Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Genetic predictors of educational attainment and intelligence test performance predict voter turnout

Nature Human Behaviour volume 5, pages 281–291 (2021)Cite this article

Subjects

Abstract

Although the genetic influence on voter turnout is substantial (typically 40–50%), the underlying mechanisms remain unclear. Across the social sciences, research suggests that ‘resources for politics’ (as indexed notably by educational attainment and intelligence test performance) constitute a central cluster of factors that predict electoral participation. Educational attainment and intelligence test performance are heritable. This suggests that the genotypes that enhance these phenotypes could positively predict turnout. To test this, we conduct a genome-wide complex trait analysis of individual-level turnout. We use two samples from the Danish iPSYCH case–cohort study, including a nationally representative sample as well as a sample of individuals who are particularly vulnerable to political alienation due to psychiatric conditions (n = 13,884 and n = 33,062, respectively). Using validated individual-level turnout data from the administrative records at the polling station, genetic correlations and Mendelian randomization, we show that there is a substantial genetic overlap between voter turnout and both educational attainment and intelligence test performance.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Heritability estimates for voting in municipal, European and national elections.
Fig. 2: Prevalence of voter turnout by polygenic score percentiles.
Fig. 3: Genetic correlations between voter turnout and non-voting traits that show significant genetic correlation after FDR adjustment in at least one of the turnout phenotypes in the two samples by election.

Similar content being viewed by others

Data availability

Owing to the consent structure of the iPSYCH and Danish law, the data cannot be shared publicly owing to its sensitive nature. The data can be accessed from secure servers59. For further information, please contact P.B.M. (pbm@econ.au.dk). For access to the data in Supplementary Table 5, please contact K.M.H. (kmh@ifs.ku.dk) as these register data also cannot be shared publicly.

Code availability

Code and scripts available from the corresponding author on request.

References

  1. Gerber, A. S., Green, D. P. & Shachar, R. Voting may be habit‐forming: evidence from a randomized field experiment. Am. J. Polit. Sci. 47, 540–550 (2003).

    Article  Google Scholar 

  2. Green, D. P. & Shachar, R. Habit formation and political behaviour: evidence of consuetude in voter turnout. Br. J. Polit. Sci. 30, 561–573 (2000).

    Article  Google Scholar 

  3. Plutzer, E. Becoming a habitual voter: inertia, resources, and growth in young adulthood. Am. Polit. Sci. Rev. 96, 41–56 (2002).

    Article  Google Scholar 

  4. Wolfinger, R. E. & Rosenstone, S. J. Who Votes? 22 (Yale Univ. Press, 1980).

  5. Dawes, C. et al. The relationship between genes, psychological traits, and political participation. Am. J. Polit. Sci. 58, 888–903 (2014).

    Article  Google Scholar 

  6. Dawes, C. T., Settle, J. E., Loewen, P. J., McGue, M. & Iacono, W. G. Genes, psychological traits and civic engagement. Philos. Trans. R. Soc. Lond. B 370, 20150015 (2015).

    Article  CAS  Google Scholar 

  7. Fowler, J. H., Baker, L. A. & Dawes, C. T. Genetic variation in political participation. Am. Polit. Sci. Rev. 102, 233–248 (2008).

    Article  Google Scholar 

  8. Klemmensen, R. et al. The genetics of political participation, civic duty, and political efficacy across cultures: Denmark and the United States. J. Theor. Polit. 24, 409–427 (2012).

    Article  Google Scholar 

  9. Loewen, P. J. & Dawes, C. T. The heritability of duty and voter turnout. Polit. Psychol. 33, 363–373 (2012).

    Article  Google Scholar 

  10. Weinschenk, A. C., Dawes, C. T., Kandler, C., Bell, E. & Riemann, R. New evidence on the link between genes, psychological traits, and political engagement. Polit. Life Sci. 38, 1–13 (2019).

    Article  Google Scholar 

  11. Mondak, J. J. Personality and the Foundations of Political Behavior (Cambridge Univ. Press, 2010).

  12. Mondak, J. J., Hibbing, M. V., Canache, D., Seligson, M. A. & Anderson, M. R. Personality and civic engagement: an integrative framework for the study of trait effects on political behavior. Am. Polit. Sci. Rev. 104, 85–110 (2010).

