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Imprint of assortative mating on the human genome

Nature Human Behaviour volume 2, pages 948–954 (2018)Cite this article

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Abstract

Preference for mates with similar phenotypes; that is, assortative mating, is widely observed in humans1,2,3,4,5 and has evolutionary consequences6,7,8. Under Fisher's classical theory6, assortative mating is predicted to induce a signature in the genome at trait-associated loci that can be detected and quantified. Here, we develop and apply a method to quantify assortative mating on a specific trait by estimating the correlation (θ) between genetic predictors of the trait from single nucleotide polymorphisms on odd- versus even-numbered chromosomes. We show by theory and simulation that the effect of assortative mating can be quantified in the presence of population stratification. We applied this approach to 32 complex traits and diseases using single nucleotide polymorphism data from ~400,000 unrelated individuals of European ancestry. We found significant evidence of assortative mating for height (θ = 3.2%) and educational attainment (θ = 2.7%), both of which were consistent with theoretical predictions. Overall, our results imply that assortative mating involves multiple traits and affects the genomic architecture of loci that are associated with these traits, and that the consequence of mate choice can be detected from a random sample of genomes.

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Fig. 1: Schematic of the effect of assortative mating on the correlation between trait-associated alleles.
Fig. 2: Estimates of assortative mating-induced GPD among TIAs in three independent cohorts.
Fig. 3: Correlation of genetic predictors of 32 complex traits and diseases in 18,984 mates pairs as function of within-individual estimates of GPD in alleles associated with these traits.

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Data availability

We used genotypic data from the Resource for Genetic Epidemiology Research on Adult Health and Aging (GERA: dbGaP phs000674.v2.p2), genotypic and phenotypic data from the Health and Retirement Study (HRS: dbGaP phs000428.v1.p1), and genotypic and phenotypic data from the UKB under project 12505.

References

  1. Pearson, K. & Lee, A. On the laws of inheritance in man: I. Inheritance of physical characters. Biometrika 2, 357–462 (1903).

    Article  Google Scholar 

  2. Spuhler, J. N. Assortative mating with respect to physical characteristics. Eugen. Q 15, 128–140 (1968).

    Article  CAS  Google Scholar 

  3. Mare, R. D. Five decades of educational assortative mating. Am. Sociol. Rev. 56, 15–32 (1991).

    Article  Google Scholar 

  4. Silventoinen, K., Kaprio, J., Lahelma, E., Viken, R. J. & Rose, R. J. Assortative mating by body height and BMI: Finnish twins and their spouses. Am. J. Hum. Biol. 15, 620–627 (2003).

    Article  Google Scholar 

  5. Stulp, G., Simons, M. J. P., Grasman, S. & Pollet, T. V. Assortative mating for human height: a meta-analysis. Am. J. Hum. Biol 29, e22917 (2016).

    Article  Google Scholar 

  6. Fisher, R. A. The correlation between relatives on the supposition of Mendelian inheritance Trans. R. Soc. Edinb. 52, 399–433 (1918).

    Article  Google Scholar 

  7. Wright, S. Systems of mating. III. Assortative mating based on somatic resemblance. Genetics 6, 144–161 (1921).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Crow, J. F. & Kimura, M. An Introduction to Population Genetics Theory (Blackburn Press, Caldwell, 2009).

  9. Shine, R., O’connor, D., Lemaster, M. P. & Mason, R. T. Pick on someone your own size: ontogenetic shifts in mate choice by male garter snakes result in size-assortative mating. Anim. Behav. 61, 1133–1141 (2001).

    Article  Google Scholar 

  10. Jiang, Y., Bolnick, D. I. & Kirkpatrick, M. Assortative mating in animals. Am. Nat. 181, E125–E138 (2013).

    Article  Google Scholar 

  11. Vandenburg, S. G. Assortative mating, or who marries whom? Behav. Genet. 2, 127–157 (1972).

    Article  CAS  Google Scholar 

  12. Hippisley-Cox, J., Coupland, C., Pringle, M., Crown, N. & Hammersley, V. Married couples' risk of same disease: cross sectional study. Br. Med. J. 325, 636 (2002).

