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

Huwenbo Shi (), Steven Gazal, Masahiro Kanai, Evan M. Koch, Armin P. Schoech, Katherine M. Siewert, Samuel S. Kim, Yang Luo, Tiffany Amariuta, Hailiang Huang, Yukinori Okada, Soumya Raychaudhuri, Shamil R. Sunyaev and Alkes L. Price ()
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Huwenbo Shi: Harvard T.H. Chan School of Public Health
Steven Gazal: Harvard T.H. Chan School of Public Health
Masahiro Kanai: Program in Medical and Population Genetics, Broad Institute of MIT and Harvard
Evan M. Koch: Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School
Armin P. Schoech: Harvard T.H. Chan School of Public Health
Katherine M. Siewert: Harvard T.H. Chan School of Public Health
Samuel S. Kim: Harvard T.H. Chan School of Public Health
Yang Luo: Program in Medical and Population Genetics, Broad Institute of MIT and Harvard
Tiffany Amariuta: Program in Medical and Population Genetics, Broad Institute of MIT and Harvard
Hailiang Huang: Analytic and Translational Genetics Unit, Massachusetts General Hospital
Yukinori Okada: Osaka University Graduate School of Medicine
Soumya Raychaudhuri: Program in Medical and Population Genetics, Broad Institute of MIT and Harvard
Shamil R. Sunyaev: Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School
Alkes L. Price: Harvard T.H. Chan School of Public Health

Nature Communications, 2021, vol. 12, issue 1, 1-15

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.

Date: 2021
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DOI: 10.1038/s41467-021-21286-1

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