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Investigating the Genetic Architecture of Non-Cognitive Skills Using GWAS-by-Subtraction

Author

Listed:
  • Perline Demange
  • Margherita Malanchini
  • Travis Mallard
  • Pietro Biroli

    (University of Zurich)

  • Simon Cox
  • Andrew Grotzinger
  • Elliot Tucker-Drob
  • Abdel Abdellaoui
  • Louise Arseneault
  • Elsje van Bergen
  • Dorret Boomsma
  • Avshalom Caspi

    (Duke University)

  • David Corcoran
  • Benjamin Domingue
  • Kathleen Harris
  • Hill Ip
  • Colter Mitchell
  • Terrie E. Moffitt

    (Duke University / King's College London)

  • Richie Poulton
  • Joseph Prinz
  • Karen Sugden
  • Jasmin Wertz
  • Ben Williams

    (George Washington University)

  • Eveline de Zeeuw
  • Daniel Belsky

    (Columbia University)

  • K. Paige Harden

    (University of Texas, Austin)

  • Michel Nivard

Abstract

Educational attainment (EA) is influenced by characteristics other than cognitive ability, but little is known about the genetic architecture of these “non-cognitive†contributions to EA. Here, we use Genomic Structural Equation Modelling and prior genome-wide association studies (GWASs) of EA (N = 1,131,881) and cognitive test performance (N = 257,841) to estimate SNP associations with EA 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 EA. Non-cognitive genetics were as strongly related to socioeconomic success and longevity as genetic variants associated with cognitive performance. Noncognitive genetics were further related to openness to experience and other personality traits, less risky behavior, and increased risk for psychiatric disorders. Non-cognitive genetics were enriched in the same brain tissues and cell types as cognitive performance, but showed different associations with gray-matter brain volumes. By conducting a GWAS of a phenotype that was not directly measured, we offer a first view of genetic architecture of non-cognitive skills influencing educational success.

Suggested Citation

  • Perline Demange & Margherita Malanchini & Travis Mallard & Pietro Biroli & Simon Cox & Andrew Grotzinger & Elliot Tucker-Drob & Abdel Abdellaoui & Louise Arseneault & Elsje van Bergen & Dorret Boomsma, 2020. "Investigating the Genetic Architecture of Non-Cognitive Skills Using GWAS-by-Subtraction," Working Papers 2020-028, Human Capital and Economic Opportunity Working Group.
    • Handle: RePEc:hka:wpaper:2020-028
    Note: ECI, HI, IP
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    References listed on IDEAS

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    Cited by:

    1. Andrew E. Clark & Conchita D'Ambrosio & Simone Ghislandi & Anthony Lepinteur & Giorgia Menta, 2021. "Maternal depression and child human capital: a genetic instrumental-variable approach," CEP Discussion Papers dp1749, Centre for Economic Performance, LSE.
    2. Hyeokmoon Kweon & Casper A.P. Burik & Richard Karlsson Linner & Ronald de Vlaming & Aysu Okbay & Daphne Martschenko & Kathryn Paige Harden & Thomas A. DiPrete & Philipp D. Koellinger, 2020. "Genetic Fortune: Winning or Losing Education, Income, and Health," Tinbergen Institute Discussion Papers 20-053/V, Tinbergen Institute, revised 01 Dec 2020.
    3. Atticus Bolyard & Peter Savelyev, 2021. "Understanding the Educational Attainment Polygenic Score and its Interactions with SES in Determining Health in Young Adulthood," Working Papers 2021-026, Human Capital and Economic Opportunity Working Group.
    4. Kourtellos, Andros & Marr, Christa & Tan, Chih Ming, 2020. "Local Intergenerational Mobility," European Economic Review, Elsevier, vol. 126(C).

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    More about this item

    Keywords

    genome-wide significant loci; socioeconomic success; longevity;
    All these keywords.

    JEL classification:

    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • I10 - Health, Education, and Welfare - - Health - - - General
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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