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September 2011 Volume 15, Number 9 pp. 375–446 Special Issue: The Genetics of Cognition Editor Stavroula Kousta Executive Editor, Neuroscience Katja Brose Journal Manager Rolf van der Sanden Journal Administrators Patrick Scheffmann and Ria Otten Advisory Editorial Board R. Adolphs, Caltech, CA, USA R. Baillargeon, U. Illinois, IL, USA N. Chater, University of Warwick, UK P. Dayan, University College London, UK S. Dehaene, INSERM, France D. Dennett, Tufts U., MA, USA J. Driver, University College, London, UK Y. Dudai, Weizmann Institute, Israel A.K. Engel, Hamburg University, Germany M. Farah, U. Pennsylvania, PA, USA S. Fiske, Princeton U., NJ, USA A.D. Friederici, MPI, Leipzig, Germany O. Hikosaka, NIH, MD, USA R. Jackendoff, Tufts U., MA, USA P. Johnson-Laird, Princeton U., NJ, USA N. Kanwisher, MIT, MA, USA C. Koch, Caltech, CA, USA M. Kutas, UCSD, CA, USA N.K. Logothetis, MPI, Tübingen, Germany J.L. McClelland, Stanford U., CA, USA E.K. Miller, MIT, MA, USA E. Phelps, New York U., NY, USA R. Poldrack, U. Texas Austin, TX, USA M.E. Raichle, Washington U., MO, USA T.W. Robbins, U. Cambridge, UK A. Wagner, Stanford U., CA, USA V. Walsh, University College, London, UK Editorial Enquiries

Editorial

375 Uncovering the genetic underpinnings of cognition

Trevor W. Robbins and Stavroula Kousta

Update FORUM: Science & Society

378 Genetics and criminal responsibility

Stephen J. Morse

Review

381 Genetics of human episodic memory: dealing with complexity

Andreas Papassotiropoulos and Dominique J.-F. de Quervain

388 The genetics of cognitive ability and cognitive ageing in healthy older people

Sarah E. Harris and Ian J. Deary

395 Dissecting the genetic architecture of human personality

Marcus R. Munafò and Jonathan Flint

401 Genetics of emotion

Laura Bevilacqua and David Goldman

409 Genetics of autism spectrum disorders

Daniel H. Geschwind

417 Understanding risk for psychopathology through imaging gene–environment interactions

Luke W. Hyde, Ryan Bogdan and Ahmad R. Hariri

428 The genetics of cognitive impairment in schizophrenia: a phenomic perspective

Robert M. Bilder, Andrew Howe, Nic Novak, Fred W. Sabb and D. Stott Parker

436 The contribution of imaging genetics to the development of predictive markers for addictions

Eva Loth, Fabiana Carvalho and Gunter Schumann

Trends in Cognitive Sciences

Cell Press 600 Technology Square Cambridge, MA 02139, USA Tel: +1 617 397 2817 Fax: +1 617 397 2810 E-mail: [email protected]

Cover: Twin, family and adoption studies have demonstrated that there is a substantial heritable component to all cognitive functions. The articles in this special issue summarize what is currently known about the genetic underpinnings of these functions and their disorders. At the same time, they highlight just how much there is yet to be discovered in this rapidly advancing field. Cover image: Adrian Neal/Lifesize/Getty Images.

Editorial

Special Issue: The Genetics of Cognition

Uncovering the genetic underpinnings of cognition Trevor W. Robbins1 and Stavroula Kousta2 1 2

Department of Experimental Psychology, University of Cambridge, Downing Street, CB2 3EB Cambridge, UK Editor, Trends in Cognitive Sciences, Cell Press, 600 Technology Square, Cambridge MA, 02142, USA

No one today can ignore the genetic approach to cognition and behavior, given the huge achievements of the Human Genome Project (http://www.ornl.gov/sci/techresources/ Human_Genome/home.shtml). The initial impact of studies of individual differences in genetic polymorphisms, such as the catechol-O-methyl transferase (COMT) gene, and their relationship to such core cognitive concepts as working memory (in this case via its modulation by prefrontal dopamine [1]) has been immense, catching the imagination of many cognitive neuroscientists. Here is a way, for example, to evaluate the effect of individual differences in neurotransmitter function without the need to administer drugs. Alternatively, possible heterogeneity in behavioral performance or patterns of neural network activation revealed through functional neuroimaging, may be resolved by taking into account genetic factors. Taken together, the contributions in this special issue address the genetic underpinnings of key aspects of cognition, such as memory, intelligence, reward processing, as well as emotion and personality, in the context of both healthy populations and in key disorders, such as schizophrenia, autism, and addiction. Collectively, the articles discuss evidence from a variety of perspectives and different approaches, including twin, linkage, candidate gene, genome-wide association, imaging genetics, gene environenvironment interaction, and gene expression studies, providing insight into the strengths and challenges for each approach. A key insight arising from several articles in this issue is that variations in complex psychological attributes such as intelligence and psychosis are usually likely to be determined by multiple genes, each exerting a small effect. Moreover, it is becoming increasingly clear that, so as to establish valid behavioral–genetic correlations, it is necessary to test large populations, in order to avoid spurious false positive effects arising from the very large number of contributing genes. Finally, the well-known mantra of the importance of ‘gene environment interaction’ has gained additional complexity from the discovery of effects of imprinted genes and epigenetic factors. As several articles in this special issue make clear, some of the inspiration for the genetic (and epigenetic) approach to cognition comes from our need to understand better the origin of disorders in mental health. The hope is that by understanding their genetic basis, we will be able to identify the causal molecular pathology of different disorders Corresponding authors: Robbins, T.W. ([email protected]); Kousta, S. ([email protected]).

and capitalize on proteomic approaches for treatments. By paying attention to possible intermediate phenotypes (‘endophenotypes’), which may have a biochemical, neural or even cognitive-behavioral basis, we may be able to bridge the present daunting gap between genes and psychopathology. Rapid advances in the field of genetics have also raised several ethical and legal questions. In the first article in this issue, Morse discusses one such question: what are the implications of genetic research for the concept of criminal responsibility? Morse explains that the concept of criminal responsibility as determined by law is based on identifying relevant mental states: was there an intention to commit a crime? And did the individual possess knowledge of wrongdoing? Genetic research, on the other hand, is concerned with mechanistic causation, and causation does not currently constitute an excusing condition in law. Unless it can be shown that biological causation is directly responsible for a particular mental state, it cannot be used either as a mitigating factor or an excuse for a criminal act. But one could argue, and some have, that an individual’s genetic make-up completely determines behaviour, hence suggesting that the concept of criminal responsibility as defined by law should be completely revised: it is biology, not mental states that matter. However, Morse argues, genetic research (or any other research) has provided no evidence to support the idea that mental states are redundant and do not have a causal role in driving behavior. And denying a critical role for mental states would do away with human motivation to do anything whatsoever, not just a criminal act. The four review articles that follow address different aspects of cognitive and emotional processes from a genetic perspective. Papassotiropoulos and De Quervain overview the literature on the genetic underpinnings of human episodic memory, discussing the findings of two key approaches: candidate gene studies based on well-established molecular pathways involved in synaptic plasticity and hypothesis-free genome-wide association studies. Although both types of approaches have yielded promising results, much remains to be discovered. Key in achieving progress, the authors argue, is understanding the phenotypic complexity of episodic memory: episodic memory is not one phenotype, and similarity of a phenotype between studies might not be sufficient for replication purposes. Moreover, most studies (both candidate-gene and genomewide association studies) still perform simple statistics, analyzing genetic variants independently of each other. This approach covers a minor part of the complexity of episodic memory, and new methods are needed to capture

1364-6613/$ – see front matter ß 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.tics.2011.07.006 Trends in Cognitive Sciences, September 2011, Vol. 15, No. 9

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Editorial the genetic variability of human memory, for instance, through additive or epistatic interactions. Cognitive ageing affects people’s quality of life and predicts dementia and death. Harris and Deary focus on the genetics of cognitive function in healthy aging, and summarize findings across several different approaches in behavioral and molecular genetics. They discuss a number of findings, best established among which is evidence that the e4 haplotype of the apoliprotein E (APOE) gene is positively associated with cognitive ageing trajectories and with cognitive function in elderly individuals. Several other loci might be implicated in cognitive function and decline with age, however, and the authors argue that welldesigned longitudinal studies, with long follow up times, are required to determine further genetic influences on cognitive ageing in healthy older individuals. Munafo` and Flint critically review the personality genetics literature, as a case study of the missing heritability problem: as with any other complex trait, the genetic architecture of personality traits, the authors argue, is likely to be the result of several hundreds, if not thousands, of small effect loci, which together nonetheless produce substantial heritability. Early candidate gene studies found positive associations of the serotonin transporter gene (SLC6A4) with measures of neuroticism, and the dopamine D4 receptor gene (DRD4) with novelty seeking. Although follow up candidate gene studies, as well as gene by environment interaction studies, abound, meta-analytic studies suggest that these genes, as well as other candidate genes identified, have a very small effect at best. In fact, a number of recent genome-wide association studies have failed to identify any locus with clear genome-wide significance. Munafo` and Flint argue that, in order to achieve progress in this field, it is important to consider issues of power, statistical stringency and independent replication; and to acknowledge that, as with other complex traits, such as blood pressure and height, variation in personality is the outcome of the combined effect of several small effect loci. In a related contribution, Bevilacqua and Goldman discuss the genetic basis of emotional processing, personality and temperament, focusing on functional variants at five genes: COMT, SLC6A4, neuropeptide Y (NPY), a glucocorticoid receptor-regulating cochaperone of stress proteins (FKBP5) and pituitary adenylate cyclase-activating polypeptide (PACAP), as illustrative of the effects of genes on emotion. They address factors that alter or confound the effects of these genes, especially gene environenvironment interactions, as emotionality is strongly influenced by exposure to stress. They acknowledge the issues highlighted in Munafo` and Flint’s contribution—the few genome-wide association studies that have examined the effects on emotion of genes identified in candidate gene studies have indeed failed to yield any significant results for these genes. And they converge with Munafo` and Flint on likely explanations (statistical power and the fact that emotionality is likely to be underpinned by several different loci of small effect). However, despite lack of genomewide significance in the few studies carried out up to now, the authors argue, these genes have large effects on metabolic responses of the brain to emotional stimuli assessed 376

Trends in Cognitive Sciences September 2011, Vol. 15, No. 9

in real time by brain imaging, hence validating the effects of these genes in emotion. The final four review articles in this issue address the genetic basis of disorders in which cognitive and emotional dysfunctions are a key aspect. Amongst them, autism genetics may be the area where most progress has been achieved, with known genetic causes for 10–20% of the cases. Geschwind discusses the current state of the art in autism genetics, highlighting the fact that autism spectrum disorders (ASD) are both highly heritable but also etiologically heterogeneous. Moreover, he points out that the autism phenotype is broad and overlaps with many other neurodevelopmental disorders. Geschwind reviews research that has identified common and rare gene variants and explains how these findings verify or refute prevailing genetic models of autism. He explains that new approaches, such as gene expression and epigenetics, have provided novel insight into the genetic basis of ASD. This more recent research has suggested that, despite their heterogeneity, the multiple genetic causes of ASD converge on a few biological pathways affected in most individuals with autism. In the context of psychopathology, Hyde, Bogdan and Hariri discuss recent advances in gene environment interactions and imaging genetics, two approaches that up to the present have been largely independent. They highlight the key strengths as well as challenges for both strands of research and argue that bridging these two approaches will provide crucial insight into risk factors for psychopathology. They lay out the foundations of an integrated approach, imaging gene environment interactions, by examining statistical methods for combining the two approaches and by discussing plausible biological mechanisms, such as epigenetics, that can explain the interplay between genes, experience, and the brain. Imaging gene environment interactions, the authors argue, holds significant promise to elucidate biological mechanisms that underlie the etiology and pathophysiology of psychopathology and for the development of personalized treatment and refined diagnostic criteria. It is well-established that, in addition to psychotic symptoms, cognitive impairment in schizophrenia is severe. Bilder and colleagues’ contribution addresses our current understanding of the genetic basis of cognitive dysfunction in schizophrenia. They conclude that, despite intense research, little progress has been made up to the present in detecting genetic associations for cognitive impairments in schizophrenia. The authors advocate a specific approach, phenomics, as offering significant promise in this field, by emphasizing the simultaneous study of multiple phenotypes across biological scales. This approach might be particularly fruitful if it is the case that the high heritability of schizophrenia and cognitive impairments is due to large numbers of variants with small effects. Bilder et al. present a new collaborative database, CogGene, to share data on genetic associations with cognitive phenotypes. They show how examining results from this database in parallel with results from a similar database on genetic associations in schizophrenia (SZGene) can provide important insight into the genetic underpinnings of cognitive impairment in schizophrenia.

Editorial Abnormalities in reinforcement behaviour (sensitivity to rewards and punishment, inhibitory control and stressrelated emotional vulnerability), particularly during adolescence, have been linked to addiction vulnerability. Loth, Carvalho and Schumann examine the genetic basis of inter-individual differences in reinforcement and reward-related processes by focusing on imaging genetic studies. These studies have addressed associations between one or a few candidate polymorphisms and taskrelated brain activation differences in a few regions of interest, and have revealed effect sizes that manifold exceed the variance typically explained with behavioural traits or cognitive measures. Despite their usefulness, however, results across studies are not always consistent, and the authors advocate strengthening the imaging genetics approach by examining large sample sizes and using


longitudinal designs (with repeated testing of the same individuals at different ages). Twin, family and adoption studies have demonstrated that there is a substantial heritable component to all cognitive functions [2]. The articles in this special issue summarize what is currently known about the genetic underpinnings of these functions and their disorders. At the same time, they highlight just how much there is yet to be discovered in this rapidly advancing field. References 1 Egan, M.F. et al. (2001) Effect of COMT Val108/158 Met genotype on frontal lobe function and risk for schizophrenia. Proc. Natl. Acad. Sci. U.S.A. 98, 6917–6922 2 Bouchard, T.J. and McGue, M. (2003) Genetic and environmental influences on human psychological differences. J. Neurobiol. 54, 4–45

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Update FORUM: Science & Society


Genetics and criminal responsibility Stephen J. Morse University of Pennsylvania Law School, Psychiatry Department and Center for Neuroscience and Society, 3400 Chestnut Street, Philadelphia, PA 19104-6204, USA

Some believe that genetics threatens privacy and autonomy and will eviscerate the concept of human nature. Despite the astonishing research advances, however, none of these dire predictions and no radical transformation of the law have occurred. Advocates have tried to use genetic evidence to affect judgments of criminal responsibility. At present, however genetic research can provide little aid to assessments of criminal responsibility and it does not suggest a radical critique of responsibility.

Internal and external interdisciplinary critiques Another discipline could influence the law by providing either an internal or an external critique. In the former, the general validity of a legal doctrine, practice or institution is accepted but the other discipline tries either to explain the legal phenomenon or to reform it. For example, findings from other disciplines might suggest that the doctrines of criminal responsibility should be altered in various ways but might not suggest that the concept of responsibility is incoherent. By contrast, an external critique suggests that the doctrine, practice or institution is invalid. Many people believe that discoveries in genetics and other sciences strongly suggest the truth of determinism (or something like it), and that if determinism is true, then no one can be genuinely responsible. For example, The Economist recently warned, ‘Genetics may yet threaten privacy, kill autonomy, make society homogeneous and gut the concept of human nature. But neuroscience could do all of these things first’ [1]. Blaming and punishing criminals is thus allegedly unfair because no one deserves such treatment [2]. The conclusion is that current conceptions of criminal justice should be abandoned because they rest on a morally mistaken foundation. External critiques are radical, whereas internal critiques produce incremental change or suggest that the current system is valid. The law’s psychology and general concept of responsibility To understand how genetics research can influence criminal law requires understanding of the law’s implicit psychology and concept of responsibility. Law is a system of rules and standards that is meant to guide human action by providing agents with reasons to act one way or another [3]. Criminal law, and indeed all law, therefore presupposes the ‘folk psychological’ view, which causally explains behavior in Corresponding author: Morse, S.J. ([email protected]).

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part by mental states such as desires, beliefs, intentions, volitions, and plans [4]. Other psychological, as well as biological and sociological variables also play a role but folk psychology considers mental states fundamental to a complete explanation of human action. The law’s concept of responsibility follows from the nature of law and the type of creature it addresses. Responsible agents are those who can be adequately guided by the law, which means, roughly, that only conscious, intentional and rational creatures with developed linguistic capacity can be responsible. This explains why young children and some people with mental abnormalities are not considered responsible [5]. Criminal law responsibility criteria Now let us be a bit more specific about the criteria for criminal liability, which are normative and not scientific facts. These criteria justify state blame and punishment because offenders who meet them deserve such treatment, and desert is at least a necessary condition for just punishment in the USA. The definitions of most criminal prohibitions include an intentional action done in a reasonably integrated state of consciousness that is accompanied by another mental state (mens rea) that indicates how culpable the action is. Note that these are both folk psychological criteria. For example, a common definition of murder is intentional killing conduct done with the purpose to kill. A neuromuscular spasm that causes the death of another is not an action, and if one is driving intentionally but completely carefully, then an entirely accidental killing of a pedestrian would not be done with the purpose to cause death. In both cases, the defendant is not culpable. Even if the agent does the prohibited act with the culpable mental state, the person will not be liable if he or she has an ‘affirmative defense’ because either the agent’s act was right or permissible under the circumstances (a justification) or the person was not a responsible agent at the time of the crime (an excuse). Intentionally killing a wrongful aggressor because the agent reasonably believes he must do so to save his own life, self-defense, is an example of the former. If the agent kills because he has the delusional belief that he is in deadly danger, then he has done wrong, but he might be excused by the defense of legal insanity because he was not a rational agent. There are two generic excusing conditions: lack of rational capacity and compulsion. The latter can be caused externally, such as cases involving dreadful ‘do-it-or-else’ threats (e.g. ‘kill or I will kill you’), or internally, such as

Update cases of strong internal desires (e.g. an addict’s desire to seek and use substances). In both cases, the agent acted intentionally but we might think it is unfair to ask him to control himself because it will be so difficult to do so. Note that affirmative defenses also involve folk psychology because they are based on mental states, including desires and beliefs. Translating genetic research for assessing criminal responsibility Genetics concerns mechanistic causation. Genes do not have mental states and do not commit crimes; people do. To make a useful internal contribution to criminal responsibility, the genetic data must be ‘translated’ into the law’s folk psychological responsibility criteria [6]. It must be shown how, precisely, the genetic data are relevant to whether a defendant acted, whether he or she possessed a particular mens rea, and whether the mental states relevant to defenses were present. It is not sufficient to indicate that genetics played a causal role in explaining the criminal behavior, even if that causal role is very powerful. Causation and predictability are not excusing conditions in law and causation is not the equivalent of legal compulsion (most action is not the causal result of dire threats or uncontrollable desires) [7]. If they were, no one would be responsible because we inhabit a causal universe, but we nonetheless hold people responsible. A genetic predisposition to criminal conduct does not per se mitigate or excuse. Causation is relevant only if it tends to show the presence of a genuine excusing condition, but it is the latter that does the legal work. Believing that causation per se mitigates or excuses responsibility is the most pernicious confusion bedeviling the attempt to relate scientific findings to criminal responsibility. I have termed it the ‘fundamental psycholegal error’ [8]. In the few legal cases in which genetic information has been used to mitigate responsibility, this error has been common [9,10]. We are reasonably confident that having a genetically induced MAO-A deficiency in interaction with childhood abuse causally increases the risk of criminal and antisocial behavior more than ninefold [11]. Nonetheless, there is no reason to believe that offenders exposed to that interaction did not act or form the required mental states. If exposure to that interaction somehow diminished their rationality or produced some type of uncontrollable internal desire, then mitigation or excuse might be warranted. Such a diminished rationality or control problem would have to be demonstrated independently by evidence other than causation data. Internal contributions unrelated to responsibility Genetic research might contribute internally to criminal law in ways unrelated to responsibility. For example, knowledge about genetic variables that predispose people to crime could enhance the accuracy of dangerousness predictions that affect sentencing and parole and it might enhance the efficacy of interventions to reduce the risk of crime. Note that if it were safe to release an offender early as a result of a successful, genetically based intervention, the reason would be public safety and cost and not because the offender deserved less punishment.


External challenges to the concept of responsibility Genetic research might also provide a radical external critique of criminal responsibility if it convincingly demonstrated that no one deserves punishment. It might do this either by lending support to a deterministic metaphysics, or by proving that our mental states play no role in explaining our behavior. The former is simply the familiar determinism and free will issue. Note that contra-causal libertarian free will is not an internal criterion in law and is not even foundational for criminal responsibility. For example, even if determinism is true, some defendants are delusional and most are not. Some offenders act with a gun at their heads but most do not. These are differences that make a moral difference according to theories of fairness we endorse. At the metaphysical level, there is a respectable view, ‘compatibilism,’ which holds that robust responsibility is possible in a deterministic world even if we lack libertarian free will [12]. Determinist ‘incompatibilists’ disagree, of course, but the dispute is not resolvable and the law cannot wait for the metaphysicians. Thus, the external critique based on determinism does not have much legal purchase, although it has proponents. Compatibilism cannot save responsibility from the second external critique because compatibilism presupposes the folk-psychological view of agency that the second critique denies. Some believe that mental states play no causal role – paradoxically, this is a motivating mental state for them – but neither genetics nor any other science at present remotely proves that our mental states are causally inert [6]. As the eminent philosopher of mind, Jerry Fodor, has written, if we are wrong about the importance of mental states, that will be about the ‘wrongest’ we have ever been about anything [13]. Finally, if our mental states, including our reasons for action, are simply an epiphenomenal sideshow the brain somehow constructs, what good reason would we have to do anything? The task of genetics and other sciences should be to explain our intentionality rather than reductively to explain it away. Acknowledgment As always, the author thanks his personal attorney, Jean Avnet Morse, for her sound sober counsel and moral support.

References 1 The Economist 23 May (2002) The ethics of brain science: open your mind, p. 77 2 Greene, J. and Cohen, J. (2006) For the law neuroscience changes nothing and everything. In Law and the Brain (Zeki, S. and Goodenough, O., eds), pp. 207–226, Oxford 3 Shapiro, S. (2000) Law, morality, and the guidance of conduct. Legal Theory 6, 127–170 4 Sifferd, K.L. (2006) In defense of the use of commonsense psychology in the criminal law. Law Philos. 25, 571–612 5 Morse, S.J. (2011) Gene-environment interactions, criminal responsibility and sentencing. In Gene-Environment Interactions in Developmental Psychopathology (Dodge, K.A. and Rutter, M., eds), pp. 207–234, Guilford 6 Morse, S.J. (2011) Lost in translation?: an essay on law and neuroscience. In Law and Neuroscience (Freeman, M., ed.), pp. 529– 562, Oxford 7 Morse, S.J. (2007) The non-problem of free will in forensic psychiatry and psychology. Behav. Sci. Law 25, 203–220 8 Morse, S.J. (1994) Culpability and control. Univ. Pa. Law Rev. 142, 1587–1660 379

Update 9 Rigoni, D. et al. (2010) How neuroscience and behavioral genetics improve. psychiatric assessment: report on a violent murder case. Front. Behav. Neurosci. 4, 1–10 10 Feresin, E. (2009) Lighter sentence for murderer with ‘bad genes’. Nature DOI: 10.1038/news.2009.1050 11 Caspi, A. et al. (2002) Role of genotype in the cycle of violence in maltreated children. Science 297, 851–854

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Trends in Cognitive Sciences September 2011, Vol. 15, No. 9 12 Lenman, J. (2006) Compatibilism and contractualism: the possibility of moral responsibility. Ethics 117, 7–31 13 Fodor, J. (1987) Psychosemantics: The Problem of Meaning in the Philosophy of Mind, MIT Press 1364-6613/$ – see front matter ß 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.tics.2011.06.009 Trends in Cognitive Sciences, September 2011, Vol. 15, No. 9

Review


Genetics of human episodic memory: dealing with complexity Andreas Papassotiropoulos1,2,3 and Dominique J.-F. de Quervain2,4 1

Department of Psychology, Division of Molecular Neuroscience, University of Basel, Basel, Switzerland University Psychiatric Clinics, University of Basel, Basel, Switzerland 3 Department Biozentrum, Life Sciences Training Facility, University of Basel, Basel, Switzerland 4 Department of Psychology, Division of Cognitive Neuroscience, University of Basel, Basel, Switzerland 2

Episodic memory is a polygenic behavioral trait with substantial heritability estimates. Despite its complexity, recent empirical evidence supports the notion that behavioral genetic studies of episodic memory might successfully identify trait-associated molecules and pathways. The development of high-throughput genotyping methods, of elaborated statistical analyses and of phenotypic assessment methods at the neural systems level will facilitate the reliable identification of novel memory-related genes. Importantly, a necessary crosstalk between behavioral genetic studies and investigation of causality by molecular genetic studies will ultimately pave the way towards the identification of biologically important, and hopefully druggable, genes and molecular pathways related to human episodic memory. Heritability of episodic memory as a prerequisite for genetic studies Episodic memory (see Glossary) is a major neurocognitive system that enables conscious recollection of past experiences (e.g. autobiographical episodes and learned material) along with their spatial and temporal contexts [1]. Episodic memory is subserved by a broad network of cortical and subcortical brain regions, is uniquely different from other memory systems, and probably unique to humans [1]. From a genetic standpoint, episodic memory can be defined as a genetically complex behavioral trait with substantial heritability estimates (i.e. genetic factors account for a significant proportion of the variance of this phenotype). Several twin studies report heritability values between 30% and 60% [2–8], indicating that naturally occurring genetic variations have an important impact on this cognitive ability. The majority of these studies treat episodic memory as a single unified construct without breaking it down to its underlying phenotypic structure, which is characterized, for example, by temporally distinct phases as assessed by short-delay recall and long-delay recall. However, recent empirical evidence from twin studies [9] revealed both overlapping and distinct genetic influences on these temporal components of episodic memory. In brief, twin studies in nonclinical populations clearly Corresponding authors: Papassotiropoulos, A. ([email protected]); de Quervain, D.J.F. ([email protected]).

demonstrate the importance of the genome for this neurocognitive trait. The significant proportion of phenotypic variability in episodic memory that is attributable to heritable factors is a prerequisite and a starting point for targeted genetic studies aimed at identifying specific molecules and the molecular pathways related to specific components of human episodic memory (Box 1). Human genetic approaches Traditionally, two main research strategies exist for the identification of genes related to heritable traits: linkage studies and association studies. In linkage studies, genetic markers of known chromosomal localization are usually examined within large pedigrees. If a certain marker is linked with the trait under study due to linkage disequilibrium with the trait-related genetic variant, then positional cloning, fine-mapping and in-depth resequencing can be performed to identify the trait-associated gene along with its causal variants. This strategy has been, and still is, particularly successful for traits and diseases following Mendelian inheritance but is also applicable to some geGlossary Allele: one of two or more forms of a gene, located on a specific position on a chromosome. Complex trait: a quantifiable property of an organism influenced by both genetic and environmental factors as well as interactions between them. Epigenetics: research field that investigates potentially heritable changes in gene expression caused by mechanisms other than changes in DNA sequence. Episodic memory: a memory system that enables conscious recollection of past experiences (e.g. autobiographical episodes and learned material) along with their spatial and temporal contexts. Mendelian inheritance: patterns of inheritance (i.e. manner in which genes and traits are passed from parents to offspring) following rules described in the 19th century by Gregor Johann Mendel, an Augustinian friar and scientist. Examples of diseases following Mendelian inheritance patterns include autosomal dominant (e.g. Huntington’s disease), autosomal recessive (e.g. cystic fibrosis) and sex chromosome-linked diseases (e.g. hemophilia A). Missing heritability: describes the fact that despite the success of large and dense genome-wide scans in the discovery of trait-associated genetic variants, a large portion of the heritability of complex traits still remains unexplained. Phenotype: physical appearance of an organism with respect to a trait (e.g. blue eye color). Polymorphism: in genetics, a difference in DNA sequence among individuals. A common form of a genetic polymorphism is a SNP, which occurs when a nucleotide — A, T, C or G — differs between individuals. The human genome contains millions of SNPs. Statistical epistasis: a concept defined about a century ago and dealing with the statistical deviation from additive interaction effects between two or more genetic polymorphisms.


