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Updated: Jun 7, 2025
08:27
Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
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A framework for conducting GWAS using repeated measures data with an application to childhood BMI
Kimberley Burrows1,2, Anni Heiskala3, Jonathan P Bradfield4,5
1MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
Nature Communications
|November 20, 2024
Summary
Investigating genetic influences on changing human traits, like childhood body mass index (BMI), reveals new insights into health and disease. This study identified novel genetic variants affecting BMI trajectories over time.
Area of Science:
- Genetics
- Human Physiology
- Biostatistics
Background:
- Genetic influences on human trait changes over time are not well understood but may have significant pathophysiological implications.
- Studying longitudinal trait changes is crucial for a comprehensive understanding of human development and disease etiology.
Purpose of the Study:
- To develop and validate a framework for analyzing age-varying genetic effects on human traits over time.
- To identify genetic loci associated with body mass index (BMI) trajectories during childhood and adolescence.
Main Methods:
- A framework incorporating data quality control and mixed models was used to evaluate trait trajectories.
- Phenotypes representing BMI changes (slope, area under the curve, peak, rebound) were estimated for 71,336 participants across six cohorts.
- Genome-Wide Association Studies (GWAS) were performed on 12 estimated BMI-related phenotypes.
Main Results:
- Twenty-eight genome-wide significant variants at 13 loci were identified through GWAS.
- One novel locus (in DAOA) was associated with childhood and adult BMI.
- The study successfully estimated age-varying genetic effects on BMI trajectories.
Conclusions:
- Genetic studies focusing on the temporal dynamics of human traits can uncover unique biological mechanisms.
- Understanding age-varying genetic effects is essential for elucidating the pathophysiology of quantitative traits.
- This framework provides a robust method for analyzing genetic influences on trait changes over the lifespan.
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