2024
Evaluation of imputation performance of multiple reference panels in a Pakistani population
Xu J, Liu D, Hassan A, Genovese G, Cote A, Fennessy B, Cheng E, Charney A, Knowles J, Ayub M, Peterson R, Bigdeli T, Huckins L. Evaluation of imputation performance of multiple reference panels in a Pakistani population. Human Genetics And Genomics Advances 2024, 6: 100395. PMID: 39696820, PMCID: PMC11759560, DOI: 10.1016/j.xhgg.2024.100395.Peer-Reviewed Original ResearchGenome-wide association studiesReference panelImputation accuracyMultiple reference panelsPakistani individualsGenotype imputationAssociation studiesGenotype dataAncestry compositionEuropean individualsSample sizeTOPMedDiverse populationsPakistani populationImputationGenomeAncestryIndividualsGenotypesPopulationVariantsFuture panels
2022
Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits
Highland H, Wojcik G, Graff M, Nishimura K, Hodonsky C, Baldassari A, Cote A, Cheng I, Gignoux C, Tao R, Li Y, Boerwinkle E, Fornage M, Haessler J, Hindorff L, Hu Y, Justice A, Lin B, Lin D, Stram D, Haiman C, Kooperberg C, Le Marchand L, Matise T, Kenny E, Carlson C, Stahl E, Avery C, North K, Ambite J, Buyske S, Loos R, Peters U, Young K, Bien S, Huckins L. Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits. American Journal Of Human Genetics 2022, 109: 669-679. PMID: 35263625, PMCID: PMC9069067, DOI: 10.1016/j.ajhg.2022.02.013.Peer-Reviewed Original ResearchConceptsAncestrally diverse populationsBody mass indexCardiometabolic traitsEuropean ancestryGene expressionTranscriptomic imputation modelsNon-EA populationsDiverse populationsWhite British participantsReference panelGenetically regulated gene expressionGene-trait associationsTissue-specific gene expressionPopulation ArchitectureUK BiobankMeasurement of gene expressionPredicting gene expressionMulti-omics datasetsRelevant tissuesSusceptibility lociMass indexComplex traitsIdentified genesNovel associationsDiverse sample
2019
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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