Supplementary Website

Understanding Chronic Fatigue Syndrome (CFS) from CAMDA

Data: A Systems Biology Approach

Vasyl Pihur, Susmita Datta, Somnath Datta^*

Department of Bioinformatics and Biostatistics, University of Louisville, KY 40292, USA

Abstract

We start by constructing gene-gene association networks based on about 300 genes whose expression values vary between the groups of CFS patients (plus control). Connected components (modules) from these networks are further inspected for their predictive ability for symptom severity and genotypes of two single nucleotide polymorphisms (SNP) known to be associated with symptom severity. We use two different network construction methods and choose the common genes identified in both for added validation. Our analysis identified eleven genes which may play important roles in certain aspects of CFS or related symptoms. In particular, the gene WASF3 (aka WAVE3) possibly regulates brain cytokines involved in the mechanism of fatigue through the p38 MAPK regulatory pathway.

^{* somnath.datta@louisville.edu}

Gene-Gene Association Network constructed using the PLS method

Gene-Gene Association Network constructed using the PC method