasted ?against different mutational classes (Templeton 1987). When this approach was first proposed in 1987, few data sets existed that were amenable to intraspecific/interspecific contingency analyses of mutational classes. The contingency approach did not gain popularity until its application by McDonald & Kreitman (1991), by which time there were many data sets appropriate for such an analysis. The following example illustrates the contingency approach using data from Ruvolo et a1 (1993). Ruvolo et a1 (1993) studied the molecular evolution of the gene encoding mitochondria1 cytochrome oxidase subunit I1 (COZr) in humans

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Homo sapiens

Pan troglodfles

Pan paniscus

Ptrl Hsa5

I

Hm6

1

I

-

11 Mutational Change First and Second Codon Position Substitutions

- Third Codon Position Substitutions

FIG. 3. The interspecific/intraspecifichaplotype tree estimated from data given in Ruvolo et a1 (1993) for COZZ sequences from humans and two species of chimpanzees (Pan troglodytes and Pan paniscus). Thin lines indicate mutations in the first or second codon positions, and thick lines indicate third codon position substitutions. The haplotype designations are those given in Ruvolo et a1 (1993).

and apes. Figure 3 shows a tree of the COZZ DNA sequences from six humans, three common chimpanzees (Pan troglodytes) and three pygmy chimpanzees (Panpaniscus). The algorithm of Templeton et a1 (1992) reveals that there is some uncertainty about the exact branch lengths of the interspecific portions of the haplotype tree, but only the most parsimonious reconstruction is used in the following analyses. Two classes of mutation can be distinguished:

Templeton

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TABLE 2 Contingency table of the evolutionary position in the COZZ haplotype tree (intraspecific polymorphic versus interspecific fixed) of mutations in two different categories: first or second codon substitutions versus third codon position substitutions. The evolutionary positions are determined from the phylogeny given in Fig. 3 Substitution

Polymorphic

First or second codon Third codon

16

6

Fixed

7 60

substitutions in the first and second codon positions, which almost always lead to amino acid replacements; and substitutions in the third codon position, which are almost always synonymous substitutions. If both types of mutation were neutral, they should occur in equal proportions in the fixed and polymorphic categories. As discussed in Templeton (1987), the hypothesis of neutrality can be tested by constructing a contingency table and testing the null hypothesis of homogeneity. In this case, a 2 x 2 table is obtained (Table 2). A standard contingent test on these data yields a x2 value of 3.76 with one degree of freedom. The probability of such a result under the null hypothesis is 0.053, and using a Fisher’s exact test the probability is 0.061. Hence, the hypothesis of homogeneity (and thus of neutrality) is not rejected at the 5% level. Table 2 shows that there is a tendency for first and second codon mutations to be relatively more abundant in the polymorphic section of the haplotype tree than in the fixed section. Hudson (1993) pointed out that these kinds of tests are cross-categorical tests of mutational classes versus positions in the haplotype tree. There is no reason, therefore, for these tests to be limited to 2 x 2 tables-one could easily have more than two mutational classes (e.g. Templeton 1987) and/or more than two positions in the haplotype tree. Moreover, this type of interspecific/ intraspecific test could easily be combined with the intraspecific tests outlined in the previous section. For example, in Table 3 the position of mutations on the haplotype tree is categorized as being in one of three categories: (1) fixed interspecific differences; (2) intraspecific polymorphisms on tip branches; and (3) intraspecificpolymorphisms on internal (intraspecific internodal) branches. Given that tip branches tend to be strongly associated with the most recent mutations (Castelloe & Templeton 1994), this expanded table accentuates the trend observed in Table 2 of most amino acid replacements, having only a short evolutionary existence relative to third codon position mutations. Because of the sparseness of the data, the data in Table 3 are analysed with an exact permutational test (Templeton & Sing 1993), which yields a probability level of 0.046. Hence, the null hypothesis is now rejected at the 5 % level, illustrating that using greater biological resolution in defining

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TABLE 3 Contingency table of the evolutionary position in the COZZ haplotype

tree

(intraspecific tip, intraspecific interior and interspecific fixed) of mutations in two different categories: first or second codon position substitutions versus third codon substitutions. The evolutionary positions are determined from the phylogeny given in Fig. 3 Substitution

Tip

First or second codon Third codon

12

6

Interior

Fixed

0 4

7 60

evolutionary categories has resulted in increased statistical power. Assuming that the third codon mutations are more likely to be neutral, these results imply that first and second codon mutations tend to have a short evolutionary existence within species and are unlikely to become fixed, thereby implying that most replacement substitutions in this gene are deleterious. Given the extremely small intraspecific samples, it is remarkable that this relatively simple procedure could yield significant results with such small sample sizes. These categorical contrasts provide a statistically simple yet biologically powerful and robust method (Hudson 1993) of determining the overall phenotypic or evolutionary significance of a class of mutations. There is no reason why this approach cannot be used to assess the significance of genetic variation in humans because the appropriate outgroup species needed for the interspecific contrasts exists, as illustrated by the COZZ example. Once again, however, this method has not been applied generally to human data for the reasons given in the previous section and because of a lack of appropriate interspecific data. If human genetic surveys produced more highly resolved examinations of small DNA regions and included comparable samples from one or more non-human ape species, the ability to study the evolutionary significance of genetic variation in humans would be greatly enhanced. As long as human genetic surveys are dominated by a ‘marker’ mentality, there is virtually no power to assess the functional significance of human variation.

Cladistic analyses of the genotypelphenotype relationship Another use of haplotype trees is to identify and localize within the haplotype tree the specific mutations that are associated with phenotypic change or disease risk. The rationale behind such a localization is simple: any mutation that causes a significant change in phenotype arose during the evolutionary process and hence is imbedded in the same historical framework defined by all mutations in the same DNA region (providing recombination is sufficiently rare). Hence, if that evolutionary history is estimated from all or a subset of the mutations in this DNA region, then whole branches (clades) of the haplotype tree should display similar phenotypic effects. This phenotypic clustering by

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clades should occur even if the causative mutation(s) were not used to construct the haplotype tree because the association arises from the shared evolutionary history in the small DNA region. Clades showing homogeneous phenotypic effects can be identified by first transforming the haplotype tree into a nested design in which haplotypes that are one mutational step from one another are nested into one-step clades, one-step clades into two-step clades, etc., using nesting rules described in Templeton et a1 (1987) and Templeton & Sing (1993). The nesting design allows the investigator to look systematically at larger and larger clades within the haplotype tree and to test the phenotypic associations of all mutational transitions within the haplotype tree. Depending on the type of data (continuous versus categorical), type of genetic situation (haploid, which includes hemizygous and the case in which one allele is common and therefore constitutes a nearly fixed genetic background, versus diploid) and type of sampling (general population versus case-control) a variety of statistical procedures can be coupled to the nested design to test the null hypothesis of phenotypic or disease risk homogeneity of all clades (Templeton 1995, Templeton et a1 1987, 1988, 1992, Templeton & Sing 1993). When the null hypothesis is rejected, the nested analysis further allows the localization of the significant phenotypic change within the haplotype tree; that is, the analysis identifies the mutational transition(s) in the haplotype tree that are most strongly associated with the phenotypic transition@). A more common strategy for detecting phenotypic associations in human genome studies is to find a polymorphic marker and contrast the phenotypes of individuals bearing the marker with those that do not. Multiple single-marker analyses are commonly used when there are several markers in a single DNA region. For example, Chartier-Harlin et a1 (1994) scored genomic DNA at five marker locations in the APOE and APOCI loci (which encode the apolipoproteins apoE and apoCI, respectively) in the 19q13.2 chromosomal region in two case-control samples for early-onset and late-onset Alzheimer’s disease. Chartier-Harlin et a1 (1994) then performed multiple single-marker marginal analyses and found significant associations with the markers in APOE (both the ~2 and the ~4 alleles) and APOCI and with an anonymous (CA), repeat marker (DZ9SZ78) that is most likely located between these two loci. Given the success of this strategy in identifying disease associations, why go to the extra trouble of estimating a haplotype tree from the markers and performing a cladistic analysis? There are several reasons why the cladistic approach is preferred over the multiple single-marker approach. Firstly, because of a shared evolutionary history, much linkage disequilibrium exists in small DNA regions; that is, the distribution of the markers is not independent in the population. Hence, multiple single-marker tests are not statistically independent of one another, so that when the null hypothesis of no association is rejected for more than one marker (as is the case in Chartier-Harlin et a1 1994), it is not clear if that

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rejection is truly associated with the marker under study, or due to linkage disequilibrium with one of the other markers or some undetected mutation. For example, Templeton (1995) estimated a haplotype tree from the data given in Chartier-Harlin et a1 (1994) and performed a cladistic analysis upon risk for Alzheimer’s disease. The results are shown in Fig. 4. Two significant phenotypic transitions for early-onset and all-onset risk (the sample sizes for late-onset were too small for localization of the phenotypic transitions) were detected, thereby dividing the haplotype tree into three risk categories for Alzheimer’s disease (high, intermediate and low). However, no change in risk is associated with any of the mutational changes at the D19S178 marker or with the APOE ~4 allele. Instead, the significant marginal associations that Chartier-Harlin et a1 (1994) reported for the D19SI78 marker and for the APOE ~4 allele are due to linkage disequilibrium with the other mutations actually associated with phenotypic transitions. These conclusions have clinical significance. For example, given the genetic state at the APOE and APOCI markers, it is worthless to score patients for the D19SZ78 marker as it adds no new information. In contrast, as shown in Fig. 4, both the APOE ~2 to ~3 transition and the APOCI marker provide independent information about the risk of Alzheimer’s disease, and hence both should be scored for accurate risk prediction. Identifying independent genetic sources of phenotypic information arises automatically from cladistic analyses, but it is impossible for multiple single-marker analyses to distinguish between redundant and independent sources of information. A second reason for preferring a cladistic analysis is that it can detect multiple phenotypic transitions. A single-marker approach is inherently bivariate, but phenotypes typically come in more than two categories. When this occurs, single-marker analyses are inherently incapable of detecting the full range of phenotypic heterogeneity. For example, the only effects that ChartierHarlin et a1 (1994) could detect even in principle with their single-marker tests is whether or not a particular marker is associated with increased or decreased risk of developing Alzheimer’s disease. For example, they concluded that the APOCI marker can divide people into higher and lower risk categories. The cladistic analysis shown in Fig. 4 also identifies the APOCI marker as being associated with a significant change in the risk of Alzheimer’s disease. Thus, the APOCZmarker does indeed provide independent information about the risk of Alzheimer’s disease from the APOE locus, but the cladistic analysis shows that the phenotypic effects associated with this marker must be placed into the context of the overall haplotype tree and all of the phenotypic associations found in that tree. Dividing people into two categories depending upon their genetic state at the APOCI marker divides people into an intermediate risk category (bearers of APOCI 1 allele) and into people with both the highest and lowest risk (bearers of APOCZ 2 allele). Note that this locus does not divide people into high and low risk categories, as mistakenly implied by the marginal

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1-1 2&44

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All Onsets

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Onsets

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Onsets

FIG. 4. The haplotype tree estimated for the APOEIAPOCZ DNA region from the data given in Chartier-HarEn et a1 (1994). Because of multiple changes in the length of the D19S178 (CA), repeat, mutations at this marker cannot be located unambiguously in the haplotype tree (see Templeton 1995 for details). Accordingly, arrows indicating mutations at other markers are connected to the middle of the arrows indicating D19S178 (CA), S/L transitions. Boxes enclose haplotypes that are nested together to form one-step clades, as designated by the notations 1-1, 1-2, 1-3 and 1-4. The significant phenotypic localizations for both early-onset and all-onset are indicated by asterisks. These two significant changes in the risk of Alzheimer’s disease (AD) divide the haplotype tree into three different risk categories (clade 1-4, clade 1-3, and clades 1-1 and 1-2 combined). The histograms summarize the Alzheimer’s disease risk data for these three branches of the haplotype tree.

analysis. Hence, the APOCI marker does indeed provide independent information about the risk of developing Alzheimer’s disease, but because of multiple phenotypic categories, that information can only be used in an accurate fashion by considering the results of the entire cladistic analysis. If that marker was used by itself as a predictor of the risk of developing Alzheimer’s disease,

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many people would be given incorrect advice because this marker lumps together those individuals with both the highest and lowest risk into a single genetic category. This inability of multiple single-marker analyses to detect multiple phenotypic transitions not only lowers the statistical power of this procedure to detect phenotypic associations relative to the cladistic approach, but also means that this approach to disease association is medically indefensible as it is frequently misleading. A third reason for preferring the cladistic analysis is that the marginal singlemarker tests tell one nothing about causative mutations, whereas the cladistic analysis at least eliminates many mutations as putative causes. It is important to keep in mind that all of these tests, including the cladistic analysis, are association tests and do not provide direct information about the mutations that are causative of phenotypic changes. Nevertheless, there has been a tendency in the literature to equate markers with strong associations as being causative. For example, a strong marginal association of the risk of developing Alzheimer’s disease with the APOE ~4 allele has been detected in several studies (Chartier-Harlin et a1 1994, Corder et a1 1993, Lucotte et a1 1994, Yu et a1 1994, Zubenko et a1 1994), and indeed this conclusion of a strong marginal association of the risk of developing Alzheimer’s disease with the APOE ~4 allele is also consistent with the cladistic analysis shown in Fig. 4 (Templeton 1995). Because of this strong association, there has been much speculation that the APOE ~4 allele somehow causes Alzheimer’s disease (e.g. Ma et a1 1994, Nathan et a1 1994, Strittmatter et a1 1994). However, the results of the cladistic analysis shown in Fig. 4 indicate that the mutation that creates the APOE ~4 allele is not associated with increased risk of Alzheimer’s disease (at least for early-onset cases), so that this allele cannot be causative for early-onset Alzheimer’s disease. Figure 4 shows that the cladistic analysis can eliminate many if not most of the mutations in the candidate DNA region as being causative simply by localizing the significant phenotypic transitions within the haplotype tree. However, a significant cladistic association does not imply causation. For example, Fig. 4 indicates that the APOE ~2 allele is associated with a decreased risk of Alzheimer’s disease, but that does not mean that the APOE ~2 allele causes a decreased risk of Alzheimer’s disease. Moreover, it does not even imply that the causative mutation is physically close to the mutation that creates the APOE 82 allele or even in the APOE locus. When dealing with DNA regions with little recombination, the pattern and magnitude of linkage disequilibrium is determined more by the temporal order of mutations as reflected in the haplotype tree than in physical distance (Heizmann et a1 1991, Zerba et a1 1991), making it virtually impossible to map accurately the physical location of the causative mutation through disequilibria with markers (Hill & Weir 1994). The cladistic approach does provide at least a temporal localization of the causative mutation, and it indicates that more detailed

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Templeton

analyses of the haplotypes that straddle the phenotypic transition in the cladogram would be the best strategy for identifying the causative mutation. This approach was recently taken by Friedlander et a1 (1995) in studies on plasmid lipid responses to dietary challenges at the APOB locus. They scored four genetic markers in this region that defined six haplotypes. A cladistic analysis indicated that a major transition in dietary response occurred between haplotypes 1 and 3. Accordingly, they sequenced within the low density lipoprotein receptor (LDLR)-binding domain of the APOB gene in individuals homozygous for these two haplotypes. Sequence differenceswere not observed, thereby indicating that mutations in the LDLR-binding domain of APOB cannot be causative of the altered dietary responses. Another example of this approach is given by the work of Maruyama & Hart1 (1991) on trans activation of mariner transposable elements in Drosophila. Several mariner elements were sequenced and scored for their ability to transpose. The sequence information was used to construct a cladogram, and the resulting cladistic analysis indicated that the ability to transpose was lost only once in the evolution of these sequences. The two haplotypes that straddled this evolutionary step differed by only two nucleotide substitutions, which were therefore identified as being the likely candidates for a functional mutation. This inference motivated the sequencing of additional mariner elements that were evolutionarily close to the haplotypes straddling the phenotypic change, and this second, more exhaustive cladistic analysis indicated that only one of these nucleotide substitutions was associated with the functional change and was, therefore, causative. This inference made completely from the cladistic analysis was compatible with the molecular effects of these two substitutiops as studied by site-directed mutagenesis (Maruyama et a1 1991). This example illustrates that cladistics offers a better strategy for identifying functional mutations than linkage disequilibria analyses of markers. Conclusions The examples given above demonstrate that much information about the evolutionary and phenotypic significance of variation in the human genome can be extracted when that variation is used to reconstruct an evolutionary history of the DNA region being surveyed. Hence, the past is often the key to understanding the present. Before this historical information can be used, it is necessary to estimate the haplotype tree, which can now be done in a quantitative fashion that explicitly calculates the statistical confidence of the estimator and can deal with limited amounts of recombination (Templeton et a1 1992). The more accurate and detailed our reconstruction of the evolutionary past, the more accurate and detailed is our analysis of the present. However, even a coarse estimate of the haplotype tree can be very useful in phenotypic association studies. Figure 4 illustrates that the nested cladistic

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analysis of haplotype/phenotype associations can be successfully executed with the low resolution genetic marker studies that are typical of surveys of human genetic variation within a small DNA region. Nevertheless, the power of the cladistic approach even for this purpose is enhanced as haplotype tree resolution increases (Templeton et a1 1987, 1988), and this increased resolution is necessary in order to reduce the number of candidate causative mutations to a small number, as shown by the work of Maruyama & Hart1 (1991). High genetic resolution surveys are also needed to execute the homogeneity and contingency type tests for general patterns of evolutionary significance of classes of variation (Fig. 1). Hence, the dominant strategy of most human genetic surveys, in which only a handful of markers in a DNA region are scored, ensures that the qualitatively new sources of information inherent in modem survey techniques cannot be utilized in a powerful fashion to test hypotheses about evolutionary significance and phenotypic association. It would be far better to have higher resolution genetic surveys, even at the expense of surveying fewer DNA regions and/or having smaller sample sizes. Moreover, if genetic surveys on chimpanzees or gorillas were performed standardly together with human surveys, the significance of the human variation could be judged far more accurately and powerfully than at present. Unfortunately, the Human Genome Project is proceeding for the most part in a manner that ignores the strength and power of the comparative method in evolutionary biology and there seems to be a lack of appreciation that genetic surveys on non-human apes would greatly enhance the utility of the human data. Thus, although the potential for gaining significant insight into the evolutionary significance of variation in the human genome and for identifying phenotypic and disease associations is thanks to molecular techniques, that potential will not be fully realized until it is coupled with the appropriate sample and genetic survey designs.

