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Investigating the genetic architecture of noncognitive skills using GWAS-by-subtraction

Abstract

Little is known about the genetic architecture of traits affecting educational attainment other than cognitive ability. We used genomic structural equation modeling and prior genome-wide association studies (GWASs) of educational attainment (n = 1,131,881) and cognitive test performance (n = 257,841) to estimate SNP associations with educational attainment variation that is independent of cognitive ability. We identified 157 genome-wide-significant loci and a polygenic architecture accounting for 57% of genetic variance in educational attainment. Noncognitive genetics were enriched in the same brain tissues and cell types as cognitive performance, but showed different associations with gray-matter brain volumes. Noncognitive genetics were further distinguished by associations with personality traits, less risky behavior and increased risk for certain psychiatric disorders. For socioeconomic success and longevity, noncognitive and cognitive-performance genetics demonstrated associations of similar magnitude. By conducting a GWAS of a phenotype that was not directly measured, we offer a view of genetic architecture of noncognitive skills influencing educational success.

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Fig. 1: GWAS-by-subtraction Genomic-SEM model.
Fig. 2: Manhattan plot of SNP associations with NonCog.
Fig. 3: Polygenic prediction and genetic correlations with IQ and educational achievement.
Fig. 4: Estimates of genetic correlations with NonCog, Cog and educational attainment.
Fig. 5: Genetic correlations with regional gray-matter volumes and white-matter tracts.

Data availability

GWAS summary data for NonCog and Cog (excluding 23andMe) have been deposited in the GWAS catalog with accession nos. GCST90011874 and GCST90011875, respectively (NonCog GWAS: ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90011874, Cog GWAS: ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90011875). For 23andMe dataset access, see https://research.23andme.com/dataset-access. Part of the AddHealth data is publicly available and can be downloaded at the following link: https://data.cpc.unc.edu/projects/2/view#public_li. For restricted access data, details of the data-sharing agreement and data access requirements can be found at the following link: https://data.cpc.unc.edu/projects/2/view. The Dunedin study datasets reported in the current article are not publicly available due to lack of informed consent and ethical approval, but are available on request by qualified scientists. Requests require a concept paper describing the purpose of data access, ethical approval at the applicant’s university and provision for secure data access. We offer secure access on the Duke, Otago and King’s College campuses. All data analysis scripts and results files are available for review (https://moffittcaspi.trinity.duke.edu/research-topics/dunedin). The E-Risk Longitudinal Twin Study datasets reported in the current article are not publicly available due to lack of informed consent and ethical approval, but are available on request by qualified scientists. Requests require a concept paper describing the purpose of data access, ethical approval at the applicant’s university and provision for secure data access. We offer secure access on the Duke and King’s College campuses. All data analysis scripts and results files are available for review (https://moffittcaspi.trinity.duke.edu/research-topics/erisk). NTR data may be accessed, on approval of the data access committee (email: ntr.datamanagement.fgb@vu.nl). Researchers will be able to obtain Texas Twins data through managed access. Requests for managed access should be sent to E. Tucker-Drob (tuckerdrob@utexas.edu) and P. Harden (harden@utexas.edu), joint principal investigators of the Texas Twin Project. The WLS data can be requested following this form: https://www.ssc.wisc.edu/wlsresearch/data/Request_Genetic_Data_28_June_2017.pdf.

Code availability

Code used to run the analyses is available at https://github.com/PerlineDemange/non-cognitive.

A tutorial on how to perform GWAS-by-subtraction is available at http://rpubs.com/MichelNivard/565885. All additional software used to perform these analyses is available online.

References

  1. 1.

    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).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  2. 2.

    von Stumm, S., Hell, B. & Chamorro-Premuzic, T. The hungry mind: intellectual curiosity is the third pillar of academic performance. Perspect. Psychol. Sci. 6, 574–588 (2011).

    Article  Google Scholar 

  3. 3.

    Tucker-Drob, E. M., Briley, D. A., Engelhardt, L. E., Mann, F. D. & Harden, K. P. Genetically-mediated associations between measures of childhood character and academic achievement. J. Pers. Soc. Psychol. 111, 790–815 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  4. 4.

    Heckman, J. J., Stixrud, J. & Urzua, S. The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior. J. Labor Econ. 24, 411–482 (2006).

