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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.

Author information

1
Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
2
Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan.
3
Center for Neuropsychiatric Research, National Health Research Institutes, Zhunan, Taiwan.
4
Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan.
5
Division of Psychiatry, National Yang-Ming University, Taipei, Taiwan.

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 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.

KEYWORDS:

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

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