Genome-Wide Gene-Environment Interaction Analysis Using Set-Based Association Tests

The identification of gene-environment interactions (G x E) may eventually guide health-related choices and medical interventions for complex diseases. More powerful methods must be developed to identify G x E. The "adaptive combination of Bayes factors method" (ADABF) has been proposed as a powerful genome-wide polygenic approach to detect G x E. In this work, we evaluate its performance when serving as a gene-based G x E test. We compare ADABF with six tests including the "Set-Based gene-EnviRonment InterAction test" (SBERIA), "gene-environment set association test" (GESAT), etc. With extensive simulations, SBERIA and ADABF are found to be more powerful than other G x E tests. However, SBERIA suffers from a power loss when 50% SNP main effects are in the same direction with the SNP x E interaction effects while 50% are in the opposite direction. We further applied these seven G x E methods to the Taiwan Biobank data to explore genex alcohol interactions on blood pressure levels. The ADAMTS7P1 gene at chromosome 15q25.2 was detected to interact with alcohol consumption on diastolic blood pressure (p = 9.5 x 10(-7), according to the GESAT test). At this gene, the P-values provided by other six tests all reached the suggestive significance level (p < 5 x 10(-5)). Regarding the computation time required for a genome-wide G x E analysis, SBERIA is the fastest method, followed by ADABF. Considering the validity, power performance, robustness, and computation time, ADABF is recommended for genome-wide G x E analyses.