Signatures of negative selection in the genetic architecture of human complex traits

Jian Zeng, Ronald De Vlaming, Yang Wu, Matthew R. Robinson, Luke R. Lloyd-Jones, Loic Yengo, Chloe X. Yap, Angli Xue, Julia Sidorenko, Allan F. McRae, Joseph E. Powell, Grant W. Montgomery, Andres Metspalu, Tonu Esko, Greg Gibson, Naomi R. Wray, Peter M. Visscher, Jian Yang*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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Abstract

We develop a Bayesian mixed linear model that simultaneously estimates single-nucleotide polymorphism (SNP)-based heritability, polygenicity (proportion of SNPs with nonzero effects), and the relationship between SNP effect size and minor allele frequency for complex traits in conventionally unrelated individuals using genome-wide SNP data. We apply the method to 28 complex traits in the UK Biobank data (N = 126,752) and show that on average, 6% of SNPs have nonzero effects, which in total explain 22% of phenotypic variance. We detect significant (P < 0.05/28) signatures of natural selection in the genetic architecture of 23 traits, including reproductive, cardiovascular, and anthropometric traits, as well as educational attainment. The significant estimates of the relationship between effect size and minor allele frequency in complex traits are consistent with a model of negative (or purifying) selection, as confirmed by forward simulation. We conclude that negative selection acts pervasively on the genetic variants associated with human complex traits.

Original languageEnglish
Pages (from-to)746-753
Number of pages8
JournalNature Genetics
Volume50
Issue number5
Early online date16 Apr 2018
DOIs
Publication statusPublished - May 2018

Funding

This research was supported by the Australian Research Council (DP160101343, DP160101056, DP160103860, and DP160102400), the Australian National Health and Medical Research Council (1107258, 1078901, 1078037, 1083656, 1078399, 1046880, and 1113400), the US National Institutes of Health (MH100141, GM099568, ES025052, and AG042568), and the Sylvia & Charles Viertel Charitable Foundation (Senior Medical Research Fellowship). R.d.V. acknowledges funding from an ERC consolidator grant (647648 EdGe, awarded to Philipp Koellinger). We thank The University of Queensland’s Research Computing Centre (RCC) for its support in this research. We thank F. Zhang for building the website for the software tool GCTB. This research was supported by the Australian Research Council (DP160101343, DP160101056, DP160103860, and DP160102400), the Australian National Health and Medical Research Council (1107258, 1078901, 1078037, 1083656, 1078399, 1046880, and 1113400), the US National Institutes of Health (MH100141, GM099568, ES025052, and AG042568), and the Sylvia & Charles Viertel Charitable Foundation (Senior Medical Research Fellowship). R.d.V. acknowledges funding from an ERC consolidator grant (647648 EdGe, awarded to Philipp Koellinger). This study makes use of data from dbGaP (accessions: phs000090 and phs000091), UK10K project (EGA accessions: EGAS00001000108 and EGAS00001000090), and UK Biobank Resource (application number: 12514). A full list of acknowledgements for these datasets can be found in part 19 of the Supplementary Note.

FundersFunder number
Australian National Health and Medical Research Council
US National Institutes of Health
National Institutes of HealthES025052, GM099568, MH100141, AG042568
Sylvia and Charles Viertel Charitable Foundation
Horizon 2020 Framework Programme692145, 647648
European Research Council
Australian Research CouncilDP160101343, DP160102400, DP160101056, DP160103860
National Health and Medical Research Council1078901, 1107258, 1078399, 1083656, 1113400, 1046880, 1078037

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