Improved multi-ancestry fine-mapping identifiescis-regulatory variants underlying molecular traits and disease risk

Abstract

AbstractMulti-ancestry statistical fine-mapping ofcis-molecular quantitative trait loci (cis-molQTL) aims to improve the precision of distinguishing causalcis-molQTLs from tagging variants. However, existing approaches fail to reflect shared genetic architectures. To solve this limitation, we present the Sum of Shared Single Effects (SuShiE) model, which leverages LD heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations, and estimate ancestry-specific expression prediction weights. We apply SuShiE to mRNA expression measured in PBMCs (n=956) and LCLs (n=814) together with plasma protein levels (n=854) from individuals of diverse ancestries in the TOPMed MESA and GENOA studies. We find SuShiE fine-mapscis-molQTLs for 16%more genes compared with baselines while prioritizing fewer variants with greater functional enrichment. SuShiE infers highly consistentcis-molQTL architectures across ancestries on average; however, we also find evidence of heterogeneity at genes with predicted loss-of-function intolerance, suggesting that environmental interactions may partially explain differences incis-molQTL effect sizes across ancestries. Lastly, we leverage estimatedcis-molQTL effect-sizes to perform individual-level TWAS and PWAS on six white blood cell-related traits in AOU Biobank individuals (n=86k), and identify 44 more genes compared with baselines, further highlighting its benefits in identifying genes relevant for complex disease risk. Overall, SuShiE provides new insights into thecis-genetic architecture of molecular traits.