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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., Trans-ethnic genetic correlation is significantly less than 1 for many diseases. Here, the authors stratify this correlation by genomic annotations, finding that loci whose causal disease effect sizes differ between ethnicities are likely impacted by selection, particularly positive selection.

Alopecia areata (AA) is a prevalent autoimmune disease with 10 known susceptibility loci. Here we perform the first meta-analysis of research on AA by combining data from two genome-wide association studies (GWAS), and replication with supplemented ImmunoChip data for a total of 3,253 cases and 7,543 controls. The strongest region of association is the major histocompatibility complex, where we fine-map four independent effects, all implicating human leukocyte antigen-DR as a key aetiologic driver. Outside the major histocompatibility complex, we identify two novel loci that exceed the threshold of statistical significance, containing ​ACOXL/​BCL2L11(​BIM) (2q13); ​GARP (​LRRC32) (11q13.5), as well as a third nominally significant region ​SH2B3(​LNK)/​ATXN2 (12q24.12). Candidate susceptibility gene expression analysis in these regions demonstrates expression in relevant immune cells and the hair follicle. We integrate our results with data from seven other autoimmune diseases and provide insight into the alignment of AA within these disorders. Our findings uncover new molecular pathways disrupted in AA, including autophagy/apoptosis, transforming growth factor beta/Tregs and JAK kinase signalling, and support the causal role of aberrant immune processes in AA.