    Article  Google Scholar 

  13. Ajslev, T. A. et al. Assortative marriages by body mass index have increased simultaneously with the obesity epidemic. Front. Genet. 3, 125 (2012).

    Article  Google Scholar 

  14. Nordsletten, A. E. et al. Patterns of nonrandom mating within and across 11 major psychiatric disorders. JAMA Psychiatry 73, 354–361 (2016).

    Article  Google Scholar 

  15. Nagylaki, T. Assortative mating for a quantitative character. J. Math. Biol. 16, 57–74 (1982).

    Article  CAS  Google Scholar 

  16. Gimelfarb, A. Quantitative characters under assortative mating: gametic model. Theor. Popul. Biol. 25, 312–330 (1984).

    Article  CAS  Google Scholar 

  17. Bulmer, M. G. The Mathematical Theory of Quantitative Genetics (Clarendon Press, Oxford, 1985).

  18. Sebro, R., Peloso, G. M., Dupuis, J. & Risch, N. J. Structured mating: patterns and implications. PLoS Genet. 13, e1006655 (2017).

    Article  Google Scholar 

  19. Sebro, R., Hoffman, T. J., Lange, C., Rogus, J. J. & Risch, N. J. Testing for non-random mating: evidence for ancestry-related assortative mating in the Framingham Heart Study. Genet. Epidemiol. 34, 674–679 (2010).

    Article  Google Scholar 

  20. Risch, N. et al. Ancestry-related assortative mating in Latino populations. Genome Biol. 10, R132 (2009).

    Article  Google Scholar 

  21. Crow, J. F. & Felsenstein, J. The effect of assortative mating on the genetic composition of a population. Eugen. Q. 15, 85–97 (1968).

    Article  CAS  Google Scholar 

  22. Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).

    Article  CAS  Google Scholar 

  23. Li, X., Redline, S., Zhang, X., Williams, S. & Zhu, X. Height associated variants demonstrate assortative mating in human populations. Sci. Rep. 7, 15689 (2017).

    Article  Google Scholar 

  24. Sampson, J., Kidd, K. K., Kidd, J. R. & Zhao, H. Selecting SNPs to identify ancestry. Ann. Hum. Genet. 75, 539–553 (2011).

    Article  Google Scholar 

  25. 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).

    Article  CAS  Google Scholar 

  26. 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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  28. Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).

    Article  CAS  Google Scholar 

  29. Cesarini, D. & Visscher, P. M. Genetics and educational attainment. npj Sci. Learn. 2, 4 (2017).

    Article  Google Scholar 

  30. Bycroft, C. et al. Genome-wide genetic data on ~500,000 UK Biobank participants. Nature 562, 203–209 (2018).

    Article  CAS  Google Scholar 

  31. 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).

    Article  CAS  Google Scholar 

  32. Kemp, J. P. et al. Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis. Nat. Genet. 49, 1468–1475 (2017).

    Article  CAS  Google Scholar 

  33. Robinson, M. R. et al. Genetic evidence of assortative mating in humans. Nat. Hum. Behav 1, 0016 (2017).

    Article  Google Scholar 

  34. Hugh-Jones, D., Verweij, K. J. H., St. Pourcain, B. & Abdellaoui, A. Assortative mating on educational attainment leads to genetic spousal resemblance for polygenic scores. Intelligence 59, 103–108 (2016).

    Article  Google Scholar 

  35. Conley, D. et al. Assortative mating and differential fertility by phenotype and genotype across the 20th century. Proc. Natl Acad. Sci. USA 113, 6647–6652 (2016).

    Article  CAS  Google Scholar 

  36. Agrawal, A. et al. Assortative mating for cigarette smoking and for alcohol consumption in female Australian twins and their spouses. Behav. Genet. 36, 553–566 (2006).

    Article  Google Scholar 

  37. Youyou, W., Stillwell, D., Schwartz, H. A. & Kosinski, M. Birds of a feather do flock together: behavior-based personality-assessment method reveals personality similarity among couples and friends. Psychol. Sci. 28, 276–284 (2017).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  40. Tenesa, A., Rawlik, K., Navarro, P. & Canela-Xandri, O. Genetic determination of height-mediated mate choice. Genome Biol. 16, 269 (2016).