    Article  Google Scholar 

  13. Charney, E. Behavior genetics and postgenomics. Behav. Brain Sci. 35, 331–358 (2012).

    Article  PubMed  Google Scholar 

  14. Charney, E. & English, W. Genopolitics and the science of genetics. Am. Polit. Sci. Rev. 107, 382–395 (2013).

    Article  Google Scholar 

  15. Frey, B. S. Why do high income people participate more in politics? Publ. Choice 11, 101–105 (1971).

    Article  Google Scholar 

  16. Tollison, R. D. & Willett, T. D. Some simple economics of voting and not voting. Publ. Choice 6, 59–71 (1973).

    Article  Google Scholar 

  17. Verba, S., Schlozman, K. L. & Brady, H. E. Voice and Equality: Civic Voluntarism in American Politics 4 (Harvard Univ. Press, 1995).

  18. Brady, H. E., Verba, S. & Schlozman, K. L. Beyond SES: a resource model of political participation. Am. Polit. Sci. Rev. 89, 271–294 (1995).

    Article  Google Scholar 

  19. Hansen, K. M. Electoral turnouts: strong social norms of voting. in Oxford Handbook of Danish Politics (eds Christiansen, P. M. et al.) 76–87 (Oxford Univ. Press, 2020).

  20. Bouchard, T. J. & McGue, M. Familial studies of intelligence: a review. Science 212, 1055–1059 (1981).

    Article  PubMed  Google Scholar 

  21. Hill, W. D. et al. A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence. Mol. Psychiatry 24, 169–181 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Plomin R., DeFries, J. C., Knopik, V. S. & Neiderhiser, J. M. Behavioral Genetics 6th edn (Worth Publishers, 2013).

  23. Rowe, D. C., Jacobson, K. C. & Van den Oord, E. J. Genetic and environmental influences on vocabulary IQ: parental education level as moderator. Child Dev. 70, 1151–1162 (1999).

    Article  CAS  PubMed  Google Scholar 

  24. Krapohl, E. et al. The high heritability of educational achievement reflects many genetically influenced traits, not just intelligence. Proc. Natl Acad. Sci. USA 111, 15273–15278 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Converse, P. E. In The Human Meaning of Social Change (eds Campbell, A. & Converse, P. E.) 263–337 (Russell Sage, 1972).

  26. Dinesen, P. T. et al. Estimating the impact of education on political participation: evidence from monozygotic twins in the United States, Denmark and Sweden. Polit. Behav. 38, 579–601 (2016).

    Article  Google Scholar 

  27. Verba, S. & Nie, N. H. Participation in America: Social Equality and Political Democracy (Harper & Row, 1972).

  28. Persson, M. Education and political participation. Br. J. Polit. Sci. 45, 689–703 (2015).

    Article  Google Scholar 

  29. Nie, N. H., Junn, J. & Stehlik-Barry, K. Education and Democratic Citizenship in America (Univ. Chicago Press, 1996).

  30. Gerber, A. S. et al. Personality traits and participation in political processes. J. Polit. 73, 692–706 (2011).

    Article  Google Scholar 

  31. Richardson, M., Abraham, C. & Bond, R. Psychological correlates of university students’ academic performance: a systematic review and meta-analysis. Psychol. Bull. 138, 353–387 (2012).

    Article  PubMed  Google Scholar 

  32. Moffitt, T. E. et al. A gradient of childhood self-control predicts health, wealth, and public safety. Proc. Natl Acad. Sci. USA 108, 2693–2698 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Mõttus, R., Realo, A., Vainik, U., Allik, J. & Esko, T. Educational attainment and personality are genetically intertwined. Psychol. Sci. 28, 1631–1639 (2017).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. 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).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  37. Sondheimer, R. M. & Green, D. P. Using experiments to estimate the effects of education on voter turnout. Am. J. Polit. Sci. 54, 174–189 (2010).

    Article  Google Scholar 

  38. Deary, I. J., Batty, G. D. & Gale, C. R. Childhood intelligence predicts voter turnout, voting preferences, and political involvement in adulthood: the 1970 British cohort study. Intelligence 36, 548–555 (2008).

    Article  Google Scholar 

  39. Hillygus, D. S. The missing link: exploring the relationship between higher education and political engagement. Polit. Behav. 27, 25–47 (2005).

    Article  Google Scholar 

  40. Sternberg, R. J., Grigorenko, E. L., & Bundy, D. A. The predictive value of IQ. Merrill Palmer Q. 47, 1–41 (2001).

  41. White, E. S. Intelligence and sense of political efficacy in children. J. Polit. 30, 710–731 (1968).

    Article  Google Scholar 

  42. White, K. R. The relation between socioeconomic status and academic achievement. Psychol. Bull. 91, 461–481 (1982).

    Article  Google Scholar 

  43. Strenze, T. Intelligence and socioeconomic success: a meta-analytic review of longitudinal research. Intelligence 35, 401–426 (2007).

