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.
Competing Interest Statement
A.R.M has consulted for 23andMe and Illumina, and she has received speaker fees from Genentech, Illumina, and Pfizer. B.M.N. is a member of the scientific advisory board at Deep Genomics and RBNC Therapeutics, Member of the scientific advisory committee at Milken and a consultant for Camp4 Therapeutics, Takeda Pharmaceutical and Biogen. D.S.P. is an employee of Genomics plc, all contributions were performed prior to him joining the company.
