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Genome-wide analysis identifies molecular systems and 149 genetic loci associated with income

Author

Listed:
  • W. David Hill

    (University of Edinburgh
    University of Edinburgh)

  • Neil M. Davies

    (University of Bristol
    University of Bristol)

  • Stuart J. Ritchie

    (King’s College London)

  • Nathan G. Skene

    (Karolinska Institutet
    UCL Institute of Neurology
    Imperial College)

  • Julien Bryois

    (Karolinska Institutet)

  • Steven Bell

    (University of Cambridge
    University of Cambridge
    Addenbrooke’s Hospital)

  • Emanuele Di Angelantonio

    (University of Cambridge
    University of Cambridge
    Addenbrooke’s Hospital
    NHS Blood and Transplant)

  • David J. Roberts

    (Health Data Research UK
    University of Oxford
    NHS Blood and Transplant – Oxford Centre)

  • Shen Xueyi

    (University of Edinburgh)

  • Gail Davies

    (University of Edinburgh
    University of Edinburgh)

  • David C. M. Liewald

    (University of Edinburgh
    University of Edinburgh)

  • David J. Porteous

    (University of Edinburgh
    Western General Hospital)

  • Caroline Hayward

    (Western General Hospital)

  • Adam S. Butterworth

    (University of Cambridge
    University of Cambridge
    Addenbrooke’s Hospital)

  • Andrew M. McIntosh

    (University of Edinburgh
    University of Edinburgh)

  • Catharine R. Gale

    (University of Edinburgh
    University of Edinburgh
    University of Southampton)

  • Ian J. Deary

    (University of Edinburgh
    University of Edinburgh)

Abstract

Socioeconomic position (SEP) is a multi-dimensional construct reflecting (and influencing) multiple socio-cultural, physical, and environmental factors. In a sample of 286,301 participants from UK Biobank, we identify 30 (29 previously unreported) independent-loci associated with income. Using a method to meta-analyze data from genetically-correlated traits, we identify an additional 120 income-associated loci. These loci show clear evidence of functionality, with transcriptional differences identified across multiple cortical tissues, and links to GABAergic and serotonergic neurotransmission. By combining our genome wide association study on income with data from eQTL studies and chromatin interactions, 24 genes are prioritized for follow up, 18 of which were previously associated with intelligence. We identify intelligence as one of the likely causal, partly-heritable phenotypes that might bridge the gap between molecular genetic inheritance and phenotypic consequence in terms of income differences. These results indicate that, in modern era Great Britain, genetic effects contribute towards some of the observed socioeconomic inequalities.

Suggested Citation

  • W. David Hill & Neil M. Davies & Stuart J. Ritchie & Nathan G. Skene & Julien Bryois & Steven Bell & Emanuele Di Angelantonio & David J. Roberts & Shen Xueyi & Gail Davies & David C. M. Liewald & Davi, 2019. "Genome-wide analysis identifies molecular systems and 149 genetic loci associated with income," Nature Communications, Nature, vol. 10(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-13585-5
    DOI: 10.1038/s41467-019-13585-5
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    Cited by:

    1. Navarro Alfredo M., 2023. "Genetics and Economics," Asociación Argentina de Economía Política: Working Papers 4677, Asociación Argentina de Economía Política.
    2. Germinario, Giuseppe & Amin, Vikesh & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2022. "What can we learn about the effect of mental health on labor market outcomes under weak assumptions? Evidence from the NLSY79," Labour Economics, Elsevier, vol. 79(C).
    3. Max Lam & Chia-Yen Chen & W. David Hill & Charley Xia & Ruoyu Tian & Daniel F. Levey & Joel Gelernter & Murray B. Stein & Alexander S. Hatoum & Hailiang Huang & Anil K. Malhotra & Heiko Runz & Tian Ge, 2022. "Collective genomic segments with differential pleiotropic patterns between cognitive dimensions and psychopathology," Nature Communications, Nature, vol. 13(1), pages 1-22, December.
    4. Gianmarco Mignogna & Caitlin E. Carey & Robbee Wedow & Nikolas Baya & Mattia Cordioli & Nicola Pirastu & Rino Bellocco & Kathryn Fiuza Malerbi & Michel G. Nivard & Benjamin M. Neale & Raymond K. Walte, 2023. "Patterns of item nonresponse behaviour to survey questionnaires are systematic and associated with genetic loci," Nature Human Behaviour, Nature, vol. 7(8), pages 1371-1387, August.
    5. 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.
    6. Fletcher, Jason M. & Lu, Qiongshi & Mazumder, Bhashkar & Song, Jie, 2023. "Understanding Sibling Correlations in Education: Molecular Genetics and Family Background," IZA Discussion Papers 15862, Institute of Labor Economics (IZA).
    7. Andrea G Allegrini & Ville Karhunen & Jonathan R I Coleman & Saskia Selzam & Kaili Rimfeld & Sophie von Stumm & Jean-Baptiste Pingault & Robert Plomin, 2020. "Multivariable G-E interplay in the prediction of educational achievement," PLOS Genetics, Public Library of Science, vol. 16(11), pages 1-20, November.
    8. Braun, Lundy & Wentz, Anna & Baker, Reuben & Richardson, Ellen & Tsai, Jennifer, 2021. "Racialized algorithms for kidney function: Erasing social experience," Social Science & Medicine, Elsevier, vol. 268(C).
    9. Robinette, Jennifer W. & Boardman, Jason D., 2021. "Cognition in context: Pathways and compound risk in a sample of US non-Hispanic whites," Social Science & Medicine, Elsevier, vol. 283(C).

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