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Measuring selection in contemporary human populations

An Erratum to this article was published on 30 November 2010

This article has been updated

Key Points

  • We can estimate selection on traits using a measure of fitness, reliable measurements of multiple traits and the phenotypic and genetic correlations among these traits.

  • This information is available for contemporary human populations in more than 19 existing databases that cover from approximately 1,000 to more than 8 million individuals.

  • A trait under selection to decrease could respond positively, negatively or not at all, depending on the quantitative contributions of phenotypic and genetic correlations.

  • To account for traits that change with age and are influenced by the environment, traits can be expressed relative to those of other individuals of similar age and environment.

  • Phenotypic evolution can be expressed in terms of phenotypic (P) and genetic (G) variance–covariances matrices and a vector of selection gradients, β.

  • Most human traits have measurable heritability and will respond to selection if they are not constrained by phenotypic and genetic correlations with other traits.

  • Traits directly related to lifetime reproductive success tend to have lower heritabilities than traits that have a less direct relationship to fitness.

  • Selection is acting in some human populations to reduce age at first reproduction, to increase age at menopause in females and to reduce total blood cholesterol.

  • A major unresolved issue is how to deal with cultural evolution and gene–culture co-evolution.

  • One next step will be to determine whether phenotypic changes are tracked by changes in the allele frequencies of the genes associated with the traits.

Abstract

Are humans currently evolving? This question can be answered using data on lifetime reproductive success, multiple traits and genetic variation and covariation in those traits. Such data are available in existing long-term, multigeneration studies — both clinical and epidemiological — but they have not yet been widely used to address contemporary human evolution. Here we review methods to predict evolutionary change and attempts to measure selection and inheritance in humans. We also assemble examples of long-term studies in which additional measurements of evolution could be made. The evidence strongly suggests that we are evolving and that our nature is dynamic, not static.

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Figure 1: Heritabilities (h2) of human traits.

Change history

  • 10 August 2010

    In the version of this article initially published online, the e-mail address for Sean G. Byars was incorrect. This has now been corrected in all versions of the article.

References

  1. Barreiro, L. B. & Quintana-Murci, L. From evolutionary genetics to human immunology: how selection shapes host defence genes. Nature Rev. Genet. 11, 17–30 (2010).

    Article  CAS  PubMed  Google Scholar 

  2. Bradley, B. J. Reconstructing phylogenies and phenotypes: a molecular view of human evolution. J. Anat. 212, 337–53 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Han, Y. et al. Evidence of positive selection on a class I ADH locus. Amer J. Hum. Genet. 80, 441–456 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  5. Tishkoff, S. A. et al. Convergent adaptation of human lactase persistence in Africa and Europe. Nature Genet. 39, 31–40 (2007). An excellent example of detecting signatures of selection in the human genome.

    Article  CAS  PubMed  Google Scholar 

  6. Perry, G. H. et al. Diet and the evolution of human amylase gene copy number variation. Nature Genet. 39, 1256–1260 (2007).

    Article  CAS  PubMed  Google Scholar 

  7. Cavalli-Sforza, L., Menozzi, P. & Piazza, A. The History and Geography of Human Genes. (Princeton University Press, Princeton, 1994).

    Google Scholar 

  8. Biswas, S. & Akey, J. Genomic insights into positive selection. Trends Genet. 22, 437–446 (2006).

    Article  CAS  PubMed  Google Scholar 

  9. Grossman, S. R. et al. A composite of multiple signals distinguishes causal variants in regions of positive selection. Science 12, 883–886 (2010).

    Article  CAS  Google Scholar 

  10. Novembre, J. & Di Rienzo, A. Spatial patterns of variation due to natural selection in humans. Nature Rev. Genet. 10, 745–755 (2009).