Acknowledgement This was work was supported by National Institutes of Health Grant 1 R01 HL39107.

References Berry M, Grosveld F, Dillon N 1992 A single point mutation is the cause of the Greek form of hereditary persistence of fetal haemoglobin. Nature 358:499-502 Castelloe J, Templeton AR 1994 Root probabilities for intraspecific gene trees under neutral coalescent theory. Mol Phylo Evol 3:102-113 Chartier-Harlin M, Parfitt M, Legrain S et a1 1994 Apolipoprotein E, ~4 allele as a major risk factor for sporadic early and late-onset forms of Alzheimer’s disease: analysis of the 19q13.2 chromosomal region. Hum Mol Genet 3569-574

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Corder EH, Saunders AM, Strittmatter WJ et al 1993 Gene dose of Apoliprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science 261:921-923 Friedlander Y, Berry EM, Eisenberg S, Stein Y, Leitersdorf E 1995 Plasma lipids and lipoproteins response to a dietary challenge-analysis of four candidate genes. Clin Genet 47:1-12 Golding B 1987 The detection of deleterious selection using ancestors inferred from a phylogenetic history. Genet Res 49:71-82 Golding B 1992 Estimating selection coefficients from the phylogenetic history. In: Takahata N, Clark AG (eds) Mechanisms of molecular evolution: introduction to molecular paleopopulation biology. Sinauer, Sunderland, MA, p 61-78 Heimann C, Kirchgessner T, K,witerovich PO et al 1991 DNA polymorphism haplotypes of the human lipoprotein-lipase gene: possible association with highdensity-lipoprotein levels. Hum Genet 86578-584 Hill WG, Weir BS 1994 Maximum-likelihood estimation of gene location by linkage disequilibrium. Am J Hum Genet 54705-714 Hudson RR 1993 Levels of DNA polymorphism and divergence yield important insights into evolutionary processes. Proc Natl Acad Sci USA 90:7425-7426 Kaplan N, Hudson RR, Iizuka M 1991 The coalescent process in models with selection, recombination and geographic subdivision. Genet Res 57:83-91 Lewontin RC 1991 25 years ago in genetics: electrophoresis in the development of evolutionary genetics: milestone or millstone? Genetics 128:657-662 Lewontin RC, Hubby JL 1966 A molecular genetic approach to the study of genic heterozygosity in natural populations. 11. Amount of variation and degree of heterozygosity in natural populations of Drosophila pseudoobscura. Genetics 54: 595-609 Lucotte G, Visvikis S, Leiningermuler B et a1 1994 Association of apolipoprotein E allele E-4 with late-onset sporadic Alzheimer’s disease. Am J Med Genet 54: 286-288 Ma J, Yee A, Brewer HB, Das S, Potter H 1994 Amyloid-associated proteins aIantichymotrypsin and apolipoprotein E promote assembly of Alzheimer 8-protein into filaments. Nature 372:92-94 Maruyama K, Hartl DL 1991 Evolution of the transposable element mariner in Drosophila species. Genetics 128:319-329 Maruyama K,Schoor KD, Hartl DL 1991 Identification of nucleotide substitutions necessary for trans-activation of mariner transposable elements in Drosophila: analysis of naturally occurring elements. Genetics 128:777-784 McDonald JH, Kreitman M 1991 Adaptive protein evolution at the Adh locus in Drosophila. Nature 35 1 :652-654 McNearney T, Hornickova Z, Templeton A et al 1995 Nef and LTR sequence variation from sequentially derived human immunodeficiency virus type. 1 isolates. Virology 2081388-398 Nathan BP, Bellosta S, Sanan DA, Weisgraber KH, Mahley RW, Pitas RE 1994 Differential effects of apolipoproteins E3 and E4 on neuronal growth in vitro. Science 2642350-852 O’Brien SJ 1991 Ghetto legacy. Curr Biol 1:209-211 Orkin SH, Kazazian HH 1984 The mutation and polymorphism of the human 8-globin gene and its surrounding DNA. Annu Rev Genet 18:131-171 Ruvolo M, Zehr S, von Dornum M, Pan D, Chang B, Lin J 1993 Mitochondria1 COIl sequences and modern human 8-globin gene and its surrounding DNA. AMU Rev Genet 18:13 1-1 71

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Satta Y,O’Huigin C, Takahata N, Klein J 1994 Intensity of natural selection at the major histocompatibility complex loci. Proc Natl Acad Sci USA 91:7184-7188 Slade RW, McCallum HI 1992 Overdominant vs frequency-dependent selection at MHC loci. Genetics 132861-862 Strittmatter WJ, Weisgraber KH, Goedert M et a1 1994 Hy$othesis: microtubule instability and paired helical filament formation in the Alzheimer disease brain are related to apolipoprotein E genotype. Exp Neurol 125:163-171 Takahata N, Nei M 1990 Allelic genealogy under overdominant and frequencydependent selection and polymorphism of major histocompatibility complex loci. Genetics 124967-978 Templeton AR 1987 Genetic systems and evolutionary rates. In: Campbell KSW, Day MF (eds) Rates of evolution. Allen & Unwin, London, p218-234 Templeton AR 1995 A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping or DNA sequencing. V. Analysis of case/control sampling designs: Alzheimer’s disease and the Apolipoprotein E locus. Genetics 140:40349 Templeton AR, Sing C F 1993 A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping. IV. Nested analyses with cladogram uncertainty and recombination. Genetics 134659469 Templeton AR, Boerwinkle E, Sing CF 1987 A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping. I. Basic theory and an analysis of alcohol dehydrogenase activity in Drosophilu. Genetics 117:343-351 Templeton AR, Sing CF, Kessling A, Humphries S 1988 A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping. 11. The analysis of natural populations. Genetics 120:1145-1 154 Templeton AR, Crandall KA, Sing CF 1992 A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping and DNA sequence data. 111. Cladogram estimation. Genetics 132:619633 Yu C, Payami H, Olson JM 1994 The apolipoprotein E/CI/CII gene cluster and lateonset Alzheimer.disease. Am J Hum Genet 5463 1 4 4 2 Zerba KE, Kessling AM, Davignon J, Sing CF 1991 Genetic structure and the search for genotype-phenotype relationships: an example from disequilibrium in the Apo-B gene region. Genetics 129525-533 Zhu Z, Vincek V, Figueroa F, Schonbach C, Klein J 1991 MHC-DRB genes of the pigtail macaque (Mucucu nemestrinu): implications for the evolution of human DRB genes. Mol Biol Evol 8563-578 Zubenko GS, Stiffler S, Stabler S et al 1994 Association of the apolipoprotein E E-4 allele with clnical subtypes of autopsy-confirmed Alzheimer’s disease. Am J Med Genet 5 4 199-205

DISCUSSION Clark: If there are numerous segregating sites in the data set, there are a number of independent tests that one could potentially use to establish a connection with the phenotype. The cladistic method works because it reduces the dimensionality of this problem. Chakraborty (1993) reduced the dimensionality by considering the occupancy distribution of genetic variation.

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Cladistics can fail to reduce the dimensionality of the cladogram in the following situations. (1) The cladogram may not be resolved when, for instance, strong selection distorts the tree. In particular, if every affected individual has an extremely rare mutation, every time one encounters an individual, they may have a different mutation, so that the cladistic approach may not work at the very tips of the tree. (2) It may also not work if there is rapid population growth, so that there is a founding population with distorted growth generating a star-shaped tree. (3) If there is a high rate of recombination and the linkage of disequilibrium breaks down, sites become independent and the cladistic approach may not work. People working with Drosophilu (e.g. Schaeffer & Miller 1993, Leicht et a1 1995) have been asking, what is the ratio between the recombination rate and the mutation rate? The parameters r and p are confounded with the effective population size, so that one can only estimate 4Nr and 4Np. However, those ratios should be proportional to r/p. In Drosophilu the value can be anywhere between one and 30, so the recombination rate can be of the same order as the mutation rate. It is not known whether this is also true for humans. (4) If the phenotype is caused by rare recessive alleles, it is possible to look only at haplotype trees. However, it is impossible to detect the effects in diploid genotypes, because a recessive trait cannot be seen in heterozygotes. (5) If there is strong epistasis, then no single gene tells you anything about the phenotype. Depending on the nature of the gene interactions, the cladistic approach assumes that there is a strong enough marginal effect of the locus whose gene tree you are analysing. (6) Finally, false positives may be obtained using the cladistic approach, if there is a genotype-by-environmentcorrelation. Templeton: We haven’t looked at how selection can distort the trees. I don’t know what effect it may have, but the effect could be serious. Star-shaped cladograms are not a problem to analyse. For example, Haviland et a1 (1995) have shown that the APO (apolipoprotein) AZ-CZZZ-AZV gene region has a star shaped phylogeny, but the cladistic approach was applied very well in this situation. The human mitochondrial DNA is also star shaped and again, the cladistic approach can be applied (Templeton 1993). We can also use geography as a phenotype, so that human mitochondrial DNA, for example, can be analysed to test for the ‘out of Africa replacement hypothesis’. This analysis showed that there was no ‘out of Africa replacement’ (Templeton 1993). We addressed recombination in a paper in 1992 (Templeton et a1 1992). If the recombination rate is sufficiently low, the rare recombinants are excluded from the analysis. They can then be reintroduced later to obtain a physical localization of the mutations that were detected in the standard cladistic analysis. We have done this for the region encoding alcohol dehydrogenase in Drosophilu melunoguster (Templeton et a1 1987). The rate of recombination was so high at the esterase-6 region in D. melunoguster that we had to give up that approach (Templeton & Sing 1993). We had to use the algorithm of Hein

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(1990, 1993) to subdivide the region into three smaller regions and then do cladistic analyses within those regions. This approach worked better because the mutants were both temporally and physically localized into one of the three regions. Therefore, recombination can actually increase, rather than decrease, the power of this analysis (Templeton & Sing 1993). Completely recessive rare alleles are a problem in any human genetic survey, particularly if the survey is a random sample. However, Haviland (1993) has addressed the issue of dominance in a cladistic analysis, but I won’t go into the details (Haviland 1993). Strong epistasis is a problem, but it is sometimes possible to get around that if a major locus exists. A genotype-by-environment correlation is a problem in any human genetic study. In such cases, the sampling method will have a large impact on the outcome of the analysis, regardless of the analytical technique. For example, when Haviland (1993) studied cholesterol levels and the low density lipoprotein receptor gene in her study in Quebec, she separated the French Canadians from the non-French Canadians because such a genotype-by-environment correlation could exist in that situation. Therefore, genotype-by-environment correlation and corrections for it by appropriate stratified sampling have to be taken into consideration in any human genetic study and not just in a cladistic analysis. W. Bodmer: Luca Cavalli-Sforza had the idea that heterogeneity in genes has to be evidence for selection. This idea was applied to HLA sequence data about 10-12 years ago. The ideas that you’re talking about have been used in the analysis of HLA sequence data by myself and others in the early 1980s, and Ceppellin coined the term haplotype in 1967 (see Tomlinson & Bodmer 1995). People in the HLA field have always looked for associations by studying the haplotype and not the individual. Numerous studies have used this information to demonstrate direct associations with particular alleles and whether these alleles are linked (see Histocompatibility Testing 1972 et seq, Munksgaard, Copenhagen). I have a technical comment on the question of recombination and mutation rates. In humans recombination occurs at a frequency of about 1% per lo6 base pairs, so that the recombination rate per site is about 1 in lo8. Mutation rates per base pair can also be estimated directly using mutationselection balance theory. In humans the mutation rate is about 1 in lo9 on average per base pair. Therefore, in humans the recombination rate per base pair site is about the same, and often slightly more, than the mutation rate. The one overriding selection that occurs in individuals with HIV is immunological. This immunological selection has little to do with the biochemical function, except that mutants will not be present that escape immunological detection and go against the biochemical function. Immunological selection, which can recognize any difference in the sequence of a virus that’s expressed, is the pressure that is creating variation and is what’s driving those trees within the individuals. How do you take that into account? A

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biochemical mutant that escapes the immune system cannot be selected against. Templeton: Cladistic analyses are not just haplotype analyses, they’re haplotypes put in a context of their genealogical history. This is different from just analysing the association of haplotypes with disease. We are studying immunological selection in HIV infection, which is part of the selection that drives the intra-individual gene tree diversity, but it is not the only selective pressure. W . B o h e r : It is the major form of selection. Templeton: There are other forms of strong selection. HIV adapts to different cell types in the body, so it affects not only just the immune system but also other cell types. Edward: Alzheimer’s disease is often due to one of several known dominant mutations. Therefore, you would have to define the particular mutation and its frequency before applying your methods. How do you obtain this statistic from the data you’re working on, and how do you determine whether it has anything to do with APOE? Templeton: These are not my data, they are case control data from ChartierHarlin et a1 (1994). We are looking at the distribution of various markers, haplotypes or clades in the case group versus a control group. It’s not risk in the way an epidemiologist would use that term. Risk in this case is measured by the difference in haplotype or clade frequencies in cases versus controls-that’s the way it’s presented in the literature. I’m not arguing that this tells us a lot about the probability of getting Alzheimer’s disease, given a certain marker. Edwards: Can it be used to determine the prognosis of the patients? Templeton: No, I would not use it to determine the prognosis. Chukruvarti: There are some cases where cladistic analysis may not be informative, such as for rare selected genes. If one could identify haplotypes for chromosomes in patients, the relationship that one gets from the genealogy of these patients within pedigrees is precisely the information that leads to their mapping as well as the isolation of where the mutant gene would be. The problem of recombination is related to the size of the pedigree, and it’s only when one goes to the limit and looks at cases where there is no apparent gehetic relationship between patients that cladistic analysis becomes useful. In populations, such as the Mennonites or Amish, the genealogy provides precisely the information that one is trying to reconstruct. Templeton: I agree. I also would like to clarify that although we are using primarily random sampling designs in our cladistic analyses, I’m not saying that the other sampling designs are inappropriate. I’m arguing that we ought to start incorporating these as additional sampling designs in human genetics because they have potential that isn’t being realized. Chukravarti: Human geneticists do think within that framework, it’s just that they do not know the genetic relationship between cases and controls.

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Cladistic analysis is one way to impose genetic relationships among haplotypes, as assessed from genetic variation in the region. If you look at mutations at those sites that have genetic variation and compare them with the total amount of recombination across the region that you’re studying, does the analysis of additional markers help or hinder? Does the phylogeny become more complicated? Templeton: One interesting aspect of the cladistic approach is that the more markers you have, the better it works. I support the intensive sampling of small DNA regions because the more finely resolved the cladogram, the more power is obtained. Subdivisions of the data are not disadvantageous because the nested design gives an automatic pooling procedure. Therefore, that power will be recovered as one goes up through the nesting hierarchy. Kidd: It’s now possible to define the phylogeny of human haplotypes that determines the ancestral haplotype and the direction of the mutation generating every polymorphic site being evaluated by looking at the ancestral state in chimpanzees, gorillas and orang-utans using PCR-based techniques. We are currently doing this for several of the haplotypes we’re studying (e.g. Castiglione et a1 1995). However, there is a point of diminishing return in terms of additional markers, at least in our experience with human haplotypes, because within any given population, and indeed maybe even within the whole species, there may only be four common haplotypes. Despite the structure of the cladogram, the markers and the complexity, if there are only four haplotypes currently existing in the population, each haplotype must be analysed independently. Chakravarti: In most regions a few of the haplotypes must be related to the nature of the population. However, most of these genetic studies are done in mixed populations over a 40-50 kb region. Therefore, there are many different haplotypes, some of which are rare, so its difficult to find out what those haplotypes are. Kidd: Most of the regions we are studying are about 20-30 kb, but we find that in populations distributed all around the world, especially the non-African populations, there are only a few common haplotypes. However, each population has several additional haplotypes that are rare, so that if you’re studying any common phenomena those rare ones are just like random noise. Templeton: No, they’re not. The mitochondria1 data also fall into this category. Rare haplotypes often have more information than the common haplotypes. Common haplotypes tend to be old because of gene flow, and they may be the ones that have spread out and lost the geographical information. Weiss: You mentioned that the star-shaped phylogeny can be analysed, but doesn’t that amount to a marginal analysis when each haplotype is independent? If there is no nesting, what’s the advantage? Templeton: Most star-shaped phylogenies do have some substructurethat is, they are not completely star shaped-and they therefore have some

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non-redundant nesting categories. In the case of a truly star-shaped phylogeny, where there is one central haplotype from which all other haplotypes branch off, there is only one nesting level, so there are no nesting hierarchies. Nevertheless, even in this case, the cladistic approach is useful because it defines the appropriate contrasts within this single level of nesting. For example, a pure star-shaped phylogeny was analysed by Haviland et a1 (1995). There were seven haplotypes in this phylogeny, so there are 21 possible pairwise contrasts among the haplotypes. However, from an evolutionary point of view, the cladistic design suggests that only six of these contrasts are meaningful: the six involving evolutionary neighbours (that is, the central haplotype contrasted with the six peripheral haplotypes). There are also six degrees of freedom, and the number of degrees of freedom always equals the number of meaningful evolutionary contrasts in any cladogram. Therefore, the cladogram gives the optimal statistical design, even for a star-shaped phylogeny.

