Genome-Wide Gene-Environment Interaction Analysis Using Set-Based Association Tests - Lin, Wan-Yu; Huang, Ching-Chieh; Liu, Yu-Li; Tsai, Shih-Jen; Kuo, Po-Hsiu (Frontiers in Genetics, 2019)
The identification of gene-environment interactions (GxE) may eventually guide health-related choices and medical interventions for complex diseases. More powerful methods must be developed to identify GxE. The “adaptive combination of Bayes factors method” (ADABF) has been proposed as a powerful genome-wide polygenic approach to detect GxE. In this work, we evaluate its performance when serving as a gene-based GxE 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 GxE tests. However, SBERIA suffers from a power loss when 50% SNP main effects are in the same direction with the SNPxE interaction effects while 50% are in the opposite direction. We further applied these seven GxE methods to the Taiwan Biobank data to explore gene x 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 = 9.5E-7 , according to the GESAT test). At this gene, the P-values provided by other six tests all reached the suggestive significance level (p < 5E-5). Regarding the computation time required for a genome-wide GxE analysis, SBERIA is the fastest method, followed by ADABF. Considering the validity, power performance, robustness, and computation time, ADABF is recommended for genome-wide GxE analyses.