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

Lookup NU author(s): Emeritus Professor Nicol Ferrier, Professor Jeremy Parr, Professor Allan Young



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


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

Publication metadata

Author(s): Maier R, Moser G, Chen G-B, Ripke S, Cross-Disorder Working Group of the Psychiatric Genomics Consortium, Coryell W, Potash JB, Scheftner WA, Shi J, Weissman MM, Hultman CM, Landen M, Levinson DF, Kendler KS, Smoller JW, Wray NR, Lee SH, Nicol Ferrier I, Parr JR, Young AH

Publication type: Article

Publication status: Published

Journal: American Journal of Human Genetics

Year: 2015

Volume: 96

Issue: 2

Pages: 283-294

Print publication date: 05/02/2015

Online publication date: 29/01/2015

Acceptance date: 08/12/2014

Date deposited: 24/08/2017

ISSN (print): 0002-9297

ISSN (electronic): 1537-6605

Publisher: Cell Press


DOI: 10.1016/j.ajhg.2014.12.006

PubMed id: 25640677


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Funder referenceFunder name
Australian Research Council (FT0991360 and DE130100614)
Genetic Cluster Computer, which is financially supported by the Netherlands Scientific Organization (NOW; 480-05-003)
Karolinska University Hospital
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.).
Karolinska Institutet
National Institute of Mental Health (NIMH) grant U01 MH085520
Netherlands Scientific Organization (NWO 645-000-003)
National Health and Medical Research Council (613608, 1011506, 1047956, and 1080157).
So¨derstro¨m Ko¨nigska Foundation
Stanley Center for Psychiatric Research
Stockholm County Council
Sylvan Herman Foundation
Swedish Research Council
Swedish schizophrenia study (NIMH R01 MH077139),