Using genetics for social science

Abstract

Social science genetics is concerned with understanding whether, how and why genetic differences between human beings are linked to differences in behaviours and socioeconomic outcomes. Our review discusses the goals, methods, challenges and implications of this research endeavour. We survey how the recent developments in genetics are beginning to provide social scientists with a powerful new toolbox they can use to better understand environmental effects, and we illustrate this with several substantive examples. Furthermore, we examine how medical research can benefit from genetic insights into social-scientific outcomes and vice versa. Finally, we discuss the ethical challenges of this work and clarify several common misunderstandings and misinterpretations of genetic research on individual differences.

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: Genetic correlations of educational attainment with traits across the entire lifespan.
Fig. 2: The influence of GWAS sample size on the accuracy of polygenic scores for two genetically complex traits with assumed SNP heritability of 20% and 40%.
Fig. 3: The relationship between a polygenic score for educational attainment and actual years of schooling in the Health and Retirement Study.
Fig. 4: How medical science can benefit from social science genetics.

Data availability

The genetic correlations reported in Fig. 1 are based on publicly available GWAS summary statistics on LDHub (http://ldsc.broadinstitute.org/ldhub/). The Health and Retirement Study data in Fig. 3 can be accessed via dbGaP and the University of Michigan.

References

  1. 1.

    Definition of social science. Merriam Webster Dictionary https://www.merriam-webster.com/dictionary/social%20science (Accessed 1 November 2018).

  2. 2.

    Turkheimer, E. Three laws of behavior genetics and what they mean. Curr. Dir. Psychol. Sci. 9, 160–164 (2000).

    Article  Google Scholar 

  3. 3.

    Polderman, T. J. C. et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47, 702–709 (2015).

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Benjamin, D. J. et al. The promises and pitfalls of genoeconomics. Annu. Rev. Econ. 4, 627–662 (2012).

    Article  Google Scholar 

  5. 5.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47D1, D1005–D1012 (2019).

  7. 7.

    Freese, J. The arrival of social science genomics. Contemp. Sociol. 47, 524–536 (2018).

    Article  Google Scholar 

  8. 8.

    Comfort, N. Nature still battles nurture in the haunting world of social genomics. Nature 553, 278–280 (2018).

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Turkheimer, E. & Paige Harden, K. Behavior Genetic Research Methods. in Handbook of Research Methods in Social and Personality Psychology 159–187 (Cambridge University Press, 2014).

  10. 10.

    Kong, A. et al. The nature of nurture: Effects of parental genotypes. Science 359, 424–428 (2018).

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Koellinger, P. D. & Harden, K. P. Using nature to understand nurture: Genetic associations show how parenting matters for children’s education. Science 359, 386–387 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Servick, K. Can 23andMe have it all? Science 349, 1472–1474, 1476–1477 (2015).

  14. 14.

    Duncan, L. E., Pollastri, A. R. & Smoller, J. W. Mind the gap: why many geneticists and psychological scientists have discrepant views about gene-environment interaction (G×E) research. Am. Psychol. 69, 249–268 (2014).

    PubMed  Article  Google Scholar 

  15. 15.

    Reich, D. E. et al. Linkage disequilibrium in the human genome. Nature 411, 199–204 (2001).

    CAS  PubMed  Article  Google Scholar 

  16. 16.

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

    PubMed  Article  CAS  Google Scholar 

  17. 17.

    Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Visscher, P. M., Brown, M. A., McCarthy, M. I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Chabris, C. F., Lee, J. J., Cesarini, D., Benjamin, D. J. & Laibson, D. I. The fourth law of behavior genetics. Curr. Dir. Psychol. Sci. 24, 304–312 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    Rapid GWAS of thousands of phenotypes for 337,000 samples in the UK Biobank. Neale Lab http://www.nealelab.is/blog/2017/7/19/rapid-gwas-of-thousands-of-phenotypes-for-337000-samples-in-the-uk-biobank (Accessed 6 November 2018).

  21. 21.

    Kyoko Watanabe, D.P. Atlas of GWAS Summary Statistics. GWAS Atlas (2017). http://atlas.ctglab.nl/ (Accessed 6 November 2019).

  22. 22.

