Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing
Chen F, Wang X, Jang S, Quach B, Weissenkampen J, Khunsriraksakul C, Yang L, Sauteraud R, Albert C, Allred N, Arnett D, Ashley-Koch A, Barnes K, Barr R, Becker D, Bielak L, Bis J, Blangero J, Boorgula M, Chasman D, Chavan S, Chen Y, Chuang L, Correa A, Curran J, David S, Fuentes L, Deka R, Duggirala R, Faul J, Garrett M, Gharib S, Guo X, Hall M, Hawley N, He J, Hobbs B, Hokanson J, Hsiung C, Hwang S, Hyde T, Irvin M, Jaffe A, Johnson E, Kaplan R, Kardia S, Kaufman J, Kelly T, Kleinman J, Kooperberg C, Lee I, Levy D, Lutz S, Manichaikul A, Martin L, Marx O, McGarvey S, Minster R, Moll M, Moussa K, Naseri T, North K, Oelsner E, Peralta J, Peyser P, Psaty B, Rafaels N, Raffield L, Reupena M, Rich S, Rotter J, Schwartz D, Shadyab A, Sheu W, Sims M, Smith J, Sun X, Taylor K, Telen M, Watson H, Weeks D, Weir D, Yanek L, Young K, Young K, Zhao W, Hancock D, Jiang B, Vrieze S, Liu D. Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing. Nature Genetics 2023, 55: 291-300. PMID: 36702996, PMCID: PMC9925385, DOI: 10.1038/s41588-022-01282-x.Peer-Reviewed Original ResearchConceptsTWAS methodsExpression quantitative trait loci (eQTL) dataQuantitative trait loci dataTranscriptome-wide associationWide association studyGenome-wide association study summary statisticsWhole genome sequencesSubsequent fine mappingEQTL datasetNew genesLoci dataFine mappingPhenotypic effectsTobacco use phenotypesDiverse ancestryAssociation studiesBiological relevanceEuropean ancestryGenesAncestryGWASSummary statisticsBiologyDrug repurposingDiversity
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