    Article  CAS  PubMed  Google Scholar 

  11. Sabeti, P. et al. Positive natural selection in the human lineage. Science 312, 1614–1620 (2006).

    Article  CAS  PubMed  Google Scholar 

  12. Byars, S. G., Ewbank, D., Govindaraju, D. R. & Stearns, S. C. Natural selection in a contemporary human population. Proc. Natl Acad. Sci. USA 107, 1787–1792 (2010). By detecting significant selection on height, weight, age at first birth and menopause, this paper illustrates in detail the methods discussed in this Review.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Castelli, W. P. & Anderson, K. A population at risk. Prevalence of high cholesterol levels in hypertensive patients in the Framingham Study. Am. J. Med. 80, 23–32 (1986).

    Article  CAS  PubMed  Google Scholar 

  14. Stearns, S. C., Nesse, R. M., Govindaraju, D. R. & Ellison, P. T. Evolutionary perspectives on health and medicine. Proc. Natl Acad. Sci. USA 107, 1691–1695 (2010). A recent overview of the diverse applications of evolutionary thought to issues of medical importance.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Boomsma, D. I. Twin registers in Europe: an overview. Twin Research 1, 34–51 (1998).

    Article  CAS  PubMed  Google Scholar 

  16. Pedersen, C. B., Gotzsche, H., Moller, J. O. & Mortensen, P. B. The Danish Civil Registration System — a cohort of eight million persons. Danish Med. Bull. 53, 441–449 (2006).

    PubMed  Google Scholar 

  17. Kaar, P., Jokela, J., Helle, T. & Kojola, I. Direct and correlative phenotypic selection on life-history traits in three pre-industrial human populations. Proc. R. Soc. Lond. B 263, 1475–1480 (1996).

    Article  CAS  Google Scholar 

  18. Charlesworth, B. Evolution in Age-Structured Populations 2nd edn (Cambridge University Press, Cambridge, 1994).

    Book  Google Scholar 

  19. McGraw, J. & Caswell, H. Estimation of individual fitness from life-history data. Am. Nat. 147, 47–64 (1996).

    Article  Google Scholar 

  20. Schonemann, P. On models and muddles of heritability. Genetica 99, 97–108 (1997).

    CAS  PubMed  Google Scholar 

  21. Stinchcombe, J. et al. Testing for environmentally induced bias in phenotypic estimates of natural selection: theory and practice. Am. Nat. 160, 511–523 (2002).

    Article  PubMed  Google Scholar 

  22. Coulson, T. & Tuljapurkar, S. The dynamics of a quantitative trait in an age-structured population living in a variable environment. Am. Nat. 172, 599–612 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Hastie, T. J. & Tibshirani, R. J. Generalized Additive Models. (Chapman and Hall, London, 1990).

    Google Scholar 

  24. Cleveland, W. S. Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Assoc. 74, 829–836 (1979).

    Article  Google Scholar 

  25. Lande, R. & Arnold, S. The measurement of selection on correlated characters Evolution 37, 1210–1226 (1983). A classic paper in which methods were presented that transformed evolutionary quantitative genetics.

    Article  PubMed  Google Scholar 

  26. Lande, R. Quantitative genetic-analysis of multivariate evolution, applied to brain–body size allometry. Evolution 33, 402–416 (1979).

    Article  PubMed  Google Scholar 

  27. Robertson, A. A mathematical model of the culling process in dairy cattle. Anim. Prod. 8, 95–108 (1966). A prescient contribution that anticipated both Lande and Arnold's approach and Price's covariance theory and provided the basis for much of selection theory in plant and animal breeding.

    Google Scholar 

  28. Blangero, J., Almasy, L., Dyer, T. & Peterson, C. Sequential oligogenic linkage analysis routings. Solar Version 1.4.1. SOLAR [online], http://solar.sfbrgenetics.org (1999).

    Google Scholar 

  29. Abecasis, G. R., Cherny, S. S., Cookson, W. O. & Cardon, L. R. Merlin — rapid analysis of dense genetic maps using sparse gene flow trees. Nature Genet. 30, 97–101 (2002).

