@article{7715,
  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.},
  author       = {Yengo, Loic and Robinson, Matthew Richard and Keller, Matthew C. and Kemper, Kathryn E. and Yang, Yuanhao and Trzaskowski, Maciej and Gratten, Jacob and Turley, Patrick and Cesarini, David and Benjamin, Daniel J. and Wray, Naomi R. and Goddard, Michael E. and Yang, Jian and Visscher, Peter M.},
  issn         = {2397-3374},
  journal      = {Nature Human Behaviour},
  number       = {12},
  pages        = {948--954},
  publisher    = {Springer Nature},
  title        = {{Imprint of assortative mating on the human genome}},
  doi          = {10.1038/s41562-018-0476-3},
  volume       = {2},
  year         = {2018},
}

@article{7728,
  abstract     = {Meta-analyses of genome-wide association studies, which dominate genetic discovery, are based on data from diverse historical time periods and populations. Genetic scores derived from genome-wide association studies explain only a fraction of the heritability estimates obtained from whole-genome studies on single populations, known as the ‘hidden heritability’ puzzle. Using seven sampling populations (n = 35,062), we test whether hidden heritability is attributed to heterogeneity across sampling populations and time, showing that estimates are substantially smaller across populations compared with within populations. We show that the hidden heritability varies substantially: from zero for height to 20% for body mass index, 37% for education, 40% for age at first birth and up to 75% for number of children. Simulations demonstrate that our results are more likely to reflect heterogeneity in phenotypic measurement or gene–environment interactions than genetic heterogeneity. These findings have substantial implications for genetic discovery, suggesting that large homogenous datasets are required for behavioural phenotypes and that gene–environment interaction may be a central challenge for genetic discovery.},
  author       = {Tropf, Felix C. and Lee, S. Hong and Verweij, Renske M. and Stulp, Gert and van der Most, Peter J. and de Vlaming, Ronald and Bakshi, Andrew and Briley, Daniel A. and Rahal, Charles and Hellpap, Robert and Iliadou, Anastasia N. and Esko, Tõnu and Metspalu, Andres and Medland, Sarah E. and Martin, Nicholas G. and Barban, Nicola and Snieder, Harold and Robinson, Matthew Richard and Mills, Melinda C.},
  issn         = {2397-3374},
  journal      = {Nature Human Behaviour},
  number       = {10},
  pages        = {757--765},
  publisher    = {Springer Nature},
  title        = {{Hidden heritability due to heterogeneity across seven populations}},
  doi          = {10.1038/s41562-017-0195-1},
  volume       = {1},
  year         = {2017},
}