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

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  • Li, Gen
    • Other Affiliation: Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, USA
  • Jima, Dereje
    • Other Affiliation: Center for Human Health and the Environment and Bioinformatics Research Center, North Carolina State University, Raleigh, USA
  • Wright, Fred A
    • Other Affiliation: Center for Human Health and the Environment and Bioinformatics Research Center, North Carolina State University, Raleigh, USA; Department of Statistics and Biological Sciences, North Carolina State University, Raleigh, USA
  • Nobel, Andrew
    • Affiliation: College of Arts and Sciences, Department of Statistics and Operations Research, Gillings School of Global Public Health, Department of Biostatistics
Abstract
  • 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.
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  • Article
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  • In Copyright
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  • The Author(s)
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  • English
Bibliographic citation
  • BMC Bioinformatics. 2018 Mar 09;19(1):95
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  • BioMed Central

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