    Article  CAS  PubMed  Google Scholar 

  30. Neumaier, A. & Groeneveld, E. Restricted maximum likelihood estimation of covariances in sparse linear models. Genet. Selection Evolution 30, 3–26 (1998).

    Article  Google Scholar 

  31. Pettay, J., Kruuk, L., Jokela, J. & Lummaa, V. Heritability and genetic constraints of life-history trait evolution in preindustrial humans. Proc. Natl Acad. Sci. USA 102, 2838–2843 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Blows, M. W. A tale of two matrices: multivariate approaches in evolutionary biology. J. Evol. Biol. 20, 1–8 (2007).

    Article  CAS  PubMed  Google Scholar 

  33. Brodie, E., Moore, A. & Janzen, F. Visualizing and quantifying natural selection. Trends Ecol. Evol. 10, 313–318 (1995).

    Article  PubMed  Google Scholar 

  34. Janzen, F. & Stern, H. Logistic regression for empirical studies of multivariate selection. Evolution 52, 1564–1571 (1998).

    Article  PubMed  Google Scholar 

  35. Kruuk, L. E. B. & Garant, D. A wake-up call for studies of natural selection? J. Evol. Biol. 20, 30–33 (2007).

    Article  CAS  PubMed  Google Scholar 

  36. Ovaskainen, O., Cano, J. M. & Merila, J. A Bayesian framework for comparative quantitative genetics. Proc. R. Soc. Lond. B 275, 669–678 (2008).

    Google Scholar 

  37. Schluter, D. Estimating the form of natural selection on a quantitative trait Evolution 42, 849–861 (1988).

    Article  PubMed  Google Scholar 

  38. Stinchcombe, J. R., Agrawal, A. F., Hohenlohe, P. A., Arnold, S. J. & Blows, M. W. Estimating nonlinear selection gradients using quadratic regression coefficients: double or nothing? Evolution 62, 2435–2440 (2008).

    Article  PubMed  Google Scholar 

  39. Blows, M. W. & Brooks, R. Measuring nonlinear selection. Am. Nat. 162, 815–820 (2003).

    Article  PubMed  Google Scholar 

  40. Schluter, D. & Nychka, D. Exploring fitness surfaces. Am. Nat. 143, 597–616 (1994).

    Article  Google Scholar 

  41. Kingsolver, J. G. et al. The strength of phenotypic selection in natural populations. Am. Nat. 157, 245–261 (2001). A comprehensive review of measurements of natural selection in a broad range of plants and animals.

    Article  CAS  PubMed  Google Scholar 

  42. Stearns, S. & Koella, J. The evolution of phenotypic plasticity in life history traits — predictions of reaction norms for age and size at maturity. Evolution 40, 893–913 (1986).

    PubMed  Google Scholar 

  43. Sear, R., Allal, N., Mace, R. & Mcgregor, I. Height and reproductive success among Gambian women. Am. J. Hum. Biol. 16, 223–223 (2004).

    Google Scholar 

  44. Mueller, U. & Mazur, A. Evidence of unconstrained directional selection for male tallness. Behav. Ecol. Sociobiol. 50, 302–311 (2001).

    Article  Google Scholar 

  45. Pawlowski, B., Dunbar, R. I. M. & Lipowicz, A. Evolutionary fitness — tall men have more reproductive success. Nature 403, 156–156 (2000).

    Article  CAS  PubMed  Google Scholar 

  46. Sear, R. Height and reproductive success — how a Gambian population compares with the West. Hum. Nat. 17, 405–418 (2006).

    Article  PubMed  Google Scholar 

  47. Nettle, D. Height and reproductive success in a cohort of British men. Hum. Nat. 13, 473–491 (2002).

    Article  PubMed  Google Scholar 

  48. Silventoinen, K., Lahelma, E. & Rahkonen, O. Social background, adult body-height and health. Int. J. Epidemiol. 28, 911–918 (1999).