    Article  Google Scholar 

  5. 5.

    Heckman, J. J., Moon, S. H., Pinto, R., Savelyev, P. A. & Yavitz, A. The rate of return to the HighScope Perry Preschool Program. J. Public Econ. 94, 114–128 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Conti, G., Heckman, J. J. & Pinto, R. The effects of two influential early childhood interventions on health and healthy behaviour. Econ. J. 126, F28–F65 (2016).

    Article  Google Scholar 

  7. 7.

    Gutman, L. M. & Schoon, I. The impact of non-cognitive skills on outcomes for young people. Educ. Endow. Found. 59, 2019 (2013).

    Google Scholar 

  8. 8.

    Garcia, E. The Need to Address Noncognitive Skills in the Education Policy Agenda (Economic Policy Institute, 2014).

  9. 9.

    Kautz, T., Heckman, J. J., Diris, R., Ter Weel, B. & Borghans, L. Fostering and Measuring Skills: Improving Cognitive and Non-cognitive Skills to Promote Lifetime Success OECD Education Working Papers No. 110 (OECD Publishing, 2014).

  10. 10.

    Heckman, J. J. Skill formation and the economics of investing in disadvantaged children. Science 312, 1900–1902 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  11. 11.

    Heckman, J. J. & Kautz, T. Hard evidence on soft skills. Labour Econ. 19, 451–464 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Rimfeld, K., Kovas, Y., Dale, P. S. & Plomin, R. True grit and genetics: predicting academic achievement from personality. J. Pers. Soc. Psychol. 111, 780–789 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    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).

    PubMed  Article  PubMed Central  Google Scholar 

  14. 14.

    Smithers, L. G. et al. A systematic review and meta-analysis of effects of early life non-cognitive skills on academic, psychosocial, cognitive and health outcomes. Nat. Hum. Behav. 2, 867–880 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Kovas, Y. et al. Why children differ in motivation to learn: insights from over 13,000 twins from 6 countries. Personal. Individ. Differ. 80, 51–63 (2015).

    Article  Google Scholar 

  16. 16.

    Loehlin, J. C. Genes and Environment in Personality Development (Sage Publications, 1992).

  17. 17.

    Tucker-Drob, E. M. & Harden, K. P. Learning motivation mediates gene-by-socioeconomic status interaction on mathematics achievement in early childhood. Learn. Individ. Differ. 22, 37–45 (2012).

    PubMed  Article  PubMed Central  Google Scholar 

  18. 18.

    Malanchini, M., Engelhardt, L. E., Grotzinger, A. D., Harden, K. P. & Tucker-Drob, E. M. ‘Same but different’: associations between multiple aspects of self-regulation, cognition, and academic abilities. J. Pers. Soc. Psychol. 117, 1164–1188 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  19. 19.

    Morris, T. T., Smith, G. D., van Den Berg, G. & Davies, N. M. Investigating the longitudinal consistency and genetic architecture of non-cognitive skills, and their relation to educational attainment. Preprint at bioRxiv https://doi.org/10.1101/470682 (2019).

  20. 20.

    Liu, J. Z., Erlich, Y. & Pickrell, J. K. Case–control association mapping by proxy using family history of disease. Nat. Genet. 49, 325–331 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  21. 21.

    Bowles, S. & Gintis, H. Schooling in Capitalist America: Educational Reform and the Contradictions of Economic Life (Basic Books, 1977).

  22. 22.

    Heckman, J. J. & Rubinstein, Y. The importance of noncognitive skills: lessons from the GED Testing Program. Am. Econ. Rev. 91, 145–149 (2001).

    Article  Google Scholar 

  23. 23.

    Ackerman, P. L., Kanfer, R. & Goff, M. Cognitive and noncognitive determinants and consequences of complex skill acquisition. J. Exp. Psychol. Appl. 1, 270–304 (1995).

    Article  Google Scholar 

  24. 24.

    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 

  25. 25.

    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 

  26. 26.

    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 

  27. 27.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Ritchie, S. J. & Tucker-Drob, E. M. How much does education improve intelligence? A meta-analysis. Psychol. Sci. 29, 1358–1369 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Ligthart, L. et al. The Netherlands Twin Register: longitudinal research based on twin and twin-family designs. Twin Res. Hum. Genet. 22, 623–636 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  30. 30.