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Review Box 1. Some prerequisites for successful genetic studies of human episodic memory Heritability: significant heritability (i.e. genetic factors account for a significant proportion of this phenotype’s variance) is a prerequisite for the reliable identification of genotype–phenotype correlations. Human episodic memory shows substantial heritability, with values ranging from 30% to 60%. Reliable and valid phenotypic assessment: episodic memory performance can be quantified by using reliable and valid instruments. Episodic memory is not a single phenotype but instead might show distinct molecular profiles, depending on the specific episodic memory task (e.g. word list vs story recall). Control for episodic memory-unrelated phenotypes: factors such as attention, concentration, motivation and working memory could influence episodic memory performance and might bias genetic association findings. Controlling for episodic memoryunrelated phenotypes is therefore crucial. Specific neural correlates: episodic memory has well definable and well quantifiable neural correlates (e.g. memory-related activity of the hippocampus and parahippocampal gyrus). This allows for further corroboration of genetic association findings at the neural systems level. Independent replication: regardless of the statistical models used and however simple or complicated they might be, successful gene identification stands and falls with independent replication of the gene–phenotype associations. This is particularly important in the era of GWAS, which screen for association between heritable traits and millions of genetic variants distributed over the entire genome, thereby introducing a serious type I error burden.

netically complex traits. Regarding episodic memory, linkage studies have successfully identified genes related to diseases characterized by impaired episodic memory, such as Alzheimer’s disease and other neurodegenerative conditions [10]. Association studies follow a different strategy. Based on theoretical considerations and previous knowledge, and in an unbiased genome-wide manner, the genotype frequencies of common genetic polymorphisms (i.e. with a minor allele frequency 1%) of groups of individuals are compared. Genetic association studies can be performed using both case-control (i.e. comparing unrelated individuals stratified into distinct phenotypic groups) and family-based designs (i.e. using relatives as a control group). Importantly (and in the case of episodic memory essentially), association studies are particularly applicable to quantitative traits in settings using both unrelated and related individuals. Candidate gene studies Whenever sufficient knowledge about the biological mechanisms underlying the physiology (and in case of disease, the pathophysiology) of a certain trait exists, candidate gene association studies, which assess the association between one or more alleles of a biologically plausible gene (or a set of genes) with the phenotype, could be readily implemented. In the case of episodic memory, sufficient biological knowledge does exist. Several animal studies in both invertebrates and vertebrates over the past three decades have identified genes and signaling molecules important for memory, including protein kinases and phosphatases, transcription factors, growth factors and receptor complexes [11–15]. These studies suggest that many memory-related molecular mechanisms are conserved across species and that molecules related to learning-related 382


synaptic plasticity, long-term potentiation, long-term depression and activity of such brain structures as the hippocampus, parahippocampal gyrus and the amygdala might represent ideal biological candidates for genetic association studies. Indeed, in 2003 two articles supported the notion that the candidate gene approach is promising by showing that functional single nucleotide polymorphisms (SNPs) in the genes encoding the 5-hydroxytryptamine (serotonin) receptor 2A (HTR2A) and the brainderived neurotrophic factor (BDNF) are associated with episodic memory performance [16,17]. Both molecules are highly expressed in the hippocampus and the cerebral cortex and have been implicated in animal learning and memory [18–20]. In humans, a common HTR2A SNP predicts an amino acid substitution (His to Tyr) at residue 452 (H452Y). Compared with carriers of the common His/His variant, heterozygous (His/Tyr) carriers show a blunted receptor response upon pharmacological stimulation with serotonin [21,22] and poorer episodic memory performance in verbal and figural tasks [16]. BDNF harbors a nonconservative SNP, which is located in the 50 pro-BDNF sequence and results in a Val to Met substitution at codon 66. The Met allele is related to deficits in the cellular distribution and regulated secretion of BDNF and to poorer episodic memory performance [17]. The demonstration of successful identification of episodic memory-related molecules through the candidate gene approach prompted subsequent studies, which reported on additional genes related to this cognitive capacity such as COMT [23], GRM3 [24], PRNP [25], CHRFAM7A [26], APOE [27,28], PDYN [29] and CPEB3 [30]. Although the majority of these studies remain to be independently replicated, they do suggest that focusing on pre-existing and well-established biological information could lead to the identification of genes related to episodic memory in humans. Genome-wide association studies (GWAS) On the one hand pre-existing biological information clearly facilitates the search for biologically meaningful candidates, on the other hand it could introduce a severe bias towards readily accessible molecular pathways and it definitely limits the potential of genetic association studies to identify novel genes and molecular pathways. Recent advances in the development of high-density genotyping platforms and analytical software now allow for high-resolution GWAS, which screen for association between heritable traits and millions of genetic variants distributed over the entire genome. This dense screening feature renders GWAS particularly useful in discovering novel molecular pathways of genetically complex traits. The first GWAS on episodic memory utilized a genotyping microarray containing more than 500 000 polymorphic markers and identified KIBRA and CLSTN2 as episodic memory-related genes [31]. Replication studies in European and Asian populations further supported the role of KIBRA in episodic memory (e.g. [32–35]), although not unequivocally [36]. Importantly, a study in two large Scottish samples suggested that KIBRA is related to processes specific to the conscious recall of item-based material, possibly reflecting hippocampal processing [32]. This high degree of specificity underscores the importance of precise

Review phenotypic assessment in behavioral genetic research, including replication studies. Subsequent GWAS in healthy participants supported the role of additional genes such as CAMTA1 [37] and NRXN1 [38] in episodic memory performance by using a variety of neuropsychological measurements. It is important to stress that the genes identified through genome-wide screening are involved in memory-related molecular pathways such as protein phosphorylation, calcium-responsive transcriptional activation and, in general, synaptic plasticity. Nevertheless, it is highly unlikely that most of these genes would have been selected a priori in a candidate gene setting, simply because very little was known regarding the biological relevance of these genes before their identification in the course of the GWAS. This demonstrates the substantial potential of this approach for the identification of novel genes and pathways related to human episodic memory. Our knowledge about the molecular underpinnings of this trait has already increased and it will undoubtedly continue to increase as larger and denser GWAS, utilizing refined analytical methodology, are expected in the near future. Genetic complexity GWAS could powerfully identify components of the genetic basis of complex multigenic traits. In the past few years, GWAS using hundreds of thousands and even millions of polymorphic markers, mainly SNPs, in samples ranging from a few hundred to several thousands of individuals have led and still lead to the identification of numerous susceptibility genes and trait-related genomic variants. However, despite this extensive use of genetic and analytical force, which has undoubtedly proven successful, a major portion of the heritability of complex traits still remains unexplained, a phenomenon commonly termed ‘missing heritability’ [39]. Although recent empirical and simulation studies suggest that modeling thousands of SNPs concurrently could explain large proportions of the missing heritability [40,41], the proportion of the observed phenotypic variance explained by the specific and statistically significant trait-associated variants is low. In other words, appropriate modeling shows that a large proportion of the heritability of a complex trait can be estimated by the genomic content of an SNP array [40]. However, when asking how much of the heritability of a complex trait can be explained by the significant GWAS SNPs identified to date, then the answer is generally low, depending on the trait of interest. Gene–gene interactions In addition to parent-of-origin effects and epigenetic factors, causal but nonexamined rare variants, and environmental influences, the negligence of analytical approaches accounting for gene–gene interaction effects, such as statistical epistasis, could partly explain the phenomenon of ‘missing heritability’ [42]. Indeed, despite the obvious conception that the analysis of genetically complex traits should account for the underlying biological and statistical complexity, the vast majority of large-scale genetic association studies to date are restricted to the use of singlemarker statistics. Clearly, this approach does not fully account for the polygenic nature of the phenotype under


study and erroneously implies that the impact of genetic variation is due to independently acting effects. The above also holds true for the study of the genetics of episodic memory. Although the large-scale analysis of statistical epistasis related to complex traits has proven feasible [43], only a few studies have addressed epistatic effects of a limited number of genetic variants on cognitive functions [44–47]. A functional magnetic resonance imaging (fMRI) study of 82 healthy participants demonstrated significant nonlinear interaction effects of two dopaminerelated genes. Specifically, both the COMT Val/Met polymorphism and the functional variable number of tandem repeat polymorphism in the 30 untranslated region of the dopamine transporter gene (SLC6A3) were independently associated with activation differences in the hippocampus during the encoding phase of a hippocampus-dependent memory task. In addition to these independent effects, interaction analyses revealed a nonlinear relationship between the two polymorphisms also at the hippocampal level [46]. Thus, focusing on main SNP effects only and neglecting the study of possible interaction effects most probably leaves a significant proportion of variability (and heritability) in episodic memory unexplained. In the light of the complex polygenic nature of episodic memory, studies accounting for the statistical interaction between few (e.g. two) genetic variants should be regarded as proof-of-principle studies and as a starting point for more complex approaches. However, studies involving comprehensive sets of genetic variants and addressing interaction (e.g. epistatic) effects on human memory are lacking. The concept of statistical epistasis was defined about a century ago and deals with the statistical deviation from additive interaction effects between genetic markers [48]. Per definition, the inclusion of such statistical interaction terms as epistasis exponentially increases the number of statistical tests performed. For example, a two-way interaction analysis between 1000 single markers requires the performance of 499 500 tests. Some strategies attempting to limit the number of necessary tests in analyses of epistasis employ a stepwise procedure by including only those interaction terms for which the corresponding marker showed a significant main effect in the first step singlemarker analysis. However, this approach is arbitrary because there is no biological rationale for considering only markers with significant main effects. Indeed, a recently published report of an exhaustive genome-wide analysis showed that significant epistatic interactions would have been missed if SNPs that did not display any main effect had been excluded a priori [49]. It remains to be shown whether these exhaustive approaches will prove feasible and successful in the study of the complex genetic architecture of episodic memory (Box 2). Gene clusters Rather than computing complex gene–gene interactions, another approach to capture the interplay between genetic variants and its impact on episodic memory focuses on the computation of compound gene and SNP clusters based on multilocus analyses. For example, a permutation-based method, termed set association, evaluates sets of polymorphic markers and provides a cluster of significant alleles 383

Review


and genotypes with a single test statistic. Importantly, such compound analyses defining gene clusters can be used for the calculation of aggregate individual genetic scores, which principally reflect a person’s number of trait-associated genetic variants weighted by the effect size of each variant. With regard to episodic memory, such approaches have proven feasible [50] and extendable to conditions of pathological cognition [51]. These multilocus methods represent an extension of the candidate gene approach and deal with sets of genes in biologically meaningful candidate pathways. A preselection of the human homologues of 47 genes with well-established molecular and biological functions in synaptic plasticity and animal memory led to the identification of a 7-gene cluster associated with episodic memory [50]. This gene cluster represents important memory-related molecules such as adenylyl cyclases, kinases and glutamate receptors. An aggregate, individual gene score based on the 7-gene cluster was also associated with activations in memory-related brain regions, such as the hippocampus and parahippocampal gyrus (Figure 1). The

Box 2. Dealing with the genetic complexity of episodic memory It is impossible to predict which statistical and bioinformatics approaches will ideally explain the relationship between genetic and phenotypic variability of such complex traits as episodic memory. It will be most probably a combination of several approaches specific to distinct memory-related phenotypes. The obvious genetic complexity of episodic memory, along with studies showing the importance of statistical methods accounting for gene–gene interactions, supports the notion that rare combinations of common variants significantly account for the variability and heritability of this trait. Therefore, the study of the genetic architecture of episodic memory will require a statistical and systems biology framework that accounts for the complexity of the interacting functional molecular networks. The current approaches (e.g. set-association [65], set-based tests [66], multifactor dimensionality reduction [67], gene set enrichment analysis [68], to name a few) are clearly a very good start. However, as more and more methods will become available, study designs based on multidisciplinary research frameworks and focusing on replication of the identified complex genetic structures will prove invaluable for the robust and reliable discovery of parts of the genetic underpinnings of episodic memory.

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Figure 1. Gene score related to human episodic memory and memory-related brain activity. (a) Squares represent a selection of the human homologues of 47 genes with well-established molecular and biological functions in synaptic plasticity and/or animal memory. Squares in red represent the seven genes for which significant association of genetic variations with human episodic memory was found. (b) The seven-SNP cluster was used for the calculation of an individual’s memory-related genetic score, termed individual memory-associated genetic score (IMAGS). (c) Regression analysis revealed a significant positive correlation between the IMAGS and learning-induced brain activations in the medial temporal lobe (MTL), including the hippocampus and parahippocampal gyrus. (d) Scatter plot illustrating the positive correlations between IMAGS and learning-induced brain activations in the hippocampus at coordinate position [24, 12, 20]. Adapted from [50].

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Review computation of aggregate genetic scores based on genetic clusters has hitherto relied on pre-existing information (i.e. a candidate pathway approach). Recently, similar gene clustering methods have been used for the calculation of genetic risk profiles by utilizing GWAS data [52]. Most probably, capitalizing on a combination of GWAS data and gene clustering methods will also facilitate the unbiased identification of novel gene clusters related to episodic memory capacity. Phenotypic complexity Episodic memory refers to memory for past experiences (e.g. autobiographical episodes and learned material), which includes information about the content of the experience and the spatial and temporal context in which it occurred [53,54]. It is important to realize that, in terms of neurobiological underpinnings, episodic memory performance could be subserved by distinct molecular profiles, depending on the specific episodic memory task used for quantifying performance. For example, the BDNF Val66Met polymorphism has been shown to be associated with delayed recall from stories of the Wechsler Memory Scale revised version but not with delayed recall of a word list taken from the California Verbal Learning Test [17], yet both tasks are referred to as episodic memory tasks. Furthermore, it is well known that emotionally arousing information is better stored into memory than neutral information and that this phenomenon depends on the activation of noradrenergic transmission [55,56]. Consequently, it has been shown that a functional deletion variant of ADRA2B, the gene encoding the a2b-adrenoceptor, is related to differential episodic memory performance for emotionally arousing pictures but not for neutral pictures [57]. Furthermore, it is important to note that several factors, which are not considered to belong to episodic memory, such as motivation, attention or concentration can have a large impact on performance in episodic memory tasks. Thus, in behavioral genetic studies on episodic memory it is crucial to control for such unspecific factors. The phenotypic complexity with regard to episodic memory not only has important and obvious implications for replication studies [32] but also for meta- and megaanalyses (i.e. the use of raw data from individual subjects across different studies): by combining several similar but not identical episodic memory phenotypes, genetic studies could identify statistically robust associations pointing to common denominators of these phenotypes but are likely to miss molecules specifically related to a distinct phenotype with distinct neurobiological features. Imaging genetics Besides the importance of replicating genetic association studies on episodic memory it is important to also consider additional methods for further corroboration and better understanding of the behavioral genetics findings. In the recent few years, brain-imaging techniques, in particular fMRI, have become increasingly popular for this purpose [31,58–60]. The rationale for combining behavioral genetic and neuroimaging methods is to validate and extend purely behavioral genetic studies by providing insight into the genetic differences in memory processes at the level of


neural circuits [61]. So far, the majority of imaging genetic studies has looked at single markers in relation to brain activation differences. However, considering the genetic and phenotypic complexity of human episodic memory, another promising approach involves studying specific genetic networks (comprising multiple markers in different genes) related to a certain episodic memory phenotype by investigating compound genetic scores in relation to brain activity [50] (Figure 1). The interpretation of genotype-dependent differences in brain activity depends crucially on the fMRI study design and the behavioral findings of the fMRI sample. Whenever significant performance differences across genotype groups exist, a possible outcome is higher activity in memoryrelated brain regions in the genotype group that shows higher memory performance [62]. In cases in which genotype groups of the fMRI sample have been matched for performance, increases in brain activity of the genotype group with low memory performance in the unmatched population can be interpreted as compensatory activity to achieve the same level of performance as the genotype group with high memory performance [31]. Interpretation becomes difficult if the fMRI study reveals genotype-dependent activity differences despite nonsignificant differences in memory performance in genotype groups unmatched for performance. This situation is common because the number of subjects used in imaging genetic studies reporting significant genotype-dependent differences in brain activity is typically between 20 and 60, whereas behavioral genetics studies usually used hundreds or thousands of subjects to consistently produce significant results [61]. A possible explanation for this observation is that neural activity is more proximate to the direct effects of functional genetic polymorphisms on gene products and their function, and might therefore be more sensitive in estimating genotypedependent differences in mental processing [61,63,64]. Nevertheless, genotype-dependent differences in brain activity that do not translate to significant differences in behavior should be interpreted with caution. Concluding remarks As with any genetically complex trait, neuroscientists focusing on the study of the genetic underpinnings of episodic memory have to deal with many complexity levels. Firstly, the trait (i.e. episodic memory) per se represents an assembly of phenotypes with overlapping but also partially distinct molecular profiles. Secondly, complexity at the genetic level is not only related to the polygenic nature of the trait itself but it also reflects the complexity of the human genome, which exerts its influence on the trait not only through simple linear gene effects but also through gene–gene interactions, gene–environment interactions and epigenetic mechanisms. Finally, the relation between genetic and phenotypic variability is not expected to follow simple and general rules applicable to every memoryrelated phenotype. Despite this obvious complexity, empirical evidence supports the notion that behavioral genetic studies of episodic memory successfully identify trait-associated molecules and pathways. As new technological and analytical approaches – both at the genetic and the phenotypic level – emerge, two 385

(Figure_2)TD$IG][ Review


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Figure 2. Crosstalk between behavioral/imaging genetic studies in humans, and behavioral and molecular genetic studies in animals. Human candidate gene studies depend on pre-existing information, which mostly originates from animal and molecular studies. GWAS often identify genes for which the function is not known or poorly understood. In such cases, behavioral and molecular genetic studies in animals could be useful to learn more about the causal role and the function of the gene.

simple and important pillars of reliable genetic research must not be forgotten. Firstly, regardless of the statistical models used and however simple or complicated they might be, successful gene identification stands and falls with independent replication of the gene–phenotype associations. Secondly, given the correlative nature of behavioral genetic research, genetic findings must be corroborated through implementation of additional phenotypic assessment and methods. Whereas fMRI is certainly a powerful tool to gain insight into the genetic differences in memory processes at the level of neural circuits, it is important to note that imaging genetic studies are also correlational in nature and do not allow causal interpretations. Methods, which allow researchers to investigate the causal role of a certain gene or genetic variant, include studies in genetically modified animals and studies aiming at intervening at the level of gene products. Such studies are especially important to gain more knowledge about genes identified in GWAS for which the function is not known or poorly understood (Figure 2). This crosstalk between genetic association and investigation of causality will ultimately pave the way towards the identification of biologically important, and hopefully druggable, genes and molecular pathways related to human episodic memory. References 1 Tulving, E. (2002) Episodic memory: from mind to brain. Annu. Rev. Psychol. 53, 1–25

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The genetics of cognitive ability and cognitive ageing in healthy older people Sarah E. Harris1,2 and Ian J. Deary2 1

Centre for Cognitive Ageing and Cognitive Epidemiology, Medical Genetics Section, University of Edinburgh, Edinburgh, EH4 2XU, UK 2 Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK

Determining the genetic influences on cognitive ability in old age and in cognitive ageing are important areas of research in an increasingly ageing society. Heritability studies indicate that genetic variants strongly influence cognitive ability differences throughout the lifespan, including in old age. To date, however, only the genes encoding apolipoprotein E (APOE) and possibly catechol-O-methyl transferase (COMT), brain-derived neurotrophic factor (BDNF) and dystrobrevin binding protein 1 (DTNBP1) have repeatedly been associated in candidate gene studies with cognitive decline or with cognitive ability in older individuals. Genome-wide association studies have identified further potential loci, but results are tentative. Advances in exome and/or whole-genome sequencing, transcriptomics, proteomics and methylomics hold significant promise for uncovering the genetic underpinnings of cognitive ability and decline in old age. Human cognitive ability and non-pathological cognitive ageing To speak of the ‘genetics of’ cognitive ability and cognitive ageing refers to the possibility that genetic differences cause some of the variation in these two phenotypes that is manifest in humans. To proceed, there must be clarification of the difference between cognitive ability and cognitive ageing, and of the phenotypes to be studied within each. Pathological states of cognitive decline (the dementias, other neurodegenerative conditions and prodromal states, such as mild cognitive impairment) are important research areas, and with their own research fields, including genetics. However, in the present overview, we focus on genetic contributions to normal, non-pathological variation in cognitive ageing. This is an important and large research area in its own right, in part because cognitive ageing affects people’s quality of life, predicts dementia and death, and is at this stage more likely to be open to amelioration [1,2]. We present evidence from twin studies that indicate that cognitive ability in old age is highly heritable. We then describe both candidate gene and genome-wide association studies (GWAS; see Glossary) that Corresponding author: Deary, I.J. ([email protected]).

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have attempted, with limited success, to determine the specific genetic variants that influence cognitive ability and cognitive ageing. Cognitive ability and cognitive ageing Consider the scores obtained by a 75-year-old individual in several mental tests; say, tests of memory, reasoning, speed of processing and so forth. This is an age at which the adult peak in such skills is typically well past [3,4]. Therefore, scores on any one test will be the combination of three things: the previous peak level of the ability, how much age-associated change there has been, and occasionspecific variance (including error variance). To separate the phenotypes of level and change in cognitive abilities, longitudinal studies are required in which people have taken mental tests on more than one occasion. This allows a cognitive trajectory to be computed for each subject. From a sample of such subjects, it is then possible to describe and find determinants of the differences in levels and rates of change in cognitive abilities. These are different things, probably with different determinants.

Glossary Allele: one of two or more versions of a gene. Candidate gene study: a study that investigates associations between genetic variants in a gene suspected of influencing a trait of interest (by virtue of the function or chromosomal location of the gene) and the trait. Cognitive change: the change in cognitive score between occasions of measurement. Note that it is common to see ‘cognitive ageing’ referred to when only one occasion of measurement has taken place. This is a common error. Cognitive level: cognitive score obtained on one occasion. General intelligence (g): operationally, g is typically the first unrotated principal component (or factor) from a battery of mental tests administered to a sample of a population. It describes the near-universal finding that all mental tests tend to correlate positively. Genome-wide association study: a hypothesis-free study whereby up to 1 million SNPs, spread throughout the genome, are investigated to identify genomic regions associated with a particular trait. Intercept and slope: given a number of occasions of cognitive measurements, a regression equation can be applied to the longitudinal data of each individual to discover their starting level of cognitive ability (intercept) and trajectory of cognitive change (slope). Therefore, one can seek the antecedents (including genetics) and outcomes of the slope and trajectory. Note that the slope might be linear, quadratic or even more complex. Single nucleotide polymorphism (SNP): a position on a chromosome where humans are known to differ with regard to which base pair occurs.


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mental test capabilities and forms taxonomies of abilities based on correlations among cognitive skills [5] (Box 1). The second approach, called the cognitive or neuropsychological approach, relies more on lesion and brain imaging studies, and tends to focus on mental capabilities within a given domain of function [6]. In the present article, we largely use the differential psychology approach, as this is the approach almost exclusively employed in behaviour genetic studies and the bulk of molecular genetic studies seeking to explain individual differences in cognitive function and change in old age.

