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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.
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
URL: https://doi.org/10.1016/j.ajhg.2014.12.006
DOI: 10.1016/j.ajhg.2014.12.006
PubMed id: 25640677
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