Supplementary Material for: Does Accounting for Gene-Environment Interactions Help Uncover Association between Rare Variants and Complex Diseases?

Objective: To determine whether accounting for gene-environment (G×E) interactions improves the power to detect associations between rare variants and a disease, we have extended three statistical methods and compared their power under various simulated disease models. Methods: To test for association of a group of rare variants with a disease, Min-P uses the lowest p value within the group of variants, CAST (Cohort Allelic Sums Test) uses an indicator variable to quantify the rare alleles within the group of variants, and SKAT (Sequence Kernel Association Test) uses a logistic regression based on kernel machine. For each method, we incorporate a term for the G×E interaction and test for association and interaction jointly. Results: When testing for disease association with a set of rare variants, accounting for G×E interactions can improve power in specific situations (pure interaction or high proportion of causal variants interacting with the environment). However, the power of this approach can decrease, in particular in the presence of main genetic or environmental effects. Among the methods compared, the optimized and weighted SKAT performed best, whether to test for genetic association or to test it jointly with G×E interactions. Conclusion: This approach can be used in specific situations but is not appropriate for a primary analysis.研究目的：为明确纳入基因-环境（G×E）交互作用是否可提升检测稀有变异与疾病关联的检验效能，本研究拓展了三种统计方法，并在多种模拟疾病模型下对比其检验效能。 研究方法：针对一组稀有变异与疾病的关联检验，Min-P采用变异组内的最小p值；CAST（Cohort Allelic Sums Test，队列等位基因总和检验）通过指示变量量化变异组内的稀有等位基因；SKAT（Sequence Kernel Association Test，序列核关联检验）则采用基于核机器的逻辑回归模型。本研究为每种方法引入G×E交互作用项，同时开展关联与交互作用检验。 研究结果：在对一组稀有变异进行疾病关联检验时，纳入G×E交互作用可在特定场景下提升检验效能（如纯交互作用场景，或与环境存在交互的致病变异占比较高的场景）。但该策略的检验效能也可能下降，尤其当存在主要遗传效应或主要环境效应时。在对比的三种方法中，优化加权SKAT表现最优，无论仅开展遗传关联检验，还是同时开展关联与G×E交互作用检验。 结论：该方法可应用于特定场景，但不适用于首要分析。