    Article  CAS  PubMed  Google Scholar 

  49. Heliovaara, M., Makela, M., Knekt, P., Impivaara, O. & Aromaa, A. Determinants of sciatica and low-back-pain. Spine 16, 608–614 (1991).

    Article  CAS  PubMed  Google Scholar 

  50. Michaud, D. S. et al. Physical activity, obesity, height, and the risk of pancreatic cancer. J. Am. Med. Assoc. 286, 921–929 (2001).

    Article  CAS  Google Scholar 

  51. Shors, A. R., Solomon, C., McTiernan, A. & White, E. Melanoma risk in relation to height, weight, and exercise (United States). Cancer Causes Control 12, 599–606 (2001).

    Article  CAS  PubMed  Google Scholar 

  52. Ramsden, E. A differential paradox: the controversy surrounding the Scottish mental surveys of intelligence and family size. J. Hist. Behav. Sci. 43, 109–134 (2007). A superb study in the recent history of science. This paper tells the intriguing story of how intelligence was predicted to decline under selection but in fact increased.

    Article  PubMed  Google Scholar 

  53. Visscher, P. M., Hill, W. G. & Wray, N. R. Heritability in the genomics era — concepts and misconceptions. Nature Rev. Genet. 9, 255–266 (2008). A masterly introduction to the heritability concept.

    Article  CAS  PubMed  Google Scholar 

  54. Mousseau, T. & Roff, D. Natural selection and the heritability of fitness components. Heredity 59, 181–197 (1987).

    Article  PubMed  Google Scholar 

  55. Stearns, S., De Jong, G. & Newman, B. The effects of phenotypic plasticity on genetic correlations. Trends Ecol. Evol. 6, 122–126 (1991).

    Article  CAS  PubMed  Google Scholar 

  56. Hill, W. G. Genetics. A century of corn selection. Science 307, 683–4 (2005).

    Article  CAS  PubMed  Google Scholar 

  57. Laurie, C. C. et al. The genetic architecture of response to long-term artificial selection for oil concentration in the maize kernel. Genetics 168, 2141–55 (2004). A thorough test of the additivity assumption of quantitative genetics. It demonstrates that responses to selection for oil content in corn continued for over 100 generations despite small population sizes.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Tait, L. Has the law of natural selection by survival of the fittest failed in the case of man? Dublin Quart J. Med. Sci. 47, 102–113 (1869).

    Article  Google Scholar 

  59. Groth, P. & Weiss, B. Phenotype data: A neglected resource in biomedical research? Curr. Bioinform 1, 347–358 (2006).

    Article  CAS  Google Scholar 

  60. Houle, D. Numbering the hairs on our heads: the shared challenge and promise of phenomics. Proc. Natl Acad. Sci. USA 107, 1793–1799 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Denny, J. C. et al. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations. Bioinformatics 26, 1205–1210 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. van Driel, M., Bruggeman, J., Vriend, G., Brunner, H. & Leunissen, J. A text-mining analysis of the human phenome. Eur. J. Hum. Genet. 14, 535–542 (2006).

    Article  CAS  PubMed  Google Scholar 

  63. Oti, M., Huynen, M. A. & Brunner, H. G. The biological coherence of human phenome databases. Am. J. Hum. Genet. 85, 801–808 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Bilder, R. M. Phenomics: Building scaffolds for biological hypotheses in the post-genomic era. Biol. Psychiatry 63, 439–440 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Bouchard, T. J. Genetic influence on human intelligence (Spearman's g): how much? Ann. Hum. Biol. 36, 527–544 (2009).

    Article  PubMed  Google Scholar 

  66. Austin, M. A. et al. Genetics of LDL subclass phenotypes in women twins — concordance, heritability, and commingling analysis. Arterioscler. Thromb. 13, 687–695 (1993).

