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A selection pressure landscape for 870 human polygenic traits

Abstract

Characterizing the natural selection of complex traits is important for understanding human evolution and both biological and pathological mechanisms. We leveraged genome-wide summary statistics for 870 polygenic traits and attempted to quantify signals of selection on traits of different forms in European ancestry across four periods in human history and evolution. We found that 88% of these traits underwent polygenic change in the past 2,000–3,000 years. Recent selection was associated with ancient selection signals in the same trait. Traits related to pigmentation, body measurement and nutritional intake exhibited strong selection signals across different time scales. Our findings are limited by our use of exclusively European data and the use of genome-wide association study data, which identify associations between genetic variants and phenotypes that may not be causal. In sum, we provide an overview of signals of selection on human polygenic traits and their characteristics across human evolution, based on a European subset of human genetic diversity. These findings could serve as a foundation for further populational and medical genetic studies.

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Fig. 1
Fig. 2: Selection pressure in the present day and in recent history.
Fig. 3: Selection pressure in recent history.
Fig. 4: Selection pressure during the pan-Neolithic period.
Fig. 5: Selection pressure analysis for humans since speciation.
Fig. 6: Relation among selection pressures at different time scales.
Fig. 7: Population-average polygenic risk score trajectory for 765 traits.
Fig. 8: Genomic architectures impacted selection pressure.

Data availability

All GWAS summary statistics analysed in the current study could be downloaded from the public domain. All data generated in the current study could be obtained from the Supplementary Information.

Code availability

Scripts used for this study is available at https://github.com/WeiCSong/selection.

References

  1. Mathieson, I. Human adaptation over the past 40,000 years. Curr. Opin. Genet. Dev. 62, 97–104 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Bersaglieri, T. et al. Genetic signatures of strong recent positive selection at the lactase gene. Am. J. Hum. Genet. 74, 1111–1120 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Kelly, A. J., Dubbs, S. L. & Barlow, F. K. An evolutionary perspective on mate rejection. Evol. Psychol. 14, 1–13 (2016).

    Google Scholar 

  4. Gibson, M. A. & Lawson, D. W. Applying evolutionary anthropology. Evol. Anthropol. 24, 3–14 (2015).

    PubMed  PubMed Central  Google Scholar 

  5. Wells, J. C. K., Nesse, R. M., Sear, R., Johnstone, R. A. & Stearns, S. C. Evolutionary public health: introducing the concept. Lancet 390, 500–509 (2017).

    PubMed  Google Scholar 

  6. Currat, M. et al. Molecular analysis of the β-globin gene cluster in the Niokholo Mandenka population reveals a recent origin of the β(S) Senegal mutation. Am. J. Hum. Genet. 70, 207–223 (2002).

    CAS  PubMed  Google Scholar 

  7. Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 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  PubMed  PubMed Central  Google Scholar 

  9. Laland, K. N., Odling-Smee, J. & Myles, S. How culture shaped the human genome: bringing genetics and the human sciences together. Nat. Rev. Genet. 11, 137–148 (2010).

    CAS  PubMed  Google Scholar 

  10. Esteller-Cucala, P. et al. Genomic analysis of the natural history of attention-deficit/hyperactivity disorder using Neanderthal and ancient Homo sapiens samples. Sci. Rep. 10, 8622 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Pardiñas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).

    PubMed  PubMed Central  Google Scholar 

  12. Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 1339–1348 (2019).

    CAS  PubMed  Google Scholar 

  13. Hejase, H. A., Dukler, N. & Siepel, A. From summary statistics to gene trees: methods for inferring positive selection. Trends Genet. 36, 243–258 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Malik, R. et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat. Genet. 50, 524–537 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Lawn, R. B. et al. Schizophrenia risk and reproductive success: a Mendelian randomization study. R. Soc. Open Sci. 6, 181049 (2019).

