Display Settings:

Format

Send to:

Choose Destination
Bioinformatics. 2012 May 15;28(10):1353-8. doi: 10.1093/bioinformatics/bts163. Epub 2012 Apr 6.

Matrix eQTL: ultra fast eQTL analysis via large matrix operations.

Author information

  • Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. shabalin@email.unc.edu

Abstract

MOTIVATION:

Expression quantitative trait loci (eQTL) analysis links variations in gene expression levels to genotypes. For modern datasets, eQTL analysis is a computationally intensive task as it involves testing for association of billions of transcript-SNP (single-nucleotide polymorphism) pair. The heavy computational burden makes eQTL analysis less popular and sometimes forces analysts to restrict their attention to just a small subset of transcript-SNP pairs. As more transcripts and SNPs get interrogated over a growing number of samples, the demand for faster tools for eQTL analysis grows stronger.

RESULTS:

We have developed a new software for computationally efficient eQTL analysis called Matrix eQTL. In tests on large datasets, it was 2-3 orders of magnitude faster than existing popular tools for QTL/eQTL analysis, while finding the same eQTLs. The fast performance is achieved by special preprocessing and expressing the most computationally intensive part of the algorithm in terms of large matrix operations. Matrix eQTL supports additive linear and ANOVA models with covariates, including models with correlated and heteroskedastic errors. The issue of multiple testing is addressed by calculating false discovery rate; this can be done separately for cis- and trans-eQTLs.

PMID:
22492648
[PubMed - indexed for MEDLINE]
PMCID:
PMC3348564
Free PMC Article

Images from this publication.See all images (1)Free text

Fig. 1.
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for HighWire Icon for PubMed Central
    Loading ...
    Write to the Help Desk