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Genetic influence on social outcomes during and after the Soviet era in Estonia

Nature Human Behaviour volume 2, pages 269–275 (2018)Cite this article

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Abstract

The aetiology of individual differences in educational attainment and occupational status includes genetic as well as environmental factors1,2,3,4,5 and can change as societies change3,6,7. The extent of genetic influence on these social outcomes can be viewed as an index of success in achieving meritocratic values of equality of opportunity by rewarding talent and hard work, which are to a large extent influenced by genetic factors, rather than rewarding environmentally driven privilege. To the extent that the end of the Soviet Union and the independence of Estonia led to an increase in meritocratic selection of individuals in education and occupation, genetic influence should be higher in the post-Soviet era than in the Soviet era. Here we confirmed this hypothesis: DNA differences (single-nucleotide polymorphisms) explained twice as much variance in educational attainment and occupational status in the post-Soviet era compared with the Soviet era in both polygenic score analyses and single-nucleotide polymorphism heritability analyses of 12,500 Estonians. Our results demonstrate a change in the extent of genetic influence in the same population following a massive and abrupt social change—in this case, the shift from a communist to a capitalist society.

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Fig. 1: Variance explained by EduYears GPS in the post-Soviet and Soviet groups.
Fig. 2: SNP heritabilities showing the proportion of variance explained by additive effects of common SNPs for the whole EGCUT sample and for the Soviet and post-Soviet groups using a cutoff of 15 years.

References

  1. Lykken, D. T., Bouchard, T. J. Jr., McGue, M. & Tellegen, A. The Minnesota Twin Family Registry: some initial findings. Acta Genet. Med. Gemellol. 39, 35–70 (1990).

    Article  CAS  PubMed  Google Scholar 

  2. Heath, A. C. et al. Education policy and the heritability of educational attainment. Nature 314, 734–736 (1985).

    Article  CAS  PubMed  Google Scholar 

  3. Branigan, A. R., Mccallum, K. J. & Freese, J. Variation in the heritability of educational attainment: an international meta-analysis. Soc. Forces 92, 109–140 (2013).

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Tambs, K., Sundet, J. M., Magnus, P. & Berg, K. Genetic and environmental contributions to the covariance between occupational status, educational attainment, and IQ: a study of twins. Behav. Genet. 19, 209–222 (1989).

    Article  CAS  PubMed  Google Scholar 

  6. Colodro-Conde, L., Rijsdijk, F., Tornero-Gómez, M. J., Sánchez-Romera, J. F. & Ordoñana, J. R. Equality in educational policy and the heritability of educational attainment. PLoS One 10, e0143796 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Lichtenstein, P., Pedersen, N. L. & McClearn, G. E. The origins of individual differences in occupational status and educational level: a study of twins reared apart and together. Acta Sociol. 35, 13–31 (1992).

    Article  Google Scholar 

  8. Adler, N. E. et al. Socioeconomic status and health: the challenge of the gradient. Am. Psychol. 49, 15–24 (1994).

    Article  CAS  PubMed  Google Scholar 

  9. Cutler, D. M. & Lleras-Muney, A. Education and Health: Insights From International Comparisons Working Paper (NBER, 2012).

  10. Cutler, D. M., Lleras-Muney, A. & Vogl, T. Socioeconomic Status and Health: Dimensions and Mechanisms Working Paper (NBER, 2008).

  11. Batty, G. D., Deary, I. J. & Gottfredson, L. S. Premorbid (early life) IQ and later mortality risk: systematic review. Ann. Epidemiol. 17, 278–288 (2007).

    Article  PubMed  Google Scholar 

  12. von Stumm, S., Deary, I. J. & Hagger-Johnson, G. Life-course pathways to psychological distress: a cohort study. BMJ Open 3, e002772 (2013).

    Article  Google Scholar 

  13. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. Genome-wide complex trait analysis (GCTA): methods, data analyses, and interpretations. Methods Mol. Biol. 1019, 215–236 (2013).

    Article  CAS  PubMed  Google Scholar 

  14. Hill, W. D. et al. Molecular genetic contributions to social deprivation and household income in UK Biobank (n = 112,151). Curr. Biol. 26, 3083–3089 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Marioni, R. E. et al. Molecular genetic contributions to socioeconomic status and intelligence. Intelligence 44, 26–32 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  16. 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 

  17. Davies, G. et al. Genome-wide association study of cognitive functions and educational attainment in UK Biobank (N = 112151). Mol. Psychiatry 21, 758–767 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hyytinen, A., Ilmakunnas, P., Johansson, E. & Toivanen, O. Heritability of Lifetime Income (Helsinki Centre of Economic Research, 2013).

