  Expression quantitative trait loci (eQTL) mapping aims to determine genomic
regions that regulate gene transcription. Expression QTL is used to study the
regulatory structure of normal tissues and to search for genetic factors in
complex diseases such as cancer, diabetes, and cystic fibrosis. A modern eQTL
dataset contains millions of SNPs and thousands of transcripts measured for
hundreds of samples. This makes the analysis computationally complex as it
involves independent testing for association for every transcript-SNP pair. The
heavy computational burden makes eQTL analysis less popular, often forces
analysts to restrict their attention to just a subset of transcripts and SNPs.
As larger genotype and gene expression datasets become available, the demand
for fast tools for eQTL analysis increases. We present a new method for fast
eQTL analysis via linear models, called Matrix eQTL. Matrix eQTL can model and
test for association using both linear regression and ANOVA models. The models
can include covariates to account for such factors as population structure,
gender, and clinical variables. It also supports testing of heteroscedastic
models and models with correlated errors. In our experiment on large datasets
Matrix eQTL was thousands of times faster than the existing popular software
for QTL/eQTL analysis. Matrix eQTL is implemented as both Matlab and R packages
and thus can easily be run on Windows, Mac OS, and Linux systems. The software
is freely available at the following address:
http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL

Genomics