Supplementary Material for: Does Accounting for Gene-Environment Interactions Help Uncover Association between Rare Variants and Complex Diseases?
datasetposted on 11.04.2013 by Kazma R., Cardin N.J., Witte J.S.
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
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.