Leveraging longitudinal data to boost statistical power for gene-environment interaction analysis.
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- Record sourced from PubMed, PMID 42304093.
- Also identified by DOI 10.1038/s43588-026-01002-z.
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Abstract
Gene-environment interaction (G×E) analyses play a crucial role in advancing genetic discovery, addressing missing heritability, and facilitating precision medicine. However, existing G×E methods are mostly designed for cross-sectional data, limiting the utility of longitudinal data. Here we propose SAGELD, a scalable and accurate genome-wide G×E method for longitudinal traits that controls for sample relatedness in large-scale datasets. SAGELD uses matrix projection to construct test statistics and the SPA<sub>GRM</sub> framework to efficiently control for sample relatedness, achieving 10- to 10,000-fold speedups over existing methods while maintaining greater power than cross-sectional analyses. We evaluated SAGELD through extensive simulations and UK Biobank analyses. Using age and body mass index as environmental exposures, we identified 74 loci with genetic × age interactions and 5 loci with genetic × adiposity interactions in the pooled analysis of longitudinal primary care data and cross-sectional assessment data. These results highlight the advantages of leveraging longitudinal data in G×E analyses.