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

Gene-environment interaction (GxE) analysis elucidates the interplay between genetic predispositions and environmental influences, offering significant potential for precision medicine. With the increasing use of electronic health records (EHR) linked to genetic data in large-scale biobanks, genome-wide association studies (GWAS) have expanded to encompass complex traits with intricate structures, such as time-to-event and ordinal categorical traits. Although these complex traits convey more phenotypic information, most existing scalable genome-wide GxE analysis approaches only focus on quantitative or binary traits. In this work, we propose a scalable and accurate analysis framework, SPAGxE_CCT, that is applicable to a wide variety of trait types. We extend SPAGxE to SPAGxE+, which can account for sample relatedness. In addition, we extend SPAGxE_CCT to SPAGxEmix_CCT, which accounts for population stratification and is applicable to include individuals from multiple ancestries or admixed populations. We applied SPAGxE_CCT, SPAGxE+, and SPAGxEmix_CCT to analyze time-to-event traits in UK Biobank. For the SPAGxE_CCT analyses, 281,149 White British individuals were included. For the SPAGxE+ analyses, 337,367 WB individuals with sample relatedness were included.  For the SPAGxEmix_CCT analyses, 338,044 individuals from all ancestries were included. SPAGxE_CCT, SPAGxE+, and SPAGxEmix_CCT are computationally efficient to analyze large datasets with hundreds of thousands of individuals, can accurately control type I error rates while remaining powerful to identify novel GxE findings.