Multi-Ancestry Meta-Analysis yields novel genetic discoveries and ancestry-specific associations
Year: 2021
Type: preprint
Abstract: ABSTRACT We present a new method, Multi-Ancestry Meta-Analysis (MAMA), which combines genome-wide association study (GWAS) summary statistics from multiple populations to produce new summary statistics for each population, identifying novel loci that would not have been discovered in either set of GWAS summary statistics alone. In simulations, MAMA increases power with less bias and generally lower type-1 error rate than other multi-ancestry meta-analysis approaches. We apply MAMA to 23 phenotypes in East-Asian- and European-ancestry populations and find substantial gains in power. In an independent sample, novel genetic discoveries from MAMA replicate strongly.
Authors: Patrick Turley, Alicia R. Martin, Grant Goldman, Hui Li, Masahiro Kanai, Raymond K. Walters, Jonathan B. Jala, Kuang Lin, Iona Y. Millwood, Caitlin E. Carey, Duncan S. Palmer, Meghan Zacher, Elizabeth G. Atkinson, Zhengming Chen, Liming Li, Masato Akiyama, Yukinori Okada, Yoichiro Kamatani, Robin G. Walters, Shawneequa Callier, David Laibson, Michelle N. Meyer, David Cesarini, Mark Daly, Daniel J. Benjamin, Benjamin M. Neale
Institutions: University of Southern California, Broad Institute, Massachusetts General Hospital, Stanley Center for Psychiatric Research, National Bureau of Economic Research, Harvard University, The University of Osaka, University of Oxford, MRC Population Health Research Unit, University of California, Los Angeles, Brown University, Peking University, Kyushu University, RIKEN Center for Integrative Medical Sciences, The University of Tokyo, George Washington University, Geisinger Medical Center, Geisinger Health System, New York University, University of Helsinki, Institute for Molecular Medicine Finland, HiLIFE – Elämäntieteiden Instituutti
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Funder: Medical Research Council
OpenAlex ID: W3158903754