    Article  CAS  PubMed  Google Scholar 

  67. Baare, W. F. C. et al. Quantitative genetic modeling of variation in human brain morphology. Cereb. Cortex 11, 816–824 (2001).

    Article  CAS  PubMed  Google Scholar 

  68. Beardsall, K. et al. Heritability of childhood weight gain from birth and risk markers for adult metabolic disease in prepubertal twins. J. Clin. Endocrinol. Metab. 94, 3708–3713 (2009).

    Article  CAS  PubMed  Google Scholar 

  69. Bella, J. N. et al. Genetic influences on aortic root size in American Indians — the Strong Heart Study. Arterioscler. Thromb. Vasc. Biol. 22, 1008–1011 (2002).

    Article  CAS  PubMed  Google Scholar 

  70. Brown, W. M. et al. Age-stratified heritability estimation in the Framingham Heart Study families. BMC Genet. 4 (Suppl. 1), 32 (2003).

    Article  Google Scholar 

  71. Busjahn, A. et al. β-2 adrenergic receptor gene variations, blood pressure, and heart size in normal twins. Hypertension 35, 555–560 (2000).

    Article  CAS  PubMed  Google Scholar 

  72. Byard, P. J., Poosha, D. V. R. & Satyanarayana, M. Genetic and environmental determinants of height and weight in families from Andhra Pradesh, India. Hum. Biol. 57, 621–633 (1985).

    CAS  PubMed  Google Scholar 

  73. Carmichael, C. M. & Mcgue, M. A cross-sectional examination of height, weight, and body-mass index in adult twins. J. Gerontol. A Biol. Sci. Med. Sci. 50, B237–B244 (1995).

    Article  CAS  PubMed  Google Scholar 

  74. Clark, P. J. The heritability of certain anthropometric characters as ascertained from measurements of twins. Am. J. Hum. Genet. 8, 49–54 (1956).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Dahlberg, G. Twin Births and Twins from a Hereditary Point of View (Tidens Tryckeri, Stockholm, 1926).

    Google Scholar 

  76. Darocha, F. J., Salzano, F. M., Callegar, S. M. & Pena, H. F. New studies on heritability of anthropometric characteristics as ascertained from twins. Acta Genet. Med. Gemellol. 21, 125–134 (1972).

    Article  Google Scholar 

  77. de Bruin, J. P. et al. The role of genetic factors in age at natural menopause. Hum. Reprod. 16, 2014–2018 (2001).

    Article  CAS  PubMed  Google Scholar 

  78. de Oliveira, C. M., Pereira, A. C., de Andrade, M., Soler, J. M. & Krieger, J. E. Heritability of cardiovascular risk factors in a Brazilian population: Baependi Heart Study. BMC Med. Genet. 9, 1–8 (2008).

    Article  Google Scholar 

  79. Decastro, J. M. Genetic influences on daily intake and meal patterns of humans. Physiol. Behav. 53, 777–782 (1993).

    Article  CAS  Google Scholar 

  80. Deng, H. W. et al. A whole-genome linkage scan suggests several genomic regions potentially containing QTLs underlying the variation of stature. Am. J. Med. Genet. 113, 29–39 (2002).

    Article  PubMed  Google Scholar 

  81. Fischbein, S. Intra-pair similarity in physical growth of monozygotic and of dizygotic twins during puberty. Ann. Hum. Biol. 4, 417–430 (1977).

    Article  CAS  PubMed  Google Scholar 

  82. Furusho, T. On manifestation of genotypes responsible for stature. Hum. Biol. 40, 437–455 (1968).

    CAS  PubMed  Google Scholar 

  83. Garner, C. et al. Genetic and environmental influences on left ventricular mass — a family study. Hypertension 36, 740–746 (2000).