    Global Biobank Engine. https://biobankengine.stanford.edu/ (Accessed 6 November 2019).

  23. 23.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    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  Article  Google Scholar 

  25. 25.

    Berg, J. J. et al. Reduced signal for polygenic adaptation of height in UK Biobank. eLife 8, e39725 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Haworth, S. et al. Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis. Nat. Commun. 10, 333 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  27. 27.

    Lawson, D. J. et al. Is population structure in the genetic biobank era irrelevant, a challenge, or an opportunity? Hum. Genet. 139, 23–41 (2020).

    PubMed  Article  Google Scholar 

  28. 28.

    Loh, P.-R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Yang, J., Zaitlen, N. A., Goddard, M. E., Visscher, P. M. & Price, A. L. Advantages and pitfalls in the application of mixed-model association methods. Nat. Genet. 46, 100–106 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  30. 30.

    Davies, N. M. et al. Within family Mendelian randomization studies. Hum. Mol. Genet. 28R2 R170–R179, https://doi.org/10.1093/hmg/ddz204 (2019).

  31. 31.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Lee, J. J., McGue, M., Iacono, W. G. & Chow, C. C. The accuracy of LD Score regression as an estimator of confounding and genetic correlations in genome-wide association studies. Genet. Epidemiol. 42, 783–795 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017).

    CAS  PubMed  Article  Google Scholar 

  34. 34.

    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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Gage, S. H., Davey Smith, G., Ware, J. J., Flint, J. & Munafò, M. R. G = E: what GWAS can tell us about the environment. PLoS Genet. 12, e1005765 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  36. 36.

    Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229–237 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Grotzinger, A. D. et al. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat. Hum. Behav. 3, 513–525 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Baselmans, B. M. L. et al. Multivariate genome-wide analyses of the well-being spectrum. Nat. Genet. 51, 445–451 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  39. 39.

    Loehlin, J. C. The Cholesky approach: A cautionary note. Behav. Genet. 26, 65–69 (1996).

    Article  Google Scholar 

  40. 40.

    Torkamani, A., Wineinger, N. E. & Topol, E. J. The personal and clinical utility of polygenic risk scores. Nat. Rev. Genet. 19, 581–590 (2018).

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    Dudbridge, F. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 9, e1003348 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Wray, N. R. et al. Research review: Polygenic methods and their application to psychiatric traits. J. Child Psychol. Psychiatry 55, 1068–1087 (2014).

    PubMed  Article  Google Scholar 

  43. 43.

    de Vlaming, R. et al. Meta-GWAS accuracy and power (MetaGAP) calculator shows that hiding heritability is partially due to imperfect genetic correlations across studies. PLoS Genet. 13, e1006495 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  44. 44.

    Rietveld, C. A. et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340, 1467–1471 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    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  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Belsky, D. W. & Harden, K. P. Phenotypic annotation: Using polygenic scores to translate discoveries from genome-wide association studies from the top down. Curr. Dir. Psychol. Sci. 28, 82–90 (2019).

    Article  Google Scholar 

  49. 49.

    Barcellos, S. H., Carvalho, L. S. & Turley, P. Education can reduce health differences related to genetic risk of obesity. Proc. Natl Acad. Sci. USA 115, E9765–E9772 (2018).

    CAS  PubMed  Article  Google Scholar 

  50. 50.

    Bansal, V. et al. Genome-wide association study results for educational attainment aid in identifying genetic heterogeneity of schizophrenia. Nat. Commun. 9, 3078 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Belsky, D. W. et al. The genetics of success: how single-nucleotide polymorphisms associated with educational attainment relate to life-course development. Psychol. Sci. 27, 957–972 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Young, A. I., Benonisdottir, S., Przeworski, M. & Kong, A. Deconstructing the sources of genotype-phenotype associations in humans. Science 365, 1396–1400 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Mostafavi, H., Harpak, A., Conley, D., Pritchard, J. K. & Przeworski, M. Variable prediction accuracy of polygenic scores within an ancestry group. eLife 9, e48376 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    DiPrete, T. A., Burik, C. A. P. & Koellinger, P. D. Genetic instrumental variable regression: Explaining socioeconomic and health outcomes in nonexperimental data. Proc. Natl Acad. Sci. USA 115, E4970–E4979 (2018).