    Harris, K. M. et al. Cohort profile: the National Longitudinal Study of Adolescent to Adult Health (AddHealth). Int. J. Epidemiol. 48, 1415–1415k (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Poulton, R., Moffitt, T. E. & Silva, P. A. The Dunedin Multidisciplinary Health and Development Study: overview of the first 40 years, with an eye to the future. Soc. Psychiatry Psychiatr. Epidemiol. 50, 679–693 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Moffitt, T. E., E-risk Team. Teen-aged mothers in contemporary Britain. J. Child Psychol. Psychiatry 43, 727–742 (2002).

    PubMed  Article  PubMed Central  Google Scholar 

  33. 33.

    Herd, P., Carr, D. & Roan, C. Cohort profile: Wisconsin Longitudinal Study (WLS). Int. J. Epidemiol. 43, 34–41 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Harden, K. P., Tucker-Drob, E. M. & Tackett, J. L. The Texas Twin Project. Twin Res. Hum. Genet. 16, 385–390 (2013).

    PubMed  Article  PubMed Central  Google Scholar 

  35. 35.

    Benyamin, B. et al. Childhood intelligence is heritable, highly polygenic and associated with FNBP1L. Mol. Psychiatry 19, 253–258 (2014).

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    Chetty, R. et al. The association between income and life expectancy in the United States, 2001–2014. JAMA 315, 1750–1766 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Case, A. & Deaton, A. Mortality and morbidity in the 21st century. Brook. Pap. Econ. Act. 2017, 397–476 (2017).

    Article  Google Scholar 

  38. 38.

    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 

  39. 39.

    Timmers, P. R. et al. Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances. eLife 8, e39856 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Almlund, M., Duckworth, A. L., Heckman, J. & Kautz, T. in Handbook of the Economics of Education Vol. 4 (eds Hanushek, E. A., Machin, S. & Wößman, L.) 1–181 (Elsevier, 2011).

  41. 41.

    Borghans, L., Duckworth, A. L., Heckman, J. J. & Weel, Bter The economics and psychology of personality traits. J. Hum. Resour. 43, 972–1059 (2008).

    Google Scholar 

  42. 42.

    Rabin, M. A perspective on psychology and economics. Eur. Econ. Rev. 46, 657–685 (2004).

  43. 43.

    Becker, A., Deckers, T., Dohmen, T., Falk, A. & Kosse, F. The relationship between economic preferences and psychological personality measures. Annu. Rev. Econ. 4, 453–478 (2012).

    Article  Google Scholar 

  44. 44.

    Linnér, R. K. et al. Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nat. Genet. 51, 245–257 (2019).

    PubMed Central  Article  CAS  Google Scholar 

  45. 45.

    Sanchez-Roige, S. et al. Genome-wide association study of delay discounting in 23,217 adult research participants of European ancestry. Nat. Neurosci. 21, 16–18 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  46. 46.

    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 

  47. 47.

    Tobacco and Genetics Consortium. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 42, 441–447 (2010).

    Article  CAS  Google Scholar 

  48. 48.

    Walters, R. K. et al. Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nat. Neurosci. 21, 1656–1669 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Schumann, G. et al. KLB is associated with alcohol drinking, and its gene product β-Klotho is necessary for FGF21 regulation of alcohol preference. Proc. Natl Acad. Sci. USA 113, 14372–14377 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  50. 50.

    Pasman, J. A. et al. GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal effect of schizophrenia liability. Nat. Neurosci. 21, 1161–1170 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Linnér, R. K. et al. Multivariate genomic analysis of 1.5 million people identifies genes related to addiction, antisocial behavior, and health. Preprint at bioRxiv https://doi.org/10.1101/2020.10.16.342501 (2020).

  52. 52.

    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 

  53. 53.

    Lo, M.-T. et al. Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders. Nat. Genet. 49, 152–156 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  54. 54.

    John, O. P., Naumann, L. P. & Soto, C. J. in Handbook of Personality: Theory and Research (eds John, O. P. et al.) 114–158 (Guilford Press, 2008).

  55. 55.

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

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  56. 56.

