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A framework for conducting GWAS using repeated measures data with an application to childhood BMI

Author

Listed:
  • Kimberley Burrows

    (MRC Integrative Epidemiology Unit at the University of Bristol
    University of Bristol)

  • Anni Heiskala

    (University of Oulu)

  • Jonathan P. Bradfield

    (Children’s Hospital of Philadelphia
    Quantinuum Research LLC)

  • Zhanna Balkhiyarova

    (University of Surrey
    University of Surrey
    Imperial College London)

  • Lijiao Ning

    (Lille University Hospital)

  • Mathilde Boissel

    (Lille University Hospital)

  • Yee-Ming Chan

    (Boston Children’s Hospital
    Harvard Medical School)

  • Philippe Froguel

    (Lille University Hospital
    Imperial College London)

  • Amelie Bonnefond

    (Lille University Hospital
    Imperial College London)

  • Hakon Hakonarson

    (Children’s Hospital of Philadelphia)

  • Alexessander Couto Alves

    (University of Surrey)

  • Deborah A. Lawlor

    (MRC Integrative Epidemiology Unit at the University of Bristol
    University of Bristol)

  • Marika Kaakinen

    (University of Surrey
    University of Surrey
    Imperial College London)

  • Marjo-Riitta Järvelin

    (University of Oulu
    Imperial College London
    Brunel University London)

  • Struan F. A. Grant

    (Children’s Hospital of Philadelphia
    Children’s Hospital of Philadelphia
    University of Pennsylvania
    University of Pennsylvania)

  • Kate Tilling

    (MRC Integrative Epidemiology Unit at the University of Bristol)

  • Inga Prokopenko

    (University of Surrey
    University of Surrey)

  • Sylvain Sebert

    (University of Oulu)

  • Mickaël Canouil

    (Lille University Hospital)

  • Nicole M. Warrington

    (University of Queensland
    University of Queensland)

Abstract

Genetic effects on changes in human traits over time are understudied and may have important pathophysiological impact. We propose a framework that enables data quality control, implements mixed models to evaluate trajectories of change in traits, and estimates phenotypes to identify age-varying genetic effects in GWAS. Using childhood BMI as an example trait, we included 71,336 participants from six cohorts and estimated the slope and area under the BMI curve within four time periods (infancy, early childhood, late childhood and adolescence) for each participant, in addition to the age and BMI at the adiposity peak and the adiposity rebound. GWAS of the 12 estimated phenotypes identified 28 genome-wide significant variants at 13 loci, one of which (in DAOA) has not been previously associated with childhood or adult BMI. Genetic studies of changes in human traits over time could uncover unique biological mechanisms influencing quantitative traits.

Suggested Citation

  • Kimberley Burrows & Anni Heiskala & Jonathan P. Bradfield & Zhanna Balkhiyarova & Lijiao Ning & Mathilde Boissel & Yee-Ming Chan & Philippe Froguel & Amelie Bonnefond & Hakon Hakonarson & Alexessander, 2024. "A framework for conducting GWAS using repeated measures data with an application to childhood BMI," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53687-3
    DOI: 10.1038/s41467-024-53687-3
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    References listed on IDEAS

    as
    1. Kathryn E. Kemper & Julia Sidorenko & Huanwei Wang & Ben J. Hayes & Naomi R. Wray & Loic Yengo & Matthew C. Keller & Michael Goddard & Peter M. Visscher, 2024. "Genetic influence on within-person longitudinal change in anthropometric traits in the UK Biobank," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
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