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A Framework for Interpreting Genome-Wide Association Studies of Psychiatric Disorders the Psychiatric GWAS Consortium Steering Committee

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A Framework for Interpreting Genome-Wide Association Studies of Psychiatric Disorders the Psychiatric GWAS Consortium Steering Committee Molecular Psychiatry (2009) 14, 10–17 & 2009 Nature Publishing Group All rights reserved 1359-4184/09 $32.00 www.nature.com/mp PERSPECTIVE A framework for interpreting genome-wide association studies of psychiatric disorders The Psychiatric GWAS Consortium Steering Committee Genome-wide association studies (GWAS) have yielded a plethora of new findings in the past 3 years. By early 2009, GWAS on 47 samples of subjects with attention-deficit hyperactivity disorder, autism, bipolar disorder, major depressive disorder and schizophrenia will be completed. Taken together, these GWAS constitute the largest biological experiment ever conducted in psychiatry (59 000 independent cases and controls, 7700 family trios and > 40 billion genotypes). We know that GWAS can work, and the question now is whether it will work for psychiatric disorders. In this review, we describe these studies, the Psychiatric GWAS Consortium for meta-analyses of these data, and provide a logical framework for interpretation of some of the conceivable outcomes. Molecular Psychiatry (2009) 14, 10–17; doi:10.1038/mp.2008.126; published online 11 November 2008 Keywords: genome-wide association; attention-deficit hyperactivity disorder; autism; bipolar disorder; major depressive disorder; schizophrenia Genome-wide association studies (GWAS) have review of GWAS per se as many excellent reviews of yielded confirmed associations for complex diseases this technically and statistically intricate methodo- in almost all medical specialties. Since 2005, there logical approach are available.7–12 This is also not a have been genetic breakthroughs in cardiology, review of the advantages and disadvantages of endocrinology, gastroenterology, hepatology, infec- different study designs and sampling strategies for tious disease, oncology, ophthalmology, neurology, the dissection of complex psychiatric traits. We pulmonology and rheumatology. The success of would like to consider how the dozens of GWAS GWAS for nonpsychiatric complex diseases has been papers that will soon be in the literature can be exceptional – as of 1 August 2008, there were 197 synthesized: what can integrated mega-analyses associations where the initial GWAS finding repli- (meta-analysis is based on summary data (for exam- cated in one or more additional samples with a ple, odds ratios) from all available studies whereas P-value < 5 Â 10–8 (a defensible choice for genome- ‘mega-analysis’ uses individual-level genotype and wide significance).1 Recent papers describing signifi- phenotype data) of all available GWAS data tell us cant and replicated associations for autism,2 bipolar about the etiology of these psychiatric disorders? This disorder3 and schizophrenia4–6 suggest that psychiatry is an exceptional opportunity as positive or negative has joined this list. By the end of 2008, GWAS on 42 results will enable us to learn hard facts about these samples of subjects of European ancestry with atten- critically important psychiatric disorders. We suggest tion-deficit hyperactivity disorder (ADHD), autism, that it is not a matter of ‘success versus failure’ or bipolar disorder, major depressive disorder and schizo- ‘optimism versus pessimism’ but rather an opportu- phrenia will have been completed. Taken together, these nity for systematic and logical approaches to empiri- GWAS constitute the largest biological experiment ever cal data whereby both positive and appropriately conducted in psychiatry: > 59 000 independent cases qualified negative findings are informative. and controls, > 7700 family trios, on the order of 500 000 single nucleotide polymorphism (SNP) geno- types per subject, and a total of > 40 billion genotypes. The Psychiatric GWAS Consortium The purpose of this article is to consider the ‘big picture’ and to provide a logical framework for the The studies that comprise the Psychiatric GWAS possible outcomes of these studies. This is not a Consortium (PGC; http://pgc.unc.edu) are shown in Table 1. GWAS data for ADHD, autism, bipolar disorder, major depressive disorder and schizophre- Correspondence: Dr P Sullivan, Department of Genetics, nia from 42 samples of European subjects should be CB#7264, 4109D Neurosciences Research Building, University available for mega-analyses by early 2009 ( > 59 000 of North Carolina, Chapel Hill, NC, 27599-7264, USA. E-mail: [email protected] independent cases and controls and > 7700 family Received 27 October 2008; accepted 27 October 2008; published trios). To our knowledge, the PGC will have access to online 11 November 2008 the largest set of GWAS data available. GWAS framework 11 Table 