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Chronic Fatigue Syndrome > Publications > Pathophysiology >

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

Lin E, Hsu SY
Pharmacogenomics. 10:35-42, 2009.

Summary

Following the 2005 Cold Spring Harbor - Banbury Center CFS Computational Challenge (C3) Workshop, CDC provided data sets from the Wichita in-hospital clinical study to Duke University for use in the Sixth International Conference for the Critical Assessment of Microarray Data Analysis (CAMDA 2006). Duke University founded CAMDA to provide a forum to critically assess different techniques used in microarray data mining. CAMDA’s aim is to establish the state-of-the-art in microarray data mining and to identify progress and highlight the direction for future effort. CAMDA utilizes a community-wide experiment approach, letting the scientific community analyze the same standard data sets. Researchers worldwide are invited to take the CAMDA challenge and those whose results are accepted are invited to present a 25 minute oral presentation. The 2006 CAMDA was the first to use a single common challenge data set, which contained all clinical, gene expression, SNP, and proteomics data from the Wichita clinical study.

This publication from the Vita Genomics Group and Department of Psychiatry – Chi Mei Medical Center, Taiwan utilized sophisticated statistical and mathematical models to examine gene to gene and gene – environmental interactions that may be involved in CFS. Their findings support the hypothesis that the gene NR3C1 and female sex may play a role in biological mechanisms involved in CFS.

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 the 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.

Page last modified on January 5, 2009

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