  19. 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 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 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 

  22. 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 

  23. Hollingshead, A. Four factor index of social status. Yale J. Sociol. 8, 21–52 (1975).

    Google Scholar 

  24. Sirin, S. R. Socioeconomic status and academic achievement: a meta-analytic review of research. Rev. Educ. Res. 75, 417–453 (2005).

    Article  Google Scholar 

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

    Article  Google Scholar 

  26. Domingue, B. W., Belsky, D. W., Conley, D., Harris, K. M. & Boardman, J. D. Polygenic influence on educational attainment. AERA Open 1, 2332858415599972 (2015).

    Article  Google Scholar 

  27. Selzam, S. et al. Predicting educational achievement from DNA. Mol. Psychiatry 22, 267–272 (2017).

    Article  CAS  PubMed  Google Scholar 

  28. Knopik, V. S., Neiderhiser, J. M., DeFries, J. C. & Plomin, R. Behavioral Genetics 7th edn (Worth Publishers, New York, NY, 2017).

    Google Scholar 

  29. Baker, L. A., Treloar, S. A., Reynolds, C. A., Heath, A. C. & Martin, N. G. Genetics of educational attainment in Australian twins: sex differences and secular changes. Behav. Genet. 26, 89–102 (1996).

    Article  CAS  PubMed  Google Scholar 

  30. Samuelsson, S. et al. Environmental and genetic influences on prereading skills in Australia, Scandinavia, and the United States. J. Educ. Psychol. 97, 705–722 (2005).

    Article  Google Scholar 

  31. Hanscombe, K. B. et al. Socioeconomic status (SES) and children’s intelligence (IQ): in a UK-representative sample SES moderates the environmental, not genetic, effect on IQ. PLoS One 7, e30320 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Laar, M. Estonia’s Way (Pegasus, Tallinn, 2007).

    Google Scholar 

  33. Laar, M. The Estonian economic miracle. Backgrounder 2060, 1–12 (2007).

    Google Scholar 

  34. Saar, E. Changes in intergenerational mobility and educational inequality in Estonia: comparative analysis of cohorts born between 1930 and 1974. Eur. Sociol. Rev. 26, 367–383 (2010).

    Article  Google Scholar 

  35. Saar, E. Transitions to tertiary education in Belarus and the Baltic countries. Eur. Sociol. Rev. 13, 139–158 (1997).

    Article  Google Scholar 

  36. Titma, M., Tuma, N. B. & Roosma, K. Education as a factor in intergenerational mobility in Soviet society. Eur. Sociol. Rev. 19, 281–297 (2003).

    Article  Google Scholar 

  37. Education Policy Outlook: Estonia (OECD, 2016).

  38. Equity and Quality in Education—Supporting Disadvantaged Students and Schools (OECD, 2011).

  39. Titma, M. & Roots, A. Intragenerational mobility in successor states of the USSR. Eur. Soc. 8, 493–526 (2006).

    Article  Google Scholar 

  40. Carnaghan, E. & Bahry, D. Political attitudes and the gender gap in the USSR. Comp. Polit. 22, 379–399 (1990).

    Article  Google Scholar 

  41. Katz, K. Gender, Work and Wages in the Soviet Union: A Legacy of Discrimination (Palgrave Macmillan, Basingstoke, 2001).

    Book  Google Scholar 

  42. Boughton, J. Tearing Down Walls: The International Monetary Fund 1990–1999 (International Monetary Fund, Washington DC, 2012).

  43. Silova, I. & Magno, C. Gender equity unmasked: democracy, gender, and education in Central/Southeastern Europe and the former Soviet Union. Comp. Educ. Rev. 48, 417–442 (2004).

    Article  Google Scholar 

  44. Young, M. The Rise of the Meritocracy (Transaction Publishers, London, 1958).

  45. Bloodworth, J. The Myth of Meritocracy (Biteback Publishing, London, 2016).

  46. Leitsalu, L. et al. Cohort profile: Estonian Biobank of the Estonian Genome Center, University of Tartu. Int. J. Epidemiol. 44, 1137–1147 (2015).

