A Bayesian approach to gene––gene and gene––environment interactions in chronic fatigue syndrome. (January 2009)

• Record Type: Journal Article
• Authors: Lin, Eugene; Hsu, Sen-Yen
• Is Part Of: Pharmacogenomics. Volume 10:Number 1(2009)
• Page Start: 35
• Page End: 42
• Publication Date: 2009-01
• Subjects: Bayesian variable selection -- chronic fatigue syndrome -- gene––environment interactions -- gene––gene interactions -- genomics -- Gibbs sampling -- single nucleotide polymorphisms; Pharmacogenomics -- Periodicals; 615.1
• DOI: 10.2217/14622416.10.1.35
• Languages: English
• ISSNs: 1462-2416

Abstract

Introduction: In the study of genomics, it is essential to address gene––gene and gene––environment interactions for describing the complex traits that involves disease-related mechanisms. In this work, our goal is to detect gene––gene and gene––environment interactions resulting from the analysis of chronic fatigue syndrome patients'' genetic and demographic factors including SNPs, age, gender and BMI.Materials & methods: We employed the dataset that was original to the previous study by the Centers for Disease Control and Prevention Chronic Fatigue Syndrome Research Group. To investigate gene––gene and gene––environment interactions, we implemented a Bayesian based method for identifying significant interactions between factors. Here, we employed a two-stage Bayesian variable selection methodology based on Markov Chain Monte Carlo approaches.Results: By applying our Bayesian based approach, NR3C1 was found in the significant two-locus gene––gene effect model, as well as in the significant two-factor gene––environment effect model. Furthermore, a significant gene––environment interaction was identified between NR3C1 and gender. These results support the hypothesis that NR3C1 and gender may play a role in biological mechanisms associated with chronic fatigue syndrome.Conclusion: We demonstrated that our Bayesian based approach is a promising method to assess the gene––gene and gene––environment interactions in chronic fatigue syndrome patients by using genetic factors, such as SNPs, and demographic factors such as age, gender and BMI.