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Front Genet. 2019 Jan 14;9:715. doi: 10.3389/fgene.2018.00715. eCollection 2018.

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

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Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan.
Center for Neuropsychiatric Research, National Health Research Institutes, Zhunan, Taiwan.
Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan.
Division of Psychiatry, National Yang-Ming University, Taipei, Taiwan.


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 ADAMTS7P1 gene at chromosome 15q25.2 was detected to interact with alcohol consumption on diastolic blood pressure (p = 9.5 × 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 × 10-5). Regarding the computation time required for a genome-wide G × 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 × E analyses.


Taiwan Biobank; diastolic blood pressure; gene-alcohol interaction; hypertension; multiple testing correction; systolic blood pressure

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