Widespread signatures of natural selection across human complex traits and functional genomic categories

被引：64

作者：

Zeng, Jian ^{[1
]}

Xue, Angli ^{[1
]}

Jiang, Longda ^{[1
]}

Lloyd-Jones, Luke R. ^{[1
]}

Wu, Yang ^{[1
]}

Wang, Huanwei ^{[1
]}

Zheng, Zhili ^{[1
]}

Yengo, Loic ^{[1
]}

Kemper, Kathryn E. ^{[1
]}

Goddard, Michael E. ^{[2
,3
]}

Wray, Naomi R. ^{[1
,4
]}

Visscher, Peter M. ^{[1
]}

Yang, Jian ^{[1
,5
,6
]}

机构：

[1] Univ Queensland, Inst Mol Biosci, Brisbane, Qld, Australia

[2] Univ Melbourne, Fac Vet & Agr Sci, Parkville, Vic, Australia

[3] Dept Econ Dev Jobs Transport & Resources, Biosci Res Div, Bundoora, Vic, Australia

[4] Univ Queensland, Queensland Brain Inst, Brisbane, Qld, Australia

[5] Westlake Univ, Sch Life Sci, Hangzhou, Zhejiang, Peoples R China

[6] Westlake Lab Life Sci & Biomed, Hangzhou, Zhejiang, Peoples R China

来源：

NATURE COMMUNICATIONS | 2021年 / 12卷 / 01期

基金：

英国医学研究理事会; 澳大利亚研究理事会;

关键词：

GENETIC ARCHITECTURE; WIDE ASSOCIATION; HERITABILITY; RISK; LOCI; SUSCEPTIBILITY; STATISTICS; PROPORTION; DISEASE;

D O I：

10.1038/s41467-021-21446-3

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Understanding how natural selection has shaped genetic architecture of complex traits is of importance in medical and evolutionary genetics. Bayesian methods have been developed using individual-level GWAS data to estimate multiple genetic architecture parameters including selection signature. Here, we present a method (SBayesS) that only requires GWAS summary statistics. We analyse data for 155 complex traits (n = 27k-547k) and project the estimates onto those obtained from evolutionary simulations. We estimate that, on average across traits, about 1% of human genome sequence are mutational targets with a mean selection coefficient of similar to 0.001. Common diseases, on average, show a smaller number of mutational targets and have been under stronger selection, compared to other traits. SBayesS analyses incorporating functional annotations reveal that selection signatures vary across genomic regions, among which coding regions have the strongest selection signature and are enriched for both the number of associated variants and the magnitude of effect sizes.

引用

页数：12

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