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

Fang Chen, Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA.
Xingyan Wang, Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA.
Seon-Kyeong Jang, Department of Psychology, University of Minnesota, Minneapolis, MN, USA.
Bryan C. Quach, RTI International, Research Triangle, NC, USA.
J Dylan Weissenkampen, Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA.
Chachrit Khunsriraksakul, Deparment of Bioinformatics and Genomics, Penn State College of Medicine, Hershey, PA, USA.
Lina Yang, Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA.
Renan Sauteraud, Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA.
Christine M. Albert, Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Nicholette D. Allred, Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA.
Donna K. Arnett, College of Public Health, University of Kentucky, Lexington, KY, USA.
Allison E. Ashley-Koch, Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA.
Kathleen C. Barnes, Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Center, Aurora, CO, USA.
R Graham Barr, Department of Medicine, Columbia University Medical Center, New York, NY, USA.
Diane M. Becker, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Lawrence F. Bielak, Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Joshua C. Bis, Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.
John Blangero, Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA.
Meher Preethi Boorgula, Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Center, Aurora, CO, USA.
Daniel I. Chasman, Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Sameer Chavan, Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Center, Aurora, CO, USA.
Yii-Der I. Chen, Department of Pediatrics, Institute for Translational Genomics and Population Sciences, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
Lee-Ming Chuang, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.
Adolfo Correa, Department of Medicine, Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS, USA.
Joanne E. Curran, Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA.
Sean P. David, University of Chicago, Chicago, IL, USA.
Lisa de Fuentes, Department of Medicine, Division of Biostatistics and Cardiovascular Division, Washington University School of Medicine, St. Louis, MO, USA.
Ranjan Deka, Department of Environmental and Public Health Sciences, College of Medicine, University of Cincinnati, Cincinnati, OH, USA.
Ravindranath Duggirala, Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA.
Jessica D. Faul, Institute for Social Research, Survey Research Center, University of Michigan, Ann Arbor, MI, USA.
Melanie E. Garrett, Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA.
Sina A. Gharib, Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.

Document Type

Journal Article

Publication Date

2-1-2023

Journal

Nature genetics

Volume

55

Issue

2

DOI

10.1038/s41588-022-01282-x

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.

Department

Medicine

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