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

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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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.

References

  1. 1.

    Johnson, T. & Barton, N. Theoretical models of selection and mutation on quantitative traits. Phil. Trans. R. Soc. Lond. B 360, 1411–1425 (2005).

    CAS  Article  Google Scholar 

  2. 2.

    Hancock, A. M. et al. Colloquium paper: human adaptations to diet, subsistence, and ecoregion are due to subtle shifts in allele frequency. Proc. Natl. Acad. Sci. USA 107(Suppl 2), 8924–8930 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Pritchard, J. K., Pickrell, J. K. & Coop, G. The genetics of human adaptation: hard sweeps, soft sweeps, and polygenic adaptation. Curr. Biol. 20, R208–R215 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Smith, J. M. & Haigh, J. The hitch-hiking effect of a favourable gene. Genet. Res. 23, 23–35 (1974).

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Pritchard, J. K. Are rare variants responsible for susceptibility to complex diseases? Am. J. Hum. Genet. 69, 124–137 (2001).

    CAS  Article  Google Scholar 

  6. 6.

    Eyre-Walker, A. Evolution in health and medicine Sackler colloquium: genetic architecture of a complex trait and its implications for fitness and genome-wide association studies. Proc. Natl. Acad. Sci. USA 107(Suppl 1), 1752–1756 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Mancuso, N. et al. The contribution of rare variation to prostate cancer heritability. Nat. Genet. 48, 30–35 (2016).

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Lee, S. H., Wray, N. R., Goddard, M. E. & Visscher, P. M. Estimating missing heritability for disease from genome-wide association studies. Am. J. Hum. Genet. 88, 294–305 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Yang, J. et al. Genome partitioning of genetic variation for complex traits using common SNPs. Nat. Genet. 43, 519–525 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Gratten, J., Wray, N. R., Keller, M. C. & Visscher, P. M. Large-scale genomics unveils the genetic architecture of psychiatric disorders. Nat. Neurosci. 17, 782–790 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Habier, D., Fernando, R. L., Kizilkaya, K. & Garrick, D. J. Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics 12, 186 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Moser, G. et al. Simultaneous discovery, estimation and prediction analysis of complex traits using a Bayesian mixture model. PLoS Genet. 11, e1004969 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    de Los Campos, G., Hickey, J. M., Pong-Wong, R., Daetwyler, H. D. & Calus, M. P. Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193, 327–345 (2013).

    Article  PubMed  Google Scholar 

  17. 17.

    Zhou, X., Carbonetto, P. & Stephens, M. Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genet. 9, e1003264 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Lloyd-Jones, L. R. et al. Inference on the genetic basis of eye and skin color in an admixed population via Bayesian linear mixed models. Genetics 206, 1113–1126 (2017).

    Article  PubMed  Google Scholar 

  19. 19.

    Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Lloyd-Jones, L. R. et al. The genetic architecture of gene expression in peripheral blood. Am. J. Hum. Genet. 100, 228–237 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Speed, D., Hemani, G., Johnson, M. R. & Balding, D. J. Improved heritability estimation from genome-wide SNPs. Am. J. Hum. Genet. 91, 1011–1021 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Lee, S. H. et al. Estimation of SNP heritability from dense genotype data. Am. J. Hum. Genet. 93, 1151–1155 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Yang, J. et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat. Genet. 47, 1114–1120 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Neal, R. M. MCMC using Hamiltonian dynamics. in Handbook of Markov Chain Monte Carlo (eds Brooks, S., Gelman, A., Jones, G. & Meng, X.-L.) 113–162 (CRC Press, Boca Raton, FL, 2011).

  25. 25.

    Fernando, R. L., Dekkers, J. C. & Garrick, D. J. A class of Bayesian methods to combine large numbers of genotyped and non-genotyped animals for whole-genome analyses. Genet. Sel. Evol. 46, 50 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Walter, K. et al. The UK10K project identifies rare variants in health and disease. Nature 526, 82–90 (2015).

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Wood, A. R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Robinson, M. R. et al. Population genetic differentiation of height and body mass index across Europe. Nat. Genet. 47, 1357–1362 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Marouli, E. et al. Rare and low-frequency coding variants alter human adult height. Nature 542, 186–190 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Turcot, V. et al. Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity. Nat. Genet. 50, 26–41 (2018).

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    de Koning, L., Merchant, A. T., Pogue, J. & Anand, S. S. Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: meta-regression analysis of prospective studies. Eur. Heart J. 28, 850–856 (2007).

    Article  PubMed  Google Scholar 

  33. 33.

    Wass, P., Waldenström, U., Rössner, S. & Hellberg, D. An android body fat distribution in females impairs the pregnancy rate of in-vitro fertilization-embryo transfer. Hum. Reprod. 12, 2057–2060 (1997).

    CAS  Article  PubMed  Google Scholar 

  34. 34.

    Day, F. R. et al. Large-scale genomic analyses link reproductive aging to hypothalamic signaling, breast cancer susceptibility and BRCA1-mediated DNA repair. Nat. Genet. 47, 1294–1303 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Fuchsberger, C. et al. The genetic architecture of type 2 diabetes. Nature 536, 41–47 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Hill, W. G., Goddard, M. E. & Visscher, P. M. Data and theory point to mainly additive genetic variance for complex traits. PLoS Genet. 4, e1000008 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Visscher, P. M., Goddard, M. E., Derks, E. M. & Wray, N. R. Evidence-based psychiatric genetics, AKA the false dichotomy between common and rare variant hypotheses. Mol. Psychiatry 17, 474–485 (2012).