Cognitive phenotypes How do we define cognitive phenotypes? If there were just, say, three types of mental work that brains performed, and if there was a perfectly valid test for each of them, this would be simple. One would apply these three tests each time one wished to assess an individual’s cognitive capabilities. This is not the case, however. There is no widely accepted taxonomy of brain functions that is isomorphic with mental tests. Therefore, what is available as phenotypes is a set of cognitive tests that are classified largely by their contents. To an extent, the contents do show isomorphisms with brain activation. Indeed, there tend to be two somewhat separate approaches to cognitive phenotypes, including how they are applied to ageing. The first is called the differential psychology (or psychometric) approach. It studies the multivariate associations among

Factors affecting age-associated cognitive decline There are changes in population mean levels of cognitive test scores as people age, but what we are interested in here is the individual differences in these life-course changes and

Box 1. The differential psychology approach to cognitive ability respect to ageing, an important distinction should be made between relatively age-sensitive cognitive domains (e.g. memory, speed of processing, spatial ability, and reasoning) and relatively age-resistant domains (e.g. verbal and numerical abilities) [50]. Furthermore, one might ask about the stability of cognitive differences, and whether age affects any or all of these three levels of cognitive variation. From youth (20 years) to later middle age (55 years) [51], and even from childhood (11 years) to old age (79 years) [52,53], there is high stability in people’s rank order of cognitive ability differences. In a study of male twins followed from age 20 to 55, genetic factors were responsible for the stability (and the same genetic influences were found at both ages) and non-shared environmental causes explained the changes [51]. With regard to how ageing affects cognitive abilities, a study of over 1200 people subjected to 12 mental tests for up to seven years found that 39% of the effect of age was on general cognitive function, 33% was at the level of domains (i.e. abstract reasoning, spatial visualization, episodic memory and processing speed) and 28% was at the level of the individual test [54]. Other longitudinal [55] and cross-sectional studies [3] also find this distribution of ageing effects across general and specific cognitive skills. To summarise: some ageing effects appear to be general and affect all cognitive skills, and some are more specific.

Put simply, the differential psychology approach asks: do individuals differ in how generally smart they are, or do they tend to differ in their specific cognitive capabilities? Both are true to some extent, and the answer lies somewhere in between. This is based on the finding that all mental tests show positive covariation, and that there are groups of types of test that show higher correlations among themselves than with other mental tests. Therefore, the sources of variance (i.e. the thing that one is trying to explore genetic contributions to) are at least on three levels [5] (Figure I). Individuals differ in how generally mentally able they are; this is often called general intelligence, or g. Approximately half of the population variation in cognitive skills is captured by variation at this level. As long as there is a reasonable number of varied mental tests applied, this g factor ranks people almost identically even when it is based on different tests [47,48]. Individuals differ in how good they are in broad mental domains (e.g. memory, processing speed, visual perception, auditory perception, retrieval ability, etc.). They also differ in their specific cognitive skills. Therefore, one can seek genetic and other contributions to variation at each of these levels. The result is that additive genetic factors contribute approximately 70% or more of the influence on g in adulthood [7,47,49]. Other levels have high heritability too, but much of this is because they are highly correlated with g and so the genetic causes of g partly also cause these lower-level differences. With

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Figure I. Schematic indicating the hierarchical structure of cognitive abilities. g reflects general intelligence (based on data from [5]).

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Review what might cause them. Multiple factors have been proposed as possible determinants of individual differences in non-pathological, age-associated cognitive change [1]. A recent and thorough review examined over 100 observational studies, and many randomised controlled trials and systematic reviews [2]. The studies included nutritional, medical, socio-economic, behavioural, toxic and genetic factors. Firm evidence was lacking for most factors, but there appeared to be relatively robust evidence for risk of increased cognitive decline from the apolipoprotein E (APOE) e4 allele, smoking and some medical conditions. There was also some evidence for the protectiveness of physical exercise and cognitive training. With regard to studies of the genetic causes of differences in cognitive ageing, the simplest division of these is into behavioural and molecular genetic studies. Behavioral genetic studies typically examine twins and adoptees. They have been informative about how environmental and genetic causes affect mental test performance in old age. Mostly, they employ quite complex statistical modelling methods, but all are based on the facts that: monozygotic twins have 100% genetic similarity whereas dizygotic twins share, on average, 50%; and that adoptees share environmental but not genetic causes with their rearing family, and the reverse with their biological parents. Behavioral genetic studies have been used to explore whether genetic and environmental causes operate at the level of general intelligence (g) or specific cognitive domains, and have also explored causes of cognitive change as well as cognitive level within old age. It is important to repeat here that an association between a genetic variant and cognitive scores in old age is not the same as studying genetic contributions to cognitive ageing. Any association based on tests taken at a single time point might just represent an association with the life-long, stable trait of intelligence; to study ageing per se, it is important to show some association with individual differences in change across two or more occasions of measurement. Heritability of cognitive ageing The total variance (from genetic and environmental influences) in cognitive ability increases with age, possibly owing to stochastic effects. Although it might drop a little in advanced old age, there is also an increase in the percentage of this total variance that has a genetic component [7]. The contribution of twin studies to cognitive level and change in old age has been summarised extensively by Lee et al. [8]. As is found in earlier periods of life, the majority of the genetic variation is in general cognitive ability. The major cognitive domains also show high heritability, largely because they derive much of their variation from g. Memory in particular tends to have its own genetic causes in addition to those that derive from general cognitive ability [9]. Much of the information about genetic causes of ability differences in old age has come from various samples based on the Swedish Twin Registry. For example, the Swedish Adoption Twin Study of Ageing estimated the heritability of g at approximately 80% in the mid-60 s, and showed a high correlation between g loadings of tests and their heritability [10]. Longitudinal study of this sample tested 390


on multiple occasions over 13 years separated the genetic and environmental causes of the level and slope of cognitive functions [11]. The heritability of cognitive level was very high, with most of the rest being caused by unique and non-shared environmental effects. Whereas g still accounted for substantial genetic contributions to the cognitive domains, an interesting finding was that the heritability in fluid and/or spatial ability decreased somewhat with age, whereas that of memory increased. The slope of cognitive function with age had linear and quadratic aspects. The causes of differences in the former were almost entirely the influence of a unique environment, whereas the quadratic aspect (accounting for a much smaller proportion of variance than the linear effect) had some genetic influence. Analyses of the genetic effects on the four cognitive domains in the SATSA tests (verbal, spatial, memory and speed) over a period of up to 16 years found significant genetic influences on the intercepts, but not on the slopes [12]. However, genetic influences did affect cognitive slopes via intercept–slope correlations; that is, there were genetic influences on cognitive level, and individuals starting at different cognitive levels did not decline at the same rates. These complex analyses came to an important conclusion concerning genetic contributions to age-related cognitive changes: that genetic variance in processing speed is largely responsible for the variation that occurs at a later time point in other cognitive factors. Therefore, researchers interested in determining genetic factors that influence cognitive ageing should look for those that influence processing speed. The Swedish study with the oldest subjects (the OctoTwin Study) reported that the heritability of g was 62% at a median age of 82 years, with the heritability of cognitive domains being between 32% and 62% [13]. The non-genetic variance was caused by non-shared environmental factors. Further analysis of this sample found that the genetic contributions to the cognitive domains of verbal ability, spatial ability, speed of processing and memory were largely those that caused differences in g [9]. Similar results and conclusions were derived from studies of the Longitudinal Study of Aging Danish Twins [14– 16]. It should be stressed that the cognitive test battery in this study was small: the number of animals that could be named in 1 min; forward and backward digit span; immediate and delayed recall of a 12-word list. Even so, at initial assessment, when subjects had a mean age of 80 years and were all over 75 years, the additive genetic contribution to the cognitive composite score was 54%, with the remainder owing to non-shared environment, which is similar to that from the comparable Swedish study [13]. Follow-up of this sample to include four waves of cognitive testing showed a heritability of 75% for cognitive level but almost zero for cognitive change [15]. The heritability estimate for slope using data from a subsequent wave was 18%, but still non-significant: most of the contribution to change appeared to come from a non-shared environment [16]. Note that this higher heritability estimate for level is because level is a latent trait summarising four individual waves of testing, each of which had heritabilities typically of approximately 50%; the estimate was lower at a subsequent follow-up [16]. Note also that the total retest time

Review was no more than 6 years, which does not allow much time for any genetic or environmental factors to influence cognitive change. A US study of male twins between 69 and 80 years old reported a heritability of 79% for a latent trait of executive control [17]. The contributing tests to this trait (digit symbol, stroop, trail making and verbal fluency) make it likely to be a reasonable measure of g. In summary, the Swedish Twins-based and other studies have demonstrated that heritability of g and the major cognitive domains is still high in old age and that the heritability of the latter is due substantially to influences on the former. It is important to note that heritability studies have been more revealing for cognitive level than for cognitive change. Cognitive change tends to suffer from a poor phenotype: being studied over too-short periods of time with insufficient assessments [8]. Having established high heritability of cognitive level in old age, and with the jury still being out on genetic contributions to cognitive change, it has been important to seek the specific genes that contribute to cognitive differences in old age. Here, we consider several approaches, including candidate gene studies and GWAS. Several more recent promising approaches are summarised in Box 2. Candidate genes Several approaches have been taken to identify candidate genes to test for association with both cognitive ability in older people and age-related cognitive decline. Generally speaking, selected genes have previously been associated with age-related diseases, traits and mechanisms. Many of these genes have been implicated in cognitive ability and cognitive ageing, but few findings have been replicated. Selected examples of candidate genes, for which there are multiple positive associations, are given below. We first discuss candidate gene studies in the context of normal cognitive function as well as in neurocognitive and psychiatric disease. We then examine findings from two particular approaches, imaging genetics and oxidative stress, which might prove promising in the study of cognitive


ability and cognitive decline. A more detailed review of candidate gene studies is provided in [18]. Normal cognitive function As memory is particularly badly affected by ageing, genes previously associated with memory phenotypes have been widely studied with regard to cognitive decline. For example, brain-derived neurotrophic factor (BDNF) has been implicated in hippocampal-dependent learning and memory in humans and non-human species and there is a steady decline in BDNF expression associated with normal ageing [19]. A single functional common polymorphism has been identified in BDNF, a valine to methionine change at amino acid 66. The methionine allele has been implicated in abnormal hippocampal function, lower hippocampal volume and reduced cognitive function. Some, but not all studies have identified an association between BDNF and cognitive function in elderly subjects [18]. Catechol-O-methyl transferase (COMT) degrades the catecholamine neurotransmitters, dopamine, norepinephrine and epinephrine. Similar to BDNF, the gene contains a single common functional polymorphism. This is a valine to methionine substitution at position 158. The methionine allele reduces the thermostability and activity of the enzyme, thus reducing dopamine and other neurotransmitter degradation. COMT has been associated mainly with executive phenotypes in several studies of both younger and elderly individuals [18]. However, a meta-analysis failed to find strong evidence for an association between COMT and cognition, finding only suggestive evidence that methionine homozygotes score slightly higher on cognitive ability tests [20]. Neurocognitive disease The genetic contribution to Alzheimer’s disease (AD) has been widely studied. Genes associated with the rare earlyonset form of the disease include those encoding amyloid precursor protein (APP), presenilin 1 (PS1) and presenilin 2 (PS2), but none has been definitively associated with cognitive function or age-related cognitive decline in

Box 2. The future Exome and/or whole-genome sequencing GWAS are designed to detect, and have been successful at detecting, common genetic variation associated with diseases and traits. However, there is evidence to suggest that a substantial amount of the variation in complex traits, such as cognition and cognitive decline, is influenced by rare genetic variants, present in less than 1% of the population [56]. These variants can only be detected by directly sequencing the DNA of each individual in the study. Until recently, this had been prohibitively expensive. However, technological developments mean that exome (i.e. coding and regulatory region) sequencing is now feasible on the large numbers of individuals required to obtain the necessary power to detect associations between rare variants and complex traits [57]. Over the next few years, it is hoped that the cost of whole-genome sequencing will drop to a level that will make it possible too. This will allow genetic variants in all regions of the genome to be examined. Transcriptomics and proteomics Studying genetic variants in DNA does not directly inform one of their function. DNA is first transcribed into RNA, which is then translated into protein. The potential of genetic variants to alter RNA and protein

levels is of great interest. Where suitable tissue is available, it is now possible to identify genome-wide gene expression (RNA) levels and to correlate these directly with levels of cognitive ability and cognitive decline [58]. The biggest obstacle to this approach is that the most appropriate tissue (the brain) is inaccessible in living subjects. Another approach is to look directly at the abundance of the proteins under the control of multiple genetic variants by using methods such as mass spectrometry. A recent pilot study, looking at cognitive extremes in old age, concluded that this is a promising technique that will help in the understanding of the biological foundations of intelligence differences [59]. Methylomics Epigenetic mechanisms refer to DNA modifications that do not alter the sequence of the DNA. The best-known epigenetic mechanism is DNA methylation. Methylation mainly occurs at CpG islands that tend to be concentrated in gene promoters where transcription is blocked in the presence of a methyl group. Methylation states are known to change with age and be altered in certain age-related diseases [60]. Techniques are now available that allow the methylation status of > 450 000 loci covering > 14 000 genes to be determined [61].

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non-demented individuals [21]. By contrast, the e4 allele of the APOE gene, associated with the more common lateonset AD (LOAD), was shown by a meta-analysis to be associated also with non-impaired cognitive function, particularly in elderly subjects [22]. e4 carriers performed significantly worse on measures of episodic memory, executive functioning, global cognitive functioning and perceptual speed. In a Scottish sample that was studied for cognitive change from age 11 to age 79 years, and then from age 79 to age 87, individuals with at least one e4 allele declined more in intelligence and memory functions across these two periods of the life course [23,24] (Figure 1). More recently, a poly-t repeat in the neighbouring translocase of outer mitochondrial membrane 40 (TOMM40) gene has been linked to the age of onset of LOAD [25]. Recent GWAS (Figure 2) identified several more genetic variants that increase an individual’s risk of developing LOAD in the genes bridging integrator 1 (BIN1), clusterin (CLU), complement component (3b/4b) receptor 1 (CR1) and phosphatidylinositol binding clathrin assembly protein (PICALM) [26–28]. However, reports on whether these variants are associated with cognitive function in non-demented elderly subjects are mixed [21,29,30].

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Figure 2. Genome-wide association study design. The exact number of individuals and single nucleotide polymorphisms (SNPs) typed at each stage will differ from study to study and will depend on the availability of subjects, effect size of individual SNPs and funds.

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Figure 1. Apolipoprotein E (APOE) e4 status and ageing of cognition in the Lothian Birth Cohort of 1921: (a) general intelligence from age 11 years to age 79 years; (b) verbal declarative memory from age 79 years to age 87 years. Based on data from [23,24], respectively.

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Psychiatric disease Cognitive deficits are present in many psychiatric diseases, including schizophrenia and bipolar disorder, and are also found at a higher rate in unaffected family members than in the general population [31]. Therefore, genes previously associated with psychiatric disease are good candidates for cognitive deficits in the absence of psychiatric disease. The gene Disrupted in Schizophrenia 1 (DISC1) was initially linked to psychiatric illness when it was identified at the breakpoint of a balanced translocation segregating within a large Scottish family suffering from multiple forms of mental illness [32]. There is some evidence implicating variation in DISC1 in cognitive function, in patients, unaffected family members and the general population. Elderly Scottish women homozygous for the cysteine allele of a non-synonymous single nucleotide polymorphism (SNP) in exon 11 had significantly lower cognitive ability scores than men, controlling for their childhood cognitive ability. This study suggests that variation in DISC1 affects cognitive ageing specifically in women [33]. However, the findings were not replicated in a second, younger Scottish cohort [34]. Distinct allelic haplotypes have been associated with psychotic and bipolar spectrum disorders along with cognitive impairments in a Finnish bipolar disorder family study [35]. Dystrobrevin binding protein 1 (DTNBP1), initially associated with schizophrenia, has been associated via metaanalysis with cognitive function in several cohorts of varying ages [36]. Imaging endophenotypes Associations between genetic variants and brain structure have been investigated, based on the assumption that individual differences in brain structure are good intermediate

Review phenotypes (so-called ‘endophenotypes’) for cognitive ability. For example, variation in adrenergic, beta-2-, receptor, surface (ADRB2), has been associated both with cognitive ability and white matter integrity (controlling for childhood ability) in a cohort of elderly Scots [37,38]. There was some evidence that white matter integrity mediated the association between ADRB2 and cognitive ability in old age. Mattay et al. [39] also reviewed genes identified by brain imaging genetic studies and related to systemic disease or cardiovascular function [angiotensin I converting enzyme (peptidyl-dipeptidase A) 1 (ACE) and methylenetetrahydrofolate reductase (NAD(P)H), (MTHFR)], or inflammatory processes [interleukin 1, beta (IL1B); tumor necrosis factor (TNF) and caspase 1, apoptosis-related cysteine peptidase (interleukin 1, beta, convertase), (CASP1; ICE)]. They concluded that neuroimaging techniques provide a promising way to identify genes associated with cognitive ageing. Oxidative stress One mechanism that has been used to identify candidate genes is oxidative stress [40]. Oxidative stress occurs during cellular respiration when the defence mechanisms of the cell fail to remove damaging by-products formed during respiration. These so-called ‘free-radicals’ can damage DNA and protein. Oxidative stress is believed to be responsible for many aspects of ageing, including cognitive decline. Therefore, anti-oxidant defence genes are good candidates for influencing cognitive decline. However, to date, no genetic variants within such genes have been definitively associated with either cognitive ability in old age or cognitive decline [40]. Summary of candidate gene studies Despite many studies being published, to date only APOE and possibly COMT, BDNF and DTNBP1 have repeatedly been associated with either cognitive ability in older people or cognitive decline [18,34]. It should also be noted that even when genetic variants are significantly associated with cognitive phenotypes, effect sizes are typically very small (1–2%). Genome-wide association studies As candidate gene studies have failed to identify genetic variants that account for much of the variance in cognitive ability or cognitive ageing, researchers have more recently turned to a hypothesis-free study design. GWAS allow multiple (up to 1 million) SNPs to be investigated in a single study (Figure 2) and have been used to identify genetic variation influencing many common diseases and traits (e.g. http://www.genome.gov/gwastudies; http:// www.gwascentral.org). A small-scale GWAS failed to identify common genetic variants associated with cognitive ability [41,42], although other GWAS have implicated WW and C2 domain containing 1 (WWC1; KIBRA); calmodulin binding transcription activator 1 (CAMTA1) and sodium channel, voltage-gated, type I, alpha subunit (SCN1A) with memory phenotypes [43–45]. A recent GWAS, which included 3500 older individuals, identified no single genetic variant associated with fluid and crystallised intelligence [46]. However, it


Box 3. Questions for future research Given sufficiently good phenotypic data on age-related cognitive changes, how heritable are these phenotypes? What are the genetic variants that influence cognitive ability and cognitive ageing and can they be identified by deep sequencing individual genomes? What are the gene expression differences at both the RNA and protein level that influence cognitive ability and cognitive ageing? Do epigenetic mechanisms influence cognitive ability and cognitive ageing?

concluded that approximately 50% of the variation in cognitive ability in later life is accounted for by multiple genetic variants in linkage disequilibrium (LD) with common SNPs, each having a very small effect. Concluding remarks In summary, despite behavioural genetic studies consistently showing that the heritability of cognitive ability in old age is high, very few specific genetic variants that influence cognitive ability and cognitive ageing have been identified. To date, the majority of studies have focussed on the identification of relatively common genetic variants that influence these traits. With the introduction of largescale whole-genome sequencing, it is hoped that multiple rare variants influencing cognitive ability and cognitive ageing will be identified. By investigating gene expression levels, researchers will be also able to identify the effects of multiple genetic variants on gene function. Finally, technological advances now allow epigenetic mechanisms to be investigated on a genome-wide scale. This might lead to new insights into mechanisms that influence variation in cognitive ability and cognitive function (Box 3). Acknowledgements The work was undertaken by The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross council Lifelong Health and Wellbeing Initiative (G0700704/84698). Funding from the Biotechnology and Biological Sciences Research Council (BBSRC), Engineering and Physical Sciences Research Council (EPSRC), Economic and Social Research Council (ESRC) and Medical Research Council (MRC) is gratefully acknowledged.

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Review


Dissecting the genetic architecture of human personality Marcus R. Munafo`1 and Jonathan Flint2 1 2

School of Experimental Psychology, University of Bristol, 12a Priory Road, Bristol BS8 1TU, United Kingdom Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, United Kingdom

The first candidate gene studies of human personality promised much but, in the fifteen years since their publication, have delivered little in the way of clear evidence for the contribution of specific genetic variants to observed variation in personality traits. This is most likely due to the very small effects conferred by individual loci. The advent of genome-wide association studies has brought growing awareness that high levels of statistical stringency, very large sample sizes, and independent replication will be minimum requirements for future genetic studies of personality. At the same time, evidence from other fields indicates that the genetic architecture of personality is likely to consist of the combined effect of many hundreds, if not thousands, of small effect loci.

many conflicting claims that have proved so difficult to substantiate, and argue that progress requires greater stringency in study design and the use of much larger sample sizes for robust discovery and replication of genetic association findings. In particular, we must be prepared for the likelihood that human personality is influenced by a very large number (possibly thousands) of genetic variants, of individually small effect.

The study of human personality: from traits to genes Thirty years ago the subject matter of personality research was the structure of personality: determining the number of personality traits and their relation to each other and to psychological and biological characteristics. Once it was accepted that self-assessment, using questionnaire instruments, yielded acceptable levels of consistency and prediction [1,2], subsequent research focused on the number of factors that explained behavioural consistency and personality variation, typically extracted from an analysis of questionnaire responses [3]. More recently, the literature has attempted to identify the genes that contribute to observed variation in individual differences in personality, given clear evidence from twin, family and adoption studies for a substantial heritable component to personality (see, for example, [4]). This has proved no less contentious than the earlier discussion about the number of dimensions needed to describe personality. Much of the current direction in personality genetics grew out of interest in testing the theories that gave rise to personality models. Understanding this history may provide clues about current difficulties in personality genetics, and suggest potential solutions (Box 1). Here we begin by reviewing genetic association studies of candidate genes (see Glossary) and the results of studies that have looked at the interaction between genes and environment in shaping personality. We then look at the results of more recently introduced hypothesis-free genetic screens: genome-wide association studies and genetic linkage studies. We consider why this field has accumulated so

Candidate gene study: Candidate genes are those thought to be involved in a phenotype based on neurobiological or other theories of personality. With a candidate gene in hand, it is now straightforward to use publicly accessible databases to identify sequence variants within the gene, which ideally should result in some change in biological function, and then to determine whether these are correlated with personality variation. Complex trait: A trait is complex when its genetic contribution does not fit simple Mendelian patterns of inheritance (i.e., recessive, dominant or Xlinked). Typically, this means there is evidence for the contribution of multiple loci. The term usually also implies that there is an important environmental contribution. Almost all behavioural traits are complex in this sense. Epistasis: In statistical genetics, epistasis (gene gene interaction) occurs when the joint effect of two (or more) variants differs from the sum of their individual, independent effects. Epistasis in this sense is an interaction term in a linear regression model. Epistasis also has a biological interpretation, where a mutation at one locus masks the effect of another. This would happen, for example, in a biochemical pathway where a mutation would block the production of an intermediate needed for a subsequent reaction. Gene environment interaction: This is perhaps best thought of as genetic susceptibility to the environment. Statistically, a gene by environment interaction (G E) occurs when phenotypic variation is not predicted by the sum of independent environmental and genetic effects. For example, exposure to the same stressful life event could produce very different outcomes in two individuals because they differ in their genetic constitution. Genome-wide association study (GWAS): A genetic association is the statistically significant correlation between allelic and phenotypic variation. Genome-wide association means testing for the presence of genetic association throughout the genome (simply put, testing whether variation in any of the 20,000 or so genes that we all have might contribute to disease susceptibility or trait variation). Linkage study: This employs a within-family design to determine which parts of the genome contribute to variation in a trait or disease susceptibility. Linkage studies set out to find co-segregration of genetic markers and phenotypes in families. Chromosomal regions within which a gene contributing to variation in the phenotype of interest may lie are identified, rather than functional genetic variants themselves. Polymorphism: Variations in DNA sequences are called polymorphisms, and may or may not be functional (that is, they may or may not result in some difference in biological function). When a single base pair is polymorphic, for example such that one chromosome has base ‘T’ and another chromosome has base ‘G’ at the same place (or locus, to use genetic terminology) in the genome, the polymorphism is called a single nucleotide polymorphism or SNP.

Corresponding author: Munafo`, M.R. ([email protected]).

