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).

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

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).

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

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).

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).

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).

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

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).

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

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).

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).

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

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

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).

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).

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

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).

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).

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

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

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

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).

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

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

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).

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).

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).

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

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

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).

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

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).

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).

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

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

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).

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).

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).

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

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).

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

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).

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

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

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

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

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).

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).

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

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

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).