    CAS  Article  PubMed  Google Scholar 

  39. 39.

    Simons, Y. B., Turchin, M. C., Pritchard, J. K. & Sella, G. The deleterious mutation load is insensitive to recent population history. Nat. Genet. 46, 220–224 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Uricchio, L. H., Zaitlen, N. A., Ye, C. J., Witte, J. S. & Hernandez, R. D. Selection and explosive growth alter genetic architecture and hamper the detection of causal rare variants. Genome Res. 26, 863–873 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Kelleher, J., Etheridge, A. M. & McVean, G. Efficient coalescent simulation and genealogical analysis for large sample sizes. PLoS Comput. Biol. 12, e1004842 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Konarzewski, M. & Książek, A. Determinants of intra-specific variation in basal metabolic rate. J. Comp. Physiol. B 183, 27–41 (2013).

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    Nyholt, D. R., Gillespie, N. A., Heath, A. C. & Martin, N. G. Genetic basis of male pattern baldness. J. Invest. Dermatol. 121, 1561–1564 (2003).

    Article  PubMed  Google Scholar 

  44. 44.

    Hyde, C. L. et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat. Genet. 48, 1031–1036 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  45. 45.

    de Moor, M. H. et al. Meta-analysis of genome-wide association studies for neuroticism, and the polygenic association with major depressive disorder. JAMA Psychiatry 72, 642–650 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Davies, G. et al. Genome-wide association study of cognitive functions and educational attainment in UK Biobank (N = 112 151). Mol. Psychiatry 21, 758–767 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Gravel, S. et al. Demographic history and rare allele sharing among human populations. Proc. Natl. Acad. Sci. USA 108, 11983–11988 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Altshuler, D. M. et al. International HapMap 3 Consortium. Integrating common and rare genetic variation in diverse human populations. Nature 467, 52–58 (2010).

    CAS  Article  PubMed  Google Scholar 

  49. 49.

    Torgerson, D. G. et al. Evolutionary processes acting on candidate cis-regulatory regions in humans inferred from patterns of polymorphism and divergence. PLoS Genet. 5, e1000592 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  50. 50.

    McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Hernandez, R. D. et al. Classic selective sweeps were rare in recent human evolution. Science 331, 920–924 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Turchin, M. C. et al. Evidence of widespread selection on standing variation in Europe at height-associated SNPs. Nat. Genet. 44, 1015–1019 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Berg, J. J. & Coop, G. A population genetic signal of polygenic adaptation. PLoS Genet. 10, e1004412 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Field, Y. et al. Detection of human adaptation during the past 2000 years. Science 354, 760–764 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Gazal, S. et al. Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection. Nat. Genet. 49, 1421–1427 (2017).

    CAS  Article  PubMed  Google Scholar 

  56. 56.

    Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Speed, D., Cai, N., Johnson, M. R., Nejentsev, S. & Balding, D. J. Reevaluation of SNP heritability in complex human traits. Nat. Genet. 49, 986–992 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Guan, Y. & Stephens, M. Bayesian variable selection regression for genome-wide association studies and other large-scale problems. Ann. Appl. Stat. 5, 1780–1815 (2011).

    Article  Google Scholar 

  59. 59.

    Zeng, J. Whole Genome Analyses Accounting for Structures in Genotype Data. PhD dissertation, Iowa State University, Chapter 2, 6-33 (2015).

  60. 60.

    Fernando, R. L. & Garrick, D. Bayesian methods applied to GWAS. Methods Mol. Biol. 1019, 237–274 (2013).

    Article  PubMed  Google Scholar 

  61. 61.

    Gelman, A. et al. Bayesian Data Analysis. (CRC Press, Boca Raton, FL, 2014).

    Google Scholar 

  62. 62.

    Fernando, R. L. et al. Controlling the proportion of false positives in multiple dependent tests. Genetics 166, 611–619 (2004).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Storey, J. D. The optimal discovery procedure: a new approach to simultaneous significance testing. J. R. Stat. Soc. Series B Stat. Methodol. 69, 347–368 (2007).

    Article  Google Scholar 

  64. 64.

    Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  65. 65.

    UK Biobank Genotyping and quality control of UK Biobank, a large-scale, extensively phenotyped prospective resource. UK Biobank http://biobank.ctsu.ox.ac.uk/crystal/docs/genotyping_qc.pdf (2015).

  66. 66.

    Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013).

    Article  PubMed Central  Google Scholar 

  67. 67.

    Messer, P. W. SLiM: simulating evolution with selection and linkage. Genetics 194, 1037–1039 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Palamara, P. F. et al. Leveraging distant relatedness to quantify human mutation and gene-conversion rates. Am. J. Hum. Genet. 97, 775–789 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Enard, D., Messer, P. W. & Petrov, D. A. Genome-wide signals of positive selection in human evolution. Genome Res. 24, 885–895 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Abecasis, G. R. et al. 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

    Article  PubMed  Google Scholar 

Download references

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.

Author information

Affiliations

Authors

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.

Corresponding author

Correspondence to Jian Yang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Text, Figures and Tables

Supplementary Figures 1–26, Supplementary Tables 1–9, and Supplementary Note

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Further reading

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing