Nature genetics · 2023

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

СтатьяПоддержка исследования: вне правительства СШАПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди

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Chen Fang, Wang Xingyan, Jang Seon-Kyeong, Quach Bryan C, Weissenkampen J Dylan, Khunsriraksakul Chachrit, Yang Lina, Sauteraud Renan, Albert Christine M, Allred Nicholette D D, Arnett Donna K, Ashley-Koch Allison E, Barnes Kathleen C, Barr R Graham, Becker Diane M, Bielak Lawrence F, Bis Joshua C, Blangero John, Boorgula Meher Preethi, Chasman Daniel I, Chavan Sameer, Chen Yii-Der I, Chuang Lee-Ming, Correa Adolfo, Curran Joanne E, David Sean P, de las Fuentes Lisa, Deka Ranjan, Duggirala Ravindranath, Faul Jessica D, Garrett Melanie E, Gharib Sina A, Guo Xiuqing, Hall Michael E, Hawley Nicola L, He Jiang, Hobbs Brian D, Hokanson John E, Hsiung Chao A, Hwang Shih-Jen, Hyde Thomas M, Irvin Marguerite R, Jaffe Andrew E, Johnson Eric O, Kaplan Robert, Kardia Sharon L R, Kaufman Joel D, Kelly Tanika N, Kleinman Joel E, Kooperberg Charles, Lee I-Te, Levy Daniel, Lutz Sharon M, Manichaikul Ani W, Martin Lisa W, Marx Olivia, McGarvey Stephen T, Minster Ryan L, Moll Matthew, Moussa Karine A, Naseri Take, North Kari E, Oelsner Elizabeth C, Peralta Juan M, Peyser Patricia A, Psaty Bruce M, Rafaels Nicholas, Raffield Laura M, Reupena Muagututi'a Sefuiva, Rich Stephen S, Rotter Jerome I, Schwartz David A, Shadyab Aladdin H, Sheu Wayne H-H, Sims Mario, Smith Jennifer A, Sun Xiao, Taylor Kent D, Telen Marilyn J, Watson Harold, Weeks Daniel E, Weir David R, Yanek Lisa R, Young Kendra A, Young Kristin L, Zhao Wei, Hancock Dana B, Jiang Bibo, Vrieze Scott, Liu Dajiang J

Аннотация

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.

Предметные рубрики MeSH

Humans Transcriptome Drug Repositioning Genome-Wide Association Study Tobacco Use Biology Polymorphism, Single Nucleotide Genetic Predisposition to Disease

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

Ошибки и ограничения

Аннотация

Предметные рубрики MeSH