| Title: | Leveraging longitudinal data to boost statistical power for gene-environment interaction analysis |
| Journal: | Nature Computational Science |
| Published: | 16 Jun 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42304093/ |
| DOI: | https://doi.org/10.1038/s43588-026-01002-z |
Publication 20725
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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 SPAGRM 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.</p>
10 Keywords
- Body Mass Index
- Computer Simulation
- Cross-Sectional Studies
- Gene-Environment Interaction
- Genome-Wide Association Study
- Humans
- Longitudinal Studies
- Models, Genetic
- Polymorphism, Single Nucleotide
- UK Biobank
10 Authors
- He Xu
- Yuzhuo Ma
- Yufei Liu
- Yin Li
- Lin Wan
- Ji-Feng Zhang
- Yanlong Zhao
- Weihua Yue
- Peipei Zhang
- Wenjian Bi
1 Application
| Application ID | Title |
|---|---|
| 78795 | Regression methods for phenome-wide association analysis on large-scale biobank data |
Enabling scientific discoveries that improve human health