AUTHOR=Lin Wan-Yu , Huang Ching-Chieh , Liu Yu-Li , Tsai Shih-Jen , Kuo Po-Hsiu TITLE=Genome-Wide Gene-Environment Interaction Analysis Using Set-Based Association Tests JOURNAL=Frontiers in Genetics VOLUME=9 YEAR=2019 URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2018.00715 DOI=10.3389/fgene.2018.00715 ISSN=1664-8021 ABSTRACT=
The identification of gene-environment interactions (G × E) may eventually guide health-related choices and medical interventions for complex diseases. More powerful methods must be developed to identify G × E. The “adaptive combination of Bayes factors method” (ADABF) has been proposed as a powerful genome-wide polygenic approach to detect G × E. In this work, we evaluate its performance when serving as a gene-based G × 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 × E tests. However, SBERIA suffers from a power loss when 50% SNP main effects are in the same direction with the SNP × E interaction effects while 50% are in the opposite direction. We further applied these seven G × E methods to the Taiwan Biobank data to explore gene× alcohol interactions on blood pressure levels. The