    PubMed  PubMed Central  Google Scholar 

  17. Bribiescas, R. G. Men: Evolutionary and Life History (Harvard Univ. Press, 2009).

  18. Morrison, J., Knoblauch, N., Marcus, J. H., Stephens, M. & He, X. Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics. Nat. Genet. 52, 1–8 (2020).

    Google Scholar 

  19. Barban, N. et al. Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nat. Genet. 48, 1–7 (2016).

    Google Scholar 

  20. Burgess, S., Davies, N. M. & Thompson, S. G. Bias due to participant overlap in two-sample Mendelian randomization. Genet. Epidemiol. 40, 597–608 (2016).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Sohail, M. et al. Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies. eLife 8, e39702 (2019).

    PubMed  PubMed Central  Google Scholar 

  23. Edge, M. D. & Coop, G. Reconstructing the history of polygenic scores using coalescent trees. Genetics 211, 235–262 (2019).

    PubMed  Google Scholar 

  24. Speidel, L., Forest, M., Shi, S. & Myers, S. R. A method for genome-wide genealogy estimation for thousands of samples. Nat. Genet. 51, 1321–1329 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Janus, L. Two corner stones of the psychobiological development of mankind - The increase in frequency of pregnancies in the neolithic revolution and ‘physiological prematurity’. Nutr. Health 19, 63–68 (2007).

    PubMed  Google Scholar 

  26. Lipson, M. et al. Parallel palaeogenomic transects reveal complex genetic history of early European farmers. Nature 551, 368–372 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Fu, Q. et al. The genetic history of Ice Age Europe. Nature 534, 200–205 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Lazaridis, I. et al. Genomic insights into the origin of farming in the ancient Near East. Nature 536, 419–424 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Sankararaman, S. et al. The genomic landscape of Neanderthal ancestry in present-day humans. Nature 507, 354–357 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Palamara, P. F., Terhorst, J., Song, Y. S. & Price, A. L. High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability. Nat. Genet. 50, 1311–1317 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Grossman, S. R. et al. A composite of multiple signals distinguishes causal variants in regions of positive selection. Science 327, 883–886 (2010).

    CAS  PubMed  Google Scholar 

  32. Mughal, M. R. & DeGiorgio, M. Localizing and classifying adaptive targets with trend filtered regression. Mol. Biol. Evol. 36, 252–270 (2019).

    CAS  PubMed  Google Scholar 

  33. Cheng, X. & DeGiorgio, M. Flexible mixture model approaches that accommodate footprint size variability for robust detection of balancing selection. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msaa134 (2020).

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

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Sardá-Espinosa, A. Time-series clustering in R using the dtwclust package. R J. 11, 22–43 (2019).

    Google Scholar 

  36. Chen, M. et al. Evidence of polygenic adaptation in Sardinia at height-associated loci ascertained from the biobank Japan. Am. J. Hum. Genet. 107, 60–71 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Daub, J. T. et al. Evidence for polygenic adaptation to pathogens in the human genome. Mol. Biol. Evol. 30, 1544–1558 (2013).

    CAS  PubMed  Google Scholar 

  38. 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Google Scholar 

  39. Rocha, J. The evolutionary history of human skin pigmentation. J. Mol. Evol. 88, 77–87 (2020).

    CAS  PubMed  Google Scholar 

  40. Luca, F., Perry, G. H. & Di Rienzo, A. Evolutionary adaptations to dietary changess. Annu. Rev. Nutr. 30, 291–314 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Little, M. A. Evolutionary strategies for body size. Front. Endocrinol. 11, 107 (2020).

    Google Scholar 

  42. Södersten, P., Brodin, U., Zandian, M. & Bergh, C. Eating behavior and the evolutionary perspective on anorexia nervosa. Front. Neurosci. 13, 596 (2019).

    PubMed  PubMed Central  Google Scholar 

  43. Ewald, P. W. & Swain Ewald, H. A. An evolutionary perspective on the causes and treatment of inflammatory bowel disease. Curr. Opin. Gastroenterol. 29, 350–356 (2013).

