{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Chen F"],"funding":["NCATS NIH HHS","U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences","NIDA NIH HHS","NIEHS NIH HHS","NIAID NIH HHS","U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases","NHLBI NIH HHS","NHGRI NIH HHS","NCI NIH HHS","U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute","NIH HHS","U.S. Department of Health & Human Services | National Institutes of Health"],"pagination":["291-300"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9925385"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["55(2)"],"pubmed_abstract":["Most 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 subse"],"journal":["Nature genetics"],"pubmed_title":["Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing."],"pmcid":["PMC9925385"],"funding_grant_id":["R01 DA044283","R56 HG011035","R01 AI132476","K08 HL136928","R01 HL077612","R01 HL093081","R01 HG011035","UL1 TR001863","P30 CA014236","R03 OD032630","R01GM126479","R56HG012358","R56HG011035","R03OD032630","R01HG011035","R21AI160138","R01HG008983","P30 ES007033","T32 ES019851","R56 HG012358","R01 HL105756"],"pubmed_authors":["Psaty BM","Schwartz DA","Kardia SLR","Rotter JI","Liu DJ","Guo X","Young KL","Bielak LF","Hokanson JE","Rich SS","Hyde TM","Oelsner EC","Watson H","Sheu WH","Chasman DI","Rafaels N","Hawley NL","Weissenkampen JD","Curran JE","de las Fuentes L","Wang X","Sauteraud R","Telen MJ","Reupena MS","Khunsriraksakul C","Yang L","Weir DR","He J","Raffield LM","Garrett ME","Kaufman JD","Moussa KA","Chen F","Albert CM","Sun X","Gharib SA","Weeks DE","Lutz SM","Lee IT","Chavan S","Vrieze S","Levy D","Johnson EO","Kelly TN","Peyser PA","Jiang B","Manichaikul AW","Jaffe AE","Duggirala R","Peralta JM","Yanek LR","Ashley-Koch AE","Irvin MR","Chuang LM","Zhao W","Blangero J","Correa A","Barr RG","Naseri T","Hobbs BD","Hwang SJ","Sims M","Hall ME","Minster RL","Boorgula MP","Hsiung CA","Marx O","Shadyab AH","Arnett DK","Moll M","Hancock DB","Smith JA","Kaplan R","Taylor KD","Deka R","Kleinman JE","Allred NDD","Martin LW","Becker DM","Bis JC","David SP","Faul JD","Kooperberg C","Jang SK","Barnes KC","Chen YI","Quach BC","Young KA","McGarvey ST","North KE"],"additional_accession":[]},"is_claimable":false,"name":"Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing.","description":"Most 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 subse","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Feb","modification":"2026-05-29T05:28:26.022Z","creation":"2025-02-18T23:44:29.668Z"},"accession":"S-EPMC9925385","cross_references":{"pubmed":["36702996"],"doi":["10.1038/s41588-022-01282-x"]}}