Leveraging Multi-Ethnic Evidence for Mapping Complex Traits in Minority Populations: An Empirical Bayes Approach

Abstract

Elucidating the genetic basis of complex traits and diseases in non-European populations is particularly challenging because U.S. minority populations have been under-represented in genetic association studies. We developed an empirical Bayes approach named XPEB (Cross-Population Empirical Bayes), designed to improve the power for mapping complex trait loci in a minority population by exploiting information from genome-wide association studies (GWAS) from another ethnic population. Taking as input summary statistics from two GWAS, a target-GWAS from an ethnic minority population of primary interest and an auxiliary base-GWAS (such as a larger GWAS in Europeans), our XPEB approach reprioritizes SNPs in the target population to compute local false discovery rates. We demonstrated, through simulations, that whenever the base-GWAS harbors relevant information, XPEB gains efficiency. Moreover, XPEB has the ability to discard irrelevant auxiliary information, providing a safeguard against inflated false discovery rates due to genetic heterogeneity between populations. Applied to a blood lipids study in African Americans, XPEB more than quadrupled the discoveries compared to the conventional approach that uses target-GWAS alone, bringing the number of significant loci from 14 to 65. Thus, XPEB offers a flexible framework for mapping complex traits in minority populations.