    CAS  PubMed  Article  Google Scholar 

  55. 55.

    Wertz, J. et al. Genetics of nurture: A test of the hypothesis that parents’ genetics predict their observed caregiving. Dev. Psychol. 55, 1461–1472 (2019).

    PubMed  Article  Google Scholar 

  56. 56.

    SSGAC Polygenic Score Data. Health and Retirement Study https://hrs.isr.umich.edu/news/ssgac-polygenic-score-data (Accessed 11 November 2019).

  57. 57.

    Young, A. I. et al. Relatedness disequilibrium regression estimates heritability without environmental bias. Nat. Genet. 50, 1304–1310 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  58. 58.

    Davey Smith, G. & Hemani, G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23R1 R89–R98 (2014).

  59. 59.

    Brumpton, B. et al. Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases. bioRxiv https://doi.org/10.1101/602516 (2019).

  60. 60.

    O’Connor, L. J. & Price, A. L. Distinguishing genetic correlation from causation across 52 diseases and complex traits. Nat. Genet. 50, 1728–1734 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  61. 61.

    Zhu, Z. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 224 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  62. 62.

    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  Article  Google Scholar 

  63. 63.

    Koellinger, P. D. & de Vlaming, R. Mendelian randomization: the challenge of unobserved environmental confounds. Int. J. Epidemiol. 48, 665–671 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  64. 64.

    Black, S. E., Devereux, P. J. & Salvanes, K. G. Why the apple doesn’t fall far: Understanding intergenerational transmission of human capital. Am. Econ. Rev. 95, 437–449 (2005).

    Article  Google Scholar 

  65. 65.

    Bates, T. C. et al. The nature of nurture: Using a virtual-parent design to test parenting effects on children’s educational attainment in genotyped families. Twin Res. Hum. Genet. 21, 73–83 (2018).

    PubMed  Article  Google Scholar 

  66. 66.

    Liu, H. Social and genetic pathways in multigenerational transmission of educational attainment. Am. Sociol. Rev. 83, 278–304 (2018).

    Article  Google Scholar 

  67. 67.

    Belsky, D. W. et al. Genetic analysis of social-class mobility in five longitudinal studies. Proc. Natl Acad. Sci. USA 115, E7275–E7284 (2018).

    CAS  PubMed  Article  Google Scholar 

  68. 68.

    Barth, D., Papageorge, N. W. & Thom, K. Genetic endowments and wealth inequality. J. Polit. Econ. 128, 1474–1522 (2020).

    Article  Google Scholar 

  69. 69.

    Holland, P. W. Statistics and causal inference. J. Am. Stat. Assoc. 81, 945–960 (1986).

    Article  Google Scholar 

  70. 70.

    Rodgers, J. & Kohler, H.-P. The Biodemography of Human Reproduction and Fertility. (Springer, 2002).

  71. 71.

    Barban, N. et al. Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nat. Genet. 48, 1462–1472 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  72. 72.

    Day, F. R. et al. Genomic analyses identify hundreds of variants associated with age at menarche and support a role for puberty timing in cancer risk. Nat. Genet. 49, 834–841 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  73. 73.

    Day, F. R. et al. Physical and neurobehavioral determinants of reproductive onset and success. Nat. Genet. 48, 617–623 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  74. 74.

    Mehta, D. et al. Evidence for genetic overlap between schizophrenia and age at first birth in women. JAMA Psychiatry 73, 497–505 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  75. 75.

    Ni, G., Gratten, J., Wray, N. R. & Lee, S. H., Schizophrenia Working Group of the Psychiatric Genomics Consortium. Age at first birth in women is genetically associated with increased risk of schizophrenia. Sci. Rep. 8, 10168 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  76. 76.

    Harden, K. P. et al. A behavior genetic investigation of adolescent motherhood and offspring mental health problems. J. Abnorm. Psychol. 116, 667–683 (2007).

    PubMed  PubMed Central  Article  Google Scholar 

  77. 77.

    Beauchamp, J. P. Genetic evidence for natural selection in humans in the contemporary United States. Proc. Natl Acad. Sci. USA 113, 7774–7779 (2016).