References Castiglione CM, Deinard AS, Speed WC et a1 1995 Evolution of haplotypes at the DRD2 locus. Am J Hum Genet, in press Chakraborty R 1993 A class of population genetic questions formulated as the generalized occupancy problem. Genetics 134:953-958 Chartier-Harlin M, Parfitt M, Legrain S et a1 1994 Apolipoprotein E, ~4 allele as a major risk factor for sporadic early and late-onset forms of Alzheimer’s disease: analysis of the 19q13.2 chromosomal region. Hum Mol Genet 3569-574 Haviland MB 1993 A cladistics approach to the genetic analysis of human quantitative data. PhD thesis, University of Michigan, Ann Arbor, MI, USA Haviland MB, Kessling AM, Davignon J, Sing CF 1995 Cladistic analysis of the apolipoprotein AZ-CZZZ-AZY gene cluster using a healthy French Canadian sample. I. Haploid analysis. Ann Hum Genet 59:211-231 Hein J 1990 Reconstructing evolution of sequences subject to recombination using parsimony. Math Biosci 98: 185-200 Hein J 1993 A heurestic method to reconstruct the history of sequences subject to recombination. J Mol Evol 36:396-405 Leicht BG, Muse SV, Hanczyc M, Clark AG 1995 Constraints on intron evolution in the gene encoding the myosin alkali light c h i n in Drosophila. Genetics 139:299-308 Schaeffer SW, Miller EL 1993 Estimates of linkage disequilibrium and the recombination parameter determined from segregating nucleotide sites in the alcohol dehydrogenase region of Drosophila pseudoobscura. Genetics 135541-552 Templeton AR 1993 The ‘Eve’ hypothesis: a genetic critique and reanalysis. Am Anthropol95:51-72 Templeton AR, Sing CF 1993 A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping. IV. Nested analyses with cladogram uncertainty and recombination. Genetics 134659469 Templeton AR, Boerwinkle E, Sing CF 1987 A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping. I. Basic

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theory and an analysis of alcohol dehydrogenase activity in Drosophila. Genetics 117~343-351 Templeton AR, Crandall KA, Sing CF 1992 A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping and DNA sequence data. 111. Cladogram estimation. Genetics 132:619-633 Tomtinson IPM, Bodmer WF 1995 The HLA system and the analysis of multifactorial genetic disease. Trends Genet 11:493498

Quantitative phenotype analysis for localization and identification of disease-related genes in a complex genetic background G. M. Lathrop The Wellcome Trust Centre for Human Genetics, University of Oxford, Windmill Road, Oxford OX3 7BN, UK

Abstract. Results from genetic investigations of blood pressure and other variables in inbred rodent models are reviewed here to illustrate the power of quantitative approaches for the detection of linkage and the ultimate identification of the underlying genes. Different studies-involving angiotensinogen and hypertension, angiotensin I-converting enzyme and cardiovascular diseases, and other trait-are used to illustrate the possibility of similar approaches to multifactorial disorders in humans. 1996 Variation in the human genome. Wiley, Chichester (Ciba Foundation Symposium 197) p 284-299

There is increasing interest in the application of genetic methods to identify genes involved in common human diseases. Often these disorders have a multifactorial basis, involving multiple genetic and environmental susceptibility factors. In many instances, it is possible to study underlying quantitative phenotypes that are either used to define disease, such as blood pressure levels in hypertension, or are strongly related to the disease, such as IgE levels in asthma. The study of the quantitative phenotype provides a powerful approach to identifying susceptibility loci involved in these disorders. When susceptibility loci are related to a quantitative phenotype, it is usual to refer to them as quantitative trait loci (QTL). In this paper, I will review some of the results from both the mapping of QTL and the characterization of the underlying genes in multifactorial disorders, particularly in cardiovascular disease. As the field has advanced through mapping of QTL in inbred rodent models of disease, I will consider this area first before addressing the investigations in humans. There is also a large body of literature on statistical techniques for QTL mapping (see Lander 284

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& Schork 1994 or Weeks & Lathrop 1995 for review), which will not be discussed here in detail.

Quantitative variation in rodent models of human disease The power of QTL mapping for identifying loci involved in multifactorial disorders is clearly illustrated in models of human disease in the mouse or rat. The advantages in studying these models are outlined below. (1) The availability of inbred, and therefore genetically homogenous, rat or mice strains with contrasting phenotypes. These could be high and low responder strains for immune traits, or hypertensive and normotensive strains for blood pressure. The animals can be crossed experimentally to produce large F2 or backcross cohorts suitable for genetic analysis. (2) Several inbred genetic models exist for disease phenotypes such as hypertension. This allows phenotypic and genetic similarities and differences to be investigated. (3) Environmental factors can be experimentally controlled to reduce their effects on the phenotypic variance. (4) Control of environmental factors also allows gene/environment interactions to be investigated. For example, it is possible to investigate genetic factors that may be specifically associated with the effect of salt intake on b’lood pressure. ( 5 ) Physiological investigations that may be difficult or impossible to conduct in humans can be undertaken in rodents. (6) The relatively short generation time allows new inbred lines to be bred based on genetic results (e.g. congeneic lines). With the appropriate choice of strains and control of environmental and experimental variables, the genetic component of the phenotypic variance can be larger than that in human populations. For example, the genetic component of blood pressure in humans is about 30% of the total variance, whereas it may be as high 60% in some crosses involving hypertensive and normotensive rats. As the crosses are obtained from genetically homogenous lines, the QTL analysis is relatively straightforward. The calculation of location scores or interval mapping with two or more marker loci can be applied to the genetic data, but simple analysis of variance with each marker and the quantitative phenotype appears to be a robust procedure with a high power to detect linkage (see Hilbert et a1 1991 for examples). Blood pressure in hypertensive rat strains is one of the most extensively investigated quantitative phenotypes, and a rapid review of recent results will illustrate the potential of QTL mapping. Inbred strains of genetically hypertensive rats include: the spontaneously hypertensive rat (SHR) and its

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sister strain, the spontaneously hypertensive/stroke-pronerat (SHR/SP); the Lyon hypertensive (LH) rat; the Milan hypertensive rat; and the Dahl saltsensitive hypertensive rat. Although matching normotensive strains have been bred from the same original colonies in some instances, the hypertensive strains can be crossed with any well-characterized normotensive strain of inbred rats, such as WKY or Brown-Norway, to produce cohorts for genetic investigations. As a technical point, it is important to verify that the colony chosen is genetically homogenous prior to the start of breeding. It is interesting to note that some of the strains are not completely fixed for markers that have been linked to blood pressure, even after many generations of selection and inbreeding (Dubay et a1 1993, Bianchi et a1 1994). It is not known if this is fortuitous or related to biological constraints in selecting for high blood pressure. When rats from different colonies are used in replicate experiments, their genetic similarity should also be verified prior to comparing and contrasting results from different studies. Linkage studies of crosses involving many different combinations of hypertensive and normotensive control strains have now led to the detection of a large number of loci implicated in blood pressure regulation. Regions showing significant evidence of linkage to date include chromosomes 1 (two regions), 2 (two regions), 3, 4, 5, 7, 10 (two regions), 13, 14, 17, 18, X and Y. (Reviews of these results are contained in Rapp & Deng 1995 and Soubrier & Lathrop 1995. For references not in these papers consult Pravenec et a1 1995 and Davidson et a1 1995.) The apparent genetic complexity revealed by this list of loci can be reduced by focusing on those regions for which confirmation has been obtained in one or more crosses, either from different strains or simply in a second experiment. The primary regionsfor which replication has been obtainedarechromosomes 1,2,10, 13 and Y. Lack of replication may be due to false positive results in the first instance (type 1 error), or it can reflect other factors, such as insufficient testing of replicates or effectsof differentexperimentalconditions (e.g. indirect versus direct blood pressure measurements or age of the rats). On the other hand, confirmation of linkage to the same chromosome regions in different crosses, with a variety of experimental procedures, provides strong evidence that these QTL are one of the major genetic factors of blood pressure regulation. The analysis of QTL linked to blood pressure regulation has revealed many interesting patterns of phenotype associations and has led to the increased understanding of the complexity of hereditary hypertension. For example, in a cross involvingthe LH rat, Dubay et a1(1993) found that the pulsatilecomponent of blood pressure, as measured by pulse pressure (the difference between diastolic and systolic blood pressures), was linked to a marker on rat chromosome 2, whereas the steady-state component (diastolic blood pressure) segregated independently of this locus. In contrast, diastolic blood pressure but not pulse pressure was found to be linked to the renin gene on rat chromosome 13. These

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results dissect blood pressure into at least two geneticallydistinct sub-phenotypes. This is interesting from a physiological viewpoint because the mechanisms that could be responsible for increased pulse pressure are likely to be different from those involved in increased diastolic blood pressure. Comparison of linkage results may also suggest that some of the genetic factors underlying different disease phenotypes are the same. For instance, D. Gauguier in Oxford and A. Ktorza in Paris have examined glucose and insulin responses in a rat model of non-insulin-dependent diabetes mellitus (the GK rat), and they have found that the two QTL map closely to regions that previously showed linkage to blood pressure in hypertensive rat strains (unpublished data 1995). The locations of the QTL are sufficiently close that they may represent the effects of the same genes, or of a closely linked family of genes. For blood pressure/hypertension, or glucose tolerance and insulin response/ non-insulin-dependent diabetes mellitus, the analogy between phenotypic explorations in human and rodents is easily drawn. In other instances, appropriate quantitative variables may be difficult to study in humans but they are accessible in the rodent model. For example, it is possible to measure the degree of insulitis (infiltration of lymphocytes into islets and reduction of islet mass) in non-diabetic mice bred from crosses involving the non-obese diabetic (NOD) mouse, and use this as a quantitative index in mapping studies. Diabetes, which is present in a relatively small subset of mice from such crosses, represents the extreme of the phenotype with complete absence of insulin-producing capacity. Susceptibility loci to non-insulin-dependent diabetes mellitus have been identified both by linkage in diabetic mice alone, and through the quantitative phenotype measured in a complete cohort of backcross mice (Todd et a1 1991, Ghosh et a1 1993). Although I have emphasized results obtained in hypertension, QTL mapping in inbred rodent strains has proved to be equally powerful for many other traits. Both non-insulin-dependent and insulin-dependent diabetes mellitus are amenable to such analyses (as cited above), and other immune-related traits have also been studied in this way. These include quantitative antibody production in the mouse (Puel et a1 1995) and IgE responses to gold salts in the rat (N. Kermarrec, unpublished data 1995). Interestingly, the latter has yielded evidence of a genetic locus in the homology region near the gene encoding interleukin 4, which has been reported to be linked to IgE in humans (Marsh et a1 1994, Meyers et a1 1994), raising the possibility that the same gene is involved in the two species. If confirmed, the susceptible rat strain (Brown-Norway) could provide an important tool for further investigations of the homologous gene.

Fine mapping and gene identification The localizations obtained for QTL mapped by linkage in experimental crosses typically span at least 15-20cM. The number of genes that could be potentially

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involved in phenotypes such as blood pressure regulation is virtually unlimited, so at least one candidate can been found for most regions of linkage. Candidate genes that have attracted initial attention for some of the blood pressurerelated QTL discussed above are the gene encoding renin on rat chromosome 13, the ACE (angiotensin I-converting enzyme) gene on rat chromosome 10, the atrial natriuretic peptide receptor A gene (also called GCA) on chromosome 2 and Sa on chromosome 1 (Sa is a candidate gene with unknown function that was initially identified because of greater expression in the kidney of SHR strains compared to WKY rats, and was subsequently mapped near the QTL). Closer examination shows that most of these regions actually contain several plausible candidate genes. For example, Rapp & Deng (1995) list five plausible candidate genes in the regions of linkage to rat chromosome 2. Many other potential candidate genes have yet to be cloned or localized, and it is certain that more candidates in the regions of all QTL will increase as new data accumulates. Further fine mapping of the genes involved in blood pressure or other phenotypes in experimental crosses will be an essential step towards gene identification. This can be undertaken with the aid of congeneic lines. Congeneic lines are bred to contain a portion of the genome from one strain, such as a region of a chromosome thought to contain a QTL from a hereditary hypertensive strain, with the remainder on the genome from a second strain, such as a normotensive rat. Phenotypic analysis of a series of congeneic rodents that contain different segments of the target chromosome region, or analysis of crosses bred for the congeneic and original lines, can aid to narrow the region of the chromosome containing the QTL. One of the first successes of congeneic lines for the analysis of blood pressure variation has occurred with a QTL linked to chromosome 10 in SHR/SP and WKY crosses. A WKY congeneic strain containing a 6cM region from chromosome 10 of SHR/SP exhibited higher blood pressure when compared to the non-congeneic WKY strain, and crosses bred from the congeneic WKY and SHR/SP strains showed absence of linkage to chromosome 10 (Kreutz et a1 1995). These results suggest that the SHR/SP region of chromosome 10 which was transferred to the congeneic strain contains one of the QTL that was previously localized on this chromosome. It is of particular interest that the region of chromosome 10 from the SHR/SP strain which is contained in the congeneic strain does not include ACE. This would appear to exclude ACE, which was originally one of the principal candidate genes from the region. Indirect arguments may aid in the identification of the genes associated with QTL. For example, Bianchi et a1 (1994) have argued that a proportion of the blood pressure differences between Milan hypertensive and normotensive rats are due to point mutational differences in genes coding for the c1 and /3 subunits of adducin. These genes reside on rat chromosomes 4 and 14 in regions of linkage to blood pressure. Bianchi et a1 (1994) postulate that adducin plays a

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role in membrane Na+ transport. The argument that variants of these genes affect blood pressure is based on the results of linkage analysis in an F2 cohort. Statistically significant interaction was found in a two-way analysis-of-variance of blood pressure and genotypes for the two loci. As the interaction of these genes has a biological basis, and it could be considered unlikely that other genes mapping to the same regions would exhibit such interactions, the authors argue for a role of adducin in blood pressure regulation. Indirect arguments have also be used by Cicila et a1 (1993) to infer that a mutation in the gene encoding 11S-hydroxylase is responsible for the linkage observed with blood pressure on rat chromosome 7 in Dahl salt-sensitive and salt-resistant hypertensive strains of rat. Ultimately, however, the demonstration of the relationship between a particular gene and quantitative variation is through modification of the phenotype after transfer of a disease-associated variant into a control strain. Interesting data on quantitative effects can also be obtained by analysis of mice bearing a gene knockout or an artificial duplication of a gene. Recently, mice have been derived either without or up to four copies of the gene encoding angiotensinogen, which has been reported to be linked with susceptibility to hypertension and plasma angiotensinogen concentration in humans. These mice have shown parallel increases of plasma angiotensinogen and blood pressure as a function of the number of copies of the gene (Smithies & Kim 1994, Smithies & Maeda 1995). Similar methods have been applied to other candidate genes, as described by these authors.

Quantitative variation in human disease Multifactorial disorders in humans have a greater complexity due to the heterogeneity of genetic and environmental causes, and to outbreeding. Nevertheless, results on the localizations and characterizations of the genes involved in conditions such as hypertension, diabetes, obesity and asthma are being obtained. In many instances, the analysis of underlying quantitative phenotypes, such as blood pressure for hypertension or glucose tolerance for diabetes, is an important approach for dissecting the genetic components involved in disease. The quantitative variable may provide a more precise definition of the phenotype, as may the ability to select extreme cases or subsets of disease. It can be studied in unaffected individuals to increase the power to detect linkage. For some disorders, the quantitative variables of interest are intermediate phenotypes that are influenced by a small number of genetic and environmental factors. Such intermediate phenotypes are often more suitable than the disease for linkage analysis, or for detection of causative DNA variants within a candidate gene. Angiotensinogen and hypertension is an example of such a situation. Linkage between hypertension and a microsatellite marker of the

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gene encoding angiotensinogen has been described, and two variants (M235T and T174M at codons 235 and 174, respectively, within the gene) were found to have different frequencies in hypertensive patient and normotensive controls (Jeunemaitre et a1 1992). A highly significant correlation has been found between plasma angiotensinogen and hypertension in a large epidemiological study. Also, higher levels of angiotensinogen have been reported in both young adults with high blood pressure and a parental history of high blood pressure (compared to normotensive controls with normotensive parents), and in children with hypertensive parents (reviewed in Soubrier & Lathrop 1995). These data suggest that the gene encoding angiotensinogen is related to the risk of hypertension through an effect on angiotensinogen concentration. Indeed, when plasma angiotensinogen concentration was examined, the quantitative variable was found to be linked to the angiotensinogen locus, and it was associated with the M235T and T174M variants (Jeunemaitre et a1 1992, F. Soubrier & G. M. Lathrop, unpublished data 1995). Further molecular and genetic investigations are warranted because M235T and T174M may simply be in linkage disequilibrium with other variants that modify the gene encoding angiotensinogen. It is likely that the quantitative character will be more useful than hypertension as a phenotypic marker for these studies because of greater ease of characterization, the ability to undertake extensive family investigations without requiring two or more hypertensive patients and a potentially stronger effect of the gene. A similar situation exists for ACE. The insertion/deletion polymorphism in exon 16 of the gene has been reported to be associated with an increased risk of myocardial infarction, left-ventricular hypertrophy and diabetic complications (reviewed in Soubrier et a1 1994), but other epidemiological studies have shown a lack of association with some of these phenotypes (e.g. Bohn et a1 1993, Lindpaintner et a1 1995). Several factors could explain such discrepancies, including differences in study design, phenotype definition or statistical artefacts of the original association data. Another possibility is that linkage disequilibrium with a causative polymorphism(s) varies between the different populations in these investigations. In contrast to the difficulties in interpreting these results, family and population studies have provided an unequivocal demonstration of linkage and association between serum ACE concentrations and the insertion/ deletion polymorphism (Rigat et a1 1990, Tiret et a1 1992). In family studies, the hypothesis of a causative role of this polymorphism or complete linkage disequilibrium with another causative polymorphism is rejected against the alternative of partial linkage disequilibrium. Thus, other variants of the gene that are more strongly related to the ACE quantitative phenotype may be detected by sequencing or other means, and it may be profitable to study these for association with disease. If ACE polymorphisms are associated

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with susceptibility to cardiovascular disease, this may be via an effect on the quantitative phenotype. Plausible biological mechanisms for this are discussed in Soubrier et a1 (1994). Recently, the analysis of family data from Jamaica, the UK and France has suggested that other genetic factors could also contribute to the regulation of serum ACE concentrations (McKenzie et a1 1995). If so, their identification could make an important contribution to understanding the possible role of ACE in a range of pathological conditions. Similar techniques of combined linkage and segregation analysis have proved to be powerful methods for unravelling the genetics of complex quantitative phenotypes in extended pedigrees, even in the presence of considerable genetic heterogeneity. For example, Thein et a1 (1994) have recently used such an approach to demonstrate complex genetic regulation of the switch from fetal to adult haemoglobin in a large five-generation pedigree in which thalassaemia and hereditary persistence of fetal haemoglobin (HPFH) are present. By estimating levels of fetal haemoglobin in the members of the pedigree through a quantitative assay, and by applying a genetic model that incorporated interactions between 8-globin and Gy-globin, both of which affect the trait, they demonstrated strong evidence of another major gene. From the estimates of the genetic parameters, it was found that this locus had a high probability of being detected through linkage analysis, and a genome screen has now been successfully completed (S. L. Thein, personal communication 1995). Characterization of a gene involved in the switch from fetal to adult haemoglobin would raise the possibility of a new therapeutic approach to disease because the severe clinical symptoms of thalassaemia are absent in patients with HPFH. Successes such as those cited above have led to the expectation that searches for linkages to other quantitative measures such blood pressure, glucose tolerance, body mass, IgE levels and other phenotypes associated with human disease will be successful. Recent examples include the results on IgE levels cited above (Marsh et a1 1994, Meyers et a1 1994). A large body of literature discussing theoretical considerations, such as sample size, ascertainment criteria and statistical techniques for data analysis, is now available. These questions have been most extensively investigated for sib pairs, which have advantages in terms of ease of collection and statistical simplicity. Recent literature in the area has been reviewed by Lander & Schork (1994) and Weeks & Lathrop (1995).