Candidate gene studies Some personality theories suggest the possible involvement of serotoninergic and dopaminergic neurotransmitter Glossary


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Review Box 1. Personality Models Studies carried out in the mid-1950s were the first to propose a fivefactor framework of personality [46]. This framework is now more or less widely accepted, bolstered by many publications using the NEO Personality Inventory (NEO-PI), a set of scales that can accommodate other questionnaires, such as those developed by Eysenck [47]. The five factors derived from the NEO-PI are openness, conscientiousness, extraversion, agreeableness and neuroticism, sometimes referred to by the acronym OCEAN. However, OCEAN is not the only five-factor model – Zuckerman, for example, introduced his own [48]. Nor is five factors the universally agreed solution – Cloninger, for example, proposed seven factors [49] and Eysenck three [50,51]. These differ in some other respects – Eysenck proposed three very broad ‘superfactors’, which subsumed a number of lower-order trait factors, while Cloninger proposed a hierarchical system with broad factors lying above narrow, more specific factors below. The frameworks proposed by Eysenck, Gray and Cloninger are based on neurobiological theories of the origin of personality. All have their basis, to some extent, in learning theory. Cloninger, for example, defines three higher-order dimensions of personality on the basis of the stimulus-response characteristics of novelty seeking, harm avoidance, and reward dependence. However, the personality dimensions they identify are not necessarily the same as those in other frameworks. Comparison of the Eysenck Personality Questionnaire (EPQ) scales and Costa and McCrae’s NEO inventory reveals that the neuroticism and extraversion factors from the two systems match well [47]. There is less overlap with Cloninger’s Tridimensional Personality Questionnaire (TPQ) scales; harm avoidance correlates 0.59 with neuroticism and novelty seeking 0.44 with extraversion[52]. Further evidence that these classificatory systems are not necessarily just alternative descriptions of the same dimensions of personality comes from twin studies. These show, for example, that Eysenck’s and Cloninger’s dimensions that ‘‘purport to describe the structure of personality in terms of three major dimensions appear instead to jointly assess five or six dimensions of genetic variability and at least six dimensions of environmental variability’’ [53]. Therefore, the identification of genetic influences on personality (and the success of this enterprise) may depend in part on the personality instrument chosen. While it should not be assumed that heritability of a personality trait implies a biological correlate (correlations between genotype and phenotype do not necessarily reflect straightforward variation in a biological process), discovering that variation in personality is associated with variation in a neurotransmitter system would support frameworks arising from relevant neurobiological theories. For example, Cloninger’s claim that harm avoidance is correlated with activity of mesolimbic serotonergic neurons would benefit from finding that genetic variants in serotoninergic genes contribute to variation in personality.

systems in specific traits (Box 1), which has provided a series of candidate genes for testing. A large literature has developed, briefly reviewed below, with contradictory claims for and against the involvement of these genes emerging over time. Perhaps in part through disappointment with the search for clear individual genetic effects, researchers have examined the possibility that genetic effects might only emerge when explored together with environmental effects. We consider below whether both must be examined in order to detect association. Direct effects on personality Three key publications in 1996 [5–7] investigated whether variation in specific candidate genes (identified on the basis of their involvement in relevant neurotransmitter systems) was associated with personality. These were the serotonin transporter (SLC6A4) gene with measures of 396


neuroticism [6], and the dopamine D4 receptor (DRD4) gene with novelty seeking [5,7]. Each reported a positive result, and gave rise to the hope that molecular genetic studies were about to open a new chapter in the study of personality. Fifteen years later, it is worth considering how much of this early promise has been realized. Rather than progressing to a deeper understanding of the biology of personality, moving from genetic variant, to gene, to the neurons and neuronal circuitry in which activity constitutes temperament, debate continues as to whether these genes are involved at all and, if they are, what their individual contribution is towards accounting for the roughly 50% heritability of personality traits indicated by twin, adoption and family studies (see, for example, [4]). First, there is still no clear consensus about the involvement of any genetic variant in personality. Studies abound, varying in the degree to which they replicate each other, but combining these using meta-analytic techniques has only resulted in the conclusion that, if the claimed effects are present at all, they must be very small [8–12]. Second, the literature is still dominated by reports on a small number of candidate genes, centered around the function of two neurotransmitters: serotonin and dopamine. By far the most widely-studied candidates are SLC6A4 [11], DRD4 [12] and catechol-O-methyltransferase (COMT) [13,14]. Interestingly, DRD2, while a favourite in the wider psychiatric genetics literature, has not gained purchase in the personality genetics literature. Given that these candidates are identified on the basis of known (or presumed) neurobiology, confirmatory evidence that they play a role in personality does not seem to provide much more information beyond, for example, ascertaining that serotonergic neurotransmission plays a role in neuroticism. Gene environment interaction studies Faced with the same stressful situation, different people react differently. It is uncontroversial to assert that this variation has, in part, a genetic origin. However, while few doubt that this interplay of genes and environment (gene environment interaction, or G E) exists, its importance has been difficult to assess. The older literature was not optimistic: Jinks and Fulker [15], using the correlation between identical twin intra-pair differences and pair sums, found little evidence for G E effects for cognitive and personality traits. More recently, the tide has turned. In an influential article from 1994, Bronfenbrenner and Ceci argued strongly that interaction needs to be taken into account in behavioural genetic studies[16]. Could it be that molecular variants will not be found unless G E is taken into account? It may be that modelling the joint effects of genes and environment is necessary to obtain sufficient statistical power to detect the effect. There are several environmental effects to which individuals may be more or less sensitive, such as parental behaviour [17] and stressful life events [18]. Unfortunately, there are few comparable G E studies which have focused on personality as an outcome. However, we can perhaps learn from the parallel literature on psychiatric outcomes. Attempts to find G E effects in the wider behaviour genetics literature have met the same problems

Review faced by candidate gene studies [19–21]. A pattern of findings has emerged similar to that observed in studies of the direct effects of candidate genes on personality: initial positive findings, followed by a series of inconsistent results, typically reporting effects considerably smaller than suggested by early publications [22]. In addition, G E studies have brought their own, unique, difficulties. Two are worth highlighting. The first difficulty is sample size. Assume a study is carried out searching for the main effect of a genetic variant on a personality trait, which then asks whether there is also an interaction effect. If the interaction is of the same magnitude as the main effect, the sample size must be increased fourfold (to maintain statistical power at the same level) [23]. However, this ‘‘increase[s] dramatically to 100 or greater for more subtle interactions that [are] smaller than 20 per cent of the overall effect’’ [23]. The second difficulty is that, once an interaction has been reported, replication does not consist of merely identifying another significant interaction for the same environment and genotype. A statistically significant interaction can arise from different combinations of factors, some of which are mutually inconsistent. For example, if the first study finds that allele A, in the presence of an environmental stressor, increases a personality measure, but study B finds that the two combine to decrease the measure, both findings could be statistically significant but one does not replicate the other [20,21]. Unfortunately quite loose definitions of ‘replication’ are commonplace. Moreover, similar issues exist for the study of gene gene interactions. Genome-wide association studies A few years ago, the state of personality genetic research was comparable to that of other complex traits, such as obesity, type-2 diabetes, and high blood pressure. All were in a similarly parlous condition, each sporting a large literature of claims and counterclaims, until consensus emerged that signals had to exceed a genome-wide threshold for statistical significance (of the order of p < 10 7). This cultural change was imposed by the multiple testing inherent in the design of genome-wide association studies (GWAS), which test at least 500,000 loci (and now, more commonly, in excess of 2 million loci). Given a parallel consensus that the effects of common genetic variants on these traits were likely to be small (and much smaller than originally anticipated), it became clear that very large sample sizes would be necessary to reliably detect real genetic effects. For a common quantitative phenotype such as height, each locus, robustly identified and replicated, explains much less than 0.5% of the phenotypic variance [24]. The Wellcome Trust Case Control Consortium (WTCCC), using 2,000 cases and 3,000 controls, identified loci for only five of seven investigated traits [25]. The WTCCC study found no loci that contributed to variation in blood pressure; that required the analysis of 34,433 subjects followed up by genotyping an additional 81,114 subjects [26,27]. Moreover, the WTCCC pointed out that ‘‘for subsamples of 1,000 cases and 1,000 controls, of the 16 loci detected in the full study, we would have been certain of seeing only 2’’ [25]. In other words, the loss of power incurred by reducing


the sample size from 3,000 to 1,000 would result in failure to detect almost 90% of the loci. Success here is relative: of the ten loci reported each accounted for between 0.05 to 0.1% of the variance, in total explaining about 1% of the variance. Applying similar stringency to the p-values obtained in the personality genetics literature would remove all ‘significant’ findings at a stroke. In general, personality genetics researchers, still focusing on candidate genes, continue to consider that the results of analyzing a single variant can be considered significant if the p-value is less than 0.05. While it is not necessary to insist on genomewide significance for a candidate gene study (since by choosing a candidate one is hopefully increasing the prior odds of a genuine association existing), it is necessary to consider how many other genes might conceivably influence personality. Since this number is presumably large (with, say, at least 10,000 genes expressed in brain), at least some level of enhanced stringency is required to accommodate the implicit (and sometimes explicit) problem of multiple statistical testing. However, this is very rarely done in practice. The effect sizes of loci contributing to variation in personality could be even smaller than those found for blood pressure. All available data point in this direction: as noted above, meta-analyses of the direct association between phenotype and genotype have typically excluded effects greater than 1% [8,9,11,12]. Four genome-wide association studies [28–31] have failed to observe any locus with clear genome-wide significance, even when samples in excess of 10,000 individuals are used [31]. Critically, none of the candidate genes from the early personality genetic literature (SLC6A4 and DRD4) have shown evidence of association with any personality traits in these genomewide studies. While the sample sizes were still modest compared to those for blood pressure, the negative results are clear, and can exclude the possibility of individual loci explaining any more than 1% of phenotypic variance. Linkage studies [32–37] deliver exactly the same message: the genetic effects contributing to variation in personality are many and very small (see Box 2). To date, the largest sample size used for genotyping in personality genetics has comprised 88,000 people [38]. In this study, the extremes of the distribution were genotyped (about 3,000 individuals) for the 5-HTTLPR variant of the serotonin transporter gene, which is equivalent to genotyping a total population of about 50,000. No evidence of association with neuroticism or related measures of depression was observed. Yet even this number is too small if the true effect sizes are comparable to those that contribute to blood pressure variation. Assuming a locus accounts for 0.05% of the variance, a sample size of 10,000 has less than 20% power to detect it, at a significance threshold of 0.05. A sample size of 50,000 has about 50% power, and sample sizes of 100,000 are needed to obtain power of more than 80%. The lesson from successful complex trait analyses is that the power of a study is more important than the statistical significance of its results. Statistical theory demonstrates that nominally significant findings from underpowered studies are likely to be false positives 397

Review


Box 2. Linkage Studies

Box 3. Questions for Future Research

In family-based linkage studies of personality, about 500 polymorphic markers across the genome are used to identify chromosomal regions that are more similar than expected among siblings with similar personalities. With one exception [33], all sibling pair linkage studies have only studied neuroticism. Two studies collected large community samples (34,371 individuals in one [35], and 88,141 in the other [32]), and then genotyped the extremes of the distribution; one study similarly analysed extremes of two large twin samples, one from Australia and one from the Netherlands [37]; one studied adolescents (measuring personality twice, at ages 14 and 16) [33], one ascertained siblings for concordance on nicotine dependence [36], and another ascertained siblings for concordance on alcohol [34]. Given the evidence for different genetic effects in males and females, some studies, but not all [36], also tested for the presence of sex specific loci. Across these studies, very few regions exceeded a 5% genomewide significance level; at most, six regions have been identified [32], and, with such weak linkage signals, establishing replication is difficult. Two studies found peaks on chromosome 12 [32,33], but there are differences in sample collection (the former recruited subjects over the age of 30, the latter subjects under 17); the 95% confidence intervals of a third study [34] overlap with the peak on chromosome 12 [32], but it remains unclear whether this is coincidence. Linkage studies have therefore failed to conclusively identify regions within which genetic variants contributing to personality may lie.

What is the genetic architecture of personality? An ongoing discussion in complex trait analysis concerns the number of loci involved and their effect sizes. Could there be a relatively small number of variants with large effects or is it all due to a very large number (probably thousands) of variants with small effects? The results of GWAS and linkage studies practically exclude common variants of large effect, but it remains possible that a small number of rare variants (occurring in less than 1% of the population) with large effect (contributing to 5% or more of the phenotypic variation) might be identified [44,54]. How should studies incorporate environmental effects? The dispute over G E has focused on the appropriate study designs for the detection of interaction. One possibility is to acquire samples specifically tailored in advance to find such interactions, for example by pre-selecting groups on the basis of exposure to different environmental stressors, or genotype, or both. Such studies will also require very stringent criteria for declaring statistical significance and replication. Are there new molecular technologies that might shed new light on personality genetics? Without doubt, the most important new development in the past few years has been the emergence of next generation sequencing technologies, which have made population scale sequencing practical as an alternative to genotyping [55]. This methodology makes it possible to detect rare variants, including types of sequence variant that are not accessible to genotyping platforms [56]. Completion of the first phase of the 1,000 Genomes project permits imputation of lowfrequency variants, further increasing the power of sequencing to access genomic variation [57]. These advances mean that sequencing large numbers of individuals is now feasible in the search for variants contributing to personality variation. How should false positives and negatives be dealt with? Many of the problems we have discussed in this article are due to the way results are published: positive findings are more likely to be published in high profile journals, while negative findings less likely to be published at all. If authors submitted, and editors accepted, more negative reports, publication bias would be reduced. At the moment, the rewards for publishing encourage false positives. We should all work to change this structure.

[39,40]. This is because the false positive rate (i.e., the alpha, or significance level) will remain constant, while the true positive rate will decrease as statistical power decreases, so that the ratio of true positives to false positives among studies that achieve statistical significance will decrease with decreasing power. Since we now know that individual genetic effects on personality is very small, we also know that the vast majority of studies to date are underpowered. Furthermore, it has been suggested for G E studies that low power and low prior probability in favour of interaction effects involving a specific candidate gene make it likely that most, if not all, of these findings are false positives [41]. Greater stringency in study design, by means of a higher threshold for declaring statistical significance, the use of considerably larger sample sizes, and the requirement for results from a replication sample to be published alongside the original discovery sample, will be necessary if genuine advances in our understanding of personality genetics are to be made [22]. Concluding remarks Our brief overview of personality genetics has highlighted a number of methodological issues, which have resulted in a series of inconsistent findings and no clear consensus regarding the role of any individual gene in personality variation. What is the way forward (see also Box 3)? One suggestion emerges from the International Schizophrenia Consortium analysis of GWAS data [42]. While very few individual loci achieve conventional genome-wide significance, this group has found that there is predictive information even in markers with p-values up to 0.5. This was shown by using results from one GWAS to predict the results in another, independent sample [42]. Wray and Visscher point out that, even with 10,000 case subjects and 10,000 controls, power to detect a variant with a relative risk of 1.05 and a frequency of 0.2, at a low threshold of 398

1 10 6, is only 0.2% [43]. However, they then go on to argue that power in the totality of published schizophrenia GWAS analyses (containing only a few thousand subjects) is such that, 72% of the time, variants of this effect size will feature in the top half of the list of all results. They showed that these single nucleotide polymorphism (SNP) sets were predictive of case-control status [42] and, most importantly, that genotyped SNPs accounted for about a third of the variance in liability. In other words, the genetic architecture of schizophrenia consists of the combined effect of many hundreds, if not thousands, of small effect loci – a model sometimes referred to as quasi-infinitesimal. Small effects dominate, and are consistent with available data on the genetic architecture of complex traits [44]. This finding suggests that the genetic architecture of schizophrenia is, in large part, simply a very long tail of infinitesimal effects, which together nevertheless produce substantial heritability. The same is likely to be true for personality. If this insight is correct, then the successful genetic dissection of behavioural and cognitive phenotypes will need much larger sample sizes than currently considered – perhaps consisting of hundreds of thousands of individuals. This will obviously be an onerous task, and one that any single research group working in isolation will be unable to achieve. While current genotyping costs make

Review it difficult to see how such studies would be funded, molecular technologies continue to improve and costs fall. The first population-scale genome sequencing has recently been achieved [45], and it is not inconceivable that within the next five years case-control samples of tens of thousands will routinely be subject to genome sequencing, with even larger samples to follow soon after. At that point we may, at last, begin to understand the molecular basis of personality. Acknowledgements MRM is a member of the UK Centre for Tobacco Control Studies, a UKCRC Public Health Research Centre of Excellence. Funding from the Economic and Social Research Council, the British Heart Foundation, Cancer Research UK, the Department of Health and the Medical Research Council, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. JF is supported by the Wellcome Trust.

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Review


Genetics of emotion Laura Bevilacqua and David Goldman Laboratory of Neurogenetics, National Institute on Alcohol Abuse and Alcoholism, NIH, Rockville, MD 20852, USA

Emotion is critical to most aspects of human behavior, and individual differences in systems recruited to process emotional stimuli, expressed as variation in emotionality, are characteristic of several neuropsychiatric disorders. We examine the genetic origins of individual differences in emotion processing by focusing on functional variants at five genes: catechol-O-methyltransferase (COMT), serotonin transporter (SLC6A4), neuropeptide Y (NPY), a glucocorticoid receptor-regulating co-chaperone of stress proteins (FKBP5) and pituitary adenylate cyclase-activating polypeptide receptor (ADCYAP1R1). These represent a range of effects of genes on emotion as well as the variety of mechanisms and factors, such as stress, that modify these effects. The new genomic era of genome-wide association studies (GWAS) and deep sequencing may yield a wealth of new loci modulating emotion. The effects of these genes can be validated by neuroimaging, neuroendocrine and other studies accessing intermediate phenotypes, deepening our understanding of mechanisms of emotion and variation in emotionality. Candidate gene studies of emotion Emotion, or the assignment of affective valence to objects, states and situations (see Glossary), is critical to most aspects of human behavior: all our actions and decisions occur in emotional contexts, and cognitive functions are colored by emotional states. Neurobiologically, emotion is linked to the response of neural circuits by which individuals assign intensity and valence to objects and situations in the environment, and to internal states. Emotionality represents variation in emotion: it varies between individuals and also varies within an individual, depending on context and life history. Variation in systems recruited to process stimuli that trigger emotion and regulate emotional outputs leads to differences in emotionality. Crucially, emotionality is a factor in a wide spectrum of psychiatric diseases including mood [1,2] and anxiety disorders [3]. Significant progress has been made in identifying the brain structures that underlie affective processing, including the prefrontal cortex (PFC), anterior cingulate cortex, hippocampus and amygdala [4,5], in visualizing and modifying the activity of the circuitry of emotion [6] and in identifying genetic variants that modulate inter-individual differences in emotionality. In this review, we focus on the genetic origins of individual differences in emotion processing. Heritable individual differences in affect, temperament and personality shape other complex behaviors, as well as responses to an ever-changing environment. These

differences can also be important predictors of vulnerability to neuropsychiatric disorders that are themselves genetically influenced. Functional variants at five genes (catecholO-methyltransferase (COMT), serotonin transporter (SLC6A4), neuropeptide Y (NPY), a glucocorticoid receptor-regulating co-chaperone of stress proteins (FKBP5) and pituitary adenylate cyclase-activating polypeptide receptor (ADCYAP1R1)) will be used to illustrate a range of effects of ‘emotion genes’ and factors that alter or confound these effects. These genes have been selected on the basis of convergent neurobiological evidence in modulation of emotional processes. Notably, each of these represents a discovery made prior to a new era in which genome-wide association studies (GWAS) and deep sequencing are expected to yield a wealth of new loci modulating emotion. As will be described, the effects of these functional alleles has to some extent been explored, and validated, both via human brain imaging and in animal models, in which the genes have been genetically manipulated, although space considerations prevent full review of the functional connections to the neurobiology of emotion for any of these genes. Emotionality is moderately heritable (40-60%) [7] but is also strongly influenced by exposure to stress in a pattern consistent with gene environment interaction. Identification of the mechanisms that give rise to inter-individual differences in emotional stability and vulnerability to stress and anxiety will deepen our understanding of human behavior and predisposition to disease. The effect sizes of genetic variants implicated in emotional responses measured as psychological traits have been small. For example, in a meta-analytical study, the serotonin transporter promoter polymorphism was found to have only a 0.106 standard deviation effect on neuroticism per copy of the low expression allele [8]. However, Glossary Allele: any of the alternative variants at a given locus. Candidate gene study: a study focused on genes thought to have a causal role. Diplotype: a genotype composed of haplotypes. Emotion: the assignment of affective valence to objects, states and situations. Emotionality: the tendency to assign strong emotional valence to objects, states or situations. Endophenotype: a heritable, disease associated, intermediate phenotype. Epistasis: non-additive interaction between alleles at different loci affecting a phenotype. Genome-wide association study: allele-based linkage using markers genotyped across the entire genome. Haplotype: a combination of alleles at loci located on the same chromosome. Intermediate phenotype: mechanism-related manifestation of a complex phenotype. Pleiotropy: different phenotypic effects of the same allele. Polymorphism: inter-individual genetic variation occurring at a frequency of >1%.

Corresponding author: Goldman, D. ([email protected]). 1364-6613/$ – see front matter . Published by Elsevier Ltd. doi:10.1016/j.tics.2011.07.009 Trends in Cognitive Sciences, September 2011, Vol. 15, No. 9

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Box 1. Heritability of emotionality and emotion-induced fMRI activations Emotionality, measured as neuroticism, introversion or harm avoidance, is moderately to highly heritable in both sexes: 40-60%, with little effect of shared environment [7,75]. Behavioral inhibition is manifested early in life and predicts anxiety disorders and depression later in life [76–78]. In humans, trait emotionality has been tied to brain metabolic measures obtained by neuroimaging. However, while these measures show long-term stability and are thus trait-like [10], they have not been shown to be heritable [79]. Non-human primates represent an alternative model for the study of the relationship of brain metabolic activity to emotionality and direct evaluation of heritability. In non-human primates, emotionality and correlated brain activations are also stable and persistent across environments [80]. As shown in Figure I, in the rhesus macaque, the central nucleus of the amygdala and anterior hippocampus are components of a neural

[(Box_1)TD$FIG]

(b)

Dorsal amygdala metabolism and AT 4

Peak FDG uptake (standardized residuals)

Peak FDG uptake (standardized residuals)

(a)

circuit predictive of anxious temperament [81]. In these monkeys, brain metabolic activity was measured by fludeoxyglucose PET (FDG PET) and anxious behavior was measured by observation under a threatening condition. Using a multigenerational pedigree, the heritability of anxious temperament was 0.36 [81,82]. Heritability of threat-provoked hippocampal metabolic activity, but surprisingly not for the amygdala, was even higher (0.65 and 0.76 in right and left hippocampus, respectively). Concerning the lack of heritability of amygdala FDG, a key question would be whether it reflects measurement properties of FDG in hippocampus vs. amygdala. These findings strongly suggest that brain metabolic responses measured by neuroimaging represent an endophenotype—an intermediate phenotype that is both heritable and disease-associated [11]—for emotionality and related disorders.

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Figure I. Correlation between anxious temperament and glucose metabolism in (a) the amygdala and (b) the hippocampus in rhesus macaques. Reproduced, with permission, from [81].

several of the same variants, including this serotonin transporter allele, have larger effects on metabolic responses of the brain to emotional stimuli accessed in real time by brain imaging. These studies using intermediate phenotypes have not only validated the effects of these genes in emotion, but deepened our understanding of mechanisms by which neural circuits alter emotional responses and cognition. As described in Boxes 1 and 2, variation in BOLD fMRI measures of emotional response is both heritable and associated with emotion in the rhesus macaque. In humans, these fMRI measures of emotion have not been shown to be heritable, but they are a temporally stable and reliable index of brain function [9,10], and correlate with emotionality traits including diseases whose heritability has been demonstrated. Thus, they are defined as endophenotypes [11]. COMT Catechol-O-methytransferase (COMT) metabolizes dopamine, noradrenaline and other catecholamines. This enzyme plays an important role in the prefrontal cortex where the dopamine transporter is not abundant [12]. Val158Met is a common functional polymorphism that alters enzyme stability [13] and is predicted to lower dopamine levels in the prefrontal cortex. Under normal 402

conditions the COMT knockout mouse may not have altered catecholamine levels but under others, for example when challenged with dihydroxyphenylalanine, region and sex-specific changes have been observed [14]. Individuals with the Val158 allele perform less well on tests of working memory and executive cognition assessed by several methods [15–18]. On the other hand, the Met158 allele, although associated with better cognitive performance, leads to higher anxiety [19] and emotionality, as summarized in the ‘warrior’ (Val158) versus ‘worrier’ (Met158) model [20]. The two alleles potentially represent an example of balanced selection: counterbalancing effects of the Val and Met alleles in stress resiliency and cognition, respectively, may account for their conservation and high abundance across populations. This serves as a reminder that many of the common alleles with roles in diseases are not disease alleles per se. Met158 leads to enhanced emotionality as visualized by emotional responses assessed by Positron Emission Tomography (PET) and functional Magnetic Resonance Imaging (fMRI). Met158 leads to reduced pain thresholds and increased emotional response to pain [21,22], apparently via diminished regional opioid responses [21]. Amygdala fMRI response to emotional challenge is stronger in Met158 carriers [23], and the effect also appears to be

Review


Box 2. The power of endophenotypes for studying the effects of emotionality genes A functional promoter polymorphism of NPY, a gene that encodes an anxiolytic neuropeptide, appears to predict emotionality and stress response [57]. This study evaluated the effect of common functional NPY haplotypes on molecular, neuroimaging and behavioral phenotypes. Illustrated in Figure I, in one of the imaging genetic studies in this report, amygdala and hippocampal activations were measured in response to threat-related facial expressions. NPY diplotypes were grouped into low (LL), intermediate (LH) and high (HH) expression groups, based on the in vitro and in vivo RNA levels associated with different haplotypes, and the identification of a functional locus within the haplotype, and located in the NPY promoter region. Task-related reactivity was predicted in an allele-

[(Box_2)TD$FIG]

dosage fashion in both right dorsal amygdala and hippocampus, accounting for 6-9% of the variance in fMRI response to emotional challenge. Emotion-induced activations were highest in individuals with low NPY expression diplotypes (LL) and lowest in those with high-expression (HH) diplotypes. In contrast with large effects of NPY on brain functional responses and measures of NPY mRNA and protein levels, the effect on trait anxiety, measured as Harm Avoidance with the Tridimensional Personality Questionnaire, was directionally congruent in an independent dataset but accounted for only 3.3-3.4% of the variance. Intermediate phenotypes, if they are heritable endophenotypes, may, therefore, more closely reflect the effects of genes.

