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Meta analysis of Chronic Fatigue Syndrome through integration of clinical, gene expression, SNP and proteomic data – Source: Bioinformation, Apr 22, 2011

  [ 14 votes ]   [ Discuss This Article ]
By Vasyl Pihur, Susmita Datta, Somnath Datta • www.ProHealth.com • May 23, 2011


[Note: See a full text pdf of this open access article, including diagrams of the gene-gene association networks the team constructed to home in on one gene that may play key role in ME/CFS fatigue, at www.bioinformation.net/006/97320630006120.pdf.]

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 10 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 11 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.

Source: Bioinformation, Apr 22, 2011;6(3):120-4. PubMed ID:21584188, by Pihur V, Datta S, Datta S. Johns Hopkins University, McKusick-Nathans Institute of Genetic Medicine, Baltimore, Maryland; Department of Bioinformatics and Biostatistics, University of Louisville, Kentucky, USA. [Email: susmita.datta@louisville.edu]





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