    CAS  PubMed  Article  Google Scholar 

  78. 78.

    Kong, A. et al. Selection against variants in the genome associated with educational attainment. Proc. Natl Acad. Sci. USA 114, E727–E732 (2017).

    CAS  PubMed  Article  Google Scholar 

  79. 79.

    Rodgers, J. L. et al. Education and cognitive ability as direct, mediating, or spurious influences on female age at first birth: behavior genetic models fit to Danish twin data. Am. J.Sociol. 114, S202–S232 (2008). Suppl.

    PubMed  PubMed Central  Article  Google Scholar 

  80. 80.

    Tropf, F. C. & Mandemakers, J. J. Is the association between education and fertility postponement causal? The role of family background factors. Demography 54, 71–91 (2017).

    PubMed  Article  Google Scholar 

  81. 81.

    Jocklin, V., McGue, M. & Lykken, D. T. Personality and divorce: a genetic analysis. J. Pers. Soc. Psychol. 71, 288–299 (1996).

    CAS  PubMed  Article  Google Scholar 

  82. 82.

    D’Onofrio, B. M., Eaves, L. J., Murrelle, L., Maes, H. H. & Spilka, B. Understanding biological and social influences on religious affiliation, attitudes, and behaviors: a behavior genetic perspective. J. Pers. 67, 953–984 (1999).

    PubMed  Article  Google Scholar 

  83. 83.

    Pilling, L. C. et al. Human longevity: 25 genetic loci associated in 389,166 UK biobank participants. Aging (Albany NY) 9, 2504–2520 (2017).

    CAS  Article  Google Scholar 

  84. 84.

    Abdellaoui, A. et al. Genetic correlates of social stratification in Great Britain. Nat. Hum. Behav. 3, 1332–1342 (2019).

    PubMed  Article  Google Scholar 

  85. 85.

    Hill, W. D. et al. Molecular genetic contributions to social deprivation and household income in UK Biobank. Curr. Biol. 26, 3083–3089 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  86. 86.

    Belsky, D. W. et al. Genetics and the geography of health, behaviour and attainment. Nat. Hum. Behav. 3, 576–586 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  87. 87.

    van der Sluis, S., Posthuma, D. & Dolan, C. V. A note on false positives and power in G × E modelling of twin data. Behav. Genet. 42, 170–186 (2012).

    PubMed  Article  Google Scholar 

  88. 88.

    Duncan, L. E. & Keller, M. C. A critical review of the first 10 years of candidate gene-by-environment interaction research in psychiatry. Am. J. Psychiatry 168, 1041–1049 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  89. 89.

    Ceci, S. J. & Papierno, P. B. The rhetoric and reality of gap closing: when the “have-nots” gain but the “haves” gain even more. Am. Psychol. 60, 149–160 (2005).

    PubMed  Article  Google Scholar 

  90. 90.

    Fletcher, J. M. Why have tobacco control policies stalled? Using genetic moderation to examine policy impacts. PLoS One 7, e50576 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  91. 91.

    Goldberger, A. S. Heritability. Economica 46, 327–347 (1979).

    Article  Google Scholar 

  92. 92.

    Jencks, C. Heredity, environment, and public policy reconsidered. Am. Sociol. Rev. 45, 723–736 (1980).

    CAS  PubMed  Article  Google Scholar 

  93. 93.

    Bronfenbrenner, U. & Ceci, S. J. Nature-nurture reconceptualized in developmental perspective: a bioecological model. Psychol. Rev. 101, 568–586 (1994).

    CAS  PubMed  Article  Google Scholar 

  94. 94.

    Dickens, W. T. & Flynn, J. R. Heritability estimates versus large environmental effects: the IQ paradox resolved. Psychol. Rev. 108, 346–369 (2001).

    CAS  PubMed  Article  Google Scholar 

  95. 95.

    Lykken, D. T., Bouchard, T. J. Jr., McGue, M. & Tellegen, A. Heritability of interests: a twin study. J. Appl. Psychol. 78, 649–661 (1993).

    CAS  PubMed  Article  Google Scholar 

  96. 96.

