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

Nature Genetics volume 50, pages 746–753 (2018)Cite this article

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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.

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Fig. 1: Estimation of the genetic architecture parameters for a simulated trait using the ARIC + GENEVA data.
Fig. 2: Posterior distributions of the genetic architecture parameters for height versus BMI using data from UKB.
Fig. 3: BayesS estimates of the genetic architecture parameters for the UKB traits.
Fig. 4: cGVE by SNPs with MAF smaller than a threshold on the x axis.
Fig. 5: Forward simulations with different types of selection.

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Acknowledgements

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.

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Authors and Affiliations

  1. Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia

    Jian Zeng, 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, Naomi R. Wray, Peter M. Visscher & Jian Yang

  2. School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Ronald de Vlaming

  3. Erasmus University Rotterdam Institute for Behavior and Biology, Rotterdam, The Netherlands

    Ronald de Vlaming

  4. Department of Computational Biology, University of Lausanne, Lausanne, Switzerland

    Matthew R. Robinson

  5. Estonian Genome Center, University of Tartu, Tartu, Estonia

    Julia Sidorenko, Andres Metspalu & Tonu Esko

  6. School of Biological Sciences and Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, GA, USA

    Greg Gibson

  7. Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia

    Naomi R. Wray, Peter M. Visscher & Jian Yang

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Contributions

J.Y., P.M.V., and R.d.V. conceived the study. J.Y., J.Z., and P.M.V. designed the experiment. J.Z. derived the analytical methods, conducted all analyses, and developed the software with assistance and guidance from J.Y., Y.W., M.R.R., L.R.L.-J., L.Y., C.X.Y., A.X., and J.S. L.R.L.-J., A.F.M., J.E.P., G.W.M., A.M., T.E., G.G., N.R.W., and P.M.V. provided the CAGE data. J.Z. and J.Y. wrote the manuscript with the participation of all authors. All authors reviewed and approved the final manuscript.

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Correspondence to Jian Yang.

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Zeng, J., de Vlaming, R., Wu, Y. et al. Signatures of negative selection in the genetic architecture of human complex traits. Nat Genet 50, 746–753 (2018). https://doi.org/10.1038/s41588-018-0101-4

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