Advanced technology in whole-genome sequencing has offered the opportunity to comprehensively investigate the genetic contribution, particularly rare variants, to complex traits. Many rare variants analysis methods have been developed to jointly model the marginal effect but methods to detect gene-environment (GE) interactions are underdeveloped. Identifying the modification effects of environmental factors on genetic risk poses a considerable challenge. To tackle this challenge, we develop a unified method to detect GE interactions of a set of rare variants using generalized linear mixed effect model. The proposed method can accommodate both binary and continuous traits in related or unrelated samples. Under this model, genetic main effects, sample relatedness and GE interactions are modeled as random effects. We adopt a kernel-based method to leverage the joint information across rare variants and implement variance component score tests to reduce the computational burden. Our simulation study shows that the proposed method maintains correct type I error rates and high power under various scenarios, such as differing the direction of main genotype and GE interaction effects and the proportion of causal variants in the model for both continuous and binary traits. We illustrate our method to test gene-based interaction with smoking on body mass index or overweight status in the Framingham Heart Study and replicate the CHRNB4 gene association reported in previous large consortium meta-analysis of single nucleotide polymorphism (SNP)-smoking interaction. Our proposed set-based GE test is computationally efficient and is applicable to both binary and continuous phenotypes, while appropriately accounting for familial or cryptic relatedness.
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