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dc.contributor.authorMaier, Robert
dc.contributor.authorMoser, Gerhard
dc.contributor.authorChen, Guo-Bo
dc.contributor.authorRipke, Stephan
dc.contributor.authorCross-Disorder Working Group of the Psychiatric Genomics Consortium (incl Kent L)
dc.contributor.authorCoryell, William
dc.contributor.authorPotash, James B
dc.contributor.authorScheftner, William A
dc.contributor.authorShi, Jianxin
dc.contributor.authorWeissman, Myrna M
dc.contributor.authorHultman, Christina M
dc.contributor.authorLandén, Mikael
dc.contributor.authorLevinson, Douglas F
dc.contributor.authorKendler, Kenneth S
dc.contributor.authorSmoller, Jordan W
dc.contributor.authorWray, Naomi R
dc.contributor.authorLee, S Hong
dc.identifier.citationMaier , R , Moser , G , Chen , G-B , Ripke , S , Cross-Disorder Working Group of the Psychiatric Genomics Consortium (incl Kent L) , Coryell , W , Potash , J B , Scheftner , W A , Shi , J , Weissman , M M , Hultman , C M , Landén , M , Levinson , D F , Kendler , K S , Smoller , J W , Wray , N R & Lee , S H 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 , no. 2 , pp. 283-94 .
dc.identifier.otherPURE: 171429323
dc.identifier.otherPURE UUID: 906be542-b8d6-4622-a1da-fe62a50ca4ce
dc.identifier.otherPubMed: 25640677
dc.identifier.otherScopus: 84925130699
dc.identifier.otherORCID: /0000-0002-5315-3399/work/60195348
dc.description.abstractGenetic 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.
dc.relation.ispartofAmerican Journal of Human Geneticsen
dc.rightsCopyright 2015. The Authors. This is an open access article under the CC BY-NC-ND license (
dc.subjectR Medicine (General)en
dc.subjectQH426 Geneticsen
dc.titleJoint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorderen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Medicineen
dc.contributor.institutionUniversity of St Andrews. Institute of Behavioural and Neural Sciencesen
dc.description.statusPeer revieweden

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