ArticleNature communications2024
A framework for conducting GWAS using repeated measures data with an application to childhood BMI.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Genetic determinants of childhood blood pressure and heart rate in relation to adult health outcomes: the consortium of childhood blood pressure.European heart journal · 2026Article
- Genetic drivers of protein changes over time: Findings, considerations, and approaches in TOPMed cohorts and UK Biobank.bioRxiv : the preprint server for biology · 2026Article
- Impact of growth markers and patterns of growth on children's cardiometabolic health: a life course perspective.World journal of pediatrics : WJP · 2026Article
- Early-life body mass index and adult fat distribution: a life-course Mendelian randomisation study.BMJ open · 2026Article
- Birth Weight Percentiles and Infant and Child Growth Dynamics.JAMA network open · 2026Article
- FEMA-Long: Modeling unstructured covariances for discovery of time-dependent effects in large-scale longitudinal datasets.PLoS genetics · 2026Article
- Insight into Genome-Wide Associations of Growth Trajectories Using a Hierarchical Non-Linear Mixed Model.Biology · 2026Article
- Distinct genetic profiles influence body mass index between infancy and adolescence.Nature communications · 2026Article
- Age-specific childhood obesity and adult cholelithiasis: association and shared transcriptomic bases.International journal of obesity (2005) · 2025Article
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20 authors.
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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.
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