    Article  Google Scholar 

  41. Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

    Article  CAS  Google Scholar 

  42. Eaves, L. J., Last, K. A., Young, P. A. & Martin, N. G. Model-fitting approaches to the analysis of human behaviour. Heredity 41, 249–320 (1978).

    Article  CAS  Google Scholar 

  43. 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).

    Article  CAS  Google Scholar 

  44. Lachin, J. M. Introduction to sample size determination and power analysis for clinical trials. Control. Clin. Trials 2, 93–113 (1981).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  46. Allen, N. et al. UK Biobank: current status and what it means for epidemiology. Health Pol. Technol. 1, 123–126 (2012).

    Article  Google Scholar 

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

    Article  Google Scholar 

  48. Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in ~700000 individuals of European ancestry. Hum. Mol. Genet. 27, 3641–3649 (2018).

    Article  Google Scholar 

  49. 1000 Genomes Project Consortium A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  Google Scholar 

  50. Sniekers, S. et al. Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nat. Genet. 49, 1107–1112 (2017).

    Article  CAS  Google Scholar 

  51. 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).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This research was supported by the Australian Research Council (DP130102666, DP160103860 and DP160102400), Australian National Health and Medical Research Council (1078037, 1078901, 1103418, 1107258, 1127440 and 1113400), National Institutes of Health (grants R01AG042568, P01GM099568 and R01MH100141) and Sylvia and Charles Viertel Charitable Foundation. The GERA study was supported by grant RC2 AG036607 from the National Institutes of Health, and grants from the Robert Wood Johnson Foundation, Ellison Medical Foundation, Wayne and Gladys Valley Foundation and Kaiser Permanente. The authors thank members of the Kaiser Permanente Medical Care Plan, Northern California Region who generously agreed to participate in the Kaiser Permanente Research Program on Genes, Environment and Health. This research has been conducted using the UKB Resource under project 12505. We thank B. Hill for helpful comments and suggestions on the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Authors and Affiliations

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

    Loic Yengo, Matthew R. Robinson, Kathryn E. Kemper, Yuanhao Yang, Maciej Trzaskowski, Jacob Gratten, Naomi R. Wray, Jian Yang & Peter M. Visscher

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

    Matthew R. Robinson

  3. Department of Psychology and Neuroscience, Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA

    Matthew C. Keller

  4. Mater Research, Translational Research Institute, Brisbane, Queensland, Australia

    Jacob Gratten

  5. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Patrick Turley

  6. Stanley Centre for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Patrick Turley

  7. National Bureau of Economic Research, Cambridge, MA, USA

    David Cesarini & Daniel J. Benjamin

  8. Department of Economics, New York University, New York, NY, USA

    David Cesarini

  9. Center for Experimental Social Science, New York University, New York, NY, USA

    David Cesarini

  10. Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA

    Daniel J. Benjamin

  11. Department of Economics, University of Southern California, Los Angeles, CA, USA

    Daniel J. Benjamin

  12. Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia

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

  13. Faculty of Veterinary and Agricultural Science, University of Melbourne, Melbourne, Victoria, Australia

    Michael E. Goddard

  14. Biosciences Research Division, Department of Economic Development, Jobs, Transport and Resources Government of Victoria, Bundoora, Victoria, Australia

    Michael E. Goddard

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  1. Loic Yengo
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Contributions

P.M.V., L.Y., M.R.R., J.Y. and M.E.G. conceived and designed the study. L.Y., M.T. and N.R.W. curated the summary statistics. L.Y. and P.M.V. derived the theory. Y.Y., M.T., J.G., K.E.K. and L.Y. performed the mate pairs analyses. M.C.K., P.T., D.J.B. and D.C. helped to develop the methodology and interpret the results. P.M.V., N.R.W., M.R.R. and L.Y. performed the sibling pairs analyses. K.E.K. and L.Y. performed quality control of the UKB data. L.Y. and M.R.R. performed statistical analyses and simulations. L.Y. and P.M.V. wrote the manuscript with the participation of all authors.

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Correspondence to Loic Yengo or Peter M. Visscher.

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Yengo, L., Robinson, M.R., Keller, M.C. et al. Imprint of assortative mating on the human genome. Nat Hum Behav 2, 948–954 (2018). https://doi.org/10.1038/s41562-018-0476-3

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