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

By Huwenbo Shi, Steven Gazal, Masahiro Kanai, Evan M. Koch, Armin P. Schoech, Katherine M Siewert-Rocks, Samuel S Kim, Yang Luo, Tiffany Amariuta, Hailiang Huang, Yukinori Okada, Soumya Raychaudhuri, Shamil Sunyaev, Alkes L. Price

Posted 15 Oct 2019
bioRxiv DOI: 10.1101/803452

Many diseases and complex traits exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We developed a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and applied S-LDXR to genome-wide association summary statistics for 31 diseases and complex traits in East Asians (EAS) and Europeans (EUR) (average N EAS=90K, N EUR=267K) with an average trans-ethnic genetic correlation of 0.85 (s.e. 0.01). We determined that squared trans-ethnic genetic correlation was 0.82× (s.e. 0.01) smaller than the genome-wide average at SNPs in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes were more population-specific in functionally important regions, including conserved and regulatory regions. In analyses of regions surrounding specifically expressed genes, causal effect sizes were 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. ### Competing Interest Statement The authors have declared no competing interest.

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