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Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing.
Thursday,2023-07-20 14:40 America/Los_Angeles — ecterry
| Title | Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing. |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Authors | Chen, 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 |
| Journal | Nat Genet |
| Volume | 55 |
| Issue | 2 |
| Pagination | 291-300 |
| Date Published | 2023 Feb |
| ISSN | 1546-1718 |
| Keywords | Biology, 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> |
| DOI | 10.1038/s41588-022-01282-x |
| Alternate Journal | Nat Genet |
| PubMed ID | 36702996 |
| PubMed Central ID | PMC9925385 |
| Grant List | R56 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 & 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 & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) / R21 AI160138 / AI / NIAID NIH HHS / United States |
ePub date:
23/02