Matrix eQTL: ultra fast eQTL analysis via large matrix operations
Open accessAbstract
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. Availability: Matlab and R implementations are available for free at http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL Contact: shabalin@email.unc.edu
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The capability to demultiplex and identify doublets using natural genetic variation reduces per-sample and per-cell library preparation cost, does not require synthetic barcodes or split-pool strategies, and captures biological variability among individual samples while limiting unwanted technical variability.
“The capability to demultiplex and identify doublets using natural genetic variation reduces the per-sample and per-cell library preparation cost of single-cell RNA-sequencing, does not require synthetic barcodes or split-pool strategies36–40, and captures biological variability among individual samples while limiting unwanted technical variability.”
From Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2018) · cites this paper as [40]
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Kalnapenkis, A., Jõeloo, M., Lepik, K., Kukuškina, V., Kals, M., Alasoo, K., Estonian Biobank Research Team, Metspalu, A., Milani, L., Nelis, M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-57966-3 · Cited by 11 · see citing works
Interpreting noncoding genetic variation in complex traits and human disease
Ward, L. D., & Kellis, M. (2012) · Nature Biotechnology · doi:10.1038/nbt.2422 · Cited by 6 · see citing works
- Authors
- Shabalin, A. A.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Shabalin, Andrey A.
- ORCID
- Shabalin, Andrey A.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-04-06
- Volume
- 28
- Issue
- 10
- Pages
- 1353-1358
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC3348564
- OpenAlex
- W2100697281
- Cited by
- 1,694
- References
- 28
- Citations by year
- 2026: 112 · 2025: 154 · 2024: 172 · 2023: 157 · 2022: 174 · 2021: 192 · 2020: 143 · 2019: 138 · 2018: 132 · 2017: 105 · 2016: 98 · 2015: 60 · 2014: 35 · 2013: 16 · 2012: 3
- FWCI
- 21.97
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5r01ca138255-03)
- National Institute of Mental Health (R01MH090936)
- National Cancer Institute (R01CA-138255)
- National Institute of Environmental Health Sciences (R01ES015241)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- expression quantitative trait loci, computational efficiency, linear models, ANOVA models, correlated errors, heteroscedastic errors, false discovery rate, multiple comparisons, cis-eQTL, trans-eQTL
- MeSH terms
- Algorithms, Cystic Fibrosis, Genotype, Humans, Models, Genetic, Software, Gene Expression, Polymorphism, Single Nucleotide, Quantitative Trait Loci
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