T value 3.5 3 2.5 2 1.5 1 0.5 0 Amygdala

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NPY diplotype TRENDS in Cognitive Sciences

Figure I. Effect of diplotype-predicted NPY mRNA expression on fMRI measured amygdala and hippocampal activation in response to threat related facial expressions. Top: Genotype-dependent NPY expression predicts the fMRI activation of amygdala and hippocampus in response to threat-related facial expressions. Bottom: Right amygdala (blue diamonds) and hippocampus (orange squares) activation predicted by NPY genotype. The lower expression NPY haplotype predicted stronger threatrelated activations. Reproduced, with permission, from [57].

additive with the low expression allele of the serotonin transporter promoter polymorphism [24]. These fMRI measures of metabolic activity of regions, such as the amygdala and hippocampus, appear to have validity for understanding emotion [25], leading to their intensive use, and creating the opportunity for imaging genetic studies. Interestingly, COMT Met/Met individuals present increased reactivity and stronger connectivity of brain circuitry implicated in generating and regulating emotional

responses [23]. This circuitry includes the amygdala, orbito-frontal and ventro-lateral prefrontal cortex and the hippocampus. It is clear that specific brain regions play an important role in emotion. However, they do not work in isolation, but are part of networks that are responsible for perception, emotion and cognitive processes [26]. Maladaptation to stress is critical in the development of many psychiatric disorders, including mood and anxiety disorders [27]. For COMT, the context-dependent effect of 403

Review the Met158 allele on emotionality was demonstrated in a study conducted with Rwandan refugees [28]. Post-traumatic stress disorder (PTSD) was more common in carriers of the Met158 alleles, except that the highest levels of stress exposure overwhelmed the resiliency effect of the Val158 allele, as might have been expected. The effects of genes on emotion is strongly influenced by prior exposure to environmental stress [29], but this study of Kolassa and colleagues [28] provides evidence that the interactions between genes and environmental stress are furthermore not necessarily linear or monophasic. Of interest is recent evidence for epigenetic regulation of the COMT Val158 allele by lifetime stress exposure [30]. Greater stress correlates with reduced methylation at the CpG dinucleotide created by the Val158 allele, possibly altering the expression of COMT and traits associated with it. 5-HTTLPR The serotonin transporter gene is responsible for the reuptake of serotonin from the synaptic cleft and is a major target in the pharmacologic treatment of depression and anxiety. A common polymorphism (5-HTTLPR) is located upstream from the gene, SLC6A4, also known as 5-HTT [31]. A 14-repeat allele (S) has reduced transcriptional efficiency compared to the 16 repeat allele (L). Moreover, the L allele frequently contains a relatively common, A [TD$INLE] G substitution that makes it functionally equivalent to the low-expression S allele via binding of a defined transcription factor [32]. Subsequent to initial association studies to anxiety, Hariri and colleagues [33] observed that the low transcription allele (S) increased activation of the amygdala after passively viewed emotional stimuli. As mentioned above, the metabolic response of the amygdala to emotional challenge is contingent on modulation by other regions [23]. Consistent with this, connectivity studies revealed that the low expression allele altered the functional coupling between the amygdala and the ventromedial prefrontal cortex (vmPFC) [34] and the perigenual cingulate [35], potentially impairing fear extinction by the medial prefrontal cortex. S allele carriers showed increased coupling between the amygdala and vmPFC and relative uncoupling between the amygdala and the perigenual region. At the neural level, multiple lines of evidence link the S allele to stronger emotional arousal (see [36], for a review). These include structural alterations in the uncinate fasciculus, which connects the amygdala and PFC [37], in the pulvinar [38] and in the amygdala itself [35]. As with COMT, there is accumulating evidence that 5HTTLPR and stress interact to determine susceptibility to disorders later in life [29]. In the case of the serotonin transporter, however, the evidence is both more extensive and more conflicting, with contradictory meta-analyses [39,40]. Carriers of the low transcribing allele appear to be more likely to be depressed and suicidal following stressful life events as compared to individuals with two copies of the high expression allele [29]. The same effect was observed in other populations, including substance abusers, who overall have high exposure to stress and a high risk of suicidality [41]. As reviewed in [36], functional findings providing clues to the mechanisms of effects of 404


5-HTT reduction-of-function genotypes on emotion include increased acquisition of conditioned fear responses [42], increased auditory startle [43,44] and increased hypothalamic pituitary adrenal (HPA) axis activation to aversive stimuli [45–48]. The findings on human 5-HTTLPR are paralleled by studies in the rhesus macaque, mice and rats. To some extent, these studies are important as validating the sometimes conflicting human findings, but they have also enabled further exploration of the effects of low and high expression serotonin transporter genotypes. The rhesus macaque has an orthologous rh-HTTLPR polymorphism that leads to increased stress responses including cortisol release, and again only in the context of early life stress exposure, namely maternal separation [49]. In the rhesus macaque, the lower expression allele is associated with lower volumes of several of the same brain regions that are affected in humans, including the amygdala, PFC and pulvinar [50], and increased metabolic activity was observed in these regions in a stress paradigm involving relocation [51]. There is no rodent orthologue of 5HTTLPR, but in both mice and rats silencing of 5-HTT heightens anxiety-like behaviors, impairs fear extinction and exaggerates HPA axis response [52,53]. The finding that 5-HTT null mice have higher excitatory dendritic spine density on amygdala neurons and increased PFC dendritic arborization [54] again points to a developmental effect of this gene and its functional variants. The plasticity of this interaction is underlined by the ability of SSRIs administered early in development to mimic the 5-HTT knockout mouse, at least in terms of anxiety behaviors [55]. NPY Neuropeptide Y (NPY) is an anxiolytic neuropeptide whose release is induced by stress. The release of NPY influences stress response and vulnerability to PTSD, for example in combat veterans [56]. At the molecular level, NPY RNA and neuropeptide expression is modulated by variation in the NPY promoter region, as shown in vitro and in vivo [57]. Individuals with low expression haplotypes exhibit stronger hemodynamic responses in the amygdala when presented with threat-related stimuli (facial expressions), lower endogenous opioid release during a pain stressor, and greater trait anxiety [57]. Also, amygdala activation to angry faces is greater in normal [57] and depressed patients [58] with low expression NPY genotypes. The dilution of effect of this functional locus from molecule to complex behavior is described in Box 2. FKBP5 Acute stress activates hypothalamic release of corticotropin-releasing hormone (CRH) from the paraventricular nulecus to the pituitary, where it stimulates the secretion of the adrenocorticotropic hormone (ACTH). CRH directly, and through the action of ACTH, regulates adrenal cortisol release, steroidogenesis and catecholamine synthesis and release by the adrenal gland. Negative feedback to block release of CRH is necessary for normal function of the HPA axis, preventing prolonged or excessive activation [59]. Glucocorticoids mediate physiologic responses to stress but also reduce release of these neuropeptides by activa-

Review tion of glucocorticoid receptors (GR) in the paraventricular nucleus [59]. GR is a ligand-activated transcription factor that translocates from the cytosol to the nucleus after binding cortisol. GR function is regulated by a large molecular complex [60]. This molecular machinery includes hsp90/hsp70 chaperones and a number of co-chaperones, including FKBP5, a co-chaperone of hsp90 [61]. When FKBP5 is bound to the GR complex, the receptor has lower affinity for cortisol. Increased expression of FKBP5 therefore leads to cellular glucocorticoid resistance [62]. Although the precise functional locus has not been mapped, functional variation in the FKBP5 gene has been associated with response to antidepressants, recurrence of depressive episodes [63], suicide attempts in bipolar patients [64] and incomplete normalization of stress-elicited cortisol secretion [65]. Moreover, it has been shown that FKBP5 interacts with childhood trauma to predict PTSD [66] and suicidal behavior [67]. In controls, several FKBP5 loci are associated with high protein expression, increased glucocorticoid resistance, and thus reduced dexamethasone suppression [63]. However, in the presence of PTSD this functional association is altered. An interaction between high expression FKBP5 alleles and childhood trauma increases risk for PTSD and these alleles are associated with increased glucocorticoid sensitivity [66]. A similar relationship is observed in depressed patients [63]. Thus, genetic variation in FKBP5 may modulate effects of childhood trauma on cortisol release and abnormal protein expression may lead to altered GR responsiveness in target organs and long-lasting alterations in HPA axis reactivity. ADCYAP1R1 Among the genes that modulate stress response, the pituitary adenylate cyclase-activating polypeptide (PACAP) and its selective PAC1 receptor gene have recently been demonstrated to play a role in abnormal stress response underlying PTSD in females [68]. In this study, PACAP blood levels were correlated with the diagnosis and symptoms of PTSD. Ressler et al. [68] found that a single nucleotide polymorphism (SNP) within the PAC1 receptor gene (ADCYAP1R1) was associated with PTSD in females but not males, in two distinct samples of highly traumatized urban subjects. Expression of this gene is induced in the amygdala by fear [68]. The SNP, which alters mRNA expression, is located in an oestrogen response element, which could explain the striking gender difference. In female rats, oestradiol was shown to increase both Adcyap1 and Adcyap1r1 transcript levels in the bed nucleus of the stria terminalis, a component of the extended amygdala. Finally, methylation of ADCYAP1R1 was associated with PTSD. Concluding remarks Emotional differences arise because of the action of hundreds of genes, complex circuitries and environmental exposures. One can reasonably argue that it will not be possible to disentangle these factors or that, if it is possible, this will require far better measurement of behavior, intermediate processes and environmental exposures. However, the fact that emotionality is genetically driven—in


Box 3. COMT and pleiotropy The COMT Val158Met polymorphism has pleiotropic effects on both emotion and cognition and it represents an example of a multilevel confirmation of the effect of a functional polymorphism. COMT was not genome-wide significant on any GWAS conducted so far, but has a functional locus that coherently influences several aspects of emotion and cognition. The understanding of this polymorphism’s effects was extended via pharmacological challenge studies and use of transgenic animal models. The effects of COMT Val158Met are pleiotropic, consistent with the multiple functions of the enzyme and its widespread distribution in the brain and body. In numerous studies and different populations including normal controls [15–17], head-injured patients [83], schizophrenic patients and their healthy siblings [16], the Met158 allele is associated with better performance on neuropsychological tests of executive cognition. The role of COMT as a cognitive predictor has been comprehensively reviewed [84] and meta-analyzed with variable results, as in Barnett et al [85], where the genotype was associated with IQ but not with other cognitive measures. Subsequently, Met158 was again associated with higher IQ in a large longitudinal dataset [86]. However, all observations made on the effects of the Val158Met polymorphism are consistent with the role played by dopamine in regulating frontal cortical function. COMT inhibitors improve working memory and attention in animals [87] and in humans [88], but the improvement is lost in Met158/Met158 homozygotes, who tend to have superior baseline prefrontal function assayed by fMRI [89]. Also, under easy task conditions, where cognitive performance of individuals with different genotypes is equivalent, there is reduced cortical efficiency in Val158/ Val158 homozygotes, consistent with lower dopamine levels.

other words, heritable—has enabled genetic approaches to be successfully applied against that large portion of the variance in emotionality which is driven by allelic differences. The five genes discussed here represent instances in which common functional variants alter pathways of emotion and stress response. Their naturally occurring genetic variants in humans, and parallel genetic models in other animals, represent a unique opportunity to disentangle the complexity of emotion and identify specific origins of individual differences in emotionality. Multiple factors complicate the task of relating genes to emotion and suggest that future efforts to relate genes to emotion should be better focused, larger in scale, as well as deeper in genomic coverage. Gene environment interactions play a critical role, suggesting that it is vital to study exposed populations, as exemplified here by the studies on Rwandan refugees [28], and that at other times environmental exposures may mask genetic effects. Most genes and environmental factors that alter brain function, alter the function of multiple neurons and circuits and may have effects elsewhere in the body, a phenomenon known as pleiotropy (see Box 3), suggesting that the genetics of emotionality is likely to be tied to several other problems. Gene gene interaction is a territory largely unexplored (see also Box 4). The complexity of the brain and its cellular molecular networks seems to suggest that variation in emotionality should arise due to the simultaneous action of variants at many genes. Under a heterogeneity model, different variants play a role in different individuals. Under a polygenic model, inheritance of many genetic variants is necessary and the effects may or may not be additive. Under the epistatic model, effects are nonadditive—it is unique combinations of alleles at different 405

Review Box 4. Priorities for future research Achieve deep (i.e., rich) phenotyping that includes the use of intermediate phenotypes or endophenotypes. Establish consistency in definition of phenotypes across studies. Carry out GWAS using intermediate phenotypes or endophenotypes. Perform genome sequencing of individuals with extreme phenotypes, including intermediate phenotypes. Identify, evaluate, select and prioritize rare and uncommon variants. Establish the function of variants. Perform gene-wide studies that incorporate multiple rare variants at the same gene. Use population isolates with founder characteristics and families for the identification of rare and uncommon genetic variants with large effects on disease. Study exposed populations and develop new measures of exposure, including epigenetic signatures.

loci that lead to the phenotype. Based on ratios of trait concordances between individuals at different degrees of relationship, emotionality, other personality traits and various psychiatric diseases may be predominantly additive in their inheritance (see, for example, [69] for a review of the MZ:DZ ratios for ten different addictive disorders). As mentioned above, the effect of COMT and 5-HTTLPR on fMRI emotion response appears to be additive [24]. However, it would be unsurprising if instances of epistatic interaction were identified: for example such instance has been reported between COMT Val158Met and various other genes [70], although the functional variants have not been identified at these other genes. Replication will be particularly important for gene gene interactions, as will care in correcting for the multiple comparisons necessitated by the search for epistatic interactions. GWAS is a hypothesis-free search strategy with great power to detect effects of relatively common alleles of moderate effect. However, and as may be a particular problem for genetic studies on emotionality, GWAS is unlikely to detect the effects of rare and uncommon alleles because, as sample size is increased, various types of within-sample heterogeneity, including consistency of phenotyping, ascertainment, environmental exposure and genetic background, are likely to detract from power. Different rare alleles at the same gene are likely to reside on different genetic backgrounds (haplotypes). A GWAS of neuroticism, a personality trait that increases the likelihood of internalizing disorders, which include depression and anxiety disorders, failed to identify any genome-wide significant loci, despite the fact that it was based on the genotyping of individuals with the most extreme phenotypes from a total sample approaching 88,000 [71]. Furthermore, this study failed to detect—at a genome-wide level—the influence on emotion of several genes that have been discovered and otherwise validated, as the genes discussed in this review. The genetic factors underlying neuroticism are largely shared with those that influence liability to internalizing disorders [72]. A powerful implication of these heritability findings is that it should be feasible to map the locations of genes that lead to neuroticism, to identify the relevant functional loci and define a pathway of causation from gene to behavior. The lack of genome-wide significant loci for this heritable trait could be 406


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Review


Genetics of autism spectrum disorders Daniel H. Geschwind1,2,3 1

Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA 2 Center for Autism Research and Treatment, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA 3 Departments of Neurology, Psychiatry, and Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA

Characterized by a combination of abnormalities in language, social cognition and mental flexibility, autism is not a single disorder but a neurodevelopmental syndrome commonly referred to as autism spectrum disorder (ASD). Several dozen ASD susceptibility genes have been identified in the past decade, collectively accounting for 10–20% of ASD cases. These findings, although demonstrating that ASD is etiologically heterogeneous, provide important clues about its pathophysiology. Diverse genetic and genomic approaches provide evidence converging on disruption of key biological pathways, many of which are also implicated in other allied neurodevelopmental disorders. Knowing the genes involved in ASD provides us with a crucial tool to probe both the specificity of ASD and the shared neurobiological and cognitive features across what are considered clinically distinct disorders, with the goal of linking gene to brain circuits to cognitive function. Autism genetics: a decade of progress In many ways, autism is a mysterious disorder because it involves core abnormalities in social cognition and language, both of which are central to what makes us human. Because of this, understanding autism will have a significant impact on our basic knowledge of these fundamental cognitive processes, in addition to the obvious crucial role that such mechanistic understanding has on therapeutic development. Until the past decade, virtually nothing was known about the neurobiological basis of autism spectrum disorder (ASD). There was no consistent neuropathology and only scant knowledge of a few causal genetic factors. The past decade has brought an explosion of genetic findings in ASD, surpassing most other common neuropsychiatric disorders, so that we now have knowledge of the etiology of ASD for between 10 and 20% of cases. Here, I review these exciting findings with respect to what they tell us about the genetic architecture of ASD and how this informs our mechanistic understanding of the disorder and in a broader sense, human brain function. Definition and evolution of ASD Autism is a developmental neuropsychiatric syndrome with onset before the age of three. The fundamental conceptualization of the disorder is based on the initial observation of Corresponding author: Geschwind, D.H. ([email protected]).

Kanner in 1943 [1], where he described 11 children with autism, mostly boys with a combination of severe social and variable language dysfunction and the presence of repetitive restrictive behaviors. Kanner made numerous interesting observations based on these case studies, including the identification of large head size in about half of the subjects and postulated a biological genetic basis for the disorder. However, until the 1980s autism was not considered a distinct disorder in the manuals of psychiatric diagnosis, nor was it considered by most to be biologically based. A major change in perspective came with the pioneering twin studies of Rutter and Folstein that demonstrated a Glossary Complex genetics: a term used in contrast to Mendelian patterns of inheritance, whereby instead of being dominantly or recessively inherited a phenotype is caused by multiple genes that could interact with each other and/ or the environment. Concordance: the rate at which a second individual has the same phenotype as the first, for example in the second sibling of a pair, or the second twin. Copy number variation: a greater than one Kb change in chromosomal complement so that a particular region or chromosome diverges from the normal diploid human copy number (aneuploidy) over that region. A deletion would yield one copy, and a duplication of the region would yield 3 copies. Copy number changes can be inherited or de novo. De novo: a genetic alteration that arises either in the gamete or in the fertilized egg during its early development but is not observed in the somatic cells of the parent. Endophenotype: a heritable phenotypic feature that is closely related to a disorder but represents a more narrow or simple component than the disorder itself. An endophenotype should be observed in first-degree relatives more frequently than in the general population and might be quantitative rather than binary. Epigenetic: non-sequence based changes to DNA that affect transcription, leading to changes in the expression of the gene product. Known major epigenetic modifications include DNA methylation and histone modifications, such as acetylation or methylation. Heritability: in general this refers to the extent to which a particular trait is due to inherited DNA sequence variation. A heritability of 1.0 indicates that a trait is 100% heritable and no environmental component contributes. However, heritability is an estimate based on the specific measurements taken in a particular population, and therefore is only a rough guide. Mendelian inheritance: a dominant or recessive pattern of inheritance of a trait first defined by Gregor Mendel. Penetrance: the frequency at which a particular phenotype is observed, given a specific genotype. Penetrance is always relative to the phenotype in question. Pleiotropy: occurs when a gene influences more than one phenotype. Polymorphism: a particular variant that is observed in more than 1% (common) in the population. A SNP is a single DNA base pair change. Proband: the index case being studied usually based on it having a particular phenotype or disorder. Whole genome or whole exome sequencing: the use of new high-throughput Next Generation Sequencing methods to sequence whole genomes or the protein coding portion of the genome (exome) in patients and controls.


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Review genetic susceptibility to the disorder, providing incontrovertible evidence as to its biological origins [2,3]. Over the past two decades, the concept of autism has broadened from the strict diagnosis of autistic disorder to include those with normal intelligence and language (Asperger syndrome) and those almost meeting the strict diagnostic criteria in all three domains (pervasive development disorder – not otherwise specified, PDD-NOS). These diagnoses are not based on etiology but on expert observation and assessment of behavior and cognition; they can be considered arbitrary: which category one fits into depends partially on subjective criteria. This is further emphasized by the current trend in the DSM-V (http://www.dsm5.org/proposedrevision/Pages/ NeurodevelopmentalDisorders.aspx), in which the category of Asperger syndrome is removed and the diagnostic criteria for autism are modified under the new heading of ASD. This change in diagnostic criteria is not based on known similarities or differences in causation between these clinically defined categories but rather on the consensus of opinions of expert clinicians. Concurrently, over the past two decades, the notion that autism represents a quantitative spectrum of impairments, rather than representing discrete disorders, has gained increasing traction for researchers [4,5]. From this perspective, the term ASD attempts to crystalize the notion that patients represent a clinically variable population that suffers from pathologic levels of quantitative variation in the major cognitive and behavioral domains that are disrupted, rather than a distinct clinical disorder. How these two varying conceptualizations, autism as a unitary disorder versus a spectrum of dysfunction, relate to underlying etiologies is a key question facing the field. Furthermore, how the clinical domains relate to underlying dysfunction in specific cognitive domains is essentially unknown, although some clues are starting to emerge [6,7]. Autism is a complex genetic disorder Perhaps the biggest advance in understanding autism pathophysiology has been the appreciation of a significant genetic contribution to the etiology of ASD. Three main areas of evidence support a genetic etiology in ASD: twin studies, comparing monozygotic twins (MZ) and dizogotic twins (DZ), family studies comparing the rate of autism in first degree relatives of affected probands (see Glossary) versus the population, and studies of rare genetic syndromes with a comorbid autism diagnosis. Because MZ twins share 100% of their genetic material and DZ twins share 50% (similar to nontwin siblings) and both share the in utero environment with their twin, higher disease cooccurrence in MZ twins than DZ twins supports a genetic etiology. This is what is observed in every twin study in ASD; overall consistent with heritability estimates of about 70–80% [8,9]. One exception is a very recent study with a large sample of twins, which, despite showing a concordance of about 0.6 for MZ twins and 0.25 for DZ twins (consistent with a Falconer heritability of 0.7), comes to the conclusion that shared environment plays a larger role than genetic factors [10]. Given that the concordance for strict autism in DZ twins (who can be considered genetic siblings who share the same in utero environment) is not clearly different from modern infant sibling recur410


rence rates [11], how shared environment would have a more major role than genetics is not clear. Moreover, studies in families show that first-degree relatives of autistic probands have a markedly increased risk for autism relative to the population, consistent with a strong familial, or genetic, effect observed in twins [12]. This is not to dispute the role of the environment but to emphasize that genes play an important role. First-degree relatives of ASD probands have an increase in behavioral or cognitive features associated with autism, such as social or language dysfunction, albeit in lesser forms, when compared with the population prevalence [13]. This has been called ‘the broader phenotype’ and includes restrictive repetitive behaviors and subthreshold deficits in social cognition, as well as language dysfunction [14–17]. For example, language delay (LD) is observed in a significant proportion of nonautistic siblings of autistic probands [18,19]. Similarly, autistic-like social impairment clearly is heritable [20] and increased in unaffected parents and children of autistic probands [21]. Studies using multiple measures of subthreshold autistic traits in population cohorts suggest that different components, separately representing language, social function and repetitive or stereotyped behaviors contribute to ASD [22–24]. On aggregate, these data suggest that different features of autism represent a quantitative continuum of function that could be inherited in distinct patterns. This is consistent with the knowledge that specific genetic factors contribute to the development and function of specific brain structures, and the hypothesis that distinct brain circuits might underlie different components of autism [25]. It has also been known for several decades that many rare medical or genetic conditions are associated with autism. Dozens of genetic syndromes including Joubert Syndrome, Smith–Lemli–Opitz syndrome, Tuberous Sclerosis and Fragile X are known to cause autism, although many with less than 50% penetrance [26,27]. Although this provided strong evidence in favor of a genetic cause for ASD, these syndromic forms were considered exceptional cases, and not relevant for common forms of idiopathic autism because each was rare; none account for more than 1% of ASD cases, and most are far less common [27]. Instead, similar to other common diseases with genetic contributions, autism was thought to fit a model in which multiple common variants, each with small to moderate effect sizes, interact with each other and perhaps in some cases, environmental factors, to lead to autism; a situation referred to as complex genetics [27]. In contrast to Mendelian genetics, under the complex genetic model, specific common variants in genes increase susceptibility to ASD but each is not on its own sufficient to be causal (Figure 1). These same variants contribute to normal variation in cognition and behavior in unaffected individuals but when combined unfortunately as they are in the autistic proband, they cause ASD. This model would explain the presence of subthreshold traits in nonautistic first-degree relatives, such as the siblings or parents of autistic individuals, because they would be expected to harbor a subset of the genetic variants that cause autism in the affected individual.



Effect size Mutation screens Frequency

Association studies TRENDS in Cognitive Sciences

Figure 1. Common and rare variants. We usually think of common and rare variants representing two extremes on the allelic spectrum. SNPs with modest effects sizes (gray oval) have been identified through association studies. Rare events that segregate with disease have also been identified through resequencing efforts and CNV studies. Less attention has been directed at events not at either extreme but also likely to modulate risk. Larger studies will permit consideration of lower frequency intermediate effect alleles (ovals with dotted lines). Current data do not support the presence of common alleles of large effect for affection status (bottom right corner). However, it is possible that such associations might be observed in the future when appropriate quantitative endophenotypes are used. Adapted with permission from [87].

