Title

Authors

Affiliation

¹The Johns Hopkins University, McKusick-Nathans Institute of Genetic Medicine, Baltimore, MD 21205; ²University of Louisville, Department of Bioinformatics and Biostatistics, Louisville, KY 40292, USA

Email

susmita.datta@louisville.edu; *Corresponding author

Article Type

Prediction model

Date

Received March 03, 2011; Accepted March 16, 2011; Published April 22, 2011

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, genotypes of two single nucleotide polymorphisms (SNP) known to be associated with symptom severity, and intensity of the ten most discriminative protein features. 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.

Keywords

CFS, gene-gene interactions, microarray, proteomics, SNP

Citation

Pihur et al. Bioinformation 6(3): 120-124 (2011)

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.