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

Maier, Robert and Moser, Gerhard and Chen, Guo-Bo and Ripke, Stephan and Cross-Disorder Working Group of the Psychiatric Genomics Consort, and Coryell, William and Potash, James B. and Scheftner, William A. and Shi, Jianxin and Weissman, Myrna M. and Hultman, Christina M. and Landén, Mikael and Levinson, Douglas F. and Kendler, Kenneth S. and Smoller, Jordan W. and Wray, Naomi R. and Lee, S. Hong. (2015) Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder. American Journal of Human Genetics, Vol. 96, H. 2. pp. 283-294.

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Official URL: http://edoc.unibas.ch/dok/A6373591

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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.
Faculties and Departments:07 Faculty of Psychology > Departement Psychologie > Forschungsbereich Klinische Psychologie und Neurowissenschaften > Klinische Psychologie und Epidemiologie (Lieb)
UniBasel Contributors:Steinhausen, Hans-Christoph
Item Type:Article, refereed
Article Subtype:Research Article
Bibsysno:Link to catalogue
Publisher:Univ. of Chicago Press
ISSN:0002-9297
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:08 May 2015 08:45
Deposited On:08 May 2015 08:45

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