Population-specific causal disease effect sizes in functionally important regions impacted by selection

TLDR

Many diseases exhibit population‑specific causal effect sizes and trans‑ethnic genetic correlations below 1, limiting cross‑ethnic polygenic risk prediction. The authors develop S‑LDXR to stratify squared trans‑ethnic genetic correlation across genomic annotations and apply it to 31 diseases in East Asian and European cohorts. S‑LDXR stratifies squared trans‑ethnic genetic correlation across genomic annotations using genome‑wide summary statistics from East Asian and European populations. Squared trans‑ethnic genetic correlation is 0.82× depleted in the top quintile of background selection, indicating that causal effect sizes are more population‑specific in functionally important regions—especially conserved and regulatory loci—with skin and immune genes most population‑specific and brain genes least, possibly due to stronger gene‑environment interaction at loci under selection.

Abstract

Abstract Many diseases exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We develop a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and apply S-LDXR to genome-wide summary statistics for 31 diseases and complex traits in East Asians (average N = 90K) and Europeans (average N = 267K) with an average trans-ethnic genetic correlation of 0.85. We determine that squared trans-ethnic genetic correlation is 0.82× (s.e. 0.01) depleted in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes are more population-specific in functionally important regions, including conserved and regulatory regions. In regions surrounding specifically expressed genes, causal effect sizes are most population-specific for skin and immune genes, and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.