Mapping gene expression quantitative trait loci by singular value decomposition and independent component analysis

Background: The combination of gene expression profiling with linkage analysis has become a powerful paradigm for mapping gene expression quantitative trait loci (eQTL). To date, most studies have searched for eQTL by analyzing gene expression traits one at a time. As thousands of expression traits are typically analyzed, this can reduce power because of the need to correct for the number of hypothesis tests performed. In addition, gene expression traits exhibit a complex correlation structure, which is ignored when analyzing traits individually. Results: To address these issues, we applied two different multivariate dimension reduction techniques, the Singular Value Decomposition (SVD) and Independent Component Analysis (ICA) to gene expression traits derived from a cross between two strains of Saccharomyces cerevisiae. Both methods decompose the data into a set of meta-traits, which are linear combinations of all the expression traits. The meta-traits were enriched for several Gene ...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Shameek Biswas, John D. Storey, Joshua M. Akey
• Keywords: BMCBI 2008, Expression Traits, Gene Expression, Gene Expression Traits