    Article  Google Scholar 

  44. Savage, J. E. et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat. Genet. 50, 912–919 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. 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).

    Article  CAS  PubMed  Google Scholar 

  46. 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  PubMed  PubMed Central  Google Scholar 

  47. Johnson, W., McGue, M. & Iacono, W. G. Disruptive behavior and school grades: genetic and environmental relations in 11-year-olds. J. Educ. Psychol. 97, 391–405 (2005).

    Article  Google Scholar 

  48. Johnson, W., McGue, M. & Iacono, W. G. Genetic and environmental influences on academic achievement trajectories during adolescence. Dev. Psychol. 42, 514–532 (2006).

    Article  PubMed  Google Scholar 

  49. Deary, I. J. & Johnson, W. Intelligence and education: causal perceptions drive analytic processes and therefore conclusions. Int. J. Epidemiol. 39, 1362–1369 (2010).

    Article  PubMed  Google Scholar 

  50. Davies, G. et al. Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Mol. Psychiatry 16, 996–1005 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Davies, G. et al. Genome-wide association study of cognitive functions and educational attainment in UK Biobank (N = 112 151). Mol. Psychiatry 21, 758–767 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Fowler, J. H. & Dawes, C. T. Two genes predict voter turnout. J. Polit. 70, 579–594 (2008).

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

  54. Ojeda, C. Depression and political participation. Soc. Sci. Q. 96, 1226–1243 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Burden, B. C., Fletcher, J. M., Herd, P., Moynihan, D. P. & Jones, B. M. How different forms of health matter to political participation. J. Polit. 79, 166–178 (2017).

    Article  PubMed  Google Scholar 

  56. Cesarini, D., Johannesson, M. & Oskarsson, S. Pre-birth factors, post-birth factors, and voting: evidence from Swedish adoption data. Am. Polit. Sci. Rev. 108, 71–87 (2014).

    Article  Google Scholar 

  57. Shultziner, D. Genes and politics: a new explanation and evaluation of twin study results and association studies in political science. Polit. Anal. 21, 350–367 (2013).

    Article  Google Scholar 

  58. Rosenstone, S. J. & Wolfinger, R. E. The effect of registration laws on voter turnout. Am. Polit. Sci. Rev. 72, 22–45 (1978).

    Article  Google Scholar 

  59. Pedersen, C. B. et al. The iPSYCH2012 case–cohort sample: new directions for unravelling genetic and environmental architectures of severe mental disorders. Mol. Psychiatry 23, 6–14 (2018).

    Article  CAS  PubMed  Google Scholar 

  60. Highton, B. Voter identification laws and turnout in the United States. Annu. Rev. Polit. Sci. 20, 149–167 (2017).

    Article  Google Scholar 

  61. Mental Disorders Affect One In Four People World Health Report (WHO, 2001); https://www.who.int/whr/2001/media_centre/press_release/en/

  62. Bullenkamp, J. & Voges, B. Voting preferences of outpatients with chronic mental illness in Germany. Psychiatr. Serv. 55, 1440–1442 (2004).

    Article  PubMed  Google Scholar 

  63. Siddique, A. & Lee, A. A survey of voting practices in an acute psychiatric unit. Ir. J. Psychol. Med. 31, 229–231 (2014).

    Article  CAS  PubMed  Google Scholar 

  64. Couture, J. & Breux, S. The differentiated effects of health on political participation. Eur. J. Publ. Health 27, 599–604 (2017).

    Google Scholar 

  65. Ojeda, C. & Pacheco, J. Health and voting in young adulthood. Br. J. Polit. Sci. 49, 1163–1186 (2017).

    Article  Google Scholar 

  66. Sund, R., Lahtinen, H., Wass, H., Mattila, M. & Martikainen, P. How voter turnout varies between different chronic conditions? A population-based register study. J. Epidemiol. Community Health 71, 475–479 (2017).

    Article  PubMed  Google Scholar 

  67. Kelly, B. D. & Nash, M. Voter participation among people attending mental health services in Ireland. Ir. J. Med. Sci. 188, 925–929 (2018).

