Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/18834
Title: Joint Analysis of Psychiatric Disorders Increases Accuracy of Risk Prediction for Schizophrenia, Bipolar Disorder, and Major Depressive Disorder
Contributor(s): Maier, Robert (author); Moser, Gerhard (author); Hultman, Christina M (author); Landen, Mikael (author); Levinson, Douglas F (author); Kendler, Kenneth S (author); Smoller, Jordan W (author); Wray, Naomi R (author); Lee, Sang Hong  (author); Chen, Guo-Bo (author); Ripke, Stephan (author); Cross-Disorder Working Group of the Psychiatric Genomics Consortium, (author); Coryell, William (author); Potash, James B (author); Scheftner, William A (author); Shi, Jianxin (author); Weissman, Myrna M (author)
Publication Date: 2015
Open Access: Yes
DOI: 10.1016/j.ajhg.2014.12.006Open Access Link
Handle Link: https://hdl.handle.net/1959.11/18834
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.
Publication Type: Journal Article
Grant Details: NHMRC/APP1080157
ARC/DE130100614
Source of Publication: American Journal of Human Genetics, 96(2), p. 283-294
Publisher: Cell Press
Place of Publication: United States of America
ISSN: 1537-6605
0002-9297
Field of Research (FOR): 070201 Animal Breeding
060408 Genomics
060412 Quantitative Genetics (incl. Disease and Trait Mapping Genetics)
Socio-Economic Objective (SEO): 970107 Expanding Knowledge in the Agricultural and Veterinary Sciences
970106 Expanding Knowledge in the Biological Sciences
970111 Expanding Knowledge in the Medical and Health Sciences
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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Appears in Collections:Journal Article

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