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

Nature Genetics volume 53, pages 35–44 (2021)Cite this article

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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.

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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.

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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).

Author information

Author notes

  1. These authors contributed equally: Perline A. Demange, Margherita Malanchini.

  2. These authors jointly supervised this work: Daniel W. Belsky, K. Paige Harden, Michel G. Nivard.

Authors and Affiliations

  1. Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands

    Perline A. Demange, Abdel Abdellaoui, Elsje van Bergen, Dorret I. Boomsma, Hill F. Ip, Eveline L. de Zeeuw & Michel G. Nivard

  2. Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, Amsterdam, the Netherlands

    Perline A. Demange

  3. Research Institute LEARN!, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands

    Perline A. Demange, Elsje van Bergen & Eveline L. de Zeeuw

  4. Department of Biological and Experimental Psychology, Queen Mary University of London, London, UK

    Margherita Malanchini

  5. Social, Genetic and Developmental Psychiatric Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK

    Margherita Malanchini, Louise Arseneault, Avshalom Caspi & Terrie E. Moffitt

  6. Department of Psychology, University of Texas at Austin, Austin, TX, USA

    Margherita Malanchini, Travis T. Mallard, Andrew D. Grotzinger, Elliot M. Tucker-Drob & K. Paige Harden

  7. Department of Economics, University of Zurich, Zurich, Switzerland

    Pietro Biroli

  8. Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK

    Simon R. Cox

  9. Population Research Center, University of Texas at Austin, Austin, TX, USA

    Elliot M. Tucker-Drob

  10. Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands

    Abdel Abdellaoui

  11. Department of Psychology & Neuroscience, Duke University, Durham, NC, USA

    Avshalom Caspi, Terrie E. Moffitt, Karen Sugden, Jasmin Wertz & Benjamin S. Williams

  12. Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA

    Avshalom Caspi, David L. Corcoran, Terrie E. Moffitt & Joseph A. Prinz

  13. Center for Genomic and Computational Biology, Duke University, Durham, NC, USA

    Avshalom Caspi & Terrie E. Moffitt

  14. Stanford Graduate School of Education, Stanford University, Palo Alto, CA, USA

    Benjamin W. Domingue

  15. Department of Sociology and Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Kathleen Mullan Harris

  16. Institute for Social Research, University of Michigan, Ann Arbor, MI, USA

    Colter Mitchell

  17. Department of Psychology and Dunedin Multidisciplinary Health and Development Research Unit, University of Otago, Dunedin, New Zealand

    Richie Poulton

  18. Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA

    Daniel W. Belsky

  19. Robert N. Butler Columbia Aging Center, Columbia University, New York, NY, USA

    Daniel W. Belsky

Authors
  1. Perline A. Demange
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  2. Margherita Malanchini
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Contributions

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.

Corresponding authors

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

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Competing interests

The authors declare no competing interests.

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Peer review information Nature Genetics thanks David Cesarini and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Note and Figs. 1–17

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Supplementary Tables

Supplementary Tables 1–22

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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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