Chen, FangFangChenWang, XingyanXingyanWangJang, Seon-KyeongSeon-KyeongJangQuach, Bryan CBryan CQuachWeissenkampen, J DylanJ DylanWeissenkampenKhunsriraksakul, ChachritChachritKhunsriraksakulYang, LinaLinaYangSauteraud, RenanRenanSauteraudAlbert, Christine MChristine MAlbertAllred, Nicholette D DNicholette D DAllredArnett, Donna KDonna KArnettAshley-Koch, Allison EAllison EAshley-KochBarnes, Kathleen CKathleen CBarnesBarr, R GrahamR GrahamBarrBecker, Diane MDiane MBeckerBielak, Lawrence FLawrence FBielakBis, Joshua CJoshua CBisBlangero, JohnJohnBlangeroBoorgula, Meher PreethiMeher PreethiBoorgulaChasman, Daniel IDaniel IChasmanChavan, SameerSameerChavanChen, Yii-Der IYii-Der IChenLEE-MING CHUANGCorrea, AdolfoAdolfoCorreaCurran, Joanne EJoanne ECurranDavid, Sean PSean PDavidFuentes, Lisa de LasLisa de LasFuentesDeka, RanjanRanjanDekaDuggirala, RavindranathRavindranathDuggiralaFaul, Jessica DJessica DFaulGarrett, Melanie EMelanie EGarrettGharib, Sina ASina AGharibGuo, XiuqingXiuqingGuoHall, Michael EMichael EHallHawley, Nicola LNicola LHawleyHe, JiangJiangHeHobbs, Brian DBrian DHobbsHokanson, John EJohn EHokansonHsiung, Chao AChao AHsiungHwang, Shih-JenShih-JenHwangHyde, Thomas MThomas MHydeIrvin, Marguerite RMarguerite RIrvinJaffe, Andrew EAndrew EJaffeJohnson, Eric OEric OJohnsonKaplan, RobertRobertKaplanKardia, Sharon L RSharon L RKardiaKaufman, Joel DJoel DKaufmanKelly, Tanika NTanika NKellyKleinman, Joel EJoel EKleinmanKooperberg, CharlesCharlesKooperbergLee, I-TeI-TeLeeLevy, DanielDanielLevyLutz, Sharon MSharon MLutzManichaikul, Ani WAni WManichaikulMartin, Lisa WLisa WMartinMarx, OliviaOliviaMarxMcGarvey, Stephen TStephen TMcGarveyMinster, Ryan LRyan LMinsterMoll, MatthewMatthewMollMoussa, Karine AKarine AMoussaNaseri, TakeTakeNaseriNorth, Kari EKari ENorthOelsner, Elizabeth CElizabeth COelsnerPeralta, Juan MJuan MPeraltaPeyser, Patricia APatricia APeyserPsaty, Bruce MBruce MPsatyRafaels, NicholasNicholasRafaelsRaffield, Laura MLaura MRaffieldReupena, Muagututi'a SefuivaMuagututi'a SefuivaReupenaRich, Stephen SStephen SRichRotter, Jerome IJerome IRotterSchwartz, David ADavid ASchwartzShadyab, Aladdin HAladdin HShadyabSheu, Wayne H-HWayne H-HSheuSims, MarioMarioSimsSmith, Jennifer AJennifer ASmithSun, XiaoXiaoSunTaylor, Kent DKent DTaylorTelen, Marilyn JMarilyn JTelenWatson, HaroldHaroldWatsonWeeks, Daniel EDaniel EWeeksWeir, David RDavid RWeirYanek, Lisa RLisa RYanekYoung, Kendra AKendra AYoungYoung, Kristin LKristin LYoungZhao, WeiWeiZhaoHancock, Dana BDana BHancockJiang, BiboBiboJiangVrieze, ScottScottVriezeLiu, Dajiang JDajiang JLiu2023-02-082023-02-082023-01-2610614036https://scholars.lib.ntu.edu.tw/handle/123456789/627722Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.enjournal article10.1038/s41588-022-01282-x367029962-s2.0-85146834444https://api.elsevier.com/content/abstract/scopus_id/85146834444