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BMC Bioinformatics. 2018 Mar 9;19(1):95. doi: 10.1186/s12859-018-2088-3.

HT-eQTL: integrative expression quantitative trait loci analysis in a large number of human tissues.

Author information

1
Department of Biostatistics, Mailman School of Public Health, Columbia University, 722 W 168 Street, New York, USA. gl2521@cumc.columbia.edu.
2
Center for Human Health and the Environment and Bioinformatics Research Center, North Carolina State University, 850 Main Campus Drive, Raleigh, 27695, USA.
3
Department of Statistics and Biological Sciences, North Carolina State University, 2311 Stinson Drive, Raleigh, 27695, USA.
4
Department of Statistics and Operations Research and Department of Biostatistics, University of North Carolina at Chapel Hill, 318 E Cameron Avenue, Chapel Hill, 27599, USA.

Abstract

BACKGROUND:

Expression quantitative trait loci (eQTL) analysis identifies genetic markers associated with the expression of a gene. Most existing eQTL analyses and methods investigate association in a single, readily available tissue, such as blood. Joint analysis of eQTL in multiple tissues has the potential to improve, and expand the scope of, single-tissue analyses. Large-scale collaborative efforts such as the Genotype-Tissue Expression (GTEx) program are currently generating high quality data in a large number of tissues. However, computational constraints limit genome-wide multi-tissue eQTL analysis.

RESULTS:

We develop an integrative method under a hierarchical Bayesian framework for eQTL analysis in a large number of tissues. The model fitting procedure is highly scalable, and the computing time is a polynomial function of the number of tissues. Multi-tissue eQTLs are identified through a local false discovery rate approach, which rigorously controls the false discovery rate. Using simulation and GTEx real data studies, we show that the proposed method has superior performance to existing methods in terms of computing time and the power of eQTL discovery.

CONCLUSIONS:

We provide a scalable method for eQTL analysis in a large number of tissues. The method enables the identification of eQTL with different configurations and facilitates the characterization of tissue specificity.

KEYWORDS:

Empirical Bayes; Expression quantitative trait loci; Genotype-tissue expression project; Local false discovery rate; Tissue specific

PMID:
29523079
PMCID:
PMC5845327
DOI:
10.1186/s12859-018-2088-3
[Indexed for MEDLINE]
Free PMC Article

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