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

Shameek Biswas,John D Storey,Joshua M Akey

doi:10.1186/1471-2105-9-244

Abstract

BackgroundThe 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.ResultsTo 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.ConclusionThese results demonstrate that dimension reduction methods are a useful and complementary approach for probing the genetic architecture of gene expression variation.

Highlights

The combination of gene expression profiling with linkage analysis has become a powerful paradigm for mapping gene expression quantitative trait loci
The expression level of each gene was treated as a quantitative trait and expression quantitative trait loci (eQTL) were identified by linkage analysis using 3312 genetic markers distributed across the genome
With more segregants compared to a previous study [8], we identify a larger set of gene expression traits that link to each of the previously described eQTL hotspots

Summary

Introduction

The combination of gene expression profiling with linkage analysis has become a powerful paradigm for mapping gene expression quantitative trait loci (eQTL). 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. The combination of gene expression profiling and classic quantitative trait locus (QTL) mapping has emerged as an important tool in dissecting the genetic basis of gene expression variation [1,2,3,4,5]. Current statistical methods that analyze high-dimensional phenotypes, such as expression traits, one trait at a time suffer from low power because of the challenges (page number not for citation purposes)

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