    Article  CAS  PubMed  Google Scholar 

  84. Hammond, C. J., Snieder, H., Spector, T. D. & Gilbert, C. E. Factors affecting pupil size after dilatation: the Twin Eye Study. Br. J. Ophthalmol 84, 1173–1176 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Hansen, P. S. et al. Genetic and environmental causes of individual differences in thyroid size: a study of healthy Danish twins. J. Clin. Endocrinol. Metab. 89, 2071–2077 (2004).

    Article  CAS  PubMed  Google Scholar 

  86. Harrap, S. B., Stebbing, M., Hopper, J. L., Hoang, H. N. & Giles, G. G. Familial patterns of covariation for cardiovascular risk factors in adults — the Victorian Family Heart Study. Am. J. Epidemiol. 152, 704–715 (2000).

    Article  CAS  PubMed  Google Scholar 

  87. Hauspie, R. C., Bergman, P., Bielicki, T. & Susanne, C. Genetic variance in the pattern of the growth curve for height — a longitudinal analysis of male twins. Annals Hum. Biol. 21, 347–362 (1994).

    Article  CAS  Google Scholar 

  88. Hawk, L. J. & Brook, C. G. D. Family resemblances of height, weight, and body fatness. Arch. Dis. Child. 54, 877–879 (1979).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Hewitt, J. K., Stunkard, A. J., Carroll, D., Sims, J. & Turner, J. R. A twin study approach towards understanding genetic contributions to body size and metabolic-rate. Acta Genet. Med. Gemellol. 40, 133–146 (1991).

    Article  CAS  PubMed  Google Scholar 

  90. Hunter, D. J., Snieder, H., March, L. & Sambrook, P. N. Genetic contribution to cartilage volume in women: a classical twin study. Rheumatology 42, 1495–1500 (2003).

    Article  PubMed  Google Scholar 

  91. Kohler, H. P. & Christensen, K. in Genetic Influences on Human Fertility and Sexuality (eds Rodgers, J. L., Rower, D. C. & Miller, W. B.) 67–84 (Kluwer, Boston, 2000).

    Book  Google Scholar 

  92. Kohler, H. P., Rodgers, J. L. & Christensen, K. Between nurture and nature: the shifting determinants of female fertility in Danish twin cohorts. Soc. Biol. 49, 218–248 (2002).

    PubMed  Google Scholar 

  93. Kosova, G., Abney, M. & Ober, C. Heritability of reproductive fitness traits in a human population. Proc. Natl Acad. Sci. USA 107, 1772–1778 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Liu, X. Q., Hanley, A. J. G. & Paterson, A. D. Genetic analysis of common factors underlying cardiovascular disease-related traits. BMC Genet. 4, S56 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Peccei, J. S. Genetic correlation between the ages of menarche and menopause. Hum. Nature 11, 43–63 (2000).

    Article  CAS  Google Scholar 

  96. Pilia, G. et al. Heritability of cardiovascular and personality traits in 6,148 Sardinians. PLoS Genet. 2, e132 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  97. Wang, Z. Q., Ouyang, Z., Wang, D. M. & Tang, X. L. Heritability of blood-pressure in 7-year-old to 12-year-old Chinese twins, with special reference to body size effects. Genet. Epidemiol. 7, 447–452 (1990).

    Article  CAS  PubMed  Google Scholar 

  98. Jokela, M. Physical attractiveness and reproductive success in humans: evidence from the late 20th century United States. Evol. Hum. Behav. 30, 342–350 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  99. Wollmering, E. Wisconsin Longitudinal Study Handbook (12.10.07) (University of Wisconsin—Madison, Madison, 2007).

    Google Scholar 

  100. Nettle, D. Women's height, reproductive success and the evolution of sexual dimorphism in modern humans. Proc. R. Soc. Lond. B 269, 1919–1923 (2002).

    Article  Google Scholar 

  101. Barker, D. J. P., Osmond, C., Forsén, T. J., Kajantie, E. & Eriksson, J. G. Trajectories of growth among children who have coronary events as adults. N. Engl. J. Med. 353, 1802–1809 (2005).

