1. Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing
- Author
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Fang Chen, Xingyan Wang, Seon-Kyeong Jang, Bryan C. Quach, J. Dylan Weissenkampen, Chachrit Khunsriraksakul, Lina Yang, Renan Sauteraud, Christine M. Albert, Nicholette D. D. Allred, Donna K. Arnett, Allison E. Ashley-Koch, Kathleen C. Barnes, R. Graham Barr, Diane M. Becker, Lawrence F. Bielak, Joshua C. Bis, John Blangero, Meher Preethi Boorgula, Daniel I. Chasman, Sameer Chavan, Yii-Der I. Chen, Lee-Ming Chuang, Adolfo Correa, Joanne E. Curran, Sean P. David, Lisa de las Fuentes, Ranjan Deka, Ravindranath Duggirala, Jessica D. Faul, Melanie E. Garrett, Sina A. Gharib, Xiuqing Guo, Michael E. Hall, Nicola L. Hawley, Jiang He, Brian D. Hobbs, John E. Hokanson, Chao A. Hsiung, Shih-Jen Hwang, Thomas M. Hyde, Marguerite R. Irvin, Andrew E. Jaffe, Eric O. Johnson, Robert Kaplan, Sharon L. R. Kardia, Joel D. Kaufman, Tanika N. Kelly, Joel E. Kleinman, Charles Kooperberg, I-Te Lee, Daniel Levy, Sharon M. Lutz, Ani W. Manichaikul, Lisa W. Martin, Olivia Marx, Stephen T. McGarvey, Ryan L. Minster, Matthew Moll, Karine A. Moussa, Take Naseri, Kari E. North, Elizabeth C. Oelsner, Juan M. Peralta, Patricia A. Peyser, Bruce M. Psaty, Nicholas Rafaels, Laura M. Raffield, Muagututi’a Sefuiva Reupena, Stephen S. Rich, Jerome I. Rotter, David A. Schwartz, Aladdin H. Shadyab, Wayne H-H. Sheu, Mario Sims, Jennifer A. Smith, Xiao Sun, Kent D. Taylor, Marilyn J. Telen, Harold Watson, Daniel E. Weeks, David R. Weir, Lisa R. Yanek, Kendra A. Young, Kristin L. Young, Wei Zhao, Dana B. Hancock, Bibo Jiang, Scott Vrieze, and Dajiang J. Liu
- Subjects
Tobacco Smoke and Health ,Human Genome ,Drug Repositioning ,Single Nucleotide ,Biological Sciences ,Medical and Health Sciences ,Brain Disorders ,Tobacco Use ,Substance Misuse ,Good Health and Well Being ,Tobacco ,Genetics ,Humans ,Genetic Predisposition to Disease ,Polymorphism ,Transcriptome ,Drug Abuse (NIDA only) ,Biology ,Genome-Wide Association Study ,Developmental Biology - 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.
- Published
- 2023