    Article  PubMed  Google Scholar 

  68. Evans, L. M. et al. Narrow-sense heritability estimation of complex traits using identity-by-descent information. Heredity 121, 616–630 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Wray, N. R. et al. Pitfalls of predicting complex traits from SNPs. Nat. Rev. Genet. 14, 507–515 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. 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  PubMed  PubMed Central  Google Scholar 

  71. Vinkhuyzen, A. A. et al. Common SNPs explain some of the variation in the personality dimensions of neuroticism and extraversion. Transl. Psychiatry 2, e102 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Benjamin, D. J. et al. The genetic architecture of economic and political preferences. Proc. Natl Acad. Sci. USA 109, 8026–8031 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Tingsten, H. Political Behavior: Studies in Election Statistics (PS King, 1937).

  74. Bhatti, Y., Dahlgaard, J. O., Hansen, J. H. & Hansen, K. M. Core and peripheral voters: predictors of turnout across three types of elections. Polit. Stud. 67, 348–366 (2019).

    Article  Google Scholar 

  75. 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  PubMed  PubMed Central  Google Scholar 

  76. Domingue, B. W. et al. The social genome of friends and schoolmates in the National Longitudinal Study of Adolescent to Adult Health. Proc. Natl Acad. Sci. USA 115, 702–707 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Lewis, C. M. & Vassos, E. Prospects for using risk scores in polygenic medicine. Genome Med. 9, 96 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Blais, A. & St-Vincent, S. L. Personality traits, political attitudes and the propensity to vote. Eur. J. Polit. Res. 50, 395–417 (2011).

    Article  Google Scholar 

  80. Denny, K. & Doyle, O. Political interest, cognitive ability and personality: determinants of voter turnout in Britain. Br. J. Polit. Sci. 38, 291–310 (2008).

    Article  Google Scholar 

  81. Gallego, A. & Oberski, D. Personality and political participation: the mediation hypothesis. Polit. Behav. 34, 425–451 (2012).

    Article  Google Scholar 

  82. Weinschenk, A. Cause you’ve got personality: political participation and the tendency to join civic groups. SAGE Open 3, 1–12 (2013).

    Article  Google Scholar 

  83. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    Article  CAS  PubMed  Google Scholar 

  85. Fowler, J. H. & Schreiber, D. Biology, politics, and the emerging science of human nature. Science 322, 912–914 (2008).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  87. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).

    Google Scholar 

  88. Mortensen, P. B. Response to “Ethical concerns regarding Danish genetic research”. Mol. Psychiatry 24, 1574–1575 (2019).

    Article  PubMed  Google Scholar 

  89. Schork, A. J. et al. A genome-wide association study of shared risk across psychiatric disorders implicates gene regulation during fetal neurodevelopment. Nat. Neurosci. 22, 353–361 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Nørgaard‐Pedersen, B. & Hougaard, D. M. Storage policies and use of the Danish newborn screening Biobank. J. Inherit. Metab. Dis. 30, 530–536 (2007).

    Article  PubMed  Google Scholar 

  91. De Moor, M. H. et al. Meta-analysis of genome-wide association studies for personality. Mol. Psychiatry 17, 337–349 (2012).

    Article  PubMed  CAS  Google Scholar 

  92. Bhatti, Y., Hansen, K. M. & Wass, H. The relationship between age and turnout: a roller-coaster ride. Elect. Stud. 31, 588–593 (2012).

    Article  Google Scholar 

  93. Duan, S., Zhang, W., Cox, N. J. & Dolan, M. E. FstSNP-HapMap3: a database of SNPs with high population differentiation for HapMap3. Bioinformation 3, 139–141 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Ge, T., Chen, C. Y., Neale, B. M., Sabuncu, M. R. & Smoller, J. W. Correction: phenome-wide heritability analysis of the UK Biobank. PLoS Genet. 14, e1007228 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Abraham, G. & Inouye, M. Fast principal component analysis of large-scale genome-wide data. PLoS ONE 9, e93766 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  96. Canty A. & Ripley B. D. boot: bootstrap R (S-Plus) functions. R package version 1.3-25 https://cran.r-project.org/web/packages/boot/index.html (2020).

  97. Davison A. C. & Hinkley D. V. Bootstrap Methods and Their Applications (Cambridge Univ. Press, 1997); http://statwww.epfl.ch/davison/BMA/

  98. Lee, S. H., Goddard, M. E., Wray, N. R. & Visscher, P. M. A. Better coefficient of determination for genetic profile analysis. Genet. Epidemiol. 36, 214–224 (2012).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

We thank S. Oskarsson and members of the political behaviour research section at the Department of Political Science, Aarhus University for comments on previous versions of this manuscript. This research was supported by the Interacting Minds Centre, Aarhus University (seed grant no. 26223). The iPSYCH consortium is supported by the Lundbeck foundation (grant nos. R1-2=A9118 and R155-2014-1724). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Lene Aarøe, Vivek Appadurai.

