Gene expression correlates of unexplained fatigue.

Abstract:

:Quantitative trait analysis (QTA) can be used to test whether the expression of a particular gene significantly correlates with some ordinal variable. To limit the number of false discoveries in the gene list, a multivariate permutation test can also be performed. The purpose of this study is to identify peripheral blood gene expression correlates of fatigue using quantitative trait analysis on gene expression data from 20,000 genes and fatigue traits measured using the multidimensional fatigue inventory (MFI). A total of 839 genes were statistically associated with fatigue measures. These mapped to biological pathways such as oxidative phosphorylation, gluconeogenesis, lipid metabolism, and several signal transduction pathways. However, more than 50% are not functionally annotated or associated with identified pathways. There is some overlap with genes implicated in other studies using differential gene expression. However, QTA allows detection of alterations that may not reach statistical significance in class comparison analyses, but which could contribute to disease pathophysiology. This study supports the use of phenotypic measures of chronic fatigue syndrome (CFS) and QTA as important for additional studies of this complex illness. Gene expression correlates of other phenotypic measures in the CFS Computational Challenge (C3) data set could be useful. Future studies of CFS should include as many precise measures of disease phenotype as is practical.

journal_name

Pharmacogenomics

journal_title

Pharmacogenomics

authors

Whistler T,Taylor R,Craddock RC,Broderick G,Klimas N,Unger ER

doi

10.2217/14622416.7.3.395

subject

Has Abstract

pub_date

2006-04-01 00:00:00

pages

395-405

issue

3

eissn

1462-2416

issn

1744-8042

journal_volume

7

pub_type

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