    Caspi, A., Roberts, B. W. & Shiner, R. L. Personality development: stability and change. Annu. Rev. Psychol. 56, 453–484 (2005).

    PubMed  Article  PubMed Central  Google Scholar 

  57. 57.

    Kessler, R. C. et al. Social consequences of psychiatric disorders, I: educational attainment. Am. J. Psychiatry 152, 1026–1032 (1995).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  58. 58.

    Breslau, J., Lane, M., Sampson, N. & Kessler, R. C. Mental disorders and subsequent educational attainment in a US national sample. J. Psychiatr. Res. 42, 708–716 (2008).

    PubMed  PubMed Central  Article  Google Scholar 

  59. 59.

    Power, R. A. et al. Polygenic risk scores for schizophrenia and bipolar disorder predict creativity. Nat. Neurosci. 18, 953–955 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  60. 60.

    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 

  61. 61.

    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 

  62. 62.

    Ruderfer, D. M. et al. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell 173, 1705–1715.e16 (2018).

    CAS  PubMed Central  Article  Google Scholar 

  63. 63.

    Jansen, P. R. et al. Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways. Nat. Genet. 51, 394–403 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  64. 64.

    Duncan, L. et al. Significant locus and metabolic genetic correlations revealed in genome-wide association study of anorexia nervosa. Am. J. Psychiatry 174, 850–858 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  65. 65.

    Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. 66.

    Arnold, P. D. et al. Revealing the complex genetic architecture of obsessive–compulsive disorder using meta-analysis. Mol. Psychiatry 23, 1181–1188 (2018).

    CAS  Article  Google Scholar 

  67. 67.

    Ripke, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

    CAS  PubMed Central  Article  Google Scholar 

  68. 68.

    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 

  69. 69.

    Nieuwboer, H. A., Pool, R., Dolan, C. V., Boomsma, D. I. & Nivard, M. G. GWIS: genome-wide inferred statistics for functions of multiple phenotypes. Am. J. Hum. Genet. 99, 917–927 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. 70.

    The GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    Article  CAS  Google Scholar 

  71. 71.

    Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  72. 72.

    Fehrmann, R. S. N. et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat. Genet. 47, 115–125 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  73. 73.

    de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, 1–19 (2015).

    Article  CAS  Google Scholar 

  74. 74.

    Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  75. 75.

    Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014.e22 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  76. 76.

    Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  77. 77.

    Nave, G., Jung, W. H., Karlsson Linnér, R., Kable, J. W. & Koellinger, P. D. Are bigger brains smarter? Evidence from a large-scale preregistered study. Psychol. Sci. 30, 43–54 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  78. 78.

    Elliott, M. L. et al. A polygenic score for higher educational attainment is associated with larger brains. Cereb. Cortex 29, 3496–3504 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  79. 79.

    Zhao, B. et al. Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits. Nat. Genet. 51, 1637–1644 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  80. 80.

    Zhao, B. et al. Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n = 17,706). Mol. Psychiatry https://doi.org/10.1038/s41380-019-0569-z (2019).

  81. 81.

    Haushofer, J. & Fehr, E. On the psychology of poverty. Science 344, 862–867 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  82. 82.

    Briley, D. A., Domiteaux, M. & Tucker-Drob, E. M. Achievement-relevant personality: relations with the Big Five and validation of an efficient instrument. Learn. Individ. Differ. 32, 26–39 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  83. 83.

    Smoller, J. W. et al. Psychiatric genetics and the structure of psychopathology. Mol. Psychiatry 24, 409–420 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  84. 84.

    Plomin, R., Haworth, C. M. A. & Davis, O. S. P. Common disorders are quantitative traits. Nat. Rev. Genet. 10, 872–878 (2009).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  85. 85.

    Meehl, P. E. Schizotaxia, schizotypy, schizophrenia. Am. Psychol. 17, 827–838 (1962).

    Article  Google Scholar 

  86. 86.

    von Stumm, S. & Ackerman, P. L. Investment and intellect: a review and meta-analysis. Psychol. Bull. 139, 841–869 (2013).

    Article  Google Scholar 

  87. 87.

    Tucker-Drob, E. M. & Harden, K. P. in Genetics, Ethics and Education (eds Grigorenko, E. L. et al.) 134–158 (Cambridge University Press, 2017).

