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

TitleMulti-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing.
Publication TypeJournal Article
Year of Publication2023
AuthorsChen, F, Wang, X, Jang, S-K, Quach, BC, J Weissenkampen, D, Khunsriraksakul, C, Yang, L, Sauteraud, R, Albert, CM, Allred, NDD, Arnett, DK, Ashley-Koch, AE, Barnes, KC, R Barr, G, Becker, DM, Bielak, LF, Bis, JC, Blangero, J, Boorgula, MPreethi, Chasman, DI, Chavan, S, Chen, Y-derI, Chuang, L-M, Correa, A, Curran, JE, David, SP, Fuentes, Lde Las, 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, S-J, Hyde, TM, Irvin, MR, Jaffe, AE, Johnson, EO, Kaplan, R, Kardia, SLR, Kaufman, JD, Kelly, TN, Kleinman, JE, Kooperberg, C, Lee, I-T, 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, M'aSefuiva, Rich, SS, Rotter, JI, Schwartz, DA, Shadyab, AH, Sheu, WH-H, 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
JournalNat Genet
Date Published2023 Feb
KeywordsBiology, Drug Repositioning, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide, Tobacco Use, Transcriptome
Abstract<p>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.</p>
Alternate JournalNat Genet
PubMed ID36702996
PubMed Central IDPMC9925385
Grant ListR56 HG012358 / HG / NHGRI NIH HHS / United States
R03 OD032630 / OD / NIH HHS / United States
UL1 TR001863 / TR / NCATS NIH HHS / United States
R01HG008983 / / U.S. Department of Health &amp; Human Services | NIH | National Human Genome Research Institute (NHGRI) /
R56 HG011035 / HG / NHGRI NIH HHS / United States
R01 HL105756 / HL / NHLBI NIH HHS / United States
K08 HL136928 / HL / NHLBI NIH HHS / United States
R01 HG011035 / HG / NHGRI NIH HHS / United States
P30 CA014236 / CA / NCI NIH HHS / United States
T32 ES019851 / ES / NIEHS NIH HHS / United States
R01GM126479 / / U.S. Department of Health &amp; Human Services | NIH | National Institute of General Medical Sciences (NIGMS) /
R21 AI160138 / AI / NIAID NIH HHS / United States
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