000897092 001__ 897092
000897092 005__ 20240507205535.0
000897092 0247_ $$2doi$$a10.1016/j.biopsych.2021.04.018
000897092 0247_ $$2ISSN$$a0006-3223
000897092 0247_ $$2ISSN$$a1873-2402
000897092 0247_ $$2altmetric$$aaltmetric:105252914
000897092 0247_ $$2pmid$$apmid:34304866
000897092 0247_ $$2Handle$$a2128/28794
000897092 0247_ $$2WOS$$aWOS:000719390100006
000897092 037__ $$aFZJ-2021-03591
000897092 082__ $$a610
000897092 1001_ $$0P:(DE-HGF)0$$aNi, Guiyan$$b0
000897092 245__ $$aA Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts
000897092 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2021
000897092 3367_ $$2DRIVER$$aarticle
000897092 3367_ $$2DataCite$$aOutput Types/Journal article
000897092 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1715084283_7023
000897092 3367_ $$2BibTeX$$aARTICLE
000897092 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000897092 3367_ $$00$$2EndNote$$aJournal Article
000897092 520__ $$aBackgroundPolygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies. PGS methods differ in which DNA variants are included and the weights assigned to them; some require an independent tuning sample to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, e.g., phenotype definition or technical factors.MethodsThe Psychiatric Genomics Consortium Working Groups for schizophrenia and major depressive disorder bring together many independently collected case-control cohorts. We used these resources (31,328 schizophrenia cases, 41,191 controls; 248,750 major depressive disorder cases, 563,184 controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and 9 methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) were compared.ResultsCompared with PC+T, the other 9 methods gave higher prediction statistics, MegaPRS, LDPred2, and SBayesR significantly so, explaining up to 9.2% variance in liability for schizophrenia across 30 target cohorts, an increase of 44%. For major depressive disorder across 26 target cohorts, these statistics were 3.5% and 59%, respectively.
000897092 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
000897092 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
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000897092 7001_ $$0P:(DE-HGF)0$$aGe, Tian$$b5
000897092 7001_ $$0P:(DE-HGF)0$$aRestuadi, Restuadi$$b6
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000897092 7001_ $$0P:(DE-HGF)0$$aNyholt, Dale R.$$b8
000897092 7001_ $$00000-0002-6759-0944$$aColeman, Jonathan R. I.$$b9
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000897092 7001_ $$00000-0003-2001-2474$$aYang, Jian$$b11
000897092 7001_ $$0P:(DE-HGF)0$$aVisscher, Peter M.$$b12
000897092 7001_ $$00000-0001-7421-3357$$aWray, Naomi R.$$b13
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