MESuSiE enables scalable and powerful multi-ancestry fine-mapping of causal variants in genome-wide association studies

Abstract

Fine-mapping in genome-wide association studies attempts to identify causal SNPs from a set of candidate SNPs in a local genomic region of interest and is commonly performed in one genetic ancestry at a time. Here, we present multi-ancestry sum of the single effects model (MESuSiE), a probabilistic multi-ancestry fine-mapping method, to improve the accuracy and resolution of fine-mapping by leveraging association information across ancestries. MESuSiE uses summary statistics as input, accounts for the diverse linkage disequilibrium pattern observed in different ancestries, explicitly models both shared and ancestry-specific causal SNPs, and relies on a variational inference algorithm for scalable computation. We evaluated the performance of MESuSiE through comprehensive simulations and multi-ancestry fine-mapping of four lipid traits with both European and African samples. In the real data, MESuSiE improves fine-mapping resolution by 19.0% to 72.0% compared to existing approaches, is an order of magnitude faster, and captures and categorizes shared and ancestry-specific causal signals with enhanced functional enrichment.