Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing.

View 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 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.

Investigators
Abbreviation
Nat Genet
Publication Date
2023-01-26
Pubmed ID
36702996
Medium
Print-Electronic
Full Title
Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing.
Authors
Chen F, Wang X, Jang SK, Quach BC, Weissenkampen JD, Khunsriraksakul C, Yang L, Sauteraud R, Albert CM, Allred NDD, Arnett DK, Ashley-Koch AE, Barnes KC, Barr RG, Becker DM, Bielak LF, Bis JC, Blangero J, Boorgula MP, Chasman DI, Chavan S, Chen YI, Chuang LM, Correa A, Curran JE, David SP, Fuentes LL, Deka R, Duggirala R, Faul JD, Garrett ME, Gharib SA, Guo X, Hall ME, Hawley NL, He J, Hobbs BD, Hokanson JE, Hsiung CA, Hwang SJ, Hyde TM, Irvin MR, Jaffe AE, Johnson EO, Kaplan R, Kardia SLR, Kaufman JD, Kelly TN, Kleinman JE, Kooperberg C, Lee IT, Levy D, Lutz SM, Manichaikul AW, Martin LW, Marx O, McGarvey ST, Minster RL, Moll M, Moussa KA, Naseri T, North KE, Oelsner EC, Peralta JM, Peyser PA, Psaty BM, Rafaels N, Raffield LM, Reupena MS, Rich SS, Rotter JI, Schwartz DA, Shadyab AH, Sheu WH, Sims M, Smith JA, Sun X, Taylor KD, Telen MJ, Watson H, Weeks DE, Weir DR, Yanek LR, Young KA, Young KL, Zhao W, Hancock DB, Jiang B, Vrieze S, Liu DJ