Efficient and accurate framework for genome-wide gene-environment interaction analysis in large-scale biobanks

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 SPAGxE_CCT, a scalable and accurate framework for diverse trait types. SPAGxE_CCT 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 SPAGxE_CCT to SPAGxEmix_CCT, which accounts for population stratification and is applicable to multi-ancestry or admixed populations. SPAGxEmix_CCT can further be extended to SPAGxEmix_CCT-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 SPAGxE_CCT and SPAGxEmix_CCT are scalable to analyze large-scale study cohort, control type I error rates effectively, and maintain power.