Acknowledgement G . M.L. is a Wellcome Trust Principal Fellow.

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References Bianchi G, Tripodi G, Casari G et a1 1994 Two point mutations within the adducin genes are involved in blood pressure variation. Proc Natl Acad Sci USA 91: 3999-4003

Bohn M, Berge KE, Bakken A, Erikssen J, Berg K 1993 Insertion/deletion (I/D) polymorphism at the locus for angiotensin I-converting enzyme and myocardial infarction. Clin Genet M292-297 Cicila GT, Rapp JP, Wang JM, St Lexin E, Ng SC, Kurtz TW 1993 Linkage of 11shydroxylase mutations with altered steroid biosynthesis and blood pressure in the Dahl rat. Nat Genet 3:346-353 Davidson AO, Schork N, Jaques BC et a1 1995 Blood pressure in genetically hypertensive rats: inthence of the Y chromosome. Hypertension 26:452-459 Dubay C, Vincent M, Samani NJ et a1 1993 Genetic determinants of diastolic and pulse pressure map to different loci in Lyon hypertensive rats. Nat Genet 3:354357 Ghosh S, Palmer SM, Rodrigues NR et a1 1993 Polygenic control of autoimmune diabetes in nonobese diabetic mice. Nat Genet 4404409 Hilbert P, Lindpaintner K,Serikawa F et a1 1991 Chromosomal mapping of two genetic loci associated with hereditary hypertension in the rat. Nature 353521-529 Jeunemaitre X, Soubrier F, Kotelevtsev W et a1 1992 Molecular basis of human hypertension: role of angiotensinogen. Cell 71: 169-180 Kreutz R,Hubner N, James MR et a1 1995 Dissection of a quantitative trait locus for genetic hypertension on rat chromosome 10. Proc Natl Acad Sci USA 92:8778-8782 Lander ES, Schork NJ 1994 Genetic dissection of complex traits. Science 265:2037-2048 Lindpaintner K, Pfeffer M, Kreutz R et a1 1995 A prospective evaluation of an angiotensin converting enzyme gene polymorphism and the risk of ischemic heart disease. New Engl J Med 332:706-711 Marsh DG, Neely JD, Breazeale DR et a1 1994 Linkage analysis of IL.4 and other chromosome 5q31.1 markers and total serum immunoglobulin E concentrations. Science 264 1152-1 156 McKenzie CA, Julier C, Forrester T et a1 1995 Segregation and linkage analysis of serum angiotensin I-converting enzyme levels: evidence for two quantitative-trait loci. Am J Hum Genet 57:1426-1436 Meyers DA, Postma DS, Panhuysen CIM et a1 1994 Evidence for a locus regulating total serum IgE levels mapping to chromosome 5. Genomics 23:464470 Pravenec M, Gauguier D, Schott JJ et a1 1995 Mapping of quantitative trait loci for blood pressure and cardiac mass in the rat by genome scanning of recombinant inbred strains. J Clin Invest 96:1973-1978 Puel A, Groot PC, Lathrop GM, Demant P, Mouton D 1995 Mapping of genes controlling quantitative antibody production in Biozzi mice. J Immunol 154 5799-5805

Rapp JP, Deng AY 1995 Detection and positional cloning of blood pressure quantitative trait loci: is it possible? Identifying the genes for genetic hypertension. Hypertension 2 5 1 121-1 128 Rigat B, Hubert C, Alhenc-Gelas F, Cambien F, Corvol P, Soubrier F 1990 An insertion/deletionin the angiotensin I-converting enzyme gene accounting for half the variance of serum enzyme levels. J Clin Invest 86:1343-1346 Smithies 0, Kim HS 1994 Targeted gene duplication and disruption for analyzing quantitative genetic traits in mice. Proc Natl Acad Sci USA 91:3612-3615 Smithies 0, Maeda N 1995 Gene targetting approaches to complex genetic diseases: atherosclerosis and essential hypertension. Proc Natl Acad Sci USA 925266-5272

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Soubrier F, Lathrop GM 1995 The genetic basis of hypertension: an update on recent studies. Curr Opin Nephrol Hypertens, in press Soubrier F, Nadaud S, Williams TA 1994 Angiotensin I converting enzyme gene: regulation, polymorphism and implications in cardiovascular diseases. Eur Heart J 15:24D-29D

Thein SL, Sampietro M, Rohde K et a1 1994 Detection of a major gene for heterocellular hereditary persistance of fetal hemoglobin after accounting for genetic modifiers. Am J Hum Genet 54:214-228 Tiret L, Rigat B, Visvikis S et a1 1992 Evidence, from combined segregation and linkage analysis, that a variant of the angiotensin I-converting enzyme (ACE) gene controls plasma ACE levels. Am J Hum Genet 51:197-205 Todd JA, Aitman TJ, Cornall RJ et a1 1991 Genetic analysis of autoimmune type 1 diabetes mellitus in mice. Nature 351542-547 Weeks D, Lathrop GM 1995 Polygenic disease: methods for mapping complex disease traits. Trends Genet 11513-519

DISCUSSION Hurtl: I would like to pose some questions relating to the genetic basis of the susceptibility of heterogenous human populations to common diseases. How many genes are involved and what is the distribution of gene frequencies? Of the genes we can identify that affect common diseases, what proportion of the variants in a population is accounted for by genes that are actually identified? To what extent are common diseases determined, not by main effects, but by interactions between genes, which are undetectable by methods of this sort? For traits beyond simple diagnostic criteria, such as diabetes and behavioural traits, the main source of variation in the trait is the diagnostician’s ability to detect it. Also, for behavioural traits, there are culturally transmitted characteristics. However, my main question that I would like you to address is what are the limitations of these kinds of techniques? Luthrop: Linkage studies represent one approach for identifying genes responsible for multifactorial diseases and interesting results are being obtained. It may not be possible to identify the majority of the genes of interest in this way. On the other hand, if we can use this approach to obtain information on 5-10% of the genes that are involved in diseases such as diabetes, we will be further advanced than at present. The limitations of the approach will probably be more apparent in two or three years time, once we have sorted out the initial results. The techniques and sample sizes that are used are adequate for detection of first-order effects but perhaps not sufficiently powerful to study gene interactions. However, some gene interactions are being described in the literature. I am thinking particularly of potential interactions between HLA and other loci in insulin-dependent diabetes mellitus (Julier et a1 1991, Hashimoto et a1 1994, Davies et a1 1994).

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W . Bodmer: An identity by descent association, which is essentially an extension of sib pair analysis, rules out that genetic factors are not involved, but it doesn’t necessarily mean that cultural factors aren’t involved. One way to show that something is genetic is to show that genetic markers are associated with it. If this is done in families, then one doesn’t have the problems of population-biased linkage disequilibrium. I would also like to mention the continual search for complexity, and the possibility that there may be interactions between genes. Obviously, you’re looking for effects of a sufficient magnitude that you can work with, but there may be numerous effects of a much smaller magnitude. Your point about estimating the variance observed in the population that can be explained by the loci is an important one. Even if that variance isn’t large, it may provide a useful handle if it can be used, for example, to reduce the incidence of cancer by 20-50%. I’m slightly puzzled by the emphasis on interactions because, if interactions are important, the frequencies of the genes involved will be high. If the gene frequencies are multiplied by the penetrance, the frequencies are low for common diseases, so interactions may not be that important. Even in the case of HLA in insulin-dependent diabetes mellitus, you’re not really looking at interactions, you’re looking at effects that could be quite independent. However, you don’t see these effects until you separate out the effect of HLA, which is the overwhelming effect in insulin-dependent diabetes mellitus. It then becomes possible, against that background, to see the effects of other markers. Luthrop: Most results in the diabetes field, both in insulin-dependent and non-insulin dependent diabetes mellitus (NIDDM), have involved splitting the data into distinct genetic subsets rather than the demonstration of interactions between genes. For NIDDM, genes accounting for a small proportion of the disease overall, but that are of major importance in individual families, have been found (Froguel et a1 1992, Vaxillaire et a1 1995, Hager et a1 1995). Ward: One of the themes at this symposium is the impact of genetic heterogeneity in humans, and the various ways we are trying to analyse this impact. In that context, I was interested in your results describing major gene effects in rats, some of which appear to be strain independent. Is there any reasonable way you could take advantage of the genetic heterogeneity in our own species in terms of these kinds of approaches? Should we be devoting more attention to replicating the studies, not merely within Caucasians, but looking at Caucasians, Chinese and Africans, for example? Lathrop: One example are the polymorphisms in the region of the insulin 5‘VNTR (variable number of tandem repeats) locus that are related to insulindependent diabetes mellitus susceptibility (Lucassen et a1 1993). The nature of this relationship has been a controversial issue in the past. For instance, I was at the Ciba Foundation Symposium on Molecular Approaches to Human Polygenic Disease at which Graeme Bell presented some results which raised considerable controversy at the time (Bell et a1 1987). The relationship has now

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been confirmed by a number of different groups both by association and family linkage studies (Julier et a1 1991). The relevant issue here is that analysis of the 5’-VNTR region shows that 10 polymorphisms are in almost complete linkage disequilibrium in Caucasian populations. The two predominant haplotypes contain either variants associated with increased susceptibility,or non-associated variants. It’s difficult from the analysis of Caucasian populations alone to determine which of the variants, or combination of variants, is required for increased susceptibility. There appears to be a considerably higher haplotype variability in African populations, not only in controls but also in diabetics. By analysing the haplotype variability in such populations, it may be possible to identify a reduced subset of variants that are associated with disease susceptibility. It is important to do so because the next experimental step is to understand how those variants might differentially regulate the expression of the insulin gene, which is very laborious. Obviously, the more variants that are eliminated through population studies, the easier will be the task to identify the causative susceptibility factors. W. Bodmer: But a major feature of the HLA and disease association is the study of those associations in different populations. Lathrop: This is not being done routinely in the quantitative trait loci (QTL) approach. Sing: In relation to the angiotensin I-converting enzyme (ACE) results that you presented, is the effect of these two loci on the levels of ACE additive? How much variation can be explained by the loci separately, and how much can be explained by their combined effect? Lathrop: Our results suggest that the ACE gene accounts for about 30% of the variation in plasma ACE levels in Jamaican families which we have studied by segregation and linkage analysis. The second locus, if it is just a single locus and not multiple loci, may account for about 50% of the variability. This suggests that together these loci explain about 80% of variability. Using the two-locus model, we rejected the hypothesis that the effects were additive in data from Jamaican and UK families (M. Lathrop, unpublished results 1995). Sing: I would like to suggest that just as the allele frequency distribution may differ among loci, the genotype/phenotype relationship may differ among genes across the genome. Harper: I would like to bring up the subject of Mendelian subsets. In our experience, Mendelian subsets are present in many disorders that have traditionally been thought of as being quantitative (such as cleft palate or congenital heart disease). These can result from alterations in either single genes or, in the case of congenital heart disease, chromosomal deletions that behave in a Mendelian fashion. The existence of Mendelian subsets has many practical applications. For instance, in the case of congenital heart disease, a few years ago there was a 2-3% recurrence risk given to families spread across

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the whole group, simply because these groups could not be subdivided. It is now possible, however, to pick out the individual high risk cases for these Mendelian subsets. The implication of this, although it’s not been absolutely proven, is that the resulting risk in what’s left is much less. How do your approaches take into account the existence of Mendelian subsets in these common diseases? Because many of these subsets may not yet have been accurately defined. Lathrop: One could do these studies with large numbers of small families such as affected sib pairs, but one would expect considerable heterogeneity in those samples. Analyses of extended families or families selected through criteria that emphasize simple genetic transmission have been profitable even when no Mendelian syndrome has been described. We’ve looked at obesity pedigrees; for example, in which the trait seems to segregate through several generations, and is perhaps determined by a small number of genes within the same pedigree. This approach has led to the possible localization of a gene that could be responsible for the character (Murray et a1 1994). However, the problem then arises of extrapolating results from highly selected families back to general forms of the disease. In the case of NIDDMfor example, in French families that have been ascertained for genetic studies by general population screening and advertising for diabetic volunteersglucokinase mutations were present in between 2-5% of all families that were studied. Glucokinase mutations are responsible for many instance of mild forms of maturity-onset diabetes in the young (MODY), which is a Mendelian subset of NIDDM (Froguel et a1 1992) present in a relatively high proportion of patients ascertained in this manner in France. However, when MODY families from the UK were studied, glucokinase mutations were found only in a small proportion of patients. The difference is probably due to the way in which the study groups were ascertained: the UK patients generally had a more severe form of the disease because they were largely selected through diabetes clinics. The glucokinase gene does not seem to be significant in other forms of NIDDM. Templeton: The cladistic approach originated from a measured genotype approach, which represents a single-locus approach. Therefore, it is already assumed that a particular DNA region is being analysed. One way of handling genetic heterogeneity is, once there is evidence of more than one candidate region, to do the cladistic analyses 6f the same phenotype but on different regions. This doesn’t really solve the problem of interactions, but the measured genotype technique can be used once the problem of interactions is recognized. Identifying interactions with measured genotypes is possible when a locus has been defined that has a large phenotypic effect. Once the genotypes have been determined at this major locus, they can be treated as a fixed effect to look for interactions with other loci. For example, Charlie Sing has pointed out that the APOE locus is regarded as a major gene for cholesterol levels. Subsequently, it

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was possible for Pedersen & Berg (1989, 1990) to detect a significant epistatic interaction between APOE and the low-density lipoprotein receptor (LDLR) locus with respect to cholesterol levels, even though the LDLR locus does not have a major marginal effect on cholesterol itself. A similar situation occurs with Charlie Sing’s results on the APOE and APOH loci (Sing et a1 1996, this volume). I agree with Charlie that there will be a spectrum of genetic architecture. It’s a mistake to dismiss interactive effects a priori and, therefore, not to even look for them in the first place. Clark: I have another example of testing for interactions after having identified the traits. One of the classical quantitative traits in Drosophila is bristle number, which has been studied at the molecular level by Trudy Mackay’s group in North Carolina (Mackay & Langley 1990, Lai et a1 1995). They asked whether the particular QTL of candidate genes explain all of the variation in bristle number. A. D. Long (personal communication) found that interaction effects are as large as main effects. We’re seeing the same thing for the effects of P element insertions on metabolic traits: pairs of P element insertions interact with effects whose magnitude is on the same order as single P element effects (A. G. Clark & L. Wang, unpublished observations 1996). Sing: How are you going to do positional cloning on these genes that appear to be regionalized by your methodologies? Lathrop: Our laboratory and other groups, including the Sanger Centre in Cambridge and the Genethon in Paris, are involved in a collaborative effort to map large numbers of cDNAs and microsatellite markers on radiation hybrid panels. This year there should be 700&10 000 microsatellite markers available for mapping and a large number of partial cDNA sequences (expressed sequence tags) should be generated. The idea is to construct a joint map so that candidate genes can be identified immediately once a disease locus has been mapped with the microsatellite markers. The candidate genes in the region could be analysed for polymorphisms and haplotype association studies could be undertaken to identify the relevant susceptibility variants very rapidly. Chakravarti: You looked at the segregation of genes in a number of different crosses and showed that there was an effect in a few but not all of the crosses. There hasn’t been a good explanation for this. Is it likely that this represents normal allelic variation within the population? If so, at any given QTL, the allelic effect may be large enough in one cross and be detected, whereas in another cross, it may be not quite large enough. In the latter case the allelic effect would not be detected, rather than the effect being seen as absent. It may be important to pool the results of the different crosses to detect minor effects. Lathrop: I agree. One of the difficulties in comparing these different results is that different experimental conditions are often involved. For instance, the techniques involved in measuring blood pressure, the time at which the blood pressure is measured and dietary conditions are all variable factors. Whether

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you detect an effect or not may depend on these differences in experimental conditions. Also, if the genetic background is different, then the magnitude of the effect at the segregating locus will be different. If there is segregation of just one locus in a particular cross, then its effect may be larger and therefore easier to detect. The different rat models that are being studied have an evolutionary relationship; they are derived historically from populations that are more or less, depending on the animal, related to each other. In some instances the hypertensive and the normotensive strains that are crossed have similar genetic regions in terms of microsatellite markers. This similarity could be due to their common origin. It is interesting that one doesn’t detect strong interactions between the loci. Some interactions have been described in the literature, but in general the interactions have not been statistically significant. This is relevant to the previous discussion of gene interaction in human disease.

