RT Journal Article
SR Electronic
T1 Population-specific causal disease effect sizes in functionally important regions impacted by selection
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 803452
DO 10.1101/803452
A1 Huwenbo Shi
A1 Steven Gazal
A1 Masahiro Kanai
A1 Evan M. Koch
A1 Armin P. Schoech
A1 Samuel S. Kim
A1 Yang Luo
A1 Tiffany Amariuta
A1 Yukinori Okada
A1 Soumya Raychaudhuri
A1 Shamil R. Sunyaev
A1 Alkes L. Price
YR 2019
UL http://biorxiv.org/content/early/2019/10/30/803452.abstract
AB 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 30 diseases and complex traits in East Asians (EAS) and Europeans (EUR) (average NEAS=93K, NEUR=274K) with an average trans-ethnic genetic correlation of 0.83 (s.e. 0.01). We determined that squared trans-ethnic genetic correlation was 0.81× (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 coding, 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.