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

Kimberley Burrows, Anni Heiskala, Jonathan P. Bradfield, Zhanna Balkhiyarova, Lijiao Ning, Mathilde Boissel, Yee-Ming Chan, Philippe Froguel, Amelie Bonnefond, Hakon Hakonarson, Alexessander Couto Alves, Deborah A. Lawlor, Marika Kaakinen, Marjo-Riitta Järvelin, Struan F. A. Grant, Kate Tilling, Inga Prokopenko, Sylvain Sebert (), Mickaël Canouil and Nicole M. Warrington ()
Additional contact information
Kimberley Burrows: MRC Integrative Epidemiology Unit at the University of Bristol
Anni Heiskala: University of Oulu
Jonathan P. Bradfield: Children’s Hospital of Philadelphia
Zhanna Balkhiyarova: University of Surrey
Lijiao Ning: Lille University Hospital
Mathilde Boissel: Lille University Hospital
Yee-Ming Chan: Boston Children’s Hospital
Philippe Froguel: Lille University Hospital
Amelie Bonnefond: Lille University Hospital
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
Marika Kaakinen: University of Surrey
Marjo-Riitta Järvelin: University of Oulu
Struan F. A. Grant: Children’s Hospital of Philadelphia
Kate Tilling: MRC Integrative Epidemiology Unit at the University of Bristol
Inga Prokopenko: University of Surrey
Sylvain Sebert: University of Oulu
Mickaël Canouil: Lille University Hospital
Nicole M. Warrington: University of Queensland

Nature Communications, 2024, vol. 15, issue 1, 1-18

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-53687-3

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