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

2015-02-05Journal article Open access
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dc.contributor.authorMaier, R.
dc.contributor.authorMoser, G.
dc.contributor.authorChen, G.B.
dc.contributor.authorRipke, S
dc.contributor.authorCross-Disorder Working Group of the Psychiatric Genomics Consortium
dc.contributor.authorCoryell, W.
dc.contributor.authorPotash, J.B.
dc.contributor.authorScheftner, W.A.
dc.contributor.authorShi, J.
dc.contributor.authorWeissman, M.M.
dc.contributor.authorHultman, C.M.
dc.contributor.authorLandén, M.
dc.contributor.authorLevinson, D.F.
dc.contributor.authorKendler, K.S.
dc.contributor.authorSmoller, J.W.
dc.contributor.authorWray, N.R.
dc.contributor.authorLee, S.H.
dc.date.accessioned2016-02-16T16:09:07Z
dc.date.available2016-02-16T16:09:07Z
dc.date.issued2015-02-05
dc.descriptionCross-Disorder Working Group of the Psychiatric Genomics Consortium - Vicente A.M.pt_PT
dc.descriptionAcessível em: http://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25640677/pt_PT
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 riskpt_PT
dc.identifier.citationAm J Hum Genet. 2015 Feb 5;96(2):283-94. doi: 10.1016/j.ajhg.2014.12.006. Epub 2015 Jan 29.pt_PT
dc.identifier.doi10.1016/j.ajhg.2014.12.006pt_PT
dc.identifier.issn0002-9297
dc.identifier.urihttp://hdl.handle.net/10400.18/3339
dc.language.isoengpt_PT
dc.publisherElsevier (Cell Press)pt_PT
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0002929714005126pt_PT
dc.subjectPsychiatric Disorderspt_PT
dc.subjectGenome-wide association studiespt_PT
dc.subjectSchizophreniapt_PT
dc.subjectBipolar Disorderpt_PT
dc.subjectDepressive Disorderpt_PT
dc.subjectPerturbações do Desenvolvimento Infantil e Saúde Mentalpt_PT
dc.titleJoint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorderpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage294pt_PT
oaire.citation.startPage283pt_PT
oaire.citation.titleAmerican Journal of Human Geneticspt_PT
oaire.citation.volume96(2)pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

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