The role of rare mutations versus common polymorphisms (CPs) in ASD A series of important findings over the past four years clearly challenges the notion that autism is mainly caused by combinations of common variants by identifying a large number of rare, recurrent and nonrecurrent mutations that lead to ASD. At the same time, whole genome association studies with common variants, although identifying a few loci with very small effect sizes, have not yielded independently replicated results [28,29]. These rare mutations, mostly in the form of submicroscopic chromosomal structural variation, called copy number variants (CNV), are now known to account for up to 10% of cases of idiopathic autism (those with no obvious clinical syndrome) [30–34]. Because many of these CNV have large effect sizes and thus are thought sufficient to cause ASD, they are predicted to significantly reduce reproductive fitness. Consistent with this, these causal CNV are often not transmitted from the parent but instead occur de novo in the germline [33,35]. However, in some cases, such as CNV at 16p11 and 15q11–13, the CNV are transmitted from an unaffected parent to cause the disorder in an offspring [33]. The genetic or epigenetic mechanism of reduced penetrance for ASD in the mutation-carrying parent is not known. However, it is also probable that the parent carriers of such CNV have more subtle neuropsychiatric or cognitive phenotypes that have not yet been systematically identified. The first key issue in genotype–phenotype mapping in ASD is reconciling the role of causal rare de novo variants that occur in probands and are not inherited by the unaffected siblings with the observations of broader subthreshold traits in siblings. The latter suggests the role of common variation but as mentioned above, the genetic evidence for specific common variants in ASD so far has been sparse. De novo events are major-effect genetic mutations that are mainly observed in probands, and are therefore not expected to contribute to the broader phenotype. In

this regard, two observations could be informative. First, de novo CNV are observed in 5–10% of ASD probands but they are also observed in 1–2% of unaffected controls; so all such CNV are not necessarily causal or fully penetrant [36]. From this perspective, some CNV could be acting as complex genetic risk factors, with intermediate effect sizes, and variable penetrance and expressivity. Such is certainly the case with 16p11 duplications, which are observed in ASD, schizophrenia (SZ) and various forms of developmental delay (DD) [37–39], as well as Neurexin 1 CNV, which are observed in about 50% of unaffected carriers [36], and observed across several neurodevelopmental disorders (Table 1). Such pleiotropy and variable penetrance and expressivity are the rule, rather than the exception (Table 1). Second, the frequency of de novo mutations is significantly lower in multiplex (familial) cases of autism versus those with only one affected proband, so called simplex families [30,31,34]. This difference in the frequency of rare de novo CNV in families with different structures (simplex vs multiplex) suggests that the contribution from different types of genetic variation between simplex and multiplex autism might differ. Recent family data based on the social responsiveness scale, which is a quantitative measure of autism-related features heavily weighted on social factors, suggests that this is indeed the case [40]. This discussion of rare and common genetic variants highlights an area of major tension in autism genetic research. The causal nature of Mendelian (recessive or dominantly acting) mutations is fairly easy to establish because there is essentially a strong correspondence between the presence of a mutation and a disease phenotype. Certainly, dominant or recessive mutations are easier to model in vitro and in vivo. However, few such variants cause autism alone but instead could cause a wide variety of phenotypes (variable expressivity) with or without autism, ranging from intellectual disability (ID), epilepsy, psychosis and global DD to neurotypical [27,41]. Thus, even for the simpler Mendelian mutations, understanding which aspects of the phenotype observed in a model system or a human patient relate to autism, and which might relate to more global aspects of cognitive dysfunction, needs to be carefully established. This provides a great opportunity for detailed phenotypic study of patients with and without autism who harbor such rare causal mutations, so as to connect specific aspects of brain structure and function with cognitive and behavioral phenotypes that underlie the clinical features of the disease. Common variants, although not causal for the disease, seem to have more subtle effects, making them harder to model in vitro or in animal models. However, perhaps because they are common in human populations and could have more subtle or specific effects on particular aspects of cognition and behavior, common risk variants might be easier to relate to particular brain phenotypes or circuits in humans. This issue will be discussed below in more detail with respect to imaging-genetic findings. Last, it should also be emphasized that although they are often presented as dichotomous models, the contributions of rare and common variants to ASD in an individual are not mutually exclusive [25]. For example, although rare de novo CNV are considered causal mutations, it is 411

Review


Table 1. Pleiotropic effects of major genes/mutations associated with ASD and allied neurodevelopmental disorders Gene/region NRXN1 CNTNAP2 16pdel 16pdup 15q13.3del 17q12del 15q11–13dup 22q11 1q21

Mechanism CNV, PM CNV, PM, CP CNV CNV CNV CNV CNV CNV CNV

Disorders ASD, SZ ASD, ID, epilepsy, LD/SLI, TS ASD, SZ, DD, LD, normal carrier SZ, ID, DD, LD, ADHD, normal carrier SZ, epilepsy, ASD, normal carrier SZ, ASD, ID ASD, SZ/psychosis ASD, ADHD, SZ, ID, epilepsy ASD, SZ, ID, epilepsy

References [36], [50,72,73] [59,61,74,75] [33,37,42,76–78] [33,39,42,78] [42,73,79] [80] [33,81] [73,82–84] [42,85,86]

Here we list genes, form of genetic risk variant, clinical disorders where the mutation has been observed, and some representative references. This table is not meant to be exhaustive but illustrative of the pleiotropic effects of known ASD genes or loci with relatively large effect sizes (OR > 5–10 for ASD). New abbreviations are as follows: TS, Tourette syndrome and PM, point mutation.

clear that mutations could have intermediate effect sizes and variable penetrance, as mentioned above [36,42], so such mutations must be interacting with other factors. Penetrance less than 50% for ASD is also observed in many Mendelian forms of ASD, such as Fragile X, Jouberts Syndrome and Tuberous Sclerosis Complex (TSC), so other genetic, epigenetic or environmental factors must be contributing to autism causation in such cases as well [27]. Thus, different rare variants could act as either susceptibility or strongly causal alleles, and, in conjunction with common variation, might contribute in differing proportions to ASD risk in individual patients [25]. Fortunately, over the next several years, whole genome or exome sequencing, if performed in large enough populations, will settle this issue by providing an empiric assessment of the relative contributions of both rare and common alleles of different effect sizes [43,44]. The molecular diversity of ASD Several recent reviews summarize the growing list of dozens of common and rare genetic variants that have been associated with ASD at varying levels of statistical evidence [27,41,45–47], so I refer the reader to these reviews for comprehensive gene lists. Here, instead, I will try to synthesize what these findings might be telling us about ASD pathophysiology at what is still an early juncture in the search for ASD susceptibility genes. The most obvious general conclusion from all of the published genetic studies is the extraordinary etiological heterogeneity of ASD. No specific gene accounts for the majority of ASD; rather, even the most common genetic forms account for not more than 1–2% of cases [27]. Further, these genes, including those mentioned earlier, represent a diversity of molecular mechanisms, ranging from cell adhesion, synaptic vesicle release and neurotransmission, synaptic structure, RNA processing/splicing, and activity-dependent protein translation. On one hand, this should not be surprising because autism is defined based on observation of cognition and behavior, not etiology. On the other hand, the diversity of potential mechanisms and the apparent lack of specificity of mutations for ASD begs the question as to whether ASD should even be viewed as a unitary disorder. Therefore, asking whether the diverse genes implicated in autism might converge on common pathways becomes an important question for understanding autism and developing new therapeutics. 412

Converging circuits and common pathways Convergence of pathways could be assessed at many levels, ranging from molecular mechanisms to brain circuitry. The earliest synthetic molecular model was based on the notion that the primary area of convergence in ASD was the postnatal, experience-dependent development of the synapse [48]. This was a highly productive model based on several forms of Mendelian mutations in ASD, which has led to successful exploration of synaptic scaffolding molecules and other synaptic genes as ASD susceptibility genes [27,46]. Interestingly, because many mRNA for synaptic genes are alternatively spliced and the proteins regulated by ubiquitination, this paper was prescient in predicting alterations in mRNA splicing and ubiquitination pathways that have since been identified [49,50]. Nevertheless, because the synapse is integral to the function of all neurons, how the cognitive and behavioral specificity of ASD emerges distinctly from ID or other synaptic disorders, is not yet explained by this molecularly focused model. Belmonte and colleagues have articulated a cogent unifying model based on imaging, electrophysiological and anatomical data, which implicates abnormal neural connectivity in disrupting timing and information processing [51,52]. This general model was subsequently anatomically refined and modified to focus primarily on reduced long-range, and increased short-range frontal lobe connectivity [53]. Since the general connectivity model was proposed, many subsequent studies provide strong supporting data, including a recent study showing weak interhemispheric functional connectivity in young toddlers with ASD [54]. This study is particularly important because it shows altered connectivity in developing language circuits to be an early (and thus potentially causal) feature of the disorder, which could not be inferred from studies of older children or adults. A more recent model [55] synthesizes several key features of the synaptic and connectivity models above in conjunction with genetic findings, and is based on developmental disconnection of frontal and temporal cortical regions. This model strives to explain the diversity of genetic findings without necessitating a particular molecular mechanism. Instead, it relies on a behavioral neurologic framework to integrate many potential levels of molecular dysfunction, from synapse formation and maturation, vesicle trafficking and signaling, to defective neuronal migration, interneuron development, dendritic



Stochastic or Environmental processes

(dup) 15q11-13 •Abnormal neuronal migration TSC1

•Abnormalities in patterning (regionalization or connectivity)

FMR1

•Faulty wiring/axon pathfinding •Aberrant synaptogenesis or

(del) 22q

synaptic function

Developmental disconnection

of critical brain circuits: •Fronto-striatal •Fronto-temporal

•Dendritic abnormalities (del) 16p

•Fronto-parietal

•Dysfunctional neural transmission

CACNA1C

ASD Genetic modifier loci (CNTNAP2, MET, rs4307059, etc…) TRENDS in Cognitive Sciences

Figure 2. Convergence of genes on neural systems. Here we illustrate a working model for how autism risk alleles with major effects, such as those shown at the left, might act to lead to autism. Because these Mendelian conditions are not specific for ASD and typically lead to ID, either environmental or genetic factors must modify their effects on brain development. The biological pathways through which such genes are known to act (shown in the center box) are myriad. Although gene expression and proteomic studies identify molecular and biological pathways that provide a source of convergence, the ultimate convergence must lie in neural systems. At a neural systems level, the convergent process will likely be disconnection of the circuits outlined in the far right box because these systems are thought to underlie the core deficits of ASD. Adapted with permission from [88].

maturation or axon path finding, all of which could contribute to functional disconnection [55] (Figure 2). Indeed several of the monogenic causes of ASD, including TSC [56] and Joubert Syndrome [57,58] show manifestations of abnormal axon path finding or migration abnormalities that are known to lead to alterations in brain structural connectivity. The study of common variants, for example in the gene CNTNAP2, also supports disconnection as a causal phenomenon based on initial imaging-genetic evidence. Two SNPs in a relatively circumscribed area of CNTNAP2 have been shown to modulate language function in ASD and other conditions [59–61]. Scott-Van Zeeland and colleagues [62] showed long-range disconnection of medial prefrontal cortex and the precuneus, and shortrange overconnectivity of the prefrontal region in carriers of the CNTNAP2 risk allele, regardless of whether they were autistic or neurotypical. This finding is particularly important because the genetic mediation of this disconnection supports its causal nature. Endophenotypes, common variants and domain specificity in ASD The relations between specific genetic variants and specific cognitive processes, such as language, highlight the notion

that the broad syndrome of ASD can be broken down into many component or intermediate phenotypes, referred to as endophenotypes. The familial segregation of endophenotypes provides a genetic basis for the broader phenotype described earlier. A logical extension of this concept is that these endophenotypes represent one end of the continuum of the normal spectrum of behavior and cognition [25]. Several groups have demonstrated that this is indeed the case with respect to CNTNAP2 [60,61,63] because variants in the same region of the gene that increase risk for LD in ASD, increase risk for specific language impairment (SLI) and modify language ability in the general population. Similarly, CNTNAP2 single nucleotide polymorphism (SNP) [64] has been shown to modulate brain morphology in several ASD-related cortical regions in normal controls [65]. However, specificity for ASD is not only an issue for rare Mendelian variants but also for common variation [66]. Because the effects of genes on behavior are mediated through neural circuits that mediate a multiplicity of functions (e.g. frontal-striatal circuits), their pleiotropic effects on behavior and cognition should not be surprising. In fact, pleiotropy should be the rule, rather than the exception. Recently, CNTNAP2 variants have been associ413

Review ated with mutism and social anxiety [67]. This complex relation between genetic variation and phenotype highlights the importance of in-depth exploration of cognitive and neurobehavioral phenotypes in ASD patients, their family members and in the general population. Phenotype discovery and the relation of core ASD-related endophenotypes to each other and specific brain circuits in patients with ASD and related neurodevelopmental disorders remains a key but underexplored area. Evidence for converging molecular pathways Several recent studies have suggested that in addition to convergent brain pathways, there could as well be convergence at the level of molecular mechanisms in ASD. One class of such studies has asked whether putative ASD susceptibility genes are enriched in members for specific molecular or biological processes more than expected by chance. The value of this approach depends on the level of experimental support for the specific genes tested and the degree to which current pathway annotations represent reality [25,68]. For genes identified within CNV this can be particularly problematic because most known pathological CNV contain more than one gene and it is not expected that all genes within the CNV contribute to ASD, potentially increasing noise in this analysis. One recent study [69] reduced such background by using a new phenotype-driven method to group genes within high confidence de novo CNV [34,69], identifying significant enrichment for several categories of genes, including axon outgrowth, synaptogenesis, cell–cell adhesion, GTPase signaling and the actin cytoskeleton. These results replicate and extend earlier composite pathway analysis of putative ASD susceptibility genes compiled from the literature [68], and CNV pathway analysis in the Autism Genetic Resource Exchange and other cohorts [32,36]. However, these studies place ASD genes within a multiplicity of pathways, several of which are broad and do not necessarily demonstrate convergence on final common molecular processes in individuals. In this regard, two recent studies use different systems biology approaches to provide a new perspective on the concept of molecular convergence. The first, an analysis of gene expression in postmortem autism brain, provides strong evidence for a shared set of molecular alterations in a majority of cases of ASD. This included disruption of the normal gene expression pattern that differentiates frontal and temporal lobes (consistent with an early developmental patterning defect), and two groups of genes dysregulated in ASD brains: one related to neuronal function, and the other to immune/inflammatory responses [49]. The neuronal function genes were enriched in genetic association signals, providing evidence that these changes were causal, rather than the consequence of the disease [49]; in contrast, the immune/inflammatory changes did not show a strong genetic signal, implicating environmental or epigenetic factors instead. It is also notable that the several of the same biological pathways identified in this gene expression study overlapped with the pathway analysis of CNV described above. This analysis of postmortem autism brain also showed downregulation of several markers of GABAergic interneurons, suggesting potential inhibitory interneuron dysfunction. These results provide 414


the first strong evidence for both a shared genetic and an environmental/epigenetic basis for ASD and the presence of an early developmental patterning defect. It is tempting to speculate that the abnormalities in cortical patterning and interneuron function provide a molecular basis for cortical–cortical and cortical–fugal disconnection, linking molecular abnormalities with anatomical, physiological and imaging findings. The second study on molecular convergence in ASD identified protein interactors of known ASD or ASD-associated genes [70]. This interactome revealed several novel interactions, including between two ASD candidate genes, Shank3 and TSC1. The biological pathways identified in this study include synapse, cytoskeleton and GTPase signaling, demonstrating a remarkable overlap with those identified by the gene expression and CNV pathway studies discussed above. This study differs from other genomewide studies (such as mRNA expression) because it begins with known ASD genes and asks about their relations. So although it is ‘–-omic’ in nature [71], it is not as unsupervised as methods that survey the genome more agnostically. Despite the significant heterogeneity in ASD, these studies using diverse methods identify several common areas of molecular convergence in ASD. Understanding how these pathways relate to individual differences now becomes an important research priority. Concluding remarks Many genes have been identified for ASD and some biologically coherent functional pathways that link these genes are emerging. Few of the genes, whether contributing by common or rare variation, are specific to ASD but instead variably contribute to genetic risk for ASD, ID, SZ, SLI, epilepsy and even attention deficit hyperactivity disorder (ADHD). Therefore, understanding the specificity of individual variants to ASD is a crucial challenge that requires several advances to be made (Box 1). Diseases of cognition and behavior have their basis in brain circuit dysfunction. Therefore, we need to understand how specific genetic risk variants lead to changes in neural circuitry and function in those with and without a specific diagnosis. This will probably involve understanding gene–gene and gene–environment interactions, as well as epigenetics, areas that have not yet been explored in ASD. We need to understand how Box 1. Priorities for future genetic research in ASD Delineate areas of molecular convergence between diverse mutational mechanisms underlying ASD susceptibility using unbiased systems biology methods. Perform whole genome sequencing in large numbers of patients with a variety of neurodevelopmental and psychiatric disorders to clearly define genetic overlap and differences in susceptibility. Define epigenetic contributions to ASD. Define appropriate endophenotypes through family case-control designs, so as to have a sound basis for understanding cross disorder genotype–phenotype relations. Translate the knowledge of mutational basis of ASD into mechanistic understanding at the cellular and circuit levels. Characterize subtypes of ASD based on molecular or genetic signatures and relate them to trajectory and treatment response. Identify environmental modifiers and gene- by- environment interactions.

Review the implicated neural circuitry relates to specific cognitive and behavioral elements, and we need to link the neural circuitry and behaviors to disease. Additionally, such circuits are related to specific aspects of human higher cognition and a more detailed notion of the autism cognitive phenotype is required. Components of this understanding have been achieved in a preliminary manner for only a few elements but to understand ASD from a systems level mechanistic perspective, we will need to integrate all of these levels of understanding [71]. This will no doubt involve the use of model systems, including mouse and invertebrates, so it will be important to be cognizant of evolutionary similarity and divergence in the interpretation of such data with respect to a disorder of human higher cognition. Regardless of the challenges, the identification of causal genetic variants provides a starting point for a new mechanistic understanding of ASD, enabling connection of genes to brain to cognition and behavior. Acknowledgments I thank Lauren Kawaguchi for her editorial assistance and Brett Abrahams for permission to use a previously published figure. Our laboratory’s work in autism and the molecular basis of human higher cognition is supported by two Autism Center of Excellence grants from NIMH: ACE Network 5R01MH081754-03 to D.H.G and ACE Center 5P50HD055784-03 (S. Bookheimer PI, D.H.G. co-PI), a Merit award from NIMH to D.H.G., 4R37MH060233-10, as well as funding from Autism Speaks and The Simons Foundation.

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Review


Understanding risk for psychopathology through imaging gene–environment interactions Luke W. Hyde1, Ryan Bogdan2 and Ahmad R. Hariri2,3 1

Department of Psychology and Center for the Neural Basis of Cognition, University of Pittsburgh, 210 South Bouquet St, Pittsburgh, PA 15260, USA 2 Laboratory of NeuroGenetics, Department of Psychology and Neuroscience, Duke University, Box 90086, 417 Chapel Drive, Durham, NC 27708, USA 3 Institute for Genome Sciences and Policy, Duke University, 417 Chapel Drive, Durham, NC 27708, USA

Examining the interplay of genes, experience and the brain is crucial to understanding psychopathology. We review the recent gene–environment interaction (G E) and imaging genetics literature with the goal of developing models to bridge these approaches within single imaging gene–environment interaction (IG E) studies. We explore challenges inherent in both G E and imaging genetics and highlight studies that address these limitations. In specifying IG E models, we examine statistical methods for combining these approaches, and explore plausible biological mechanisms (e.g. epigenetics) through which these conditional mechanisms can be understood. Finally, we discuss the potential contribution that IG E studies can make to understanding psychopathology and developing more personalized and effective prevention and treatment. Genes, experience and the brain A burgeoning synergy of disciplines and technologies are providing unique insights into how the dynamic interplay between genes, brain and experience shapes individual risk for psychopathology. This interplay is being articulated at multiple levels of analysis from molecules to cells to neural circuits, from emotional responses to cognitive functions to personality, and from populations to families to individuals [1–4]. Here, we briefly review recent endeavors that highlight the potential value of such interdisciplinary research. We then provide perspectives on how existing approaches and methods could be leveraged further to advance understanding of the etiology, pathophysiology and, ultimately, treatment and prevention of psychopathology. Gene–environment interaction (G E) [5] and imaging genetics [6] studies have both been very useful approaches to studying psychopathology. G E studies have emphasized the transactional nature of experience and the genome in the development of behavior, and imaging genetics studies have provided more proximal phenotypes and plausible mechanisms through which genes affect Corresponding author: Hyde, L.W. ([email protected]).

Glossary 5-HTTLPR: serotonin (5-HT) transporter gene linked polymorphic region. The 5HTTLPR is a variable number of tandem repeats polymorphism in the promoter region of the serotonin transporter gene. The serotonin transporter mediates active reuptake of synaptic serotonin and is thus crucial to regulating the duration and magnitude of serotonin signaling. Candidate gene: a gene whose protein product suggests that it could be involved in a phenotype of interest or a construct relevant to the phenotype or a gene that has been linked to a phenotype through GWAS. Epistasis: interaction between two or more polymorphisms so that the observed phenotype differs from what would be expected by either polymorphism independently. Gene–environment correlation (rGE): occurs when exposure to environmental conditions is dependent on one’s genotype. For example, the correlation between an ‘environmental’ risk factor such as harsh parenting and aggression could actually reflect a genetic pathway (mothers who are harsh could pass on genes to their children that increase the likelihood that they are aggressive). Genetic polymorphism: a variation in DNA with a frequency of at least 1% in the population. Functional genetic polymorphisms could reflect changes in a single (or multiple) base pair that can affect subsequent transcription of a gene or the structure of the resulting translated protein. Genome-wide association study (GWAS): an examination of genetic variation across the entire genome. Heritability: extent to which individual genetic differences contribute to phenotypic individual differences. Statistically, heritability represents the relative contribution of ‘genetics’ as compared to ‘environment’ when conceptualized as independent forces in shaping behavior and thus is a measure of the reliability estimate of the passage of traits from parent to offspring [4]. Hidden heritability: variance accounted for in twin studies of phenotypes that is unaccounted for by molecular genetic studies. Latent variables: mathematically inferred variables that represent the underlying commonality between directly measured variables. In practice, latent variables are variables that model the shared variance of similar predictor variables and thus decrease the error inherent in any one individual measure. For example, a measure of harsh parenting that includes observations of parenting, self-reports of parenting and reports of parenting by a significant other would more precisely model the underlying harsh parenting construct. Minor allele: less common allele at a polymorphic locus. Penetrance: likelihood that a genotype will result in a phenotype. Statistical mediation/Indirect effects: occurs when the link between a predictor and dependent variable is dependent on the effects of the predictor variable on an intermediate variable. This intermediate variable could serve as the mechanism linking the independent and dependent variable. Similarly, indirect effects denote the extent to which the independent variable affects the dependent variable through the independent variable’s effect on the mediator (and the mediator’s effect on the dependent variable). Note that consistent with others [58], we use the terms mediation and indirect effects interchangeably in this paper and thus do not imply that direct effects must be present between independent and dependent variables in order to find indirect effects. Statistical moderation: occurs when a ‘moderator’ variable affects the direction and/or strength of the relationship between a predictor variable and a dependent variable. A moderator variable is thus one that qualifies a relationship between two other variables. In other words, the relationship between variable A and B differs depending on the level of variable C.


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behavior. However, these approaches are not yet well integrated even though they have great potential to inform each other. In designing and carrying out studies that combine these methods, it is crucially important for researchers to address and understand challenges to progress inherent in each approach and to consider approaches that address these challenges. Moreover, in order to fruitfully combine these approaches, it is also important to consider statistical methods for analyzing these studies and to have an appreciation for biological mechanisms (e.g. epigenetics) through which genes and experience affect subsequent brain function and behavior. With careful consideration of all of these points, future research that combines G E and imaging genetics approaches has the potential to greatly inform our understanding of psychopathology and delineate more personalized and successful prevention and interventions. Gene-Environment Interactions G E occurs when the relationship between an environmental experience (e.g. exposure to toxins, trauma, stress) and the emergence of altered physiological or behavioral responses (e.g. compromised immune function, psychopathology) is contingent on individual differences in genetic make-up (i.e. genetic polymorphisms) (see Glossary) [5]. With G E, the effect of an environmental experience on outcome is conditional on genetic background (i.e. genotype) or, conversely, the effect of individual genotype on behavior or health is conditional on an environmental experience (Figure 1). For example, in key early work, Caspi and colleagues demonstrated longitudinally that well-established links between life stress and subsequent depression were contingent on serotonin transporter linked polymorphic region (5-HTTLPR) genotype [7]. Specifically, individuals with the transcriptionally less efficient short allele (fewer transporter molecules available to remove serotonin from the synapse) had a strong and positive relationship between life stress and depressive phenotypes, whereas those with the long allele had little or no relationship between life stress and depression. These findings are supported by meta-analysis [8] and animal

[(Figure_1)TD$IG]

(a) G × E

models [3], and a wealth of other G E studies have demonstrated similar associations across other genes, environments and phenotypes; for example monoamine oxidase A (MAOA) genotype moderates the relationship between maltreatment and antisocial behavior [9], and catechol O-methyltransferase (COMT) genotype moderates the relationship between cannabis use and psychosis [10]. Theoretical reviews have revealed several key principles for conducting G E research and evaluating resulting patterns [2,5]: researchers should consider the broad heritability of the target behavior, and then leverage knowledge generated in physiology and neuroscience to select polymorphisms in candidate genes that are of functional significance in the biological response to the environmental experience. Moreover, there should be evidence of variability in the response to the selected environmental experience, for which accurate measurement and, ideally, quantification should be available. Finally, there should be causal evidence linking the environmental experience with psychopathology. Because this approach does not presuppose a large main effect of single genetic variants (or experiences) on behavior but rather emphasizes an interaction with experience, carefully conducted studies of G E are instrumental in addressing issues of ‘hidden heritability’ (see glossary [11]), the generally weak penetrance of polymorphisms in candidate genes [11], and the lack of consistent replication in genetic association studies of psychopathology [2,12]. G E research often represents a more plausible model of disease in which individual experiences and genetic make-up interact across development to influence relative risk rather than more simplistic models hypothesizing independent effects of particular genes or experiences. Challenges to progress and possible advances in the field Although G E research has already advanced our understanding of the etiology of psychopathology, there are outstanding issues that deserve further consideration.

(b) Imaging genetics 2D

Genetic variation Neural functioning 1A

Gene × environment

1B

1C

Environments

Behavior & psychopathology

2A

Genetic variation

2B

2C

Behavior & psychopathology TRENDS in Cognitive Sciences

Figure 1. A conceptual and statistical diagram of G E and imaging genetics studies. (a) G E framework. Genes and environments might each have a ‘main effect’ on behavior (paths 1A and 1C) but the focus of these studies is on the interaction term which is modeled as a product of the two variables. (b) An ideal imaging genetics framework. Genetic variation in individuals leads to individual variability in neural functioning (path 2A), individual variability in neural functioning leads to differences in behavior or psychopathology (path 2B). Genetic variation might or might not have a direct impact on distal complex behavior (path 2C). Genetic variation has an indirect or mediated effect on behavior via its effect on neural functioning (arrow 2D: note that this path is not actually modeled statistically but is provided for conceptual clarity; this effect can be modeled as the product of the 2A and 2B paths).

