Research Article

A Bayesian approach to gene–gene and gene–environment interactions in chronic fatigue syndrome

Eugene Lin

† Author for correspondence

Vita Genomics, Inc., 7th floor, No. 6, Sec. 1, Jung-Shing Road, Wugu Shiang, Taipei, Taiwan.

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Email the corresponding author at eugene.lin@vitagenomics.com

Sen-Yen Hsu

Department of Psychiatry, Chi Mei Medical Center, Liouying, Tainan, Taiwan

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Published Online:23 Dec 2008https://doi.org/10.2217/14622416.10.1.35

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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.

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Papers of special note have been highlighted as: ▪ of interest ▪▪ of considerable interest

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Published online 23 December 2008

Published in print January 2009

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Acknowledgements

The authors extend their sincere thanks to Vita Genomics, Inc. for funding this research. The authors would also like to thank C Wang for helpful suggestions and thank the anonymous reviewers for their constructive comments, which improved the context and the presentation of this paper.

Financial & competing interests disclosure

This research was funded by Vita Genomics. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

The authors state that they have obtained appropriate institutional review board approval or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved.

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