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dc.contributor.authorMarquez-Luna, Carla
dc.contributor.authorLoh, Po-Ru
dc.contributor.authorPrice, Alkes L
dc.contributor.authorPinidiyapathirage, Janani
dc.date.accessioned2021-11-08T00:17:23Z
dc.date.available2021-11-08T00:17:23Z
dc.date.issued2017
dc.identifier.issn0741-0395en_US
dc.identifier.doi10.1002/gepi.22083en_US
dc.identifier.urihttp://hdl.handle.net/10072/409900
dc.description.abstractMethods for genetic risk prediction have been widely investigated in recent years. However, most available training data involves European samples, and it is currently unclear how to accurately predict disease risk in other populations. Previous studies have used either training data from European samples in large sample size or training data from the target population in small sample size, but not both. Here, we introduce a multiethnic polygenic risk score that combines training data from European samples and training data from the target population. We applied this approach to predict type 2 diabetes (T2D) in a Latino cohort using both publicly available European summary statistics in large sample size (Neff= 40k) and Latino training data in small sample size (Neff= 8k). Here, we attained a >70% relative improvement in prediction accuracy (from R2= 0.027 to 0.047) compared to methods that use only one source of training data, consistent with large relative improvements in simulations. We observed a systematically lower load of T2D risk alleles in Latino individuals with more European ancestry, which could be explained by polygenic selection in ancestral European and/or Native American populations. We predict T2D in a South Asian UK Biobank cohort using European (Neff= 40k) and South Asian (Neff= 16k) training data and attained a >70% relative improvement in prediction accuracy, and application to predict height in an African UK Biobank cohort using European (N = 113k) and African (N = 2k) training data attained a 30% relative improvement. Our work reduces the gap in polygenic risk prediction accuracy between European and non-European target populations.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherJohn Wiley and Sonsen_US
dc.relation.ispartofpagefrom811en_US
dc.relation.ispartofpageto823en_US
dc.relation.ispartofissue8en_US
dc.relation.ispartofjournalGenetic Epidemiologyen_US
dc.relation.ispartofvolume41en_US
dc.subject.fieldofresearchGeneticsen_US
dc.subject.fieldofresearchHealth services and systemsen_US
dc.subject.fieldofresearchPublic healthen_US
dc.subject.fieldofresearchcode3105en_US
dc.subject.fieldofresearchcode4203en_US
dc.subject.fieldofresearchcode4206en_US
dc.subject.keywordsScience & Technologyen_US
dc.subject.keywordsLife Sciences & Biomedicineen_US
dc.subject.keywordsGenetics & Heredityen_US
dc.subject.keywordsMathematical & Computational Biologyen_US
dc.subject.keywordsgenome-wide association studyen_US
dc.titleMultiethnic polygenic risk scores improve risk prediction in diverse populationsen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dcterms.bibliographicCitationMarquez-Luna, C; Loh, P-R; Price, AL; Pinidiyapathirage, Janani, Multiethnic polygenic risk scores improve risk prediction in diverse populations, Genetic Epidemiology 2017, 41 (8), pp. 811-823en_US
dcterms.dateAccepted2017-08-30
dc.date.updated2021-11-08T00:13:43Z
gro.hasfulltextNo Full Text
gro.griffith.authorPinidiyapathirage, Janani


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