Methodology article
Mapping gene expression quantitative trait loci by singular value decomposition and independent component analysis
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* Corresponding author: Joshua M Akey akeyj@u.washington.edu
1 Department of Genome Sciences, University of Washington, 1705 NE Pacific Street, Seattle, WA, 98195, USA
2 Department of Biostatistics, University of Washington, 1705 NE Pacific Street, Seattle, WA, 98195, USA
BMC Bioinformatics 2008, 9:244 doi:10.1186/1471-2105-9-244
Published: 20 May 2008Abstract
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 Ontology categories including metabolic pathways, stress response, RNA processing, ion transport, retro-transposition and telomeric maintenance. Genome-wide linkage analysis was performed on the top 20 meta-traits from both techniques. In total, 21 eQTL were found, of which 11 are novel. Interestingly, both cis and trans-linkages to the meta-traits were observed.
Conclusion
These results demonstrate that dimension reduction methods are a useful and complementary approach for probing the genetic architecture of gene expression variation.