  88. 88.

    Tucker-Drob, E. M. in Handbook of Competence and Motivation: Theory and Application 2nd edn (eds Elliot, A., Dweck, C. & Yeager, D.) 471–486 (Guilford Press, 2017).

  89. 89.

    Tucker-Drob, E. M. & Harden, K. P. Intellectual interest mediates gene × socioeconomic status interaction on adolescent academic achievement: intellectual interest and G×E. Child Dev. 83, 743–757 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  90. 90.

    Malanchini, M. et al. Reading self-perceived ability, enjoyment and achievement: a genetically informative study of their reciprocal links over time. Dev. Psychol. 53, 698–712 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  91. 91.

    Westfall, J. & Yarkoni, T. Statistically controlling for confounding constructs is harder than you think. PLoS ONE 11, e0152719 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  92. 92.

    de la Fuente, J., Davies, G., Grotzinger, A. D., Tucker-Drob, E. M. & Deary, I. J. A general dimension of genetic sharing across diverse cognitive traits inferred from molecular data. Nat. Hum. Behav. https://doi.org/10.1038/s41562-020-00936-2 (2020).

  93. 93.

    Tucker-Drob, E. M. & Briley, D. A. Continuity of genetic and environmental influences on cognition across the life span: a meta-analysis of longitudinal twin and adoption studies. Psychol. Bull. 140, 949–979 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  94. 94.

    Tropf, F. C. et al. Hidden heritability due to heterogeneity across seven populations. Nat. Hum. Behav. 1, 757–765 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  95. 95.

    Duncan, L. et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nat. Commun. 10, 3328 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  96. 96.

    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 

  97. 97.

    Klein, A. & Tourville, J. 101 labeled brain images and a consistent human cortical labeling protocol. Front. Neurosci. 6, 171 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  98. 98.

    Klein, A. Mindboggle-101 manually labeled individual brains. Harvard Dataverse, V2 https://doi.org/10.7910/DVN/HMQKCK (2016).

  99. 99.

    Gorgolewski, K. J. et al. NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain. Front. Neuroinform. 9, 8 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  100. 100.

    Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  101. 101.

    Willemsen, G. et al. The Adult Netherlands Twin Register: twenty-five years of survey and biological data collection. Twin Res. Hum. Genet. 16, 271–281 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  102. 102.

    Highland, H. M., Avery, C. L., Duan, Q., Li, Y. & Harris, K. M. Quality Control Analysis of AddHealth GWAS Data https://www.cpc.unc.edu/projects/addhealth/documentation/guides/AH_GWAS_QC.pdf (2018).

  103. 103.

    Vilhjálmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  104. 104.

    Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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Acknowledgements