    Article  CAS  PubMed  Google Scholar 

  102. Pesonen, A.-K. et al. Reproductive traits following a parent–child separation trauma during childhood: a natural experiment during World War II. Am. J. Hum. Biol. 20, 345–351 (2008).

    Article  PubMed  Google Scholar 

  103. Abney, M., McPeek, M. S. & Ober, C. Estimation of variance components of quantitative traits in inbred populations. Am. J. Hum. Genet. 66, 629–650 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Glasson, E. J. et al. Perinatal factors and the development of autism — a population study. Arch. Gen. Psychiatry 61, 618–627 (2004).

    Article  PubMed  Google Scholar 

  105. Mitchell, B. D. et al. Genetic and environmental contributions to cardiovascular risk factors in Mexican Americans. The San Antonio Family Heart Study. Circulation 94, 2159–2170 (1996).

    Article  CAS  PubMed  Google Scholar 

  106. Niazi, T. N., Cannon-Albright, L. A. & Couldwell, W. T. Utah Population Database: a tool to study the hereditary element of nonsyndromic neurosurgical diseases. Neurosurg. Focus 28, E1 (2010).

    Article  PubMed  Google Scholar 

  107. Higgins, M. et al. NHLBI Family Heart Study: objectives and design. Am. J. Epidemiol. 143, 1219–1228 (1996).

    Article  CAS  PubMed  Google Scholar 

  108. Siest, G. et al. Objectives, design and recruitment of a familial and longitudinal cohort for studying gene–environment interactions in the field of cardiovascular risk: the Stanislas cohort. Clin. Chem. Lab. Med. 36, 35–42 (1998).

    Article  CAS  PubMed  Google Scholar 

  109. Helle, S., Lummaa, V. & Jokela, J. Are reproductive and somatic senescence coupled in humans? Late, but not early, reproduction correlated with longevity in historical Sami women. Proc. R. Soc. Lond. B 272, 29–37 (2005).

    Google Scholar 

  110. Helle, S. A tradeoff between reproduction and growth in contemporary Finnish women. Evol. Hum. Behav. 29, 189–195 (2008).

    Article  Google Scholar 

  111. Kirk, K. M. et al. Natural selection and quantitative genetics of life-history traits in Western women: a twin study. Evolution 55, 423–435 (2001).

    Article  CAS  PubMed  Google Scholar 

  112. Weeden, J., Abrams, M. J., Green, M. C. & Sabini, J. Do high-status people really have fewer children? Education, income, and fertility in the contemporary US. Hum. Nat. 17, 377–392 (2006).

    Article  PubMed  Google Scholar 

  113. Kaar, P., Jokela, J., Helle, T. & Kojola, I. Direct and correlative phenotypic selection on life-history traits in three pre-industrial human populations. Proc R. Soc. Lond. B 263, 1475–1480 (1996).

    Article  CAS  Google Scholar 

  114. Mealey, L. The relationship between social-status and biological success — a case-study of the Mormon religious hierarchy. Ethol. Sociobiol. 6, 249–257 (1985).

    Article  Google Scholar 

  115. Bailey, S. M. & Garn, S. M. Socioeconomic interactions with physique and fertility. Hum. Biol. 51, 317–333 (1979).

    CAS  PubMed  Google Scholar 

  116. Vetta, A. Fertility, physique, and intensity of selection. Hum. Biol. 47, 283–293 (1975).

    CAS  PubMed  Google Scholar 

  117. Pawlowski, B., Dunbar, R. & Lipowicz, A. Evolutionary fitness — tall men have more reproductive success. Nature 403, 156–156 (2000).

    Article  CAS  PubMed  Google Scholar 

  118. Fieder, M. & Huber, S. The effects of sex and childlessness on the association between status and reproductive output in modern society. Evol. Hum. Behav. 28, 392–398 (2007).

