| Title: | Efficient and accurate framework for genome-wide gene-environment interaction analysis in large-scale biobanks |
| Journal: | Nature Communications |
| Published: | 29 Mar 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40157913/ |
| DOI: | https://doi.org/10.1038/s41467-025-57887-3 |
Publication 15815
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Abstract
Gene-environment interaction (G×E) analysis elucidates the interplay between genetic and environmental factors. Genome-wide association studies (GWAS) have expanded to encompass complex traits like time-to-event and ordinal traits, which provide richer phenotypic information. However, most existing scalable approaches focus only on quantitative or binary traits. Here we propose SPAGxECCT, a scalable and accurate framework for diverse trait types. SPAGxECCT fits a genotype-independent model and employs a hybrid strategy including saddlepoint approximation (SPA) for accurate p value calculation, especially for low-frequency variants and unbalanced phenotypic distributions. We extend SPAGxECCT to SPAGxEmixCCT, which accounts for population stratification and is applicable to multi-ancestry or admixed populations. SPAGxEmixCCT can further be extended to SPAGxEmixCCT-local, which identifies ancestry-specific G×E effects using local ancestry. Through extensive simulations and real data analyses of UK Biobank data, we demonstrate that SPAGxECCT and SPAGxEmixCCT are scalable to analyze large-scale study cohort, control type I error rates effectively, and maintain power.</p>
9 Keywords
- Biological Specimen Banks
- Computer Simulation
- Gene-Environment Interaction
- Genome-Wide Association Study
- Genotype
- Humans
- Models, Genetic
- Phenotype
- Polymorphism, Single Nucleotide
4 Authors
- Yuzhuo Ma
- Yanlong Zhao
- Ji-Feng 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