Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder

Robert Maier, Gerhard Moser, Guo Bo Chen, Stephan Ripke, William Coryell, James B. Potash, William A. Scheftner, Jianxin Shi, Myrna M. Weissman, Christina M. Hultman, Mikael Landén, Douglas F. Levinson, Kenneth S. Kendler, Jordan W. Smoller, Naomi R. Wray, S. Hong Lee, Devin Absher, Ingrid Agartz, Huda Akil, Farooq AminOle A. Andreassen, Adebayo Anjorin, Richard Anney, Dan E. Arking, Philip Asherson, Maria H. Azevedo, Lena Backlund, Judith A. Badner, Anthony J. Bailey, Tobias Banaschewski, Jack D. Barchas, Michael R. Barnes, Thomas B. Barrett, Nicholas Bass, Agatino Battaglia, Michael Bauer, Mònica Bayés, Frank Bellivier, Sarah E. Bergen, Wade Berrettini, Catalina Betancur, Thomas Bettecken, Joseph Biederman, Elisabeth B. Binder, Donald W. Black, Douglas H.R. Blackwood, Cinnamon S. Bloss, Michael Boehnke, Dorret I. Boomsma, Hugh Gurling, Cross-Disorder Working Group of the Psychiatric Genomics Consortium

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Abstract

Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk.

Original languageEnglish (US)
Pages (from-to)283-294
Number of pages12
JournalAmerican Journal of Human Genetics
Volume96
Issue number2
DOIs
StatePublished - Feb 5 2015

Bibliographical note

Funding Information:
This study was supported by the Australian Research Council (FT0991360 and DE130100614) and the National Health and Medical Research Council (613608, 1011506, 1047956, and 1080157). The Psychiatric Genomics Consortium is supported by National Institute of Mental Health (NIMH) grant U01 MH085520. We acknowledge the funding that supported the Swedish schizophrenia study (NIMH R01 MH077139), the Stanley Center for Psychiatric Research, the Sylvan Herman Foundation, the Karolinska Institutet, Karolinska University Hospital, the Swedish Research Council, the Stockholm County Council, the S?derstr?m K?nigska Foundation, and the Netherlands Scientific Organization (NWO 645-000-003). Statistical analyses were carried out on the Genetic Cluster Computer, which is financially supported by the Netherlands Scientific Organization (NOW; 480- 05-003). The GenRED GWAS project was supported by NIMH R01 grants MH061686 (D.F.L.), MH059542 (W.C.), MH075131 (W.B. Lawson), MH059552 (J.B.P.), MH059541 (W.A.S.), and MH060912 (M.M.W.).

Publisher Copyright:
© 2015 The Authors. This is an open access article under the CC BY-NC-ND license.

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