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Review First, it is unclear if G E pertains only to harsh environments and undesirable outcomes [13]. Some authors have argued that ‘crossover’ effects (in which a specific polymorphism is disadvantageous in some environments but advantageous in others) suggest that some polymorphisms cannot be cast as simply conferring relative ‘risk’ but rather as shaping the range or ‘plasticity’ or ‘differential susceptibility’ to environmental triggers or contexts [14,15]. Although a ‘plasticity’ model is appealing, others have argued that the limited empirical data thus far suggest that the hypothesized ‘plasticity’ effects might not fall within a meaningful range of the data (i.e. actually observed in the real world) [16]. Fortunately, this question can be addressed through continued research, especially research that addresses enriching environments and positive outcomes. A second outstanding issue reflects controversy in the use and definition of ‘environment’ in G E research [17]. Typically, environment refers to both experiential phenomena, including childhood abuse or adult stressors such as divorce or unemployment [7], and exposure to physical forces such as toxins, natural disasters (e.g. hurricanes, tornadoes) and acts of violence (e.g. war, terrorism) [18]. However, experiential phenomena and physical forces differ crucially in the degree to which the affected individual contributes to the environmental trigger: little to none with physical forces but possibly a significant amount with experiential factors. Reflected in the latter is gene–environment correlation (rGE), which captures the influence of genetically driven variability in behavior as a precipitator or correlate of specific experiential triggers (e.g. difficult temperament resulting in harsh parenting). Thus, some G E studies might be biased by rGE [19]. This issue of rGE has been addressed through using designs including behavior genetic approaches [20] (e.g. adoption [21], twin studies [22]), natural disaster [23] and natural experiments [24], experimental manipulation in humans [25] and non-human primates [26], and treatment designs [27]. Third, although G E research alone has increased the depth and complexity of our understanding of factors influencing the etiology of psychopathology, it is certain that even greater complexity exists in the form of G E E and G G E [28–30]. For example, in a G E E study, the authors report that the 5-HTTLPR genotype maltreatment interaction predicting depressive symptoms originally reported by Caspi and colleagues [7] was further moderated by social support wherein only short homozygotes with a history of childhood maltreatment and low social support showed increased depressive symptoms [28]. These results emphasize the complex and multifaceted nature of these systems in which some experiences exacerbate risk (maltreatment), whereas others are protective (high social support). Consistently replicating such increasingly complex interactions requires sample sizes and statistical power not present in even the largest datasets published, particularly when analyzing interactions using canonical approaches that involve identifying first the main effects of each variable (e.g. genotype 1, genotype 2, environment 1, environment 2) [31]. In this approach, the interaction is limited in power by inherent distributional properties of the interaction term in nonexperimental


studies and by the need to account for main effects before examining interactions. Moreover, this limitation in power is often compounded by the frequency of the minor allele of the polymorphism, the rate at which individuals are exposed to a given trigger (and severity of the exposure [32]) and the frequency (and error of measurement) of possible dichotomous psychiatric diagnosis ([3,33] although see [34]) (see [31] for a discussion of approaches that might yield more power and note that experimental studies have much greater power to detect interactions). Fourth, it is also important to be cautious of spurious G E findings, which could arise due to selected sampling as well as scaling artifacts within logistic regression. Ideally, Eaves [35] suggests that these issues can be addressed by evaluating transformed data to examine if the interaction remains and using continuous variables and random sampling when possible (for more details see [33,36]). Fifth, it is important for G E findings to be replicated and these findings to be supported by meta-analysis. Conflicting reports on the interaction of the 5-HTTLPR and life stress predicting depression underscore this point. After initial findings (e.g. [7]), a meta-analysis suggested no reliable effect of this interaction on depression diagnosis [37]. However, this meta-analysis has been criticized for a biased selection of included studies. Specifically, authors have noted that included studies were characterized by relatively poor stress measurement [38], and an emphasis on dichotomous outcomes [33]. In line with these concerns, and in contrast to the conclusions of this meta-analysis [37], more thorough and inclusive meta-analyses support the reliability of the 5-HTTLPR stress interaction predicting depression [8,38]. Moreover, recent reviews have documented this interaction effect across model species (e.g. rhesus macaque and transgenic mice) and methodologies [3]. Nevertheless, this ongoing debate clearly highlights the importance of good construct measurement (of both environment and outcome). Finally, beyond issues of measurement, demographic variables such as age [39] and gender [40], as well as race/ ethnicity [41–43] and possible genetic substructure [44] are all likely to influence findings and require careful control and examination as additional moderators. G E research has provided a more nuanced understanding of the interplay between biology and environment in shaping risk for psychopathology. However, G E alone has not revealed the specific biological mechanisms for this risk [45]. Ultimately, for a genetic or environmental variable to affect behavior, it must ‘get under the skin’ [29,36,46]. G E must be instantiated in the brain if it is to affect behavior and the etiology of psychopathology. Imaging genetics Linking common genetic polymorphisms to variability in brain structure, function and connectivity is the foundation of imaging genetics [6,47,48]. This foundation is important for several reasons: first, by connecting genetic variation to an intermediate biological phenotype (the brain), a plausible mechanism is provided through which genes affect behavior (Figure 1). For example, several studies have demonstrated a link between the short allele of the 5-HTTLPR and increased amygdala reactivity to threat 419

Review [6,47], as well as altered functional connectivity between the amygdala and prefrontal regions [48]. Given links between increased amygdala reactivity and anxiety and depression [49,50], these studies address how and why variation in the 5-HTTLPR might affect risk for these psychopathologies. Second, when the target polymorphism is of known functionality (e.g. altered gene transcription), the genetic variant serves as a proxy for individual differences in brain chemistry and thus offers clues into the molecular mechanisms through which differences in brain arise at the genetic and molecular (e.g. neurotransmitter) level. For example, in the case of the 5-HTTLPR, the short allele has been linked to decreased transcription of the serotonin transporter [51] which affects serotonin signaling. Third, the neural and genetic variables of interest allow for more effective synergy with animal models (e.g. transgenic mouse models, optogenetics), which in turn can advance the detailed understanding of molecular and cellular events ultimately linking genetic variation to brain to behavior [3,45,52]. In addition, imaging genetics using multimodal positron emission tomography (PET)/functional magnetic resonance imaging (fMRI) [53] and pharmacological fMRI designs [54] has the potential to further illuminate specific molecular pathways mediating genetic effects on brain [1,3]. Fourth, by focusing on dimensional and relatively objective intermediate phenotypes (e.g. regional brain activation to specific stimuli), analyses are not limited by broad nosological definitions (e.g. DMS-IV diagnoses) that are often plagued by heterogeneity in symptoms/behaviors or inherent biases in self-report (e.g. [55]). Moreover, by using a biological phenotype (i.e. behaviorally relevant brain structure and function) more proximal to the functional effects of genetic variants, imaging genetics gains power relative to research with more distal behavioral phenotypes, and is poised to uncover novel candidate genetic variants (possibly through GWAS). As these novel candidates identified through imaging genetics will necessarily provide demonstrated effects on specific neurobiological pathways, they can in turn be targeted in association studies with behavioral and/or clinical phenotypes [56]. In sum, imaging genetics offers new insight into psychopathology by mapping predictive links between genes, brain and behavior, furthering our understanding of the etiology of disorders at the genetic and molecular level. Challenges to progress and possible advances in the field As in G E, imaging genetics studies have contributed to our understanding of psychopathology but some major issues are worth noting. For example, a majority of imaging genetics studies, especially early research, established links between genetic polymorphisms and brain but failed to link either directly to meaningful differences in behavior [47,48]. Recently, imaging genetics studies have begun to establish such meaningful links by modeling indirect pathways from genes to behavior via the brain [49,57]. Studies that draw indirect pathways between gene and behavior through the brain, when no direct gene–behavior link exists [49], emphasize the importance of using statistical approaches that can model indirect (mediated) pathways 420


[58]. Moreover, similar to G E studies, imaging genetics studies demonstrate that there are important relationships between genes and behavior even when large direct relationships are not evident. Another crucial challenge is to model even greater complexity of genetic effects on the brain. G E studies clearly demonstrate the importance of environmental experience in understanding the ultimate effects of genetic variation on behavior and thus the environment should be modeled in future studies (see description of imaging gene– environment interactions (IG E) below). Beyond issues of the environment, as in G E, the issue of epistasis and the probable small effect of any single polymorphism highlights the need for novel analytic approaches such as investigating G G interactions [59,60], constructing cumulative genetic profiles [61], attempting hypothesis-free imaging GWAS [62] as has been done with G E [63] (although greater application of GWAS to G E and neuroimaging are both needed), examination of rare gene or copy number variants [45], and novel statistical approaches to integrate multiple genes into models [64,65]. Furthermore, beyond interaction effects (G E, G G), future studies that incorporate complementary techniques (e.g. neuroreceptor PET, pharmacologic challenge, animal models) or approach the modeling of neural reactivity in novel ways (e.g. machine learning [66], graph theory [67]) will better capture the molecular mechanisms mediating genetic effects on brain [1,68]. Imaging G E Both G E and imaging genetics research examine potential relationships between genetic variation and individual differences in behavior and risk for psychopathology. In G E, the relationship is conditional (statistical moderation) on experiences that are necessary to unmask genetic effects (or vice versa). In imaging genetics, a biological mechanism can be specified (statistical mediation/indirect effects) in which variability in brain links genes and behavior. Here, we advocate for an integration of these approaches to help understand conditional mechanisms through which genes, environments and the brain interact to predict behavior and risk for psychopathology. We term this integrative strategy: imaging gene-environment interactions (IG E) (Figure 2). Several recent reviews [3,46,69] have demonstrated possible IG E by combining findings from research in animal models, G E and imaging genetics to explain the interactions of genetic variants with environmental variables to predict learning, memory and psychopathology. Although these reviews are exciting, empirical studies are only beginning to test components of IG E directly [70,71] and thus we explore how these conditional mechanisms can be specified statistically and conceptually in a human neuroimaging study. Conceptual models Statistically, the concept of IG E can be modeled by a moderated mediation framework (also called conditional indirect effects) [58] in which mediated/indirect effects are moderated by a third variable. In this framework, any or all paths within a mediation framework (gene to brain, brain to behavior, gene to behavior via brain) could differ



IG × E Neural functioning

b Genetic variation

d c

b

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e

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a a

rGE

a Environments

Behavior & psychopathology TRENDS in Cognitive Sciences

Figure 2. A conceptual model of IG E. To understand how IG E might be modeled conceptually and statistically, we demonstrate the relationship of the variables through highlighting traditional G E and imaging genetics paths as well as new paths possible in IG E studies. The a paths (green) model typical G E relationship. The b paths (purple) model the paths from an ideal imaging genetics study. The c path (blue) models the direct effect of the environment on neural functioning demonstrated in epigenetic studies. The d path (gold) models a G E predicting neural functioning (IG E effect). In this interaction, a gene would be more predictive or have a greater effect on a neural phenotype in some environments but not others (or the reverse: the environment would be predictive of neural functioning for those with one genetic variant but not another). The e path (red) represents another interaction: the possibility that genetic variation or an environmental variable could interact with neural functioning to predict behavior. For example, those with a variant in a gene affecting endocannabinoid signaling show greater correlation between reward-related brain reactivity and a measure of impulsivity [93]. Additionally, those with low social support have a greater relationship between their threat-related neural reactivity and trait anxiety [94]. Interactions involving the environment could be between gene and environment predicting neural function (d path) or between gene and neural functioning predicting behavior (e path) but in typical G E studies both of these interactions would be equivalent even though these interactions are likely to be due to very different mechanisms. Note that indirect and mediated pathways can be connected between many of the variables (e.g. G E to behavior through neural functioning) and thus an ideal IG E finding would be that the G E interaction term predicts behavior through neural functioning. Finally, within an SEM model, modeling a continuous interaction, the covariance between a genetic variant and an environment can be modeled which reflects the rGE between the specific genetic variant and specific environment.

depending on the level of a moderator variable (e.g. presence or absence of childhood abuse). As seen in Figure 2, there are multiple ways in which genetic, neural, environmental and behavioral variables could interact, and each model yields answers to slightly different questions (see also [58]). A particularly intuitive IG E model is a G E in which the interaction term predicts behavior through its effect on brain function (Figure 2, path D). In this case there are direct effects of both genetic and environmental variables on brain function but their interaction predicts nonadditive unique variance, which in turn predicts behavior. For example, genetic variation in serotonin signaling predicts increased amygdala reactivity [47], as do experiences of extreme early environmental deprivation [72], and individuals with both this genetic variation and environmental experience could show a synergistic increase in amygdala reactivity which could then predict increased anxiety symptoms. Alternatively, a positive environment such as parental warmth could negate any relationship between genetic variation in serotonin signaling and amygdala reactivity, and this lowered amygdala reactivity could then predict average levels of anxiety symptoms (Figure 3). This particular interaction (G E predicting brain function) also underlies much of the potential of IG E approaches. By combining the power of proximal intermediate phenotypes and the potential of G E to clarify such relationships, IG E can provide further insight into the conundrum of hidden heritability. For example, if a genetic variant has no association with a neural or behavioral phenotype in most

circumstances but has a robust association in relatively rare environments (e.g. physical abuse), IG E might be able to detect this association particularly with more proximal neural phenotypes. Such relationships can be tested using path or Structural Equation Modeling (SEM). As in G E studies, the way these relationships are tested (and graphed) can affect the interpretation of the results. For example, following from imaging genetics models, the environment could be seen in IG E studies as the moderator of the paths in an imaging genetics analysis (Figure 4). However, this approach privileges genetic factors as the ‘direct’ predictors of neural activity even though there is evidence that experience can affect the brain in direct and causal ways (see epigenetics section below) and G E studies often model genes as the moderator. Beyond this conceptual distinction, the analysis method can also affect results and their interpretation. For example, moderation in SEM is often tested in a multigroup model in which the path between genetic variation and brain function could be compared across two groups of subjects (e.g. those with or without a history of abuse). This multigroup model is best for dichotomous moderators (e.g. two different alleles of a polymorphism or an environmental extreme) and could be more easily and intuitively understood by readers. However, multigroup approaches have less value when otherwise continuous variables (e.g. continuously measured parenting) are dichotomized (e.g. harsh versus warm parenting). The alternative is to specify continuous interactions between variables of interest, even 421



(a)

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Short allele carriers have less 5-HT synaptic clearance which leads to increased amygdala reactivity to threat

Two separate mechanisms cause very high chronic levels of amygdala reactivity and broader cortico-limbic reactivity Abuse increases amygdala reactivity by sensitizing amygdala to environmental threat

Above average level of anxiety symptoms

(b)

Genetic variant (e.g., 5-HTTLPR)

Environmental experience (e.g., high levels of social support)

Short allele carriers have less 5-HT synaptic clearance which leads to increased amygdala reactivity to threat

Amygdala reactivity initially increased through greater 5-HT signaling

Social support helps child learn coping mechanisms to deal with anxiety thoughts, helps child learn stimuli are not threatening

Prefrontal cortex is strengthened through learning coping strategies and down-regulates amygdala reactivity

Average level of anxiety symptoms


Figure 3. Possible biological models of IG E interactions within the brain. As research suggests there are plausible causal biological mechanisms through which experience affects transcriptional effects of genes on neural functioning, it is helpful to specify how genes and environments might interact at a conceptual level to bring out the statistical relationships that could be found in IG E studies. (a) A synergistic model. Both genes and environments directly act on one brain area on similar mechanisms at the synapse. For example, short carriers of 5-HTTLPR could have increased 5-HT signaling in the amygdala [47], leading to greater reactivity to threat, and abuse or extreme neglect could increase the transcription of nonindividually varying sequences in genes that affect amygdala function [72] causing parallel increases in amygdala reactivity to threat. Thus the amygdala could have two pushes towards being more reactive to threat and show a multiplicatively exaggerated response. (b) A buffering model. Although a carrier of the short allele of the 5-HTTLPR has increased amygdala 5-HT signaling, high levels of social support cause changes in areas of the prefrontal cortex which are able to downregulate amygdala reactivity leading to normal reactivity to threat (alternatively abuse could affect the prefrontal cortex diminishing its ability to regulate the amygdala [84]). In both (a) and (b) interactions could occur within the same brain area or across multiple brain areas within a related circuitry (e.g. a corticolimbic circuitry). Note: it is also important to keep in mind that all of these relationships are ‘probabilistic’, not deterministic, and thus these models offer possibilities as a way of understanding IG E.

latent variables, and common statistical packages have recently made estimation of continuous latent interactions possible [58]. Continuous interactions are likely to provide more power and reflect the dimensional nature of many of 422

the variables (e.g. environmental experiences, neural function), as well as allow for the modeling and evaluation of the rGE between the specific genetic and environmental variables in the model (Figure 2).



Neural reactivity Targets: Brain function, structure and connectivity Novel approaches: Factor analysis of resting BOLD data to identify distributed functional circuitries; Graph theory and machine learning approaches

Genetic variability

Behavior

Targets: Genotypes with demonstrated functional effects Novel approaches: Cumulative genetic profiles; Modeling cascades of multiple interacting genes and molecular patthways; Regression trees and recursive partitioning

Targets: Personality, mood and affect, psychopathology Novel approaches: Latent factors to identify underlying constructs across measures; Observational methods

rGE Environmental experiences Targets: Experiences that precipitate individual variability in response; Experiences linked to psychopathology; Experiences linked to changes in biology or gene expression; Extreme environments likely to have larger effects on brain and behavior Novel approaches: Observational methods, longitudinal measurement of environments, experimental manipulation or intervention studies TRENDS in Cognitive Sciences

Figure 4. Specific targets for G E, imaging genetics and IG E studies. Importantly, novel approaches across each domain are needed to help progress understanding across all models. Moreover, similar to Figure 2, this model emphasizes the interaction between the environment and biology (genes, neural reactivity) as these variables predict behavior. More transparent arrows signify links made in traditional research. Bolded arrows represent newly proposed paths specific to IG E models. BOLD: blood oxygen level-dependent.

Considerations The above promise of IG E, like that of its parent strategies, is not without challenges. First, the challenges noted in the G E and imaging genetics sections generally apply to IG E models (Box 1; Figure 4). Second, IG E models test statistical correlations in humans specifying possible relationships and thus need to be paralleled by work in animal models or with experimental designs (e.g. drug treatment protocols, adoption studies) that can infer causality [4]. Moreover, as we discuss below, these models should be guided by biologically plausible relationships between variables. Third, these complex models require significantly larger samples than those currently available to have acceptable levels of power. Moderated mediation models require starting sample sizes in the range of 500– 1000 subjects to examine the expected small to moderate effects of each variable [58]. Moreover, this estimate does not include issues such as low minor allele frequencies and environmental exposure rates, which could necessitate even larger samples. Although samples of this size might sound untenable in neuroimaging, there are already pub-

lished studies with samples of this size (e.g. [73]) and consortium projects are addressing this issue by pooling data across sites/studies (e.g. [74]). Fourth, it is important to understand that development plays a large role in the unfolding of gene–environment–brain–behavior relationships. For example, many studied genetic variants (e.g. MAOA, 5-HTTLPR) likely function in utero or very early in development [75–77]. Moreover, environmental experiences differ in their impact depending on the developmental stage of the individual (e.g. types of stressors for a child might differ from those for an adult) [78,79] and epigenetic studies demonstrate that certain experiences have a greater biological impact during ‘sensitive periods’ of development [4]. Finally, just as G E and imaging genetics studies required researchers to bridge several areas and/ or work in multidisciplinary teams, IG E studies require even greater knowledge and collaboration. We hope that the conceptual models introduced in IG E will garner even greater appreciation for the work of colleagues in disparate fields (e.g. animal neurophysiology, biostatistics, epidemiology, experimental psychology). 423

Review


Box 1. Challenges to progress and outstanding questions Genes Challenges: single polymorphisms are of small effect. Issues such as epistasis and developmental regulation of genes have not been addressed in most studies. Solutions: genetic risk profiles representing the cumulative impact of multiple functional polymorphisms within a system (e.g. dopamine) and statistical models combining polymorphisms within and between systems (recursive partitioning, regression trees) can identify small genetic effects and their interactions. Longitudinal studies of genetic effects in animals and humans can inform when and how each genetic variant might affect brain and behavior. Outstanding questions: when and how do most genes of interest have their effect on brain and behavior? Are there more complex mechanisms or organized ways in which genes interact across development? Environments Challenges: many G E studies have relied on self-report or other measures with substantial error (e.g. retrospective reports). For many environmental variables it is not clear when certain experiences might have their effect on brain or behavior. For many experiences, G E studies have not paid attention to whether it is the objective account or the subjective report that matters. Solutions: observational measures and multiple well-validated measures of the same construct can help decrease error of measurement, as can modeling latent constructs of these variables. Prospective longitudinal studies can address developmental cascades and determine ‘sensitive periods’ during which certain experiences might have the greatest impact. Studies with multiple informants and methods can compare the impact of subjective versus objective accounts of experiences. Outstanding questions: are there certain experiences that have an impact no matter when they occur? Are there experiences that interact differently with genetic polymorphisms depending on when they occur? Are there experiences for which objective or subjective reporting is more important? Brain Challenges: much imaging genetics research focuses on a single brain region or the simple relationships between two regions whereas behavior reflects complex interactions within and across

Plausible biological mechanisms Imaging genetics studies in humans and non-human primates (e.g. [26,77]) as well as studies of strain differences in laboratory mice (e.g. [52]) convincingly link interindividual genetic variability to differences in brain and behavior. What about the environment: does it alter biology in ways that affect brain and behavior? For many biologists, including neuroscientists, the obvious answer might be ‘yes’, but given the ‘nature–nurture’ debates in some areas of psychology [4] it is important to specify models whereby experiences are transduced into functional biological signals that affect brain function and subsequent behavior. A fundamental example of such transduction comes from molecular studies demonstrating that learning is supported by long-term changes (i.e. long-term potentiation and depression) in synaptic physiology, which are mediated by changes in gene expression [80,81]. Thus, activity-dependent gene regulation drives changes in protein expression and adaptations in the molecular machinery for neurons and neuronal circuits supporting behavior. Importantly, such environmentally induced changes ultimately manifest in the 424

multiple brain regions. fMRI studies are relatively indirect measures of cellular activity. Solutions: exploratory statistical techniques such as machine learning, factor analysis and graph theory analysis use a data-driven approach to identify complex circuit function and whole-brain network organization. Multimodal human and animal studies can help address cellular and molecular mechanisms underlying brain activity. Mediation analyses can be used in multimodal studies to provide plausible pathways (e.g. does brain structure mediate gene– brain function relationship? Do receptor levels, assayed with PET, mediate the gene–brain function relationship?). In addition, experimental studies (e.g. experiments that manipulate the environment, pharmacological studies to manipulate neural chemistry) and animal studies can address mechanisms from a causal perspective. Outstanding questions: are studies finding relationships between single brain areas (e.g. the amygdala) and behavior the result of more complex interactions between multiple brain structures we do not yet understand? How does the interaction between brain regions map onto behavior? To what extent do genetic variants affect behavior through their influence on function versus structure versus connectivity in the brain? Outcome behavior/psychopathology Challenges: our conceptualization and resulting measurement of psychopathology is still rudimentary and based on observable behavior, which can lead to increased error in diagnosis. Dichotomous diagnoses limit statistical and inferential power, and miss the likely dimensional nature of most psychopathology. Solutions: imaging genetics and IG E provide intermediate continuous phenotypes, which might be more objectively measured and more powerful. Continuous and hierarchical models of broad psychopathology can increase power and model the high comorbidity found in studies of psychopathology. Observational methods of behavior can provide more reliable measures particularly when combined in latent constructs with multiple converging self-report measures. Outstanding questions: how do we account for the high levels of comorbidity across most psychopathology? Can intermediate phenotypes and, ultimately, the genetic polymorphisms by which they are predicted, usefully inform diagnosis and treatment? How can we define and delineate subgroups within broad diagnostic categories (e.g. Antisocial Personality Disorder) that express more homogeneous alterations in behavior and, by extension, brain dysfunction?

reorganization of brain circuits and their functional responses [80,81]. Another fundamental mechanism governing the transduction of experience into changes in biology and behavior is epigenetics [4,82,83]. Reviews of epigenetic regulation of brain and behavior are available elsewhere [e.g. 4,82–84]. Briefly, epigenetic regulation refers broadly to the local (i.e. cell specific) modification of gene expression of the DNAhistone complex and resulting accessibility of specific genes for transcription. Studies have demonstrated that early experience can alter epigenetic markers and subsequent patterns of transcription in a way that affects brain structure and function as well as behavior [4]. Chief among studies of epigenetic regulation of behavior are those conducted by Meaney and colleagues demonstrating that in rats, maternal care of offspring affects later adult behavior through epigenetic regulation of hypothalamic– pituitary–adrenal (HPA) axis reactivity to stress. Specifically, higher levels of maternal licking and grooming and arched-back nursing (LG-ABN) of rat pups during the first week of life leads to increased serotonin levels, which drive the expression of nerve growth factor-inducible protein A

Review (NGFI-A). Increased NGFI-A, in turn, leads to decreased methylation and increased acetylation of the promoter region of the glucocorticoid receptor (GR) gene in hippocampal neurons. This pattern of decreased methylation and increased acetylation results in increased gene expression and higher GR numbers in the hippocampi, which mediate negative feedback regulation of the HPA axis response to stress. These changes persist throughout the lifespan and promote adult behavior that is characterized by relative stress resilience and increased subsequent maternal care. Thus, through this epigenetic mechanism, high LG-ABN mothers beget relatively stress-resilient pups that become high LG-ABN mothers by experience-dependent mechanisms [4,85]. In these and similar studies, early experience affects epigenetic modifications triggering a cascade of changes in cellular signaling (particularly in the brain), which shape adult behaviors. In a compelling extension of this research to humans, a study of postmortem hippocampal tissues from individuals who committed suicide (compared to others who had accidental deaths) found increased methylation of the human GR promoter and decreased GR mRNA. However, this difference was only observed in a subset of suicide completers who had been abused as children and not in completers without history of abuse. Thus, there could be a remarkable conservation of epigenetic mechanisms regulating brain and behavior across species, which gives us confidence in developing plausible biological models of IG E in humans based on findings in animal models [86]. Similar epigenetic effects have been documented in other genes and brain regions associated with psychopathology [84,87]. Collectively these studies suggest that the environment has a very direct and long-lasting effect on biology at the epigenetic and neural level and that these effects translate into differences in behaviors, thus emphasizing that G E is the rule rather than the exception when understanding variability in behavior [4]. Trying to parse main effects of genetic versus environmental variables is to ignore that the genome and environment are in constant interaction [4]: the biological primacy of G E is apparent from the realization that transcription factors can be and often are controlled by environmental signals [82]. Thus, these biological mechanisms indicate that the impact of genetic variation on relative risk and resilience for psychopathology will be experience and context dependent [88]. It is unclear, however, if such changes can be examined in the context of human IG E research because data demonstrating that epigenetic markers in peripheral human tissue (e.g. blood cells) are faithful proxies for changes in the brain is lacking ([83,84], although see [87,89]). Moreover, future studies are needed that examine the impact of epigenetic mechanisms on genetic polymorphisms, especially promoter variants, to test true epigenetic G E relationships [84,90]. Looking forward With the emergence of detailed measures for both genes and brain, IG E research is poised to accelerate the pace of scientific discovery by fueling novel exchanges between studies in humans and those in animals. Specific brain


substrates (e.g. amygdala reactivity), environments (e.g. childhood neglect) and genes (e.g. 5-HTT) identified through human IG E research can generate the next set of targets in animal models that can delve into the detailed molecular mechanisms that link these larger elements. Likewise, research in animals, especially studies that identify novel molecular and genetic factors in the regulation of brain and behavior, can generate targets for human research, which can model these factors through common polymorphisms in the genes of interest and fMRI probes of the relevant brain circuits. Dynamic exchanges across human studies and animal models promise to elucidate tractable biological mechanisms that can inform the etiology and pathophysiology of psychopathology. Within human studies, an IG E approach connects the pieces of the puzzle: whereas G E studies of the past have implied that the mechanism through which G E affects behavior is the brain, and whereas imaging genetics studies have missed the interaction of biology with experience, IG E studies can elucidate conditional mechanisms through which genes and experience interact to affect neural structure and function and ultimately behavior and psychopathology. Specifying these models through careful statistical and methodological approaches in well-characterized samples is crucial for the ability of IG E to inform our understanding of psychopathology. The treatment implications of such work are critically important as medicine moves towards greater personalization [91]. For example, IG E studies could lead to intervention and prevention trials that target those at specific genetic and/or environmental risk [4,27] by identifying more homogenous subgroups of individuals within the same diagnosis [92]. Thus, future IG E research might inform the development of frameworks for determining when and for whom certain treatments will work (e.g. which environments could sabotage the treatment process, which genes could predict treatment success, which combinations of genes and environments could be the targets of early preventative intervention projects) and might help to refine diagnostic criteria. Overall, IG E can provide a more nuanced and complex model of human nature in health and disease by extending beyond nature–nurture debates and revealing specific mechanisms through which the constantly interacting environment and genome can be understood at the level of brain function and behavior. Acknowledgments We would like to thank funding sources (NIH grants 5R01-DA026222, T32-GM081760 and P30-DA023026), as well as Stephen Manuck and the Pitt Genetics Journal Club for thoughtful comments on ideas in this article.

