Genetic analyses of diverse populations improves discovery for complex traits
Wojcik G, Graff M, Nishimura K, Tao R, Haessler J, Gignoux C, Highland H, Patel Y, Sorokin E, Avery C, Belbin G, Bien S, Cheng I, Cullina S, Hodonsky C, Hu Y, Huckins L, Jeff J, Justice A, Kocarnik J, Lim U, Lin B, Lu Y, Nelson S, Park S, Poisner H, Preuss M, Richard M, Schurmann C, Setiawan V, Sockell A, Vahi K, Verbanck M, Vishnu A, Walker R, Young K, Zubair N, Acuña-Alonso V, Ambite J, Barnes K, Boerwinkle E, Bottinger E, Bustamante C, Caberto C, Canizales-Quinteros S, Conomos M, Deelman E, Do R, Doheny K, Fernández-Rhodes L, Fornage M, Hailu B, Heiss G, Henn B, Hindorff L, Jackson R, Laurie C, Laurie C, Li Y, Lin D, Moreno-Estrada A, Nadkarni G, Norman P, Pooler L, Reiner A, Romm J, Sabatti C, Sandoval K, Sheng X, Stahl E, Stram D, Thornton T, Wassel C, Wilkens L, Winkler C, Yoneyama S, Buyske S, Haiman C, Kooperberg C, Le Marchand L, Loos R, Matise T, North K, Peters U, Kenny E, Carlson C. Genetic analyses of diverse populations improves discovery for complex traits. Nature 2019, 570: 514-518. PMID: 31217584, PMCID: PMC6785182, DOI: 10.1038/s41586-019-1310-4.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesComplex traitsBiology of complex traitsDiverse populationsEvidence of effect-size heterogeneityGenome-wide effortsLarge-scale genomic studiesReduce health disparitiesNon-European individualsHighest burden of diseaseMulti-ethnic participantsEffect-size heterogeneityBurden of diseaseRepresentation of diverse populationsGWAS associationsNovel lociRisk prediction scoreAdmixed populationsFine-mappingGenetic architectureAssociation studiesGenomic studiesHealth disparitiesHealthcare disparitiesPopulation Architecture
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