    Tucker-Drob, E. M. & Harden, K. P. Early childhood cognitive development and parental cognitive stimulation: evidence for reciprocal gene-environment transactions. Dev. Sci. 15, 250–259 (2012).

    PubMed  Article  Google Scholar 

  97. 97.

    Klahr, A. M., Thomas, K. M., Hopwood, C. J., Klump, K. L. & Burt, S. A. Evocative gene-environment correlation in the mother-child relationship: a twin study of interpersonal processes. Dev. Psychopathol. 25, 105–118 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  98. 98.

    Scarr, S. & McCartney, K. How people make their own environments: a theory of genotype greater than environment effects. Child Dev. 54, 424–435 (1983).

    CAS  PubMed  Google Scholar 

  99. 99.

    Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  100. 100.

    Wehby, G. L., Domingue, B. W. & Wolinsky, F. D. Genetic risks for chronic conditions: Implications for long-term wellbeing. J. Gerontol. A Biol. Sci. Med. Sci. 73, 477–483 (2018).

    PubMed  Article  Google Scholar 

  101. 101.

    Mallard, T. T., Harden, K. P. & Fromme, K. Genetic risk for schizophrenia is associated with substance use in emerging adulthood: an event-level polygenic prediction model. Psychol. Med. 49, 2027–2035 (2019).

    PubMed  Article  Google Scholar 

  102. 102.

    Okbay, A. et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 48, 624–633 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  103. 103.

    Wootton, R. E. et al. Evaluation of the causal effects between subjective wellbeing and cardiometabolic health: mendelian randomisation study. Br. Med. J. 362, k3788 (2018).

    Article  Google Scholar 

  104. 104.

    Kevles, D.J. In the Name of Eugenics: Genetics and the Uses of Human Heredity. (Harvard University Press, 1995).

  105. 105.

    Rawls, J. A Theory of Justice. (Belknap Press, 1999).

  106. 106.

    Herrnstein, R.J. & Murray, C. The Bell Curve: Intelligence and Class Structure in American Life (Free Press, 1996).

  107. 107.

    Jensen, A. How much can we boost IQ and scholastic achievement. Harv. Educ. Rev. 39, 1–123 (1969).

    Article  Google Scholar 

  108. 108.

    Yudell, M., Roberts, D., DeSalle, R. & Tishkoff, S. Taking race out of human genetics. Science 351, 564–565 (2016).

    CAS  PubMed  Article  Google Scholar 

  109. 109.

    Smedley, A. & Smedley, B. D. Race as biology is fiction, racism as a social problem is real: Anthropological and historical perspectives on the social construction of race. Am. Psychol. 60, 16–26 (2005).

    PubMed  Article  Google Scholar 

  110. 110.

    Henrich, J., Heine, S. J. & Norenzayan, A. The weirdest people in the world? Behav. Brain Sci. 33, 61–83 (2010). discussion 83–135.

    PubMed  Article  Google Scholar 

  111. 111.

    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  Article  Google Scholar 

  112. 112.

    Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  113. 113.

    Panofsky, A. & Donovan, J. Genetic ancestry testing among white nationalists: From identity repair to citizen science. Soc. Stud. Sci. https://doi.org/10.1177/0306312719861434 (2019).

  114. 114.

    Novembre, J. & Barton, N. H. Tread lightly interpreting polygenic tests of selection. Genetics 208, 1351–1355 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

We thank C. Burik for preparing Fig. 1 and the Social Science Genetic Association Consortium (https://www.thessgac.org/) for Fig. 3. P.D.K. was financially supported by an ERC consolidator grant (647648 EdGe). K.P.H. was supported by the Jacobs Foundation, the Templeton Foundation and Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) grants R01-HD083613 and 5-R24-HD042849 (to the Population Research Center at the University of Texas at Austin). The funders had no role in the conceptualization, preparation or decision to publish this work.

Author information

Affiliations

Authors

Corresponding authors

Correspondence to K. Paige Harden or Philipp D. Koellinger.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Editor recognition statement: Primary handling editor: Stavroula Kousta.

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Harden, K.P., Koellinger, P.D. Using genetics for social science. Nat Hum Behav (2020). https://doi.org/10.1038/s41562-020-0862-5

Download citation