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Trends in Cognitive Sciences September 2011, Vol. 15, No. 9 75 Fowler, J.S. et al. (2007) Evidence that brain MAO A activity does not correspond to MAO A genotype in healthy male subjects. Biol. Psychiatry 62, 355–358 76 Sibille, E. and Lewis, D.A. (2006) SERT-ainly involved in depression, but when? Am. J. Psychiatry 163, 8–11 77 Jedema, H.P. et al. (2009) Cognitive impact of genetic variation of the serotonin transporter in primates is associated with differences in brain morphology rather than serotonin neurotransmission. Mol. Psychiatry 15, 512–522 78 Sroufe, L.A. and Rutter, M. (1984) The domain of developmental psychopathology. Child Dev. 55, 17–29 79 Kendler, K.S. and Myers, J. (2009) A developmental twin study of church attendance and alcohol and nicotine consumption: a model for analyzing the changing impact of genes and environment. Am. J. Psychiatry 166, 1150–1155 80 Tada, T. and Sheng, M. (2006) Molecular mechanisms of dendritic spine morphogenesis. Curr. Opin. Neurobiol. 16, 95–101 81 Kandel, E.R. (1991) Cellular mechanisms of learning and the biological basis of individuality. In Principles of Neural Science 4th edn (Kandel, E.R. et al., eds), pp. 1247–1279, McGraw-Hill 82 Zhang, T.Y. and Meaney, M.J. (2010) Epigenetics and the environmental regulation of the genome and its function. Annu. Rev. Psychol. 61, 439– 466 83 Mill, J. (2011) Epigenetic effectson gene function and their role in mediating gene-environment interactions. In The Dynamic Genome and Mental Health (Kendler, K.S. et al., eds), pp. 145–171, Oxford 84 Roth, T.L. and Sweatt, J.D. (2011) Annual Research Review: epigenetic mechanisms and environmental shaping of the brain during sensitive periods of development. J. Child Psychol. Psychiatry 52, 398–408 85 Weaver, I.C.G. et al. (2004) Epigenetic programming by maternal behavior. Nat. Neurosci. 7, 847–854 86 McGowan, P.O. et al. (2009) Epigenetic regulation of the glucocorticoid receptor in human brain associates with childhood abuse. Nat. Neurosci. 12, 342–348 87 Tsankova, N. et al. (2007) Epigenetic regulation in psychiatric disorders. Nat. Rev. Neurosci. 8, 355–367 88 Masten, A.S. (2001) Ordinary magic. Resilience processes in development. Am. Psychol. 56, 227–238 89 Fraga, M.F. et al. (2005) Epigenetic differences arise during the lifetime of monozygotic twins. Proc. Natl. Acad. Sci. U.S.A. 102, 10604–10609 90 Ursini, G. et al. (2011) Stress-related methylation of the catechol-Omethyltransferase Val158 allele predicts human prefrontal cognition and activity. J. Neurosci. 31, 6692–6698 91 Willard, H.F. and Ginsburg, G.S. (2009) Essentials of Genomic and Personalized Medicine, Academic Press 92 Mehta, D. et al. (2011) Using polymorphisms in FKBP5 to define biologically distinct subtypes of posttraumatic stress disorder: evidence from endocrine and gene expression studies. Arch. Gen. Psychiatry DOI: 10.1001/archgenpsychiatry.2011.50 93 Hariri, A.R. et al. (2009) Divergent effects of genetic variation in endocannabinoid signaling on human threat- and reward-related brain function. Biol. Psychiatry 66, 9–16 94 Hyde, L.W. et al. (2011) Social support moderates the link between amygdala reactivity and trait anxiety. Neuropsychologia 49, 651–656

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Review


The genetics of cognitive impairment in schizophrenia: a phenomic perspective Robert M. Bilder1, Andrew Howe2, Nic Novak3, Fred W. Sabb1 and D. Stott Parker2 1

Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, and Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095, USA 2 Department of Computer Science, Samueli School of Engineering, University of California, Los Angeles, CA 90095, USA 3 Interdepartmental Program in Neuroscience, University of California, Los Angeles, CA 90095, USA

Cognitive impairments are central to schizophrenia and could mark underlying biological dysfunction but efforts to detect genetic associations for schizophrenia or cognitive phenotypes have been disappointing. Phenomics strategies emphasizing simultaneous study of multiple phenotypes across biological scales might help, particularly if the high heritabilities of schizophrenia and cognitive impairments are due to large numbers of genetic variants with small effect. Convergent evidence is reviewed, and a new collaborative knowledgebase – CogGene – is introduced to share data about genetic associations with cognitive phenotypes, and enable users to meta-analyze results interactively. CogGene data demonstrate the need for larger studies with broader representation of cognitive phenotypes. Given that meta-analyses will probably be necessary to detect the small association signals linking the genome and cognitive phenotypes, CogGene or similar applications will be needed to enable collaborative knowledge aggregation and specify true effects. Cognitive impairment in schizophrenia: in search of a genomic basis Cognitive impairment has been seen as a hallmark of schizophrenia at least since Emil Kraepelin described the syndrome of dementia praecox in 1893 [1] but in the past few decades it has assumed new importance at least in part due to hopes that the cognitive functions might prove more tractable targets for genetic study than are the characteristic symptoms used to diagnose schizophrenia (Box 1). Despite heritability of the schizophrenia phenotype estimated at near 80%, initial family-based association studies, and then case-control genome-wide association studies (GWAS) (see Glossary) have failed to identify any common genetic variants with large effects. A handful of reported associations surpass conventional levels of genome-wide significance ( p < 5 10 8) but none accounts for much of the morbidity associated with schizophrenia; see SZGene (SchizophreniaGene) Field Synopsis of Genetic Corresponding author: Bilder, R.M. ([email protected]).

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Association Studies (http://www.szgene.org) for regularly updated meta-analytic findings. It seems probable that many genetic variants (perhaps thousands) interact with each other and the environment to account for schizophrenia risk, and indeed this risk is likely to be shared at least with bipolar disorder and probably autism and other complex neurodevelopmental disorders [2,3]. Glossary Candidate gene study: a study of association between genotype and phenotype(s) in which the genetic variation is selected based on prior evidence of association. Copy number variation (CNV): a variation between individuals of the same species in the structural sequence of nucleotides due to repetition or deletion of part of the sequence; for instance, a ‘trinucleotide repeat’ is the repetition of three nucleotides; ‘CAG repeats’ are one example already identified as responsible for multiple neurological disorders including Huntington’s disease [34]. Endophenotype/Intermediate phenotype: the term ‘endophenotype’ as it is now widely used in psychiatry stems from original usage by Gottesman and Shields who referred to ‘internal features’, ‘only knowable after aid to the naked eye’, such as the help provided by ‘a biochemical test or a microscopic examination’ ([35], p. 19). More recently, Gottesman and Gould defined endophenotypes as ‘measurable components unseen by the unaided eye along the pathway between disease and distal genotype’ ([36], p. 636). We believe this added specification is important because it implicates endophenotypes as being the product of genetic variation that is part of the ‘causal path’ between genotype and some other phenotype of interest (usually one that is either the diagnosis or part of the diagnosis). The terms ‘endophenotype’ and ‘intermediate phenotype’ are now widely used interchangeably to refer to any phenotype that is presumed to be ‘closer’ to the level of gene action than is the observable phenotype of interest (for example, ‘molecular expression’ is considered closer to gene action than behavioral ‘symptoms’ of schizophrenia). We believe the term ‘intermediate phenotype’ better reflects the putative causal attributes of phenotypes that actually are part of the causal path from genotype to some higher-level phenotype; but so far few such intermediate phenotypes have been proven. Genome-wide association study (GWAS): a study of association between genotypes and phenotype(s) in which genetic variations are sampled widely over the entire genome, theoretically in a manner that provides ‘coverage’ of the entire genome. Paraphenotype: a phenotype at the same level of expression with respect to causal models of the relation between a genotype and some other phenotype of interest; a phenotype that shares causal precedents with another phenotype (Figure 1). Phenomics: the systematic study of phenotypes on a genome-wide scale, emphasizing the simultaneous analysis of multiple phenotypes. Single-nucleotide polymorphism (SNP): a variation between individuals of the same species in the structural sequence of nucleotides in the DNA molecule at a single location involving the substitution of one nucleotide for another. Given that there are four nucleotides: adenosine, thymine, cytosine and guanine; a SNP is said to exist when one of these nucleotides is replaced by another.


Review Box 1. Cognitive impairment in schizophrenia: why is it important? The cognitive impairment associated with schizophrenia is severe, widespread and apparent long before overt signs of psychosis emerge [37–39]. There were initially hopes that new antipsychotic drug treatments might ameliorate these deficits but large-scale trials with these agents have shown limited success. Large-scale effectiveness studies suggest no significant cognitive benefit for new antipsychotic agents [40,41], and so far no agent has been approved by the US Food and Drug Administration for the indication of cognitive impairment associated with schizophrenia despite wide interest. There remains hope that in the future pharmacogenomic strategies could yield treatment benefits for schizophrenia [42], and innovative strategies are being advanced particularly to identify new molecular targets linked directly to cognitive dimensions rather than the traditional symptomatic dimensions of schizophrenia [43]. These findings highlight the importance of identifying genetic bases of cognitive dysfunction in schizophrenia both for increasing understanding of pathophysiology and for developing more effective treatments.

Investigations focused on ‘endophenotypes’ or ‘intermediate phenotypes’, including cognitive phenotypes, are now emerging [4], with the hope that these might offer more traction in deciphering the complex genetics of brain dysfunction in schizophrenia and perhaps other neurodevelopmental syndromes. Meanwhile, most investigators are likely to agree that unraveling the genetic bases of schizophrenia and its associated cognitive impairment is proving more difficult than was hoped earlier [5]. Major questions remain as to how we might best gain traction on the elusive biological roots of schizophrenia. The new transdiscipline referred to as ‘phenomics’ (the systematic study of phenotypes on a genome-wide scale) could offer one perspective [6]. Most efforts so far have targeted the syndromal phenotype of schizophrenia in case-control studies in hopes of finding genetic association. By contrast, phenomics approaches consider multiple phenotypes, including those that could be measured on different biological scales, to better define biologically plausible traits. It is assumed that by combining information from multiple levels (from the level of gene expression through proteomic, metabolomic, cellular and systems levels) we could better characterize the biological contributions of specific genetic variants and their interactions in a way that ultimately permits personalization of diagnosis and rational treatment. In research on schizophrenia, for example, it has been hoped that we might gain clearer insights from the simultaneous focus on the symptoms that mark the syndrome and the cognitive deficits that consistently accompany the syndrome. In this article we aim to: (i) provide a brief synopsis of current knowledge about the genetic basis of cognitive deficits in schizophrenia; (ii) highlight selected conceptual issues that we believe will be important to make further progress in finding the genetic bases for cognitive phenotypes in schizophrenia and (iii) introduce a new freely available resource – CogGene – that we hope can serve the field by helping to aggregate, visualize and analyze relevant evidence for those interested in the genetic bases of cognitive function in schizophrenia and other disorders. An important point about a knowledgebase such as CogGene is that it might help advance understanding of the


genetic bases of cognitive deficits in schizophrenia even if the knowledgebase is focused selectively on findings in healthy people. In brief, we hope a tool such as CogGene can help researchers ‘triangulate’ genetic association findings: if a specific genetic variation is associated with the diagnosis of schizophrenia AND the same genetic variation is associated with cognitive impairment in otherwise healthy people, then it increases the likelihood that this variant could be related to BOTH schizophrenia and cognitive impairment through a common mechanism. Indeed this strategy might be more informative than examining association of a genetic variant with cognitive deficits within schizophrenia samples because cognitive impairment is ‘confounded with’ the diagnosis of schizophrenia. What do we know so far about the genetics of cognitive impairment in schizophrenia? It has long been known that both schizophrenia and cognitive impairment are highly heritable and it has long been assumed that some genetically mediated anomaly, probably a neurodevelopmental anomaly, underlies the vulnerability to both schizophrenia and the cognitive impairment that invariably accompanies the syndrome. The heritability of schizophrenia is estimated at greater than 0.8, whereas the heritability of cognitive phenotypes is most often found to be near 0.5, regardless of whether the estimate is derived from healthy or ill groups [7,8]. Compelling evidence has also been provided to show that many different cognitive abilities could be linked, not only by their covariation within individuals but further by their shared genetic correlations; indeed this has led to the ‘generalist gene’ hypothesis that many presumably diverse cognitive functions are likely to be associated with a common set of genetic variations [9–11]. Recent work has begun to identify the shared genetic components of cognitive phenotypes and syndromal phenotypes such as schizophrenia; in brief, the cognitive phenotypes and schizophrenia are significantly correlated, and the lion’s share of this covariance (72% to 92%) is due to shared genetic effects [12–14]. Despite the relatively high heritability of these phenotypes and their high genetic correlation, we so far have in hand no well-validated candidate genes that explain much of the variance in either schizophrenia or cognitive phenotypes (with the exception of selected rare genes in which mutations cause large effects on cognition, as described below). Meanwhile it increasingly looks as though the shared liability for schizophrenia and cognitive impairment is most likely to be identified through a relatively large number of genetic influences, some coming from larger impacts of rare variants, and some coming from larger numbers of more common variants with very small effects working in combination to undermine healthy brain development and signaling. The search for cognitive phenotypes Cognitive phenotypes as intermediate phenotypes or as paraphenotypes One frequently asked question is whether cognitive deficits or symptoms are epiphenomenal: that is, do cognitive deficits cause schizophrenia or are cognitive deficits caused by schizophrenia? Although this distinction might be seen 429

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[(Figure_1)TD$IG]

as an irrelevant exercise in semantics, it is important for modeling. We see both symptomatic and cognitive measures as similar in level of explanation, given that both are behavioral manifestations of neural systems activity. This point of view casts skepticism on the likelihood that cognitive phenotypes will serve well as intermediate phenotypes because they are not really intermediate in the sense that this term is used in causal models (i.e. they are not likely to be mediating variables for symptoms, at least in schizophrenia). Cognitive phenotypes might nevertheless be of value as ‘paraphenotypes’ (i.e. phenotypes that are at the same hierarchic level within a causal model, and are alongside each other) because they are better validated with respect to neural systems phenotypes (Figure 1b: if path coefficient x>y). Adding cognitive measures to a multivariable phenotype could thus help constrain the neural system phenotypes to a subset of all neural systems that might be part of the mechanistic path from genome to syndrome, and thereby help increase statistical power for detecting associations with ‘lower’ level biological processes including genetic variation. It should be recognized, however, that such arguments are at this point largely theoretical and there are few confirmatory or disconfirmatory examples in practice. So far we can say only that cognitive phenotypes have not shown ‘simpler’ genetic architecture than complex syndromal phenotypes [15]. The genetic bases of cognitive phenotypes There are multiple potential windows on the genetic bases of cognitive phenotypes. Some of the earliest and most successful approaches found genetic associations with cognitive impairment syndromes, particularly mental retardation. Indeed mental retardation could be seen as a phenotype for which genetic studies have been particularly successful, with approximately 300 identified monogenic causes; however, it should be recognized that these are rare (i.e. most account for only 0.01% of all cases) [16].

(a) Intermediate phenotype or endophenotype model Neural system phenotype

Cognitive phenotype

Symptom phenotype

(b) Parallel, common parent, or paraphenotype model

x

Cognitive phenotype

Neural system phenotype

y

Symptom phenotype TRENDS in Cognitive Sciences

Figure 1. Relations of cognitive phenotypes to neural systems and diagnosis. Path diagrams schematizing different points of view regarding the role of cognitive phenotypes as: (a) intermediate phenotypes or endophenotypes or (b) as different behavioral effects – paraphenotypes – that differ primarily in the strength of relations to neural system activity.

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Despite the low frequency of these conditions, they could be informative about mechanisms important to brain development and cognition. For example, the study of Fragile X syndrome (a genetic condition involving changes in part of the X chromosome) has led to multiple insights about the genetics of trinucleotide repeats, X-linked genetic disorders, and the enormous pleiotropy of single-gene deficits on neural and other systems [17]. Similarly the study of neurofibromatosis (a genetic disorder of the nervous system, which mainly affects how nerve cells form and grow), and the NF1 gene, has yielded major insights into the molecular basis of these syndromes, yielded novel transgenic rodent models in which mutants have superior abilities, and might stimulate novel treatment development [18,19]. It should be recognized that even when genetic studies reveal compelling associations that are considered significant at genome-wide levels and replicated, the identified variants might still account for only a small amount of the known heritability. Human height is a good example phenotype: despite a heritability near 80%, only about 5% of phenotypic variance is explained by more than 40 known loci [20]. This has been referred to as the problem of ‘missing heritability’ or the ‘dark matter’ of heritability, and could be due to many reasons, including: (i) variants that the GWAS arrays are missing, that is the single nucleotide polymorphisms (SNPs) that have yielded association findings might not be the causative SNPs, and the true causative SNPs might have larger effects; (ii) gene–gene interactions (epistasis) and/or gene–environment interaction effects too complicated to assess given current sample sizes and analytic strategies; (iii) epigenetic effects; (iv) much larger numbers of genetic variants with even smaller effects remaining to be found; (v) inadequate accounting for shared environmental variance among relatives [21]. Although work so far using GWAS to detect associations with cognitive phenotypes has been unsuccessful at replicating results from some prior ‘candidate gene’ studies, it is worth noting that the results remain consistent with the hypothesis that cognitive impairment could be associated with an increase in genetic variants each with small effect [22]. Sabb and colleagues summarized prior work on candidate genes for which investigators reported associations with cognitive phenotypes comprising ‘memory’ (51 effects) and ‘intelligence’ (42 effects) [23]. They found generally modest associations of candidate genes with varying cognitive phenotypes, with most effect sizes (Cohen’s d for the effect distinguishing alleles) ranging from 0.09 to 0.23. An interesting result of this survey was that among genes investigated, two had relations specifically with intelligence (CHRM2, DRD2), two had relations specifically with memory phenotypes (5-HTT, KIBRA), and four had reported links to both intelligence and memory phenotypes (DTNB1, COMT, BDNF, APOE). Others have highlighted the replication of selected findings related to rare variants in key genetic regions (such as PDE10A, CYSIP1, KCNE1/ KCNE2, CHRNA7) and their possible connection to both

Review schizophrenia and cognitive impairment phenotypes [24]. It must be recognized, however, that these findings could still reflect false positive reports. Sources of bias include the targeting of certain genes as candidates without very strong a priori evidence, considering different SNPs within a gene as replications of a specific gene finding, and as highlighted by Sabb and colleagues, dubious measurement of cognitive phenotypes (for example, one of the measures of ‘memory’ was the Mini Mental State Exam, and another was ‘Fluency’, despite the fact that these would be questioned by most investigators). Sabb and colleagues established an open database to share knowledge about these associations; see Phenowiki: an Online Collaborative Database for Phenotype Annotation (http://www.Phenowiki. org) [7]. Hopefully, continued development of such resources will ultimately enable convergence on the meaningful associations and refinement of phenotype definitions perhaps narrowing down to those that have the clearest relations for broader population studies. CogGene: a collaborative knowledgebase for documenting genetic associations with cognitive phenotypes With this background, we aimed to determine to what extent data regarding genetic associations with the schizophrenia phenotype might be enriched by examining correlations of the same genetic targets with cognitive phenotypes. This approach could be considered a triangulation of genes at the intersection of schizophrenia and cognitive impairment. To gather data relevant to this we elaborated on the Phenowiki database architecture and created a new knowledgebase and web service specifically to represent genetic association findings for cognitive phenotypes: UCLA CogGene (http://www.CogGene.org). We aimed to have an interface similar in some ways to those available in SZGene and AlzGene, which provide forest plots of effect sizes, but our data differ insofar as the phenotypes to be represented are quantitative trait scores (rather than categorical diagnoses). Further, due to the richness of the cognitive phenotype data (which includes both test names and then specific measurement variables or ‘indicators’ within each test), we created features in CogGene to enable dynamic sorting and computation of weighted effect size statistics over groups of results that can be selected simply by clicking on the effect labels. The data discussed in this paper and the CogGene system are now viewable at UCLA CogGene, and information is posted on the site regarding how to submit additional contributions. The findings described here were culled from publications identified through literature mining if they cited the names of at least one gene and related polymorphism, and at least one cognitive test: the names are from a lexicon developed in the Consortium for Neuropsychiatric Phenomics at UCLA (http://www.phenomics. ucla.edu). From these publications we selected those with usable data (i.e. with data specifying at least one statistical association between a specific SNP and a specific cognitive test indicator), and extracted quantitative effect sizes for associations between SNPs and cognitive test indicators. We highlight that these data were selected to represent results from healthy samples, in order to maximize the


independence of findings from those in SZGene (and thus enabling us to inspect possible overlaps in the ‘top hits’ free of the potential confounds between schizophrenia and cognitive impairment phenotypes). The results can now be browsed, sorted and reanalyzed using custom-designed software tools that permit visualization and execution of ‘meta-analysis’ (sample-size-weighted averaging) over selected effects under user control. In brief, the CogGene system permits visualization of the effect sizes for specific allelic variants on the cognitive trait scores (expressed as Cohen’s d statistic, which is the standardized difference between group means), and the 95% confidence intervals around these difference scores, in an interactive forest plot. This is similar to the representation of genetic association data in other widely used resources (SZGene, AlzGene) which use similar (but static) forest plots to show allelic associations with case-control differences (but in these examples, the effect sizes are expressed as odds ratios rather than group differences). A typical screenshot of CogGene is shown in Figure 2. The SZGene database contains information from 1727 studies, reporting data on 1008 genes, and 8788 polymorphisms; this database has 287 meta-analyses [25]. SZGene ranks its ‘top results’ using the HuGENet interim guidelines published by Ioannidis and colleagues [26], which consider the ‘amount’ of evidence (i.e. Grade ‘A’ is given to studies in which the total number of minor alleles exceeds 1000), ‘consistency’ of evidence (i.e. Grade ‘A’ is given only when inconsistency is modest, for example I2

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