Authors and Affiliations

  1. Department of Political Science, Aarhus University, Aarhus, Denmark

    Lene Aarøe & Michael Bang Petersen

  2. Institute of Biological Psychiatry, Mental Health Center Sct Hans, Roskilde, Denmark

    Vivek Appadurai & Andrew J. Schork

  3. The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark

    Vivek Appadurai, Andrew J. Schork, Thomas Werge, Ole Mors, Anders D. Børglum, David M. Hougaard, Merete Nordentoft, Preben B. Mortensen, Wesley Kurt Thompson, Alfonso Buil & Esben Agerbo

  4. Department of Political Science, University of Copenhagen, Copenhagen, Denmark

    Kasper M. Hansen

  5. Institute of Biological Psychiatry, Copenhagen Mental Health Services, Copenhagen, Denmark

    Thomas Werge

  6. Psychosis Research Unit, Aarhus University Hospital—Psychiatry, Aarhus, Denmark

    Ole Mors

  7. Department of Biomedicine and Centre for Integrative Sequencing, iSEQ, Aarhus University, Aarhus, Denmark

    Anders D. Børglum

  8. Center for Genomics and Personalized Medicine, Aarhus University, Aarhus, Denmark

    David M. Hougaard

  9. Danish Center for Neonatal Screening, Statens Serum Institut, Copenhagen, Denmark

    David M. Hougaard

  10. Mental Health Centre Copenhagen, Capital Region of Denmark, Copenhagen University Hospital, Copenhagen, Denmark

    Merete Nordentoft

  11. NCRR-National Center for Register-Based Research, Business and Social Sciences, Aarhus University, Aarhus, Denmark

    Preben B. Mortensen

  12. CIRRAU–Centre for Integrated Register-Based Research, Aarhus University, Aarhus, Denmark

    Preben B. Mortensen

  13. Department of Family Medicine and Public Health, University of California, San Diego, CA, USA

    Wesley Kurt Thompson

  14. Department of Economics and Business Economics—National Centre for Register-based Research, Aarhus University, Aarhus, Denmark

    Esben Agerbo

Authors
  1. Lene Aarøe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vivek Appadurai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kasper M. Hansen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrew J. Schork
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Thomas Werge
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ole Mors
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Anders D. Børglum
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. David M. Hougaard
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Merete Nordentoft
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Preben B. Mortensen
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Wesley Kurt Thompson
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Alfonso Buil
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Esben Agerbo
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Michael Bang Petersen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.A., E.A. and M.B.P. conceived the study. L.A., V.A., A.J.S., A.B., E.A. and M.B.P. designed the study. L.A., V.A. and A.J.S. drafted the manuscript. L.A., V.A., A.J.S., K.M.H., E.A., A.B. and M.B.P. discussed the results and revised the manuscript. V.A. and A.J.S. conducted all of the analyses except that K.M.H. conducted the analyses for Supplementary Table 5. K.M.H. collected the turnout data. P.B.M., M.N., A.D.B., D.M.H., T.W., O.M. and E.A. designed, implemented, and/or oversaw the collection and generation of the IPSYCH data. All of the authors (L.A., V.A., K.M.H., A.J.S., T.W., O.M., A.D.B., D.M.H., M.N., P.B.M., W.K.T., A.B., E.A. and M.B.P.) approved the final version of the manuscript.

Corresponding author

Correspondence to Esben Agerbo.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information: Primary Handling Editor: Charlotte Payne.

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

Supplementary information

Supplementary Information

Details on the iPSYCH case–cohort sample and the turnout data, summary statistics, power analysis, family-based heritability estimates; information about the genetic score analyses; information about the GWAS; information about the genetic correlations using LDHub; and information about the Mendelian randomization analyses.

Reporting Summary

Supplementary Tables

Genetic correlations between voter turnout and non-voting traits (LDSC analyses).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aarøe, L., Appadurai, V., Hansen, K.M. et al. Genetic predictors of educational attainment and intelligence test performance predict voter turnout. Nat Hum Behav 5, 281–291 (2021). https://doi.org/10.1038/s41562-020-00952-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41562-020-00952-2

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing