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

Perline A. Demange; Margherita Malanchini; Travis T. Mallard; Pietro Biroli; Simon R. Cox; Andrew D. Grotzinger; Elliot M. Tucker-Drob; Abdel Abdellaoui; Louise Arseneault; Elsje van Bergen; Dorret I. Boomsma; Avshalom Caspi; David L. Corcoran; Benjamin W. Domingue; Kathleen Mullan Harris; Hill F. Ip; Colter Mitchell; Terrie E. Moffitt; Richie Poulton; Joseph A. Prinz; Karen Sugden; Jasmin Wertz; Benjamin S. Williams; Eveline L. de Zeeuw; Daniel W. Belsky; K. Paige Harden; Michel G. Nivard

doi:10.1038/s41588-020-00754-2

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

Perline A. Demange, Margherita Malanchini, Travis T. Mallard, Pietro Biroli, Simon R. Cox, Andrew D. Grotzinger, Elliot M. Tucker-Drob, Abdel Abdellaoui, Louise Arseneault, Elsje van Bergen, Dorret I. Boomsma, Avshalom Caspi, David L. Corcoran, Benjamin W. Domingue, Kathleen Mullan Harris, Hill F. Ip, Colter Mitchell, Terrie E. Moffitt, Richie Poulton, Joseph A. PrinzKaren Sugden, Jasmin Wertz, Benjamin S. Williams, Eveline L. de Zeeuw, Daniel W. Belsky, K. Paige Harden, Michel G. Nivard^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

108 Citations (Scopus)

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.

Original language	English
Pages (from-to)	35-44
Number of pages	10
Journal	Nature genetics
Volume	53
Issue number	1
Early online date	1 Oct 2020
DOIs	https://doi.org/10.1038/s41588-020-00754-2
Publication status	Published - Jan 2021

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10.1038/s41588-020-00754-2

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Demange, P. A., Malanchini, M., Mallard, T. T., Biroli, P., Cox, S. R., Grotzinger, A. D., Tucker-Drob, E. M., Abdellaoui, A., Arseneault, L., van Bergen, E., Boomsma, D. I., Caspi, A., Corcoran, D. L., Domingue, B. W., Harris, K. M., Ip, H. F., Mitchell, C., Moffitt, T. E., Poulton, R., ... Nivard, M. G. (2021). Investigating the genetic architecture of noncognitive skills using GWAS-by-subtraction. Nature genetics, 53(1), 35-44. https://doi.org/10.1038/s41588-020-00754-2

@article{a69dad0e3e5540569864e6ac55458d77,

title = "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.",

author = "Demange, {Perline A.} and Margherita Malanchini and Mallard, {Travis T.} and Pietro Biroli and Cox, {Simon R.} and Grotzinger, {Andrew D.} and Tucker-Drob, {Elliot M.} and Abdel Abdellaoui and Louise Arseneault and {van Bergen}, Elsje and Boomsma, {Dorret I.} and Avshalom Caspi and Corcoran, {David L.} and Domingue, {Benjamin W.} and Harris, {Kathleen Mullan} and Ip, {Hill F.} and Colter Mitchell and Moffitt, {Terrie E.} and Richie Poulton and Prinz, {Joseph A.} and Karen Sugden and Jasmin Wertz and Williams, {Benjamin S.} and {de Zeeuw}, {Eveline L.} and Belsky, {Daniel W.} and Harden, {K. Paige} and Nivard, {Michel G.}",

note = "Funding Information: 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: {\textquoteleft}Twin-family database for behavior genetics and genomics studies{\textquoteright} (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 {\textquoteleft}Aggression in Children: Unraveling gene-environment interplay to inform Treatment and InterventiON strategies{\textquoteright} 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.191G.030) and an NIH grant (no. R01MH120219). Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive licence to Springer Nature America, Inc. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",

year = "2021",

month = jan,

doi = "10.1038/s41588-020-00754-2",

language = "English",

volume = "53",

pages = "35--44",

journal = "Nature genetics",

issn = "1061-4036",

publisher = "Nature Research",

number = "1",

}

Demange, PA, Malanchini, M, Mallard, TT, Biroli, P, Cox, SR, Grotzinger, AD, Tucker-Drob, EM, Abdellaoui, A, Arseneault, L, van Bergen, E, Boomsma, DI, Caspi, A, Corcoran, DL, Domingue, BW, Harris, KM, Ip, HF, Mitchell, C, Moffitt, TE, Poulton, R, Prinz, JA, Sugden, K, Wertz, J , Williams, BS, de Zeeuw, EL, Belsky, DW, Harden, KP & Nivard, MG 2021, 'Investigating the genetic architecture of noncognitive skills using GWAS-by-subtraction', Nature genetics, vol. 53, no. 1, pp. 35-44. https://doi.org/10.1038/s41588-020-00754-2

TY - JOUR

T1 - Investigating the genetic architecture of noncognitive skills using GWAS-by-subtraction

AU - Demange, Perline A.

AU - Malanchini, Margherita

AU - Mallard, Travis T.

AU - Biroli, Pietro

AU - Cox, Simon R.

AU - Grotzinger, Andrew D.

AU - Tucker-Drob, Elliot M.

AU - Abdellaoui, Abdel

AU - Arseneault, Louise

AU - van Bergen, Elsje

AU - Boomsma, Dorret I.

AU - Caspi, Avshalom

AU - Corcoran, David L.

AU - Domingue, Benjamin W.

AU - Harris, Kathleen Mullan

AU - Ip, Hill F.

AU - Mitchell, Colter

AU - Moffitt, Terrie E.

AU - Poulton, Richie

AU - Prinz, Joseph A.

AU - Sugden, Karen

AU - Wertz, Jasmin

AU - Williams, Benjamin S.

AU - de Zeeuw, Eveline L.

AU - Belsky, Daniel W.

AU - Harden, K. Paige

AU - Nivard, Michel G.

N1 - Funding Information: 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.191G.030) and an NIH grant (no. R01MH120219). Publisher Copyright: © 2021, The Author(s), under exclusive licence to Springer Nature America, Inc. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/1

Y1 - 2021/1

N2 - 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.

AB - 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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U2 - 10.1038/s41588-020-00754-2

DO - 10.1038/s41588-020-00754-2

M3 - Article

AN - SCOPUS:85098876287

SN - 1061-4036

VL - 53

SP - 35

EP - 44

JO - Nature genetics

JF - Nature genetics

IS - 1

ER -

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

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