Journal
BIOINFORMATICS
Volume 28, Issue 10, Pages 1353-1358Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/bts163
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Funding
- National Institutes of Health [R01-MH090936, R01-ES015241]
- US Environmental Protection Agency [STAR RD83382501, RD83272001]
- National Cancer Institute [R01-CA138255]
- National Science Foundation [DMS-0907177]
- National Institute of Mental Health [R01-MH090936]
- Gillings Innovation Laboratory in Statistical Genomics
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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.
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