Greg C Imholte1, Marie-Pier Scott-Boyer, Aurélie Labbe, Christian F Deschepper, Raphael Gottardo. 1. Department of Statistics, University of Washington, Seattle, WA 98195, USA, Institut de recherches cliniques de Montréal and Université de Montréal, Montréal, Quebec, Canada H2W 1R7, Faculty of Medicine, Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Quebec, Canada H3A 1A2 and Vaccine and Infections Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
Abstract
MOTIVATION: Recently, mapping studies of expression quantitative loci (eQTL) (where gene expression levels are viewed as quantitative traits) have provided insight into the biology of gene regulation. Bayesian methods provide natural modeling frameworks for analyzing eQTL studies, where information shared across markers and/or genes can increase the power to detect eQTLs. Bayesian approaches tend to be computationally demanding and require specialized software. As a result, most eQTL studies use univariate methods treating each gene independently, leading to suboptimal results. RESULTS: We present a powerful, computationally optimized and free open-source R package, iBMQ. Our package implements a joint hierarchical Bayesian model where all genes and SNPs are modeled concurrently. Model parameters are estimated using a Markov chain Monte Carlo algorithm. The free and widely used openMP parallel library speeds up computation. Using a mouse cardiac dataset, we show that iBMQ improves the detection of large trans-eQTL hotspots compared with other state-of-the-art packages for eQTL analysis. AVAILABILITY: The R-package iBMQ is available from the Bioconductor Web site at http://bioconductor.org and runs on Linux, Windows and MAC OS X. It is distributed under the Artistic Licence-2.0 terms. CONTACT: christian.deschepper@ircm.qc.ca or rgottard@fhcrc.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Recently, mapping studies of expression quantitative loci (eQTL) (where gene expression levels are viewed as quantitative traits) have provided insight into the biology of gene regulation. Bayesian methods provide natural modeling frameworks for analyzing eQTL studies, where information shared across markers and/or genes can increase the power to detect eQTLs. Bayesian approaches tend to be computationally demanding and require specialized software. As a result, most eQTL studies use univariate methods treating each gene independently, leading to suboptimal results. RESULTS: We present a powerful, computationally optimized and free open-source R package, iBMQ. Our package implements a joint hierarchical Bayesian model where all genes and SNPs are modeled concurrently. Model parameters are estimated using a Markov chain Monte Carlo algorithm. The free and widely used openMP parallel library speeds up computation. Using a mouse cardiac dataset, we show that iBMQ improves the detection of large trans-eQTL hotspots compared with other state-of-the-art packages for eQTL analysis. AVAILABILITY: The R-package iBMQ is available from the Bioconductor Web site at http://bioconductor.org and runs on Linux, Windows and MAC OS X. It is distributed under the Artistic Licence-2.0 terms. CONTACT: christian.deschepper@ircm.qc.ca or rgottard@fhcrc.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Vincent J Carey; Adam R Davis; Michael F Lawrence; Robert Gentleman; Benjamin A Raby Journal: Bioinformatics Date: 2009-04-05 Impact factor: 6.937
Authors: Marie Pier Scott-Boyer; Gregory C Imholte; Arafat Tayeb; Aurelie Labbe; Christian F Deschepper; Raphael Gottardo Journal: Stat Appl Genet Mol Biol Date: 2012-07-12