    CAS  PubMed  Google Scholar 

  44. Hill, W. G. & Robertson, A. The effect of linkage on limits to artificial selection. Genet. Res. (Camb.). 89, 311–336 (2008).

    Google Scholar 

  45. Maruyama, T. The age of a rare mutant gene in a large population. Am. J. Hum. Genet. 26, 669–673 (1974).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Charlesworth, B. Fundamental concepts in genetics: effective population size and patterns of molecular evolution and variation. Nat. Rev. Genet. 10, 195–205 (2009).

    CAS  PubMed  Google Scholar 

  47. Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Hemani, G. et al. The MR-base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).

    PubMed  PubMed Central  Google Scholar 

  49. Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304–314 (2016).

    PubMed  PubMed Central  Google Scholar 

  50. Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).

    PubMed  PubMed Central  Google Scholar 

  51. Burgess, S., Bowden, J., Fall, T., Ingelsson, E. & Thompson, S. G. Sensitivity analyses for robust causal inference from mendelian randomization analyses with multiple genetic variants. Epidemiology 28, 30–42 (2017).

    PubMed  Google Scholar 

  52. Verbanck, M., Chen, C. Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Galinsky, K. J. et al. Fast principal-component analysis reveals convergent evolution of ADH1B in Europe and East Asia. Am. J. Hum. Genet. 98, 456–472 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Purcell, S. et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Schloerke, B. et al. ggobi/ggally: v2.1.2. Zenodo, https://doi.org/10.5281/zenodo.5009047 (2021).

  56. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. McVicker, G., Gordon, D., Davis, C. & Green, P. Widespread genomic signatures of natural selection in hominid evolution. PLoS Genet. 5, e1000471 (2009).

    PubMed  PubMed Central  Google Scholar 

  58. Zeng, J. et al. Bayesian analysis of GWAS summary data reveals differential signatures of natural selection across human complex traits and functional genomic categories. Preprint at https://doi.org/10.1101/752527 (2019).

  59. Haller, B. C. & Messer, P. W. SLiM 3: forward genetic simulations beyond the Wright–Fisher model. Mol. Biol. Evol. 36, 632–637 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Berisa, T. & Pickrell, J. K. Approximately independent linkage disequilibrium blocks in human populations. Bioinformatics 32, 283–285 (2016).

    CAS  PubMed  Google Scholar 

  62. Berndt, D. J. & Clifford, J. Using dynamic time warping to find patterns in time series. KDD Work. 10, 359–370 (1994).

    Google Scholar 

  63. Hothorn, T., Hornik, K., Wiel, M. Avande & Zeileis, A. Implementing a class of permutation tests: the coin package. J. Stat. Softw. 28, 1–23 (2008).

    Google Scholar 

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Acknowledgements

The study was supported by National Natural Science Foundation of China (no. 81971292, G.N.L.), the Natural Science Foundation of Shanghai (no. 21ZR1428600, G.N.L.), the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning (grant no. 1610000043, G.N.L.) and the Interdisciplinary Program of Shanghai Jiao Tong University (no. YG2019QNA59, G.N.L.). The authors thank all researchers and consortia that share their GWAS summary statistics with the scientific community. W.S. thanks J. Song for his inspiration on this study.

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G.N.L. designed and supervised the study. Y.S. collected and preprocessed the data. W.S. analysed the data and drafted the manuscript. W.P., W.Q. and W.W. assisted with the methodology. Y.S., S.Y. and M.Z. interpreted the data. All authors read, revised and approved the manuscript.

Corresponding author

Correspondence to Guan Ning Lin.

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The authors declare no competing interests.

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Peer review information Nature Human Behaviour thanks Eugenio Lopez-Cortegano and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Song, W., Shi, Y., Wang, W. et al. A selection pressure landscape for 870 human polygenic traits. Nat Hum Behav 5, 1731–1743 (2021). https://doi.org/10.1038/s41562-021-01231-4

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