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Characterising the genetic architecture of changes in adiposity during adulthood using electronic health records

Author

Listed:
  • Samvida S. Venkatesh

    (University of Oxford
    University of Oxford)

  • Habib Ganjgahi

    (University of Oxford
    University of Oxford)

  • Duncan S. Palmer

    (University of Oxford
    University of Oxford)

  • Kayesha Coley

    (University of Leicester)

  • Gregorio V. Linchangco

    (Emory University Rollins School of Public Health
    Atlanta VA Health Care System)

  • Qin Hui

    (Emory University Rollins School of Public Health
    Atlanta VA Health Care System)

  • Peter Wilson

    (Atlanta VA Health Care System
    Emory University School of Medicine)

  • Yuk-Lam Ho

    (Veterans Affairs Boston Healthcare System)

  • Kelly Cho

    (Veterans Affairs Boston Healthcare System
    Harvard Medical School)

  • Kadri Arumäe

    (University of Tartu)

  • Laura B. L. Wittemans

    (Novo Nordisk Research Centre Oxford
    University of Oxford)

  • Christoffer Nellåker

    (University of Oxford
    University of Oxford)

  • Uku Vainik

    (University of Tartu
    University of Tartu
    University of McGill)

  • Yan V. Sun

    (Emory University Rollins School of Public Health
    Atlanta VA Health Care System)

  • Chris Holmes

    (University of Oxford
    University of Oxford
    The Alan Turing Institute)

  • Cecilia M. Lindgren

    (University of Oxford
    University of Oxford
    University of Oxford
    Broad Institute of Harvard and MIT)

  • George Nicholson

    (University of Oxford)

Abstract

Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 24.5 million primary-care health records in over 740,000 individuals in the UK Biobank, Million Veteran Program USA, and Estonian Biobank, to discover and validate the genetic architecture of adiposity trajectories. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI by 14%. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (APOE missense variant). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology of quantitative traits in adulthood.

Suggested Citation

  • Samvida S. Venkatesh & Habib Ganjgahi & Duncan S. Palmer & Kayesha Coley & Gregorio V. Linchangco & Qin Hui & Peter Wilson & Yuk-Lam Ho & Kelly Cho & Kadri Arumäe & Laura B. L. Wittemans & Christoffer, 2024. "Characterising the genetic architecture of changes in adiposity during adulthood using electronic health records," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49998-0
    DOI: 10.1038/s41467-024-49998-0
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