References Bell GI, Xiang K, Horita S, Sanz N, Karam JH 1987 The molecular genetics of diabetes mellitus. In: Molecular approaches to human polygenic disease. Wiley, Chichester (Ciba Found Symp 130) p 167-183 Davies JL, Kawaguchi Y, Bennett ST et a1 1994 A genome-wide search for human type1 diabetes susceptibility genes. Nature 371:130-136 Froguel Ph, Vaxillaire M, Sun F et a1 1992 Close linkage of glucokinase locus on chromosome 7p to early-onset non-insulin-dependent diabetes mellitus. Nature 356162-164 Hager J, Vaisse C, Vionnet N et a1 1995 A missense mutation in the glucagon receptor gene is associated with non-insulin-dependentdiabetes mellitus. Nat Genet 9:299-304 Hashimoto L, Habita C, Beressi JP et a1 1994 Genetic mapping of a susceptibility locus for insulin-dependent diabetes mellitus on chromosome 1lq. Nature 371: 161-163 Julier C, Hyer RN, Davies J et a1 1991 The insulin-IGF2 region encodes a gene implicated in HLA-DRCdependent diabetes susceptibility. Nature 354 155-159 Lai C, Lyman RF, Long AD, Langley CH, Mackay TFC 1995 Naturally occurring variation in bristle number and DNA polymorphisms at the scabrous locus in D . melanogaster. Science 266: 1697-1702 Lucassen AM, Julier C, Beressi JP et al 1993 Susceptibility to insulin-dependentdiabetes mellitus maps to a 4.1 kb segment of DNA spanning the insulin gene and associated VNTR. Nat Genet 4305-310 MacKay TFC, Langley CH 1990 Molecular and phenotypic variation in the achaetescute region of Drosophila melanogaster. Nature 348:64-66 Murray JD, Bulman DE, Ebers GC, Lathrop GM, Rice GPA 1994 Linkage of morbid obesity with polymorphic microsatellite markers on chromosome lq31 in a 3 generation Canadian kindred. Am J Hum Genet 55: 1145 (abstr 197) Pedersen JC, Berg K 1989 Interaction between low-density lipoprotein receptor (LDLR) and apolipoprotein-E (ApoE) alleles contributes to normal variation in lipid level. Clin Genet 35331-337

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Pedersen JC, Berg K 1990 Gene-gene interaction between the low-density-lipoprotein receptor and apolipoprotein-E loci affects lipids levels. Clin Genet 38:287-294 Sing CF, Howland MB, Reilly SL 1996 Genetic architecture of common multifactorial diseases. In: Variation in the human genome. Wiley, Chichester (Ciba Found Symp 197) ~211-232 Vaxillaire M, Bocci0 V, Philippi A 1995 A gene for early onset non-insulin-dependent diabetes mellitus (Maturity onset diabetes of the young) maps to chromosome 12q. Nat Genet 9:418-423

The genetics of common diseases: the implications of population variability D. J. Weatherall

Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Headington, Oxford OX3 9DU, U K

Abstract. The results of recent work on the inheritance of susceptibility to malaria suggest that, over what may have been a relatively short evolutionary period, a remarkably diverse series of gene families have been modified in response to the selective drive of this infectious disease. The phenotypic consequences are not confined to the erythrocyte, they also involve the immune system, cytokines and many other systems. It seems likely that the mechanisms of genetic susceptibility to current environmental agents will reflect at least a similar degree of complexity and, if the selective pressures have been present for longer periods of our evolutionary history, may be even more diverse. These issues are discussed in the light of current efforts to identify some of the major genes involved in variation in susceptibility to the common non-infectious diseases of the developed world. 1996 Variation in the human genome. Wiley, Chichester (Ciba Foundation Symposium 197) p 300-311

Currently, there is considerable interest in the possibility of identifying the genes responsible for relative susceptibility or resistance to the environmental agents that may cause some of the common intractable diseases of the developed world. There are several reasons why this work is valuable. First, it is likely that comparison of the differences in the gene products between susceptible and non-susceptible individuals will improve our understanding of the pathogenesis of these diseases. Second, if it is possible to identify individuals at particular risk, that should enable future efforts at preventive medicine to be concentrated on smaller and more manageable subsets of the population. However, before this stage is reached, it is important to try to assess the degree of complexity that we are likely to encounter when we try to identify susceptibility genes of this kind. There is increasing evidence that epidemics of infectious diseases have greatly modified our genetic make-up. Haldane (1948) was among the first to suggest that infectious diseases have been a major selective force in human 300

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evolution over the past 5000 years, a notion that was outlined some years earlier by A. E. Garrod (Garrod 1931). More recently, Murphy (1993) has compared sequences of genes common to rodents and humans and found that host defence genes are more diverse than other families of proteins. This observation also suggests that selection in many species has resulted from exposure to different infectious agents. I will review briefly recent work on the genetics of susceptibility to malaria, with particular reference to what this might tell us about the complexities of analysing the role of our genetic make-up in the genesis of some of the common non-infectious diseases of the developed world. Genetic factors in susceptibility to malaria: the erythrocyte Haldane (1948) was the first to suggest that the high frequency of thalassaemia in Mediterranean populations might have resulted from heterozygote protection against severe malaria. Early studies in Africa suggested that this hypothesis was broadly correct with respect to the high frequency of the sicklecell trait, and it has been found recently that heterozygosity for haemoglobin S provides 80-95% protection against the particularly important manifestations of Plasmodium faleiparum infection, notably severe anaemia and cerebral involvement (Hill et al 1991). Until recently, there was scant evidence for the protective effect of thalassaemia against malaria (Weatherall & Clegg 1981). However, over the past few years considerable evidence has been obtained that malaria is the major selective factor that has maintained both a-thalassaemia and /Ithalassaemia at extremely high frequencies in many parts of the world (Flint et al 1993). Briefly, wherever there is malaria there is thalassaemia, often at high frequencies. Similarly, but with a few exceptions, where there is no malaria, these disorders are absent or rare. Furthermore, where these exceptions have been analysed in detail they can usually be explained by gene flow, the action of drift on small populations, and other mechanisms that are still compatible with the malaria hypothesis. Clear-cut relationships have been demonstrated between the frequency of the a-thalassaemias and present or past malaria. Similar, although less extensive data have been obtained for the pthalassaemias (reviewed in Flint et al 1993). In addition, it is now apparent that in every part of the world where the thalassaemias are particularly common, there are completely different patterns of a or p globin gene mutations set in the background of different patterns of restriction fragment length polymorphisms (RFLPs) in the a and /3 gene clusters. This suggests that these mutations have arisen locally and have reached a high frequency by selection. Interesting differences between the mechanisms of selection are emerging. In the case of P-thalassaemia, like that of the sickle-cell trait, it is clear that Haldane’s original predictions that these are balanced polymorphisms are

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correct. However, the mechanism of selection for a-thalassaemia, probably the most common human genetic disorder globally, may be more complex. There are two a globin genes per haploid genome (aa). The most common forms of a-thalassaemia result from deletions of either one or both of these genes; individuals with a single deleted a gene ( - a ) are said to have a+-thalassaemia, whereas those with both genes deleted (- -), have a"-thalassaemia. a+-Thalassaemia is extremely common throughout many tropical populations, and recent studies suggest (although not unequivocally) that only homozygotes ( - a / - a) are resistant to P. fulcipurum malaria. Work in Africa suggests that they may be protected to a level of about 25% (Yates et a1 1995), which is considerably less than sickle-cell heterozygotes. Furthermore, extensive studies have not been able to demonstrate any deleterious effect of the - a / - a genotype. These observations suggest that the a+-thalassaemias represent transient rather than balanced polymorphisms that would, if the selective pressure of malaria had continued, have gone to fixation. The extraordinarily high gene frequencies for this condition, much higher than those for any other haemoglobinopathy, are also compatible with this notion. Another structural haemoglobin variant, haemoglobin E, occurs at extremely high frequencies throughout the eastern parts of the Indian subcontinent, the Union of Myanmar and South-East Asia. This variant has the phenotype of a mild form of /3-thalassaemia and, hence, it is assumed that it has also reached its high frequencies by conferring protection against P. fulcipurum malaria (Flint et a1 1993). In short, protection against malaria seems to have been responsible for the generation of over 120 different /3-thalassaemia mutations, many different a-thalassaemia mutations and an extremely high frequency of other structural haemoglobin variants, notably haemoglobins C and E. Interestingly, there is virtually no haemoglobin S east and no haemoglobin E west of the Indian subcontinent. Current evidence suggests that haemoglobin S probably arose at least twice. It is not clear why these extremely successful polymorphisms are distributed in this way, but these observations, together with data that are emerging on the relationships between the different /3-thalassaemia mutations and the associated patterns of RFLPs in the /3 globin gene cluster, hint that the major haemoglobinopathies may have been generated fairly recently in evolutionary history (Flint et a1 1993). Genetic modification of erythrocytes by selection for malaria is not restricted to haemoglobin. Many other genes involved in erythrocyte structure and metabolism have been modified. There is now clear evidence that the high frequency of glucose-6-phosphate dehydrogenase deficiency is due to protection of both heterozygous females and hemizygous males (Ruwende et a1 1995). Another example of a malaria-related balanced polymorphism involves a mutation in the gene encoding the band 3 protein of the erythrocyte membrane, which leads to Melanesian elliptocytosis, an extremely common

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condition throughout Melanesia, which appears to be lethal in homozygotes (Jarolim et a1 1991). Also, there has been a recent resurgence of interest in the protective effects of different blood group antigens against malaria (Miller 1994). As well as the well-recognized protective effect against Plasmodium vivax in Duffy-negative individuals, there is increasing evidence that blood group antigens and other erythrocyte surface antigens may have been modified by past exposure to malaria. Malaria related polymorphisms in other systems Genetic variability due to selection by malaria is not confined to erythrocytes. Work in the Gambia has shown that a single class I HLA haplotype, HLA B53, is associated with a substantial (40%) protection against both cerebral malaria and severe malarial anaemia (Hill et a1 1991). Furthermore, a single African HLA class I1 DR-DQ haplotype, HLA-DRB 1* 1302-DQB1*0501, has also been implicated in protection, particularly against severe malarial anaemia. Similar observations with respect to different HLA class I1 haplotypes have been observed in other parts of Africa. Subsequent studies have identified a peptide from the parasite liver-stage antigen 1, which is an epitope for specific CD8+ cytotoxic T lymphocytes that lyse target cells expressing this antigen or peptide (Hill et a1 1992). These observations suggest that parasite-specific cytotoxic T lymphocytes are present after natural infection, and that this may be the mechanism of the HLA-B53 association. These, and related studies on the mechanisms of the protection mediated through the class I1 genes of the HLA-DR system, provide a clear basis for the remarkable variability in the products of these genes in different populations, a phenomenon that may have profound implications for genetic responses to other environmental agents at different periods of our evolutionary history. Another polymorphic system that has been uncovered, and that is clearly related to malaria, involves the gene encoding tumour necrosis factor a (TNFa). A single base change at position - 308 of the promoter is associated with a markedly increased risk of cerebral malaria and death (McGuire et a1 1994). Subsequently, this polymorphism has been implicated in increased levels of TNF-a expression in vitro, an observation that is compatible with a clinical finding that high plasma levels of TNF-a are observed in malarial patients who have a poor clinical outcome (Kwiatkowski et a1 1990, Kwiatkowski 1995). Very recently, a further promoter polymorphism, in this case at position - 238, has been found to be associated with severe malarial anaemia in the Gambia. Further, it has been proposed that malaria may have played a much wider role in modifying responses to environmental factors than has hitherto been realized. For example, Miller (1994) has suggested that the high frequency of hypertension in Black populations may reflect the relatively high sodium and low potassium content of their erythrocytes, and that this may, in turn,

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represent a genetic response to malaria. In keeping with this notion is the observation that there are no malarias of animals-such as dogs, cats, cows and horses-that have high sodium content erythrocytes. Miller (1994) also suggests that the common genetic form of haemochromatosis in sub-Saharan Africa may have arisen by selection of heterozygotes against malaria. This idea is based on recent observations of the malarial parasite's ability to toxify intracellular haem-derived iron and its requirement for an extracellular source of iron for its development. It is clear, therefore, that during the period in which we have been exposed to malarial infection, our genetic make-up has been profoundly changed. And these remarkable adaptations are not only restricted to the primary site of action of the malarial infection, i.e. the erythrocyte, but also involve the immune system, a variety of cytokines and other molecules that regulate response to infection, and metabolic pathways such as those that control intracellular cation concentrations or iron metabolism. Indeed, it is beginning to look as though we have only scratched the surface of the remarkable genetic diversity that has resulted from adaptation to this single infectious agent, and it is likely that many other infections also have resulted in such genetic variation.

Selection and other common monogenic diseases Although no other monogenic diseases approach the frequency of the thalassaemias, there are a few that are particularly common. It seems likely, for example, that the high frequency of cystic fibrosis in North-Europeans, and the fact that there is one particularly common mutation in this racial group, reflects selection against a major infectious disease, possibly one of the diarrhoea1 illnesses that swept across Europe in the past. Genetic epidemiological studies have suggested that the high frequency of Tay-Sachs disease in certain Jewish populations reflects heterozygote resistance to tuberculosis in some of the ghettos of eastern Europe (OBrien 1991). The extremely high frequency of haemochromatosis in many North-European populations suggests that it also must have come under strong selection. Although the full phenotype of this disease is only fully expressed in middle age, it is clear that the defect resulting in increased iron absorption is expressed throughout life. It is quite conceivable that this could have been advantageous in the past during periods of dietary depletion of iron.

Selection and common chronic illness Genetic constitutions that protected humans against epidemics of infectious illnesses or other environmental hazards in the past may not be suited to the different conditions of today. However, there is evidence that some of the traits that were selected make us more or less likely to develop the common diseases

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of the developed world. This evidence has come from observations on the distribution of blood groups and secretor status in patients with common diseases: peptic ulcer or cancer of the stomach, for example (Mourant et a1 1978). More recent evidence has related secretor status to susceptibility to several important infections (see Weatherall et a1 1988). The idea that the intractable diseases of the developed world are the result of our contemporary environments and lifestyles is based largely on the observation that the prevalence of these diseases in emigrants from countries in which they are less common increases after they adapt to their new environment. But there are many major inconsistencies; in particular, it is becoming apparent that the response of different populations to westernization varies considerably, suggesting that genetic factors play an important role in modifying the effects of environmental insults. This is exemplified by the extraordinarily high frequencies of obesity and non-insulin-dependent diabetes mellitus that are now being observed world-wide. For example, the population of Pima has a 19-fold greater incidence of insulin-resistant diabetes than Caucasian populations in other parts of the Americas, and in some of the island populations of Micronesia and Polynesia frequencies of this disorder of over 60% of the adult population have been encountered. Weiss et a1 (1984) have identified a constellation of diseases in the Pima and other American Indian populations, which they have called ‘the New World syndrome’. This is characterized by a high prevalence of obesity in young adults, insulin-resistant diabetes, and gallstones and gall-bladder cancer, especially in women. They suggest that it reflects a high frequency of a gene, or genes, that causes susceptibility to environmental agents associated with westernization, and that exists by virtue of selective advantage during or before the initial peopling of the Americas. Some years ago, when it started to become apparent that there was a dramatic increase in the prevalence of diabetes in contemporary human populations, Nee1 (1962) suggested that this might be due to dietary plenty imposed on what he called a ‘thrifty genotype’,which had been selected to take advantage of sporadic food availability in early societies. This notion has been extended recently (Weiss et a1 1984,Zimmet et a1 1990). It is suggested that the gene or genes related to food storage in the form of fat may have been particularly advantageous in allowing women to become fertile or nurse their offspring in times of Arctic unpredictability or other periods of dietary insufficiency. A fat storage, or ‘thrifty’, metabolic genotype could have had a selective advantage and might explain the New World syndrome and the rapid increases in the prevalence of diseases resulting from overexposure to today’s excessive diets. Given what has been learnt about the remarkable diversity of genetic variation resulting from selective pressures imposed by malaria, what degree of complexity is likely when it comes to searching for some of the major genes that underlie polymorphisms of this kind which may well be considerably

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older? Recent studies on both insulin-dependent and non-insulin-dependent diabetes mellitus, in both humans and murine models, provide clear evidence that many different genes may be involved (Davies et a1 1994, Bennett et a1 1995, Vaxillaire et a1 1995). The recent discovery of the gene that appears to be responsible for a breed of mice which is obese and shows features of the insulin-resistant phenotype in man, and the identification of its human homologue, provides further encouragement that, ultimately, it may be possible to define at least some of the major genes involved in this type of disorder (Zhang et a1 1994). Considerable progress has also been made in defining susceptibility genes for cardiovascular disease, Alzheimer’s disease, post-menopausal osteoporosis and some forms of familial cancer, notably of the breast and colon. Although there seems little doubt that it will be possible to identify some of the important genes and their polymorphisms involved in varying susceptibility to some of the common diseases of the developed world, it may be a long time before it is possible to use this information for predictive genetics. From what is emerging from work on the diverse genetic mechanisms that have been utilized over a relatively short time to yield the phenotype of relative resistance to malaria, and if this is not an unusual adaptive response to severe infection, it seems likely that many of the genetic polymorphisms that underlie varying responses to non-infectious chronic illnesses will be equally or even more complex. Adaptations that were of value in the past, a thrifty metabolic phenotype or a particularly effective haemostatic response to injury, which may be inappropriate for today’s high energy diets and sedentary lifestyles, may reflect complex genetic heterogeneity involving many different biochemical pathways. Most of the chronic illnesses that afflict the developed world are complicated by the ill-understood pathology of ageing. There is increasing evidence that this, too, may be modified by genetic factors. Although little is known about what these may be in higher organisms, work in Drosophila, yeast and Cuenorhabditis elegans suggests that at least some of them may involve the repair of endogenous damage to DNA (Gibbon 1990). There is also evidence that oxidant damage may be involved in a number of chronic illnesses including atheroma, cancer, cataract and dementia (Ames et a1 1993). The picture of tumorigenesis that is emerging for common tumours such as colon cancer is of the steady acquisition of mutations of different oncogenes. Some of these mutations may be inherited, but most of them are acquired, either through the action of endogenous agents that damage DNA or from the environment. It seems likely that ldany of our chronic diseases arise in the same way. In this context, ‘susceptibility genes’ reflect a mechanism for making such complicated chains of events more likely to occur. Given that there may be more than 50 genes involved in cholesterol metabolism alone, and adding those that regulate blood coagulation and the metabolism of the vessel wall, it is easy

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to see why the mechanisms of susceptibility to vascular disease will be so difficult to establish quantitatively. The way forward will undoubtedly be to continue to define some of the genes involved in these multigenic disorders, and then to test the predictive strength of their particular polymorphisms with prospective, community-based studies. Although this may be difficult in the case of diseases that occur predominantly in middle and old age, studies of this type can now be initiated for disorders such as type 1 diabetes and other conditions that present relatively early in life; but it may be a long time before information of this type can be used in programmes of predictive screening.