    Article  Google Scholar 

  119. Hopcroft, R. L. Sex, status, and reproductive success in the contemporary United States. Evol. Hum. Behav. 27, 104–120 (2006).

    Article  Google Scholar 

  120. Fieder, M. & Huber, S. The effects of sex and childlessness on the association between status and reproductive output in modern society. Evol. Hum. Behav. 28, 392–398 (2007).

    Article  Google Scholar 

  121. Nettle, D. & Pollet, T. V. Natural selection on male wealth in humans. Am. Nat. 172, 658–666 (2008).

    Article  PubMed  Google Scholar 

  122. Bean, F. D. & Wood, C. H. Ethnic variations in relationship between income and fertility. Demography 11, 629–640 (1974).

    Article  CAS  PubMed  Google Scholar 

  123. Mulder, M. B. On cultural and reproductive success — Kipsigis evidence. Am. Anthropol. 89, 617–634 (1987).

    Article  Google Scholar 

  124. Low, B. S. Occupational status, landownership, and reproductive behavior in 19th-century Sweden: Tuna Parish. Am. Anthropol. 92, 457–468 (1990).

    Article  Google Scholar 

  125. Wiessner, P. Hunting, healing, and hxaro exchange — a long-term perspective on!Kung (Ju/'hoansi) large-game hunting. Evol. Hum. Behav. 23, 407–436 (2002).

    Article  Google Scholar 

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Acknowledgements

D.R.G. thanks C. Lee for hospitality and facilities. The authors thank the National Evolutionary Synthesis Center for supporting their collaboration and B.P. Stearns for detailed, constructive comments.

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FURTHER INFORMATION

Stephen C. Stearns' homepage

Atherosclerosis Risk in Communities Study (ARIC)

Coronary Artery Risk Development in Young Adults (CARDIA)

Danish National Board of Health

Finnish National Institute for Health and Welfare

Framingham Heart Study

Honolulu Heart Program

Jackson Heart Study

MERLIN

Norwegian Board of Health

Nurses' Health Study

Rotterdam Study

SOLAR

Statistics Iceland

Statistics Norway

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VCE

Wisconsin Longitudinal Study

Women's Health Study

Glossary

Longitudinal study

An observational study in which individuals are followed for a long period of time, often many decades, and in which the same traits are measured repeatedly.

Pleiotropy

The action of a single gene on two or more distinct phenotypic characters.

Linkage disequilibrium

A measure of whether alleles at two loci coexist in a population in a nonrandom fashion; one common cause is that the loci are neighbours on the same chromosome and therefore do not recombine.

Directional selection

Natural selection that favours values of a quantitative trait at one extreme of the population distribution. In positive directional selection, natural selection favours values of a quantitative trait at the upper extreme of the population distribution.

Generalized additive model

A statistical model that blends properties of generalized linear models with additive models (parametric or non-parametric) and is often used to estimate smoothing functions for scatter plots.

Heritability

The proportion of the total phenotypic variation in a trait that can be attributed to genetic effects.

Selection differential

The average superiority of the selected parents; it is expressed as the mean phenotypic value of the individuals selected as parents and expressed as a deviation from the population mean.

Additive genetic variance

The part of the total genetic variation that is due to the main (or additive) effects of alleles on a phenotype. The additive variance determines the response to selection.

Narrow-sense heritability

The proportion of phenotypic variation that can be accounted for by additive genetic effects. By contrast, broad-sense heritability includes effects of interactions among genes that are caused by dominance and epistasis.

Quadratic regression

A quadratic regression estimates the parameters of an equation for a parabola that best fits the data. Here it is incorporated in a larger model that also has linear elements.

Stabilizing selection

Natural selection that favours intermediate values of a quantitative trait.

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Stearns, S., Byars, S., Govindaraju, D. et al. Measuring selection in contemporary human populations. Nat Rev Genet 11, 611–622 (2010). https://doi.org/10.1038/nrg2831

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