標題: | Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing |
作者: | 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 LEE-MING CHUANG Correa, Adolfo Curran, Joanne E David, Sean P Fuentes, Lisa de Las 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 |
公開日期: | 26-一月-2023 |
來源出版物: | Nature genetics |
摘要: | 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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/627722 |
ISSN: | 10614036 |
DOI: | 10.1038/s41588-022-01282-x |
顯示於: | 醫學系
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