References Ames BN, Shigenaya MK, Hagen TM 1993 Oxidants, antioxidants and the degenerative disease of ageing. Proc Natl Acad Sci USA 90:79 15-7922 Bennett ST, Lucassen AM, Gough SCL et al 1995 Susceptibility to human type 1 diabetes at IDDM2 is determined by tandem repeat variation at the insulin gene minisatellite locus. Nat Genet 9:284292 Davies JL, Kawaguchi Y,Bennett ST et a1 1994 A genome-wide search for human type 1 diabetes susceptibility gene. Nature 371: 130-136 Flint J, Harding RM, Boyce AJ, Clegg JB 1993 The population genetics of the haemoglobinopathies. Clin Haematol 6:215-262 Garrod AE 1931 The inborn factors in disease: an essay. Oxford University Press, Oxford Gibbon A 1990 Gerontology research comes of age. Science 250:622425 Haldane JBS 1948 The rate of mutation of human genes. Proceedings of the Eighth International Congress of Genetics and Heredity. Hereditas (suppl) 35:267-273 Hill AVS, Allsopp CEM, Kwiatkowski D et a1 1991 Common West African HLA antigens are associated with protection from severe malaria. Nature 352:595400 Hill AVS, Elvin J, Willis AC et al 1992 Molecular analysis of the association of HLAB53 and resistance to severe malaria. Nature 360:434439 Jarolim P, Palek J, Amato D et a1 1991 Deletion in erythrocyte band 3 gene in malariaresistant Southeast Asian ovalocytosis. Proc Natl Acad Sci USA 88:11022-I1026 Kwiatkowski D 1995 Malarial toxins and the regulation of parasite density. Parasitol Today 11:206-212 Kwiatkowski D, Hill AVS, Sambou I et a1 1990 TNF concentration in fatal cerebral, non-fatal cerebral, and uncomplicated Plasmodium faleiparum malaria. Lancet 336: 1201-1 204 McGuire W, Hill AVS, Allsopp CEM, Greenwood BM, Kwiatkowski D 1994 Variation in the TNF-a promoter region associated with susceptibility to cerebral malaria. Nature 371508-511 Miller LH 1994 Impact of malaria on genetic polymorphism and genetic diseases in Africans and African Americans. Proc Natl Acad Sci USA 91:2415-2419 Mourant AE, Kopec AC, Domaniewska-Sobezak K 1978 Blood groups and disease. Oxford University Press, Oxford Murphy PM 1993 Molecular mimicry and the generation of host defense protein diversity. Cell 72:823-826

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Nee1 JV 1962 ‘Thrifty’ genotype rendered detrimental by progress? Am J Hum Genet 14:353-361 O’Brien SJ 1991 Ghetto legacy. Curr Biol 1:209-211 Ruwende C, Khoo SC, Snow RW et a1 1995 Natural selection of hemizygotes and heterozygotes for glucose-6-phosphate dehydrogenase deficiency in Africa by resistance to severe malaria. Nature 376:246-249 Vaxillaire M, Boccio V, Philippi A et a1 1995 A gene for early-onset non-insulindependent diabetes mellitus (Maturity Onset Diabetes of the Young) maps to chromosome 12q.Nat Genet 9:418423 Weatherall DJ, Bell JI, Clegg JB et al 1988 Genetic factors as determinants of infectious disease transmission in human communities. Philos Trans R SOCLond B Biol Sci 321:327-348 Weatherall DJ, Clegg JB 1981 The thalassaemia syndromes, 3rd edn. Blackwell Scientific, Oxford Weiss KM, Ferrell RE,Harris CL 1984 A New World syndrome of metabolic diseases with a genetic and evolutionary basis. Yearb Phys Anthropol 27:153-178 Yates SNR, Snow RW, Allsopp CEM et al 1995 Resistance of homozygotes but not heterozygotes for a+ thalassaemia to severe malaria: impiications for the time depth of malarial selection. Proc Brit SOCParasitol 5th Malaria Meeting, p 13-14 Zhang YY, Proenca R, Maffei M, Barone M, Leopold L, Friedman JM 1994 Positional cloning of the mouse obese gene and its human homologue. Nature 372:425-432 Zimmet P, Dowse G, Finch C, Sejeantson S, King H 1990 The epidemiology and natural history of NIDDM: lessons from the South Pacific. Diabetes Metab Rev 6:91-124

DISCUSSION Freimer: Why don’t you go back and screen the entire genome for the genes that might be involved with malaria; i.e. as if you didn’t have data for the few genes relevant for malaria risk that you already know about? Weatherall: I would probably be tempted to d o that in a place like Vanuatu, where there are no major differences in the transmission rates of malaria from Africa, but where it is a totally different disease (Maitland et a1 1995). It is possible that the parasite has changed in Vanuatu, but the American military records suggest that malaria decimated populations in that region as recently as the 1940s. We also know from the experiences of expatriates that the parasite is extremely virulent. Therefore, it looks as though there are other host factors in those island populations which we still don’t understand. It might be feasible to use modern linkage technology to try to identify them. Weiss: Have any of the South-East Asian haemoglobin variants been found in Madagascar? Because Madagascar was settled to some extent by South-East Asians. Weatherall: As far as I know, this has not been studied.

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W. Bodmer: Human erythrocytes don’t have HLA, and so they cannot have a T cell response. This contrasts to mouse erythrocytes, and it creates problems in using the mouse as a model because H-2 is present to a significant extent on mature mouse erythrocytes. I would like your comments on the importance of the T cell response. Is it important when it’s not against erythrocytes, or is it important when it’s present in the early stages of erythrocyte maturation when there may be enough HLA on the surface for a T cell response to be relevant? Weatherall: The immune response is probably most important at the liver stage because HLA is expressed on liver parenchyma cells. W. Bodmer: But liver parenchyma cells are very low in HLA. Weatherall: Yes. But it is still possible that liver cells could present malarial antigens to appropriate subsets of T cells (Hill et a1 1991, Miller 1994). W. Bodmer: Have you looked at whether the HLA class I expression increases when the liver is infected? Because it’s generally low or absent in liver parenchyma cells. Weatherall: That would be a difficult experiment to do! W. Bodmer: I was surprised that Grenadans have a high frequency of sicklecell anaemia, compared to the North American Black population. Two factors may be involved. One is that there seems to be much less European admixture in that population, and the other is that malaria was present until comparatively recently. One could imagine that those two factors maintained the high frequency. What is your opinion on the high frequency in the West Indies? Weatherall: We’ve done extensive haplotyping in Jamaica, and we have found that, even allowing for a lot of racial admixture, the situation is roughly what one would expect from the African slave movement into the Caribbean (Flint et a1 1993). There is possibly slightly more haplotype variation than we expected. One sometimes finds some of these genes at a high frequency in parts of the world where malaria has never existed, and that’s slightly awkward to explain. For example, there are parts of Polynesia where malaria has never occurred. John Clegg and his colleagues have analysed the types of thalassaemia in these populations (Flint et a1 1993). They make a strong case, by looking at the distribution of mutations in populations throughout the South Pacific, that the patchy distribution of a-thalassaemia in some nonmalarious regions reflects population movements out of malarious areas, with subsequent local expansion of individual mutations. Armour: Is it possible to use ancient DNA to construct the evolutionary histories of some of these loci? This may be a direct approach to address the issue of the ancient history of malaria in humans. Has anyone tried to find mosquitoes embedded in amber, for example? Weatherall: We think of nothing else! It is possible to detect thalassaemia mutations in old tissues and I suppose it may be feasible to identify parasite DNA.

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It is certainly difficult to explain why nobody has detected these mutations at high frequency in the indigenous populations of South America. Perhaps this simply reflects the short time that this part of the world has been exposed to malaria. The high frequencies in the Old World may, however, have been generated in less than 5000 years. Judging by the patchiness of the distribution of some of the mutations, it is conceivable that this period of expansion may be even shorter. Chakravarti: You’ve talked about the polymorphisms and variants that have been associated with resistance and susceptibility. What is known about parasite variation? Weatherall: Very little, I suspect. A remarkable amount of information is being obtained about antigenic variation in P . falciparum and about conservation of certain antigens. However, I think less progress has been made, so far, in relating this type of phenomenon to the clinical severity of malaria due to P . falciparum. We have certainly collected parasites from Vanuatu and they are being studied in this way but it is quite difficult to know what questions to ask at the moment. Perhaps the best test would be to introduce a non-immune human like yourself into Vanuatu, without malaria protection and see what happens. There seem to be genuine clusters of clinical severity of P. falciparum malaria in parts of Kenya (K. Marsh, personal communication 1995). It seems likely, though not certain, that this reflects alterations in parasite virulence. Kidd: Did I understand you correctly that there is a lot of genetic variation in the parasite within an individual? Is this because of post-infection evolution, as in the case of HIV, or from simple multiple infections? Weatherall: Both mechanisms are probably involved. There is some variation in the parasites from a single individual. Cloning experiments suggest that this may reflect anything from several to a thousand populations, although the smaller estimates seem more likely to be nearer the mark. Edwardr: Do mosquitoes have any variations in susceptibility,or are they all susceptible? Also, how did they get to Grenada? Were they transported by boat, or are they natives? Weatherall: One theory is that they went across with Columbus. There are malaria parasites in many human populations, but there is no evidence that P . falciparum was present in South America much before a few hundred years ago. It would be interesting to study the antigenic make-up of South American parasite populations and compare it with other parts of the world.

References Flint J, Harding RM, Boyce AJ, Clegg JB 1993 The population genetics of the haemoglobinopathies. Clin Haematol 6:215-262

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Hill AVS, Allsopp CEM, Kwiatkowski D et a1 1991 Common West African HLA antigens are associated with protection from severe malaria. Nature 352:595400 Maitland K, Williams TN, Pet0 T et a1 1995 Absence of cerebral malaria and malariaspecific mortality in children on Espiritu Santo, Vanuatu, South Pacific, an area of hyperendemic malaria, submitted Miller LH 1994 Impact of malaria on genetic polymorphism and genetic diseases in Africans and African Americans. Proc Natl Acad Sci USA 91:2415-2419

Final discussion

Weiss: Several people have used the phrase “the per cent of variation explained by this or that” in a variety of ways. What are the different meanings of this phrase, and are they being used in a consistent way? Edwards: The practical problem is how do these percentages actually relate to what would happen to the population if it were possible to remove the allele? Templeton: When I use that phrase, I use it in the standard analysis of variance context. That is, one can start with a total array of phenotypic variance after adjusting the data for concomitant variables, and then do a cladistic analysis. One may generate nested categories, or clades, for example, that have different phenotypic effects. One can then define the variance between these clades and determine what proportion of the total phenotypic variance that is represented by the variance can be explained by differences among the clades. However, there are other ways of talking about the meaning of that phrase. Sing: Are you talking about fixed effects or random effects? Templeton: That depends, in part, on the phenotype. I am talking mostly about fixed effects because I am analysing measured genotypes, which are regarded as fixed effects. Sing: Then you are partitioning the total samplesum of the squares rather than the total phenotypic variance. Templeton: I’ve done both. For example, I have studied random effects in my analysis of Drosophila. Lathrop: The per cent of variation explained by linkage is often reported for analyses of disease phenotypes in F2 or backcross cohorts obtained by crossing inbred strains of rats or mice. One important point is that people often refer to the results of classical analyses of variance of the marker locus, and this does not take into account that the trait locus may be at some distance from the marker and recombine with it. The actual variance explained by the trait locus may, therefore, be larger than that which is reported. W. Bodmer: Can someone clarify the difference between fixed and random effects? Templeton: This difference refers to the sampling model that you use and what population you’re sampling from. Fixed effects constitute the universe you’re interested in, whereas random effects are a sample of a broader universe of possible effects. 312

Final discussion

313

W. Bodmer: Do you take into account the gene frequencies in the population? Templeton: No, not necessarily. For example, I have looked at the effects on fecundity in Drosophila (Templeton et a1 1993). I had one measured genotype called abnormal abdomen, and so I could characterize at the molecular level whether this gene was present or not. Therefore, abnormal abdomen was a fixed effect in my model. W. Bodmer: So you were not taking into account the frequency of the gene. Templeton: I was drawing several different lines that all had the same abnormal abdomen genotype but whose genetic background was drawn at random from a population. I did not have any measurements on the other genes but I knew that there were modifiers. I regarded each strain within the abnormal abdomen category and each strain within the non-abnormal abdomen category as random effect models, i.e. each strain was a treatment but it was a random effect treatment out of a gene pool of which I did not know anything about the alleles or their frequencies. W. Bodmer: Do you assume that the fixed effect has no variance? Templeton: Yes. A fixed effect has no variance. W. Bodmer: There are no biological effects that I know of that don’t have a variance. Templeton: But you’re looking at a variance within the genotype, which has been fixed for that particular set of stocks. W. Bodmer: So in this case a random effect is simply random background variation. Sing: Yes. In one case you’re decomposing the estimate of the population variance and in the other case you’re decomposing the samplesum of the squares. There are many ways of looking at this. John Edwards’ question is important because in the literature on measured genotype effects, especially in the field of cardiovascular disease, it’s difficult to tell whether people are talking about the subdivision of the sum of the squares or the estimation of variance components. Chukraborty: In my opinion, John Edwards’ question related to a much broader issue. We estimate variance components by either of two approaches. In one approach, the strata are unequivocally defined, such as in the cladistic approach that Alan Templeton described or in the quantitative trait loci approach that Mark Lathrop presented. In the other approach, the components of variance have reference sources for which the strata are unspecified (e.g. genetic, environmental, etc). Once we have estimates of the various components-for example those explained by specific loci (or specific mutations at specific l o c i b w e can ask what would happen to a population if a factor is removed. This would resemble the situation where specific mutations are eliminated by gene therapy, for example, from the population. Such a question can be answered by evaluating what is called, in epidemiology, the

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attributable risk. In contrast, for unspecified strata (genes versus environment), the relative contributions to the total sum of squares cannot be equated in the above fashion. Attributable risk is not identical to heritability. Although they are related concepts, one cannot be obtained from the other without some extra information (Chakraborty 1984).

