Integrated Analysis of Gene Expression and Genotype Variation Data for Chronic Fatigue Syndrome

Abstract

In the past few years, high throughput technologies, such as gene expression microarrays and genotyping techniques, have provided efficient ways to measure gene expression levels and genotype variation on a genome-wide scale [Schena et al., 1995; Howell et al., 1999]. Various approaches have been proposed to analyse gene expression data and genotype variation data, in order to discover the complex network of biochemical processes of complex diseases such as chronic fatigue syndrome (CFS) [Presson et al., 2008]. In the analysis of gene expression data, for example, the identification of differentially expressed genes between two groups has been of great interest, and various statistical tests have been conducted [Ghazalpour et al., 2008; Brem et al., 2002; Kang et al., 2008]. In analysing genotype variation data, logistic regression has been commonly used to model the relationship between binary clinical outcomes and discrete predictors, such as genotypes [Henshall & Goddard, 1999; Coffey et al., 2004].