作者
Fang Chen, Xingyan Wang, Seon-Kyeong Jang, Bryan C Quach, J Dylan Weissenkampen, Chachrit Khunsriraksakul, Lina Yang, Renan Sauteraud, Christine M Albert, Nicholette DD 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 LR 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 HH 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, Dajiang J Liu
发表日期
2023/2
期刊
Nature genetics
卷号
55
期号
2
页码范围
291-300
出版商
Nature Publishing Group US
简介
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 …
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F Chen, X Wang, SK Jang, BC Quach… - Nature genetics, 2023