1 Summary of GWAS samples expected by the end of 2008 Disorder Samples Casesa Controlsb Trios ADHD 6 1418 0 2443 Autism 6 652 6000 4661 Bipolar disorder 10 7075 10 559 0 Major depressive disorder 9 12 926 9618 0 Schizophrenia 11 9588 13 500 650 Totalc 42 31 659 26 945 7772 Abbreviation: ADHD, attention-deficit hyperactivity disorder. aAll cases are independent. bControls within each disorder are independent. cColumn totals excludes duplicates and are not necessarily a simple sum of the rows. A major change in human genetics in the past 5 years ture.15 There are two subaims. (2a) Conduct mega- has been in the growth of controlled-access data analysis to identify genetic variation convincingly repositories, and individual phenotype and genotype associated with conventional definitions of two or data are now available for many of the studies in Table more disorders. This nosological aim could assist in 1. When the PGC mega-analyses are completed, most delineating the boundaries of this set of disorders. data will be available to researchers via the NIMH (2b) An expert working group will convert epidemio- Human Genetics Initiative (http://nimhgenetics.org). logical and genetic epidemiological evidence into Although the ready availability of GWAS data is a explicit hypotheses about overlap among these dis- benefit to the field by allowing rapid application of a orders, and then conduct mega-analyses based on wide range of analytic strategies to GWAS data, there these definitions (for example, to examine the lifetime are potential disadvantages. GWAS mega-analysis is presence of idiopathic psychotic features without complex and requires considerable care and expertise regard to diagnostic context). to be done validly. For psychiatric phenotypes, there We anticipate that interim mega-analyses for Aims is the additional challenge of working with disease 1 and 2a will be completed by late 2008 and final entities based largely on clinical description, with mega-analyses by mid-2009. Updated results will be unknown biological validity and having both sub- posted on the PGC web site (http://pgc.unc.edu). The stantial clinical variation within diagnostic categories standard of evidence applied will be strict and as well as overlaps across categories.13 Given the conform to that recommended in human genetics.9 urgent need to know if there are replicable genotype– Statistical power should be superior to any earlier phenotype associations, a new type of collaboration study in psychiatric genetics. As a rough illustration, was required. the minimum detectable genotypic relative risk for The purpose of the PGC is to conduct rigorous and Aim 1 is 1.161 (assuming 10 000 cases for one comprehensive within- and cross-disorder GWAS disorder, 10 000 controls, a =1Â 10–8,1 minor allele mega-analyses. The PGC began in early 2007 with frequency = 0.25, and log-additive model). For the the principal investigators of the four GAIN GWAS,14 nosological Aim 2a, the minimum detectable geno- and within six months had grown to 110 participating typic relative risk is 1.100 (same assumptions but scientists from 54 institutions in 11 countries. The with 25 000 cases and 25 000 controls). Effect sizes PGC has a coordinating committee, five disease- from the literature for other complex traits are usually working groups, a cross-disorder group, a statistical larger than these minima.16 analysis and computational group, and a cluster computer for statistical analysis. It is remarkable that A framework for interpreting GWAS of psychiatric almost all investigators approached agreed to partici- disorders pate and that no one has left the PGC. Most effort is donated but we have obtained funding from the Approach NIMH, the Netherlands Scientific Organization, Her- We emphasize that the framework presented here is senstichting Nederland and NARSAD. not the only possible formulation. Indeed, the recent The PGC has two major specific aims. (1) Within- history of human genetics has been full of unantici- disorder mega-analyses: conduct separate mega-ana- pated empirical results and it is quite possible that lyses of all available GWAS data for ADHD, autism, the future will eventuate differently from the frame- bipolar disorder, major depressive disorder, and work presented below. At the same time, we believe it schizophrenia to attempt to identify genetic variation important that we think about these studies in an convincingly associated with any one of these five integrated and systematic manner. disorders. (2) Cross-disorder mega-analyses: the clini- The goal of the PGC is to identify convincing cally-derived DSM-IV and ICD-10 definitions may not genetic variation-disease associations. A convincing directly reflect the fundamental genetic architec- association would be extremely unlikely to result Molecular Psychiatry GWAS framework 12 from chance, show consistent effect sizes across all or from almost all samples.
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