The present study was developed with support from the Jacobs Foundation at a meeting organized by D.W.B. and K.P.H.; E.M.T.-D. and C.M. provided support, and co-authors B.W.D., J.W., M.G.N. and P.B. also attended. We thank K. Mannik and F. Tropf for contributions to the meeting, and the Jacobs Foundation Fellowship team, who made the meeting possible. D.W.B., K.P.H., M.G.N., E.M.T.-D. and C.M. are fellows of the Foundation. J.W. is a Jacobs Foundation Young Scholar. The present study used GWAS summary statistics published by the SSGAC and additional data obtained from 23andMe. We thank the research participants and employees of 23andMe for making this work possible. We thank the SSGAC and COGENT consortia for sharing their summary statistics of the GWASs of educational attainment and cognitive performance, especially A. Okbay for her quick and repeated help with providing these data. The present study used data from the NTR, the Texas Twin Study, the AddHealth, the Dunedin Longitudinal Study, the E-Risk Study and the WLS. The NTR is supported by: ‘Twin-family database for behavior genetics and genomics studies’ (NWO 480-04-004), Longitudinal data collection from teachers of Dutch twins and their siblings (NWO-481-08-011); Twin-family study of individual differences in school achievement (NWO 056-32-010) and Gravitation program of the Dutch Ministry of Education, Culture and Science and the Netherlands Organization for Scientific Research (NWO 0240-001-003); NWO Groot (480-15-001/674): Netherlands Twin Registry Repository: researching the interplay between genome and environment; NWO-Spi-56-464-14192 Biobanking and Biomolecular Resources Research Infrastructure (BBMRI—NL, 184.021.007 and 184.033.111); European Research Council (ERC-230374); the Avera Institute for Human Genetics, Sioux Falls, South Dakota (USA) and the National Institutes of Health (NIH, R01D0042157-01A); and the National Institute of Mental Health Grand Opportunity grants (grant nos. 1RC2MH089951-01 and 1RC2 MH089995-01). The Texas Twin Project is supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development grants (nos. R01HD083613 and R01HD092548). AddHealth is supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development grant (no. P01HD31921), and GWAS grants (nos. R01HD073342 and R01HD060726), with cooperative funding from 23 other federal agencies and foundations. The Dunedin Multidisciplinary Health and Development Study is supported by the NZ HRC, NZ MBIE, National Institute on Aging grant (no. R01AG032282), and a UK Medical Research Council grant (no. MR/P005918/1). The E-Risk Study is supported by a UK Medical Research Council grant (no. G1002190) and Eunice Kennedy Shriver National Institute of Child Health and Human Development grant (no. R01HD077482). The WLS is supported by National Institute on Aging grants (nos. R01AG041868 and P30AG017266). Some of the work used a high-performance computing facility partially supported by grant no. 2016-IDG-1013 from the North Carolina Biotechnology Center. The Population Research Center at the University of Texas at Austin is supported by NIH grant no. P2CHD042849. P.A.D. is supported by grant no. 531003014 from the Netherlands Organisation for Health Research and Development (ZonMW). P.B. is supported by the NORFACE-DIAL grant no. 462-16-100. S.R.C. is supported by the UK Medical Research Council grant no. MR/R024065/1 and NIH grant no. R01AG054628. E.M.T.-D. is supported by NIH grant nos. R01AG054628 and R01HD083613. A.A. is supported by the Foundation Volksbond Rotterdam and by ZonMw (grant no. 849200011). E.v.B. is supported by NWO VENI (grant no. 451-15-017). D.I.B. acknowledges the Royal Netherlands Academy of Science (KNAW) Professor Award (PAH/6635). B.W.D. is supported by award no. 96-17-04 from the Russell Sage Foundation and the Ford Foundation. H.F.I. was supported by the ‘Aggression in Children: Unraveling gene-environment interplay to inform Treatment and InterventiON strategies’ project (ACTION). ACTION received funding from the European Union Seventh Framework Program (FP7/2007-2013) under grant agreement no. 602768. J.W. is supported by a postdoctoral fellowship by the AXA Research Fund. D.W.B. is a fellow of the Canadian Institute for Advanced Research Child Brain Development Network. K.P.H. and E.M.T.-D. are Faculty Research Associates of the Population Research Center at the University of Texas at Austin, which is supported by a grant (no. 5-R24-HD042849) from the Eunice Kennedy Shriver National Institute of Child Health and Human Development. M.G.N. is supported by ZonMW grants (nos. 849200011 and 531003014), a VENI grant awarded by NWO (VI.Veni.191 G.030) and an NIH grant (no. R01MH120219).

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D.W.B., K.P.H., M.G.N., P.A.D. and M.M. conceived and designed the experiment and the idea for the study, with assistance from E.M.T.-D., B.W.D., P.B., C.M. and J.W. P.A.D., M.M., T.T.M., P.B., B.W.D., D.W.B., D.L.C., K.S., S.R.C., M.G.N., A.A. and H.F.I. analyzed the data. D.W.B., K.P.H., M.G.N., M.M., P.A.D. and E.M.T.-D. wrote the paper with helpful contributions from P.B., B.W.D. and S.R.C. A.D.G., L.A., E.v.B., D.I.B., A.C., K.M.H., T.E.M., R.P., J.A.P., B.S.W., E.L.d.Z. and previously mentioned authors contributed to the interpretation of data, provided critical feedback on manuscript drafts and approved the final draft.

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Correspondence to Daniel W. Belsky, K. Paige Harden or Michel G. Nivard.

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Demange, P.A., Malanchini, M., Mallard, T.T. et al. Investigating the genetic architecture of noncognitive skills using GWAS-by-subtraction. Nat Genet 53, 35–44 (2021). https://doi.org/10.1038/s41588-020-00754-2

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