Hauptseite > Publikationsdatenbank > A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts > print

001     897092
005     20240507205535.0
024 7 _ |a 10.1016/j.biopsych.2021.04.018
|2 doi
024 7 _ |a 0006-3223
|2 ISSN
024 7 _ |a 1873-2402
|2 ISSN
024 7 _ |a altmetric:105252914
|2 altmetric
024 7 _ |a pmid:34304866
|2 pmid
024 7 _ |a 2128/28794
|2 Handle
024 7 _ |a WOS:000719390100006
|2 WOS
037 _ _ |a FZJ-2021-03591
082 _ _ |a 610
100 1 _ |a Ni, Guiyan
|0 P:(DE-HGF)0
|b 0
245 _ _ |a A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts
260 _ _ |a Amsterdam [u.a.]
|c 2021
|b Elsevier Science
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1715084283_7023
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a BackgroundPolygenic 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.
536 _ _ |a 5251 - Multilevel Brain Organization and Variability (POF4-525)
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Marc 21