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

Autor: Burrows, Kimberley, Heiskala, Anni, Bradfield, Jonathan P., Balkhiyarova, Zhanna, Ning, Lijiao, Boissel, Mathilde, Chan, Yee-Ming, Froguel, Philippe, Bonnefond, Amelie, Hakonarson, Hakon, Alves, Alexessander Couto, Lawlor, Deborah A., Kaakinen, Marika, Järvelin, Marjo-Riitta, Grant, Struan F. A., Tilling, Kate, Prokopenko, Inga, Sebert, Sylvain, Canouil, Mickaël, Warrington, Nicole M.
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Zdroj: Nature Communications; 11/20/2024, Vol. 15 Issue 1, p1-18, 18p
Abstrakt: 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. This article presents a framework to conduct GWAS of longitudinal data where the trait of interest follows a non-linear change over time. The framework is applied to childhood BMI, identifying 13 loci with age-varying genetic effects. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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