References Chakraborty R 1984 Relative contributions of gene and environment: attributable risk and heritability. J Indian Anthropol SOC19:147-152 Templeton AR, Hollocher H, Johnston JS 1993 The molecular through ecological genetics of abnormal abdomen in Drosophila mercatorum. V. Female phenotypic expression on natural genetic backgrounds and in natural environments. Genetics 134475485

Summary Kenneth M. Weiss Department of Anthropology, The Pennsylvania State University, 409 Carpenter Building, University Park, PA 16802-3404, USA

In the course of this symposium I learned that there had been other Ciba Foundation symposiaon the general topic of human genetic variation. The first of these, entitled ‘Biochemistryof human genetics’,was in 1959 and it concerned the blood-borne polymorphisms and haemoglobin variants that could be detected with the technology at that time. Although only 35 years ago, the resulting collection of papers on diverse aspects of this subject seems rather quaint. The 1978 symposium entitled ‘Human genetics: possibilities and realities’ addressed human variation in a more disease-related context. The view expressed by several attending that meeting was that human genetics was on the threshold of a new age of discovery, based on the newly arrived methods in DNA technology. Only one short DNA sequence appears in that volume, and the papers seem to lack a sense of common technology or approach. Nonetheless, some of the issues discussed then clearly foreshadowed concerns that were raised at this symposium. During the 1978 meeting, Walter Bodmer remarked that ‘the question of what is the history of the genome is an extremely interesting and important one that can only really be approached at the molecular level.’ He also observed that ‘submission to a model of quantitative inheritance [for complex traits] based on variance analysis is a surrender of despair to complexity which shouldn’t and needn’t be made. It is even less necessary if the number of basic genetic functions is not so large, so that we really do have a chance of finding something out about most, or at least many of them.’ Participants also remarked on the high level of public hyperbole about genetics, the promises being made, and the dangers that lay therein. In his closing remarks at the 1978 symposium the chairman, Sydney Brenner, said that ‘If natural complex systems resemble any man-made structure we know that they would be full of compromises, patches and even useless appendages. Perhaps we should be hesitant to read into what we observe something of the logic of the system itself, if indeed it has any logic to speak of, apart from the fact that it survives.’ We may not have resolved these issues, merely just revisited them. However, we have at least done so in the light of vastly more knowledge and better 315

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techniques than were available 20 years ago. We have grappled with interpreting these new data to make inferences about the genetic logic of human phenotypes. Several issues considered in this symposium had common themes, which may reflect revised aspects of genetic variation, viewed in its evolutionary context. One theme is the history of human genes. Because of their rarity and hierarchical structure, we can trace mutations in populations and look more critically at the factors that influence variation than was previously possible. Some of these factors include population size, stochastic variation in the gene and population tree, and the history of subdivision and interactions among human populations. We discussed the complex, but historically understandable, patterns of variation observed for relatively simple Mendelian, or single-locus diseases. Although the details vary-as we clearly saw in regard to the nucleotide repeat regions-the picture is essentially consistent for all such traits that have been looked at in detail. There are tens or hundreds of different alleles at human loci, which are geographically distributed in a way that reflects population history. In a frustrated sotto voce aside during one of the discussions, Julia Bodmer remarked that ‘we’ll never come to the end of all these variants!’ This aptly reflects the challenge of understanding the causal basis of genetic disease in the face of so much variation and its effect on phenotypes related to chronic diseases, such as cancer, diabetes or heart disease. It may not be easy to assess the effect of each of these alleles on risk, which would be required for effective counselling. In addition, the large number and regional localization of variant alleles poses problems for screening and detection procedures, which may need to be tailored specifically to different regions of the world. It is interesting that even in the seemingly simplest diseases, such as cystic fibrosis, consensus could not be reached as to whether their dispersion and frequency patterns can be explained by positive balancing selection. Alternatively, these may merely reflect bias in the retrospective ascertainment of the clinical systems in industrialized nations. Systematic studies of variation from large samples of normal individuals in different populations may some day be more informative on this score. Ryk Ward and I structured this symposium to look first at simple and then at complex traits. In both cases a similar pattern of variation at the level of the individual locus is clearly observed. However, complex traits involve additional interactions among the alleles at the loci, and interactions between their alleles and their environments. Charlie Sing stated that different instances of the same disease may be caused by different ‘models’, i.e. by different combinations of genetic and environmental risk factors and interactions. This is what we should expect for phenotypes whose origin was filtered through the evolutionary sieving processes.

Summary

317

The problem of explaining complex traits led to lively discussions on the reductionist approach of modem science. Reductionism is a term used with different nuances by different individuals. In its strongest sense, it is the search for specific causal factors whose effects can be dissected from a complex whole. Individual alleles with strong effects on chronic disease risk, for example the AF508 mutation for cystic fibrosis, have been effectively identified by the repertoire of molecular methods now available. But are such clear-cut cases the exceptions? Their relative rarity may prove the rule that most genetic variation, having been generated by rather noisy evolutionary processes, has rather little effect on phenotypes. If this is more accurate, then most cases of chronic disease in our population result from variable combinations and interactions among these individually less important factors. Simple reductionism-the search to explain each step, separately, in a tractable way-may fail to provide major public health benefits in this case. But reductionism as an approach in genetics justly received spirited defence at this symposium. Our methods for identifying and characterizing genes are powerful and effective, even for complex traits, as we see reflected in the journals every week. That not all problems have been worked out cannot deter us from the vigorous use of those methods. Genetics has never been so in flower as it is today. However, new approaches may be needed to deal with the causal genetic complexity within and among populations. Fortunately, molecular genetics has revealed deep similarities in the genetic architecture of all the types of traits considered during this symposium. Unifying generalizations are one result of evolutionary approaches to molecular genetics and generalizations are at the heart of any science. For example, we have already seen how cladistic methods may ‘reduce’ the causal dimensions of genetic variation by taking the hierarchical history of mutational processes into account. I would also like to mention a third Ciba Foundation symposium, Molecular Approaches to Human Polygenic Disease, which was held in 1986. That symposium clearly foreshadowed much of what we have discussed here, although the data and methods are, even in the short decade since then, considerably more advanced. Until recently, the major challenge in human genetics has been to identify, locate and characterize genes. Most work has relied on families segregating a clear-cut risk of disease. But for every locus subsequently studied in detail, a wealth of data on variation has been generated, which we are not yet fully able to digest. Classical models of genetic causation still serve, but they are showing limitations. Interpreting the variation in the human genome is likely to be the next major challenge in human genetics. Much is expected of us but the problems are daunting.

Index of contributors

Non-participating co-authors are indicated by asterisks. Entries in bold type indicate papers; other entries refer to discussion contributions. Indexes compiled by Liza Weinkove.

Armour, J., 67, 68, 69, 70, 115, 130,

Donnelly, P., 25, 40,41, 42, 43, 44,45, 46, 95, 117, 130

131, 133, 135, 193, 309

Balmain, A., 134, 135, 156, 169, 170, 171, 172, 173, 174, 175, 177, 191, 192

*Barnes, R. I., 194 Beighton, P., 49, 90, 91, 95, 114, 153, 178, 255, 256

Bertranpetit, J., 20,69,95,97, 114, 115, 116, 117, 254, 255, 256 Bodmer, J., 170, 233, 253, 254, 255, 256, 257 Bodmer, W. F., 20, 24, 41, 42, 46, 47, 48, 49, 68, 69, 70, 92, 94, 114, 115, 116, 128, 129, 130, 131, 132, 133, 135, 154, 169, 171, 173, 174, 175, 176, 177, 178,181, 189,190, 191,192, 193,229, 230, 231,232,254,255,256,257,258, 279, 280,294,295, 309, 312,313, 314 Bowcock, A. M., 19, 47, 69, 70, 128, 170, 173, 257, 258 *Bryson, S., 156 *Byck, S., 73

*Calafell, F., 97 Cao, A., 137, 152, 153, 154 Chakraborty, R., 21, 22,40,42, 68, 69, 70, 71, 130, 132, 230, 255, 313, 314 Chakravarti, A., 22, 23, 43, 45, 71, 92, 94, 115, 116, 117, 131, 134, 172, 176, 177, 189, 190,192,207,230,280,281, 297, 310 Clark, A., 43, 48, 130, 132, 134, 172, 173, 174, 175, 277, 297

*Cohen, J., 194 318

Edwards, J. H., 118, 133, 171, 176,208, 255, 257, 280, 310, 312

*Fee, F., 156 Freimer, N . B., 51, 68,69, 71, 117, 129, 132, 134, 154, 173, 175,230,254,257, 308

*Galanello, R., 137 *Gaw,A., 194 Harper, P. S., 44, 49, 93, 94, 126, 128, 129, 131, 132, 133, 231, 232, 295

Hartl, D. L., 42, 45, 48, 93, 94, 114, 117, 293

*Hadand, M. B., 211 *Hoang, L., 73 Hobbs, H. H., 194, 206,207,208,209 Kidd, K. K., 23,45,47, 69, 70,94, 117, 129, 131, 134,253,254,256,257,281, 310

*Landschulz, K., 194 Lathrop, G. M., 284, 293, 294, 295, 296, 297, 312

*Nagase, H., 156 *PAH Mutation Analysis Consortium, 73 *Prevost, L., 73

319

Index of contributors

*Reilly, S. L., 211 *Richards, R. I., 119 *Rosatelli, C., 137 Scriver, C. R., 44,49,73,91,92,93,94, 95. 114. 153, 154.. 176.. 208 Sing, C. F:, 43, 95, 171, 172, 190, 191, 192, 211,229,230, 232, 295, 297, 312, 313 *Slatkin, M., 51 Sutherland, G. R., 70, 119, 126, 128, 129, 131, 132, 133, 134, 135 Templeton, A. R., 20, 23, 116, 192, 208, 231,254,259, 278,280, 281, 296, 312, 313

*Tomfinson, I., 181 *Valencia, D., 6 Ward, R. H., 6, 19, 20, 21, 22, 71, 256, 294 Weatherall, D. J., 93, 95, 133, 151, 152, 154, 174, 300, 308, 309, 310 Weiss, K. M., 1, 20, 21, 22, 23, 41, 44, 46,49,69,70,91,93,94,95, 114, 115, 116, 126, 128, 132, 133, 152, 168, 169, 172, 176, 177, 178, 190, 191,207,229, 231, 232, 281, 308, 312, 315 Zechner, R., 135, 206, 207, 209

Subject index

abetalipoproteinaemia, 196, 209 abnormal abdomen, 3 13 ACE (angiotensin I-converting enzyme) gene, 288, 290-291, 295 adducin genes, 288-289 adenomatous polyposis coli familial see familial adenomatous polyposis coli gene see APC African Blacks cystic fibrosis, 114-1 15 dynamic mutation disorders, 130, 131, 132 HLA haplotypes, 237, 241, 255-256 phenylketonuria, 91 age APOE polymorphism effects and, 223 of onset, Huntington’s disease, 129 plasma LDL-cholesterol levels and, 208 tumour development and, 169, 177, 181 ageing, 306 alleles, 3-4 size in dynamic mutation disorders, 128, 129-130 microsatellites, 54, 55-59, 6142, 63-64, 69 Alzheimer’s disease, 246, 270-274, 280 Amerindians, 6 2 4 , 71 diabetes mellitus, 305 genomic variability and ancestral inference, 14-16 HLA alleles, 248-249 intertribal phylogeography, 16-17 intratribal phylogeography, 16 mitochondria1 DNA variability, 9-10 population coalescence, 10-14 ancestor, most recent common (MRCA), 27, 3 4 3 6

ancestral coalescence see coalescence, ancestral angiotensin I-converting enzyme (ACE) gene (ACE), 288, 290-291, 295 serum levels, 290-291, 295 angiotensinogen gene, 289-290 ankylosing spondylitis, 246, 247, 256257 anticipation, 124, 133-134 APC, 157, 177 mutations in mice, 165, 171 tumour development and, 182-1 83, 187-188, 191 APOAI/CIZI/AIV gene cluster, 43, 278 apoB, 43, 274 mutations, 196 plasma LDL-cholesterol levels and, 196, 200,208-209 apoCI, 270, 271-273 apoE, 95 Alzheimer’s disease and, 270, 271, 272, 273-274 coronary artery disease and, 221-224, 23 1 plasma LDL-cholesterol levels and, 196, 297 apolipoprotein B-100 (apoB-loo), 195 familial defective, 195-196 gene see apoB apolipoprotein E (apoE), 195 gene see apoE apoptosis, 183-184 cancer model incorporating, 184-188, 189-192 ascertainment bias, 92-93, 124, 133-134 atherosclerosis, 206-207, 218-219 atrial natriuretic peptide receptor A gene (GCA), 288 attributable risk, 314 autoimmune diseases, 248, 255, 257

320

Subject index

Baconian-Cartesian-NewtonianDarwinianxomtean (BCNDC) research strategy, 215-217, 225 band 3 protein, erythrocyte membrane, 302-303 BehGet’s disease, 246, 247 blood group antigens, 303 blood pressure variation, 285-287, 288289, 297-298 see also hypertension bottleneck effects, 37, 99, 248 breast cancer, 176-177 cancer, 21 1 heritability, 174-178 HLA haplotypes and, 241, 255 mortality rates, 233, 234 see also colorectal cancer; tumorigenesis; tumours causal agents complex organization, 2 17-2 18 relationship between multiple, 215217 CFTR see cystic fibrosis transmembrane regulator Chibcha tribe, 16 chimpanzees, 45, 130-131, 251, 275 cytochrome oxidase subunit I1 haplotypes, 267-268 microsatellites, 58, 59, 63-64, 71 Chinese, 237, 248 cholera, 101, 102, 114, 117 cholesterol, plasma apoE polymorphism and, 222, 223, 231. 297 LDLR and, 201, 203, 279 see also hypercholesterolaemia; low density lipoprotein-cholesterol (LDL-C), plasma cholesteryl ester transfer protein (CETP), 201, 206, 207 chorionic villus sampling, 140, 148, 151 chromosome abnormalities, murine skin tumours, 158

c1-

defective transport, 98, 99 -losing diarrhoea, 101, 114 cladistic analyses, 259-283,296-297, 312 see also gene genealogies; haplotype trees

321

coalescence ancestral, 7-9 in Amerindian tribes, 16, 17 mitochondria1 DNA variability and, 14-16 lineage, 11-13, 17 population, 1&14, 17, 20 times, 11-13, 17, 61 coalescent theory, 25-50, 1 1 6 117 haplotype tree structure and, 26 1-262 microsatellite evolution and, 59-61 colorectal cancer, 177, 181, 183-184 APC mutations and, 182-183, 191 hereditary non-polyposis (HNPCC), 183, 184 microsatellite changes, 52-53, 55 common multifactorial diseases biological model for aetiologies, 21822 1, 225 cladistic approaches, 259-283 genetic architecture, 21 1-232 mouse models, 171 population variability and, 300-3 11 quantitative phenotype analysis, 284285,289-291, 293-298 selection and, 304-307 complexity, 4, 219-221, 225, 229-232, 317 of aetiologies of health, 217-218 apoE studies and, 221-224 microsatellite, 6 2 4 4 congeneic lines, 288 congenital malformations, 192-193, 295-296 contingency approach, 266-269 Corfu GB-thalassaemia, 145 coronary artery disease, 194-195, 21 1 apoE polymorphisms and, 221-224, 23 1 biological model for aetiologies, 218-221, 225 genetic architecture, 21 1-212, 2 15-21 7 counselling, genetic, 139-140, 154 cyclin D1, 158, 164 cystic fibrosis, 97-1 18, 316 in different populations, 91, 92 reasons for high frequency, 49, 99-101, 114-118, 304

322

cystic fibrosis transmembrane regulator (CFTR), 98-99 dF508 mutation, 98-99, 101-102, 115, 117-118 age of, 104-112, 115-116 geographical distribution, 104, 117-1 18 maximum parsimony haplotype tree, 110, 111 unique origin, 106-107 microsatellite mutation pattern, 108 mutation rate of microsatellites, 107-108 original microsatellite haplotype, 108-110 spectrum of mutations, 99, 101-104 cytochrome oxidase subunit I1 (COZI), 266-268 Dahl salt-sensitive hypertensive rat, 286, 289 Darwin, C., 1, 2 denaturing gradient gel electrophoresis (DGGE), 77, 143, 145 dentatorubral-pallidolyusian atrophy, 124 diabetes mellitus, 21 1 insulin-dependent, 246, 247, 248, 293-295, 306 maturity-onset, in the young (MODY), 295, 296 non-insulin-dependent (NIDDM), 287, 294,296, 305-306 rodent models, 287 diarrhoea1 diseases, 101, 114, 304 diet apoB haplotypes and, 274 fat content, 206, 207, 208-209, 223 differentiation, cancer model incorporating, 184-188, 189-192 DMBA (7,lZdimethyl- 1,2-benzanthracene), 157-158, 164, 172, 175 DNA methylation, 121, 123 mitochondrial see mitochondria1 DNA repair, 52-53, 173, 183-184, 192, 306 sequencing, 9, 62-64, 143,260-261 Drosophila, 134, 274, 278-279, 297, 313 dynamic mutations see mutations, dynamic dyslexia, 231-232

Subject index

education, on /?-thalassaemia, 138-139 effective migration rate, 62 effective population size, 37, 67-68 elliptocytosis, Melanesian, 303 environment cancer development and, 175 common multifactorial diseases and, 212, 213, 214 correlation with genotype, 278, 279 interactions with genes, 2, 285 epidermal growth factor receptor, 158 epistasis, 278, 279 ethnic groups, HLA and disease associations, 241-247 Europeans, 21, 23 cystic fibrosis, 99, 101, 103-104, 117-1 18 HLA haplotypes, 237 evolution, 1-3, 4 HLA genes, 236 human, 9 microsatellites, 51-72 significance of genetic variation, 261-265 evolutionary history estimation in humans, 261 populations, 10-1 1, 25-50 evolutionary medicine, 4-5 familial adenomatous polyposis coli, 182-183, 191 mouse model, 165, 171 fast protein liquid chromatography (FPLC), 201, 202, 203 fat intake, dietary, 206, 207, 208-209, 223 fetal blood analysis, 140, 148, 149 fibrinogen, 230 fixed effects, 3 12-3 13 FMRI, 64, 121 founder effects, 49, 71 cystic fibrosis frequency and, 100 PAH allele frequencies/ distribution, 84, 86, 91 FRAIIB, 123 FRAMA, 122, 123 fragile sites, 119-120, 123 folate-sensitive, 124 fragile X syndrome. 119-136 Gheritaice, 120

Subject index

linkage disequilibrium and, 121-122, 129, 131-132 molecular genetics, 121 timing of allele expansion, 122-123 FRAXA (fragile X site), 119-120, 123, 130-131 FRAXE, 123 FRAXF, 123 French Canadians, 43-44, 153 PAH mutations, 81-86, 9&91 FsTstatistic, 47, 62 Gaucher disease, 49 gender apoE polymorphism effects and, 223 autoimmune diseases and, 257 cancer susceptibility and, 176 gene(s) environmental interactions, 2, 285 flow, 20, 23-24 interactions between, 2, 216-217 gene genealogies, 7 4 , 22-23 coalescent approach, 25-50 founding population size and, 4 3 4 microsatellites, 60 population size variations and, 10-11, 28, 29-30, 35, 36-38, 54-55 population structure and, 30-34, 35 star-shaped, 29, 35, 36-37, 4041, 43-44, 55 genealogical trees; gene trees see gene genealogies genetic diseases, 44 microsatellite mutations, 52, 65 see also cystic fibrosis; fragile X syndrome; phenylketonuria; p-thalassaemia genetic distances, CFTR mutation frequencies, 103-104, 105, 116 genetic drift, 69 cystic fibrosis frequency and, 115-118 in microsatellites, 69, 70 in myotonic dystrophy, 130 PAH allele frequencies/distribution and, 84, 86, 93-95 genetic surveys, 260-261, 275 genotype correlation with environment, 278, 279 phenotype relationship, 3,4, 269-274