    Article  PubMed  Google Scholar 

  47. Ganzeboom, H. B. G. A new International Socio-Economic Index [ISEI] of occupational status for the International Standard Classification of Occupation 2008 [ISCO-08] constructed with data from the ISSP 2002–2007; with an analysis of quality of occupational measurement in ISS. Paper presented at the Annual Conference of the International Social Survey Programme , Lisbon (2010); http://go.nature.com/2FUt99E

  48. Ganzeboom, H. B. & Treiman, D. J. in Advances in Cross-National Comparison. A European Working Book for Demographic and Socio-Economic Variables (eds Hoffmeyer-Zlotnik, J. H. P. & Wolf, C.) 159–193 (Kluwer Academic, New York, NY, 2003).

  49. Wolf, C. The ISCO-88 International Standard Classification of Occupations in cross-national survey research. Bull. Methodol. Sociol. 54, 23–40 (1997).

    Article  Google Scholar 

  50. Kromhout, H. The use of occupation and industry classifications in general population studies. Int. J. Epidemiol. 32, 419–428 (2003).

    Article  PubMed  Google Scholar 

  51. 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  PubMed  PubMed Central  Google Scholar 

  52. Delaneau, O., Zagury, J.-F. & Marchini, J. Improved whole-chromosome phasing for disease and population genetic studies. Nat. Methods 10, 5–6 (2013).

    Article  CAS  PubMed  Google Scholar 

  53. Howie, B. N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Euesden, J., Lewis, C. M. & O’Reilly, P. F. PRSice: polygenic risk score software. Bioinformatics 31, 1466–1468 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Fisher, R. On the probable error of a coefficient of correlation deduced from a small sample. Metron 1, 3–32 (1921).

    Google Scholar 

  57. Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. 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 

  59. Visscher, P. M., Hill, W. G. & Wray, N. R. Heritability in the genomics era—concepts and misconceptions. Nat. Rev. Genet. 9, 255–266 (2008).

    Article  CAS  PubMed  Google Scholar 

  60. Lehmann, E. Nonparametric Statistical Methods Based on Ranks (Holden-Day, San Francisco, CA, 1975).

    Google Scholar 

  61. Van Der Waerden, B. L. On the sources of my book Moderne Algebra. Hist. Math. 2, 31–40 (1975).

    Article  Google Scholar 

  62. Visscher, P. M. et al. Statistical power to detect genetic (co)variance of complex traits using SNP data in unrelated samples. PLoS Genet. 10, e1004269 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Palla, L. & Dudbridge, F. A fast method that uses polygenic scores to estimate the variance explained by genome-wide marker panels and the proportion of variants affecting a trait. Am. J. Hum. Genet. 97, 250–259 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors acknowledge the ongoing contribution of the participants in the Estonian Genome Centre University of Tartu. R.P. is supported by the UK Medical Research Council (MR/M021475/1 and previously G0901245), with additional support from the US National Institutes of Health (HD044454 and HD059215). K.R., E.K. and S.S. are supported by a Medical Research Council studentship. R.P. is supported by a Medical Research Council Research Professorship award (G19/2) and a European Research Council Advanced Investigator award (295366). J.R.I.C. is funded by the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. T.E. and A.M. were supported by grants Est.RC IUT 20-60 (A.M.) and PUT-1660 (T.E) and by CoEx for Genomics and Translational Medicine (GENTRANSMED). 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. Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

    Kaili Rimfeld, Eva Krapohl, Jonathan R. I. Coleman, Saskia Selzam & Robert Plomin

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

    Maciej Trzaskowski

  3. National Institute for Health Research Biomedical Research Centre, South London and Maudsley National Health Service Trust, London, UK

    Jonathan R. I. Coleman

  4. Department of Speech and Hearing Sciences, University of New Mexico, Albuquerque, NM, USA

    Philip S. Dale

  5. Estonian Genome Centre, University of Tartu, Tartu, Estonia

    Tonu Esko & Andres Metspalu

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Contributions

K.R. and R.P. conceived and designed the experiments. K.R., M.T., E.K. and J.R.I.C. analysed the data. K.R., M.T., E.K., T.E., A.M. and R.P. wrote the paper. All authors approved the final draft of the paper.

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Correspondence to Kaili Rimfeld.

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Supplementary Tables 1–6, Supplementary Figures 1–12.

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Rimfeld, K., Krapohl, E., Trzaskowski, M. et al. Genetic influence on social outcomes during and after the Soviet era in Estonia. Nat Hum Behav 2, 269–275 (2018). https://doi.org/10.1038/s41562-018-0332-5

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