Matrix eQTL: ultra fast eQTL analysis via large matrix operations

By Andrey A. Shabalin. Bioinform., 2011

1,611 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b088e0beee73075d4ebcc35469df4b67b70e51f5

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Citations in the corpus

Covariate powered cross‐weighted multiple testing (2017, methodology): “Therefore, a common choice made by implementations such as Matrix eQTL (Shabalin, 2012) to reduce storage requirements is to only report p-values below some threshold (e.”
Data-driven hypothesis weighting increases detection power in genome-scale multiple testing (2016, methodology): “p-values for association were calculated as described in the original paper [16] using Matrix eQTL [48].”
Innate Immune Activity Conditions the Effect of Regulatory Variants upon Monocyte Gene Expression (2014, methodology): “The eQTL analysis was performed with the R package MatrixEQTL (18) using an additive linear model.”
Pangenomics enables genotyping known structural variants in 5,202 diverse genomes (2021, background): “MatrixEQTL ( 49 ) identified SV-eQTLs while controlling for sex and population structures, as summarized by the first four principal components.”
Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2017, methodology): “All variants within a window of 100kbp of each gene were tested with linear regression using Matrix eQTL 58 .”
A survey of best practices for RNA-seq data analysis (2016, background): “Many studies have focused on testing only SNPs in the cis region surrounding the gene in question, and computationally efficient approaches have been developed recently to allow extremely swift mapping of eQTLs genome-wide [136].”
RNA Sequencing and Analysis (2015, positive): “To mitigate this computational burden, software has been developed such as Matrix eQTL to ef ﬁ ciently test the associations by modeling the effect of genotype as either additive linear (least squares model) or categorical (ANOVA model) (Shabalin 2012).”
Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma. (2018, background): “4.11) NA https://github.com/uscepigenomecenter/EGC.tools FACETS Shen and Seshan, 2016 https://github.com/mskcc/facets DNA methylation background correction Triche et al., 2013 Genome Analysis Toolkit Van der Auwera et al., 2013 https://software.broadinstitute.org/gatk/ GISTIC, GISTIC2 Mermel et al.,”