323

GK rat, 287 globins analysis of genes, 141-145 unbalanced alp chain synthesis, 140141 glucose-6-phosphate dehydrogenase deficiency, 302 gorillas, 45, 275 H-ras gene, 157-158, 164, 165, 177 haemochromatosis, 258, 304 haemoglobin A2 (HbAZ), 140-141, 143 E, 302 fetal (HbF) hereditary persistence (HPHF), 141, 291 in p-thalassaemia, 141, 147, 152 S, 302 haemophiliacs, 249-25 1, 254 haplotype trees, 261, 274275, 278-282 CFTR dF508 mutation. 110. 11 1 genotype/phenotype relationsdip and, 269-274 interspecific vs intraspecific aspects, 265-269 structure, 261-265 haplotypes, 9, 24 HDL see high density lipoprotein health genetic architecture of, 217-218 model for human, 218-221, 223 heart disease mortality rates, 233, 234 see also coronary artery disease hereditary non-polyposis colorectal cancer (HNPCC), 183, 184 heterozygosity, 26, 36, 45 heterozygote advantage, 49, 100-101 H d , 165 high density lipoprotein (HDL), 195, 206 apoE polymorphism and, 222, 223 in LDLR knockout mice, 201, 202 Hirschsprung disease, 193 HIV HLA alleles and, 249-251, 254-255, 279-280 long terminal repeat (LTR) loci, 262-263, 264, 265 ~~

Subject index

324 HIV (cont.) nefi 262-263, 264, 265 HLA, 233-258, 279 absence of allele specificity in a population, 249 allele frequency distribution, 237-241, 242-245, 253-254 interaction with disease, 248-249, 254-255 Class I, 235-236 Class 11, 235-236 diabetes mellitus and, 246, 247, 248, 293-294 disease associations, 241, 246, 255, 256-258 constancy in different populations, 241-247 differences in different populations, 248 in future, 251 disease resistance and, 248 haplotypes, 237, 253 malaria susceptibility and, 303, 309 organization of genes, 233-236 polymorphism, 236-237, 238-240 restriction, 249-25 1 selection and, 47, 236, 249 system, 233-237 vaccines and, 251-252 HLA-A2, 237, 249-251, 253-254 HLA-B27, 247, 256-257 Hodgkin’s disease, 241, 255 Human Genetic Diversity Project, 76 Human Genome Project, 1, 7, 231, 232, 264, 275 human T cell lymphotropic virus (HTLV), 19 Huntington’s disease, 124, 126-129, 133 11p-hydroxylase gene, 289 hypercholesterolaemia familial, 195, 196, 207 associated with normocholesterolaemia, 197-201, 209 mouse model, 206 polygenic, 207-208 hyperphenylalaninaemia, 74, 76, 87 newborn screening, 76, 81,92, 93 pros and cons of studying, 76-17 see also phenylketonuria hypertension candidate genes, 288-289

malaria and, 303-304 QTL mapping in rodents, 285-287 quantitative variation in humans, 289-29 1 rodent models, 285-286, 298 hypobetalipoproteinaemia, familial, 196 identity by descent (IBD), 3, 22-23, 64 identity by state (IBS), 3, 22-23, 64 IgE, 287, 291 Igflr, 164-165 immunosuppression, tumour development and, 170 imprinting in fragile X syndrome, 120, 122 skin tumours in mice and, 173-174 inbreeding, 157, 173, 285 infectious diseases HLA associations, 241 mortality rates, 233, 234 selection pressures from, 300-30 1, 304-305 infertility, 102 infinite alleles model, 53, 102-103, 117 inheritance cancer, 174-178 fragile X syndrome, 120 Ink4 (p16ink4)~ IS9, 165 insulin gene, 5‘ VNTR region, 294-295 insulin-like growth factor 2 (IGFZ), 173-174 intermediate density lipoprotein (IDL), 195 iron depletion, 304 Jacobsen syndrome, 123 Japan, 93, 249, 256 K-rus, 164 Khoi, 241, 256 Knudson’s two-stage model, 182, 191

169, 178,

latitude, cystic fibrosis and, 104 LDL see low density lipoprotein LDLR, 195, 196, 223, 279, 297 knockout mice, 201-204, 206

Subject index

missense mutation, 197-199 plasma LDL-cholesterol levels and, 207-208 lineage coalescence, 11-13, 17 loss, 11, 12 sharing, 17, 22 linkage analysis candidate LDL-lowering genes, 20020 1 M . spretus tumour resistance (Spr) genes, 160, 161, 172-173 see also quantitative trait loci (QTL) mapping linkage disequilibrium, 270-27 1 CFTR gene, 100, 106-107, 114 in fragile X syndrome, 121-122, 129, 131-132 HLA genes, 236, 237, 247, 248 in myotonic dystrophy, 129 in PAH gene, 80 lipids, plasma apoBand, 274 apoE polymorphism and, 221-223 in LDLR knockout mice, 201-203 locus, 24 low density lipoprotein (LDL), 194-195, 206 fractional catabolic rate (FCR), 200 mean production rate, 200 receptor (LDLR), 195, 207 gene see LDLR in the Puerto Rican (P.) family, 197-199 low density lipoprotein-cholesterol (LDL-C), plasma, 194210 candidate genes causing lowered, 20020 1 genetic defects affecting, 195-196 in LDLR knockout mice, 201-204 family, 197, in the Puerto Rican (P.) 199-200 lung cancer, 175-176, 183 Lyon hypertensive rat (LH), 286 magnification, 134 major histocompatibility complex (MHC), 63, 265 see also HLA malaria, 301-304, 308-310

325 mammary tumours, 170, 171 mariner transposable elements, 274 marriage partner, choice of, 154 maturity-onset diabetes in the young (MODY), 295, 296 mean corpuscular volume-mean corpuscular haemoglobin (MCV-MCH), 140, 141, 143 mental retardation, 76, 119 methylation, DNA, 121, 123 microsatellites, 8, 19, 51-72 CFTR intragenic, 98, 106-1 10, 112 changes in tumours, 52-53, 69-70, 134-135 evidence for complexity, 6 2 4 4 exploiting mutation models, 5942 mechanisms of mutation, 52, 64-65, 67, 68, 70 modelling mutation process, 53-59, 69-70 see also trinucleotide repeats microsomal triglyceride transfer protein, 196, 209 migration, 11, 20-24 PAH allele frequencies/distribution and, 81, 84, 86 rate effective, 62 gene genealogies and, 3 1-34 Milan hypertensive rat, 286, 288 Min mouse, 165, 173 minisatellite variant repeat mapping, 8, 130 mitochondria1 DNA, 9-17, 19-23, 38, 46, 54, 278 ancestral coalescence estimation, 14-16 intertribal phylogeography, 1 6 1 7 intratribal phylogeography, 16 variability in Amerindians, 9-10 Mom1 gene, 165 mortality rates, 233, 234 most recent common ancestor (MRCA), 27, 34-36 mouse mammary tumour virus (MMTV), 170 MTSl locus, 159 multifactorial diseases, common see common multifactorial diseases multilocus approach, 3, 38-39, 44

326

Subject index

multiple endocrine neoplasia 2A nef, 262-263, 264, 265 neighbour-joining tree, 104, 10s (MENZA), 177 multiple sclerosis, 246, 248 neonates, screening for phenylketoMus spretus, 171 nuria, 76, 81, 92, 93 Mus musculus crosses, 159, 160, neurofibromatosis, 178 neurological disorders, 120, 124, 133 172-1 74 tumour resistance (Spr) genes, neutral theory, 45,4849 160-163, 164-165 neutrality contingency approach, 268-269 mutation rates, 248-249, 278, 279 CFTR, 99-100 in dynamic mutation disorders, 127128 CFTR intragenic microsatellites, 107-108, 112 ‘New World syndrome’, 305 non-obese diabetic (NOD) mouse, 287 estimation, 36, 193 Nuu-Chah-Nulth, 9-17 fragile X syndrome, 120 gene tree structure and, 40-41 microsatellites, 53, 59, 64, 65, 67-68 obesity, 296, 305, 306 population coalescence and, 13 ochratoxin A, 87 tumour development and, 188, 191, overdominance 193 associative, 62 mutations, 3 cystic fibrosis frequency and, 100-101, cladistic analysis, 273-274, 275 114-1 15 contingency approach, 268-269 dynamic, 126-128 16ink4 (Ink@, 159, 165 in fragile X syndrome, 120, 121 in non-human species, 123-124, 130 p.53, 157, 177-178, 183-184, 188 unusual inheritance and, 124 knockout mice, 165, 171, 177-178, gene tree structure and, 25, 26, 34-36, 193 38, 40-43 in skin tumours, 158-159, 165, 171 germline, 68, 156-157, 182 PAH gene, 73-96 haplotype tree structure and, 262-263 disease-producing mutations, ‘ 74-75 intraspecific vs interspecific in French Canadians, 81-86, 90-91 distributions, 266-269 global distribution of mutations, microsatellite, 51-72 86-87,90-93 recurrent haplotypes, 80 in CFTR gene, 100, 115 mutations, 77-80 PAH allele frequencies/distribution PAH Mutation Analysis Consortium, and, 84, 85-86 77 in skin tumours, 157-159 pairwise sequence differences, 26, 36, 46 stepwise (one-step) model, 54-55, 59, papillomaviruses, 170 69-70 Pasl, 164 pemphigus vulgaris, 247 in tumour development, 181-183, 189-192, 306 penetrance, of fragile X mutation, 120 two-phase model, 55, 59, 69 phenotypes MUTPRED program, 77-80 genotype relationship, 3, 4, 269-274 myotonic dystrophy, 55, 124, 126-134 multiple transitions, 27 1-273 myotonin protein kinase, 124, 132 phenylalanine hydroxylase gene see PAH gene phenylketonuria, 74, 76, 117-1 18 frequencies, 84-85, 90-92 Na-Dene tribe, 17 heterozygote advantage, 49, 87 narcolepsy, 246, 247, 248, 257-258 newborn screening, 76, 81,92, 93 nasopharyngeal carcinoma, 246, 248

Subject index

pros and cons of studying, 7 6 7 7 phylogenetic analysis, 7-8 phylogeny(ies), 1 demographic change and, 10-1 1 star-shaped, 11, 278, 281-282 phylogeography, 6-24 intertribal, 16-17 intratribal, 16 polygenic model, 3 polymerase chain reaction (PCR), 62, 80, 143 population(s) coalescence see coalescence, population effective size, 37, 67-68 evolutionary history, 10-1 1, 25-50 fission-fusion, 11, 20-21, 23-24 founder size variation, 4 3 4 genetics of health and, 217-218 genetics of tumours, 181-193 size changes, 46 ancestral humans, 9 gene genealogies and, 10-11, 28, 29-30, 35, 36-38, 54-55 structure in Amerindian tribes, 11 gene genealogies and, 3G34, 35 trees, 20, 22-23 pregnancy termination, 148, 154 premutations, in fragile X syndrome, 121, 122-123, 132 prenatal diagnosis, 8-thalassaemia, 140, 148, 149, 151-153 primates, 45, 275 fragile X site, 130-131 microsatellites, 58, 59, 6 3 4 4 see also chimpanzees protein electrophoresis, 6-7, 260 psoriasis, 246, 248, 257, 258 psychiatric disorders, 21 1 Puerto Rican (P.) family, 197-201, 209 mouse model simulating, 201-204 quantitative trait loci (QTL)mapping, 206, 284-299 fine, and gene identification, 287-289 in human multifactonal diseases, 284-285, 289-291, 293-298 M.spretus tumour resistance (Spr) genes, 160, 161, 172-173 in rodent models, 285-287

327

random effects, 3 12-3 13 ras, 163-164, 174, 187-188 see also H-rus Rb (retinoblastoma), 157, 170-171, 178, 189 recessive alleles, rare, 278, 279 recombination, 26, 261 cladistic approach and, 261, 278-279 PAH allele frequencies/distribution and, 83-85 reductionism, 216, 230-231, 317 Reiter’s disease, 256 religious aspects, 151-152, 153 renin gene, 286-287, 288 replication slippage see slippage, replication response elements, 4 restriction fragment length polymorphisms (RFLPs), 8, 21, 47 ancestral resolution from, 14, 15 CFTR gene, 98, 106 restriction site mapping, 260-261 ret, 177, 193 retinoblastoma gene (Rb), 157, 170-171, 178, 189 retroviral elements, 19 RFLPs see restriction fragment length polymorphisms rheumatoid arthritis, 246, 247 RsT statistic, 62 Sagene, 288 sample size, 28-29, 4 M 1 sampling strategies, 21-22, 38-39, 44 San (Bushmen), 241, 256 Sardinia, 54-55, 137-1 55 Sardinian G/3-thalassaemia, 141-143, 147 SCAI, 64, 122 screening phenylketonuria, 76, 81, 92, 93 /3-thalassaemia, 138-139 secretor status, 305 selection, 2, 4, 4 6 4 9 coalescent approach and, 4 4 4 5 , 48 common multifactorial diseases and, 304-307 cystic fibrosis frequency and, 100-101, 102, 114-1 15, 117, 304 haplotype tree structure and, 262, 263-265, 218

328 Selection (cont.) heterozygote advantage and, 49, 100-101 in HIV infection, 279-280 on HLA genes, 47, 236, 249 infectious diseases and, 300-301, 304 on microsatellite loci, 62 PAH allele frequencies/distribution and, 80, 85, 87, 94 P-thalassaemia and, 301-302 sex see gender sex workers, Kenyan, 249, 254 Sherman paradox, 120, 121 sickle-cell disease, 95, 307 simple sequence repeats, 5 1 see also microsatellites; trinucleotide repeats single-locus analysis, 38-39, 44 multifactorial diseases, 2 15-21 6 skin tumours, 156-180 candidate susceptibility genes, 164165 future prospects, 165-166 genetic risk of malignant conversion, 160-164 linkage mapping of M. spretus resistance genes, 160, 161, 172-173 papillomaviruses and, 170 predisposition of mouse strains to, 159, 174 slippage, replication microsatellite mutations and, 52, 64, 67, 68, 70 PAH allele frequencies/distribution and, 85,86 smoking, 175-176, 183, 230 South Africa, 49, 90-91, 255-256 spinocerebellar ataxia type 1, 124 spontaneously hypertensive rat (SHR), 285-286, 288 spontaneously hypertensive stroke-prone rat (SHR/SP), 286, 288 Spr genes, 160-163, 164-165 star shaped genealogies, 29, 35, 36-37, 4 0 4 1 , 4 3 4 , 55 star shaped phylogenies, 11, 278, 28 1-282 stem cells, 184-186 stwwise (one-step) mutation model, * 54-55, 59, 69-70

Subject index

strain differences atherosclefosis susceptibility, 206-207 LDLR-deficient mice, 201-204 skin tumour predisposition, 159, 174 systemic lupus erythematosus, 246, 248 T cell receptors (TCRs), 236 Tay-Sachs disease, 153, 154, 262, 304 a-thalassaemia, 143-144, 301, 302, 309 fl-thalassaemia, 137-1 55 a-thalassaemia double heterozygotes, 140, 143-144, 146 carrier detection methods, 140-1 41 counselling, 139-140, 154 8-thalassaemia double heterozygotes, 140, 141 education and screening, 138-139 efficacy of programme, 148-149 globin gene analysis, 141-145 intermedia, 146-148 major, 145 malaria susceptibility and, 301-302 prediction of phenotype, 145-148, 152 prenatal diagnosis, 140, 148, 149, 151-153 a-thalassaemia, 144145, 146 Gfl-thalassaemia, 141, 145 Sardinian, 141-143, 147 ‘thrifty genotype’ hypothesis, 305 TPA (12-0-tetradecanoyl-phorbol-13acetate), 157-158 traditional societies, 6-24 transgene studies trinucleotide repeats, 123-124, 130, 135 tumour susceptibility, 165 tribes, 7 see also Amerindians trinucleotide repeats disorders involving expansion, 126-134 in fragile X syndrome, 120, 121, 122, 126 mechanisms of unstable expansion, 52, 64 in non-human species, 123-124, 130 transgene studies, 123-124, 130, 135 unusual inheritance patterns and, 124 see also microsatellites tuberculosis, 115, 262, 304

Subject index

tumorigenesis, 181-182, 306 Knudson’s two-step model, 169, 178, 182, 191 model involving differentiation/ apoptosis, 184-188, 189-192 skin, in mice, 156-180 tumour necrosis factor u (TNF-a), 303 tumour suppressor genes, 182-1 83 see also APC; p53; Rb tumours candidate predisposing genes, 164-165 microsatellite instability, 52-53, 69-70, 134-1 35 population genetics, 181-193 predisposition of mouse strains, 159, 174 progression, 159, 160-164, 187-188, 190

329

see also cancer; tumorigenesis two-phase mutation model, 55, 59, 69 uveitis, 246, 256 vaccines, 251-252 very low density lipoproteins (VLDL), 195, 209 viral peptide restriction, 251 von Willebrand factor gene, 70-71 waist-to-hip ratios, 222, 223 Wright-Fisher model, 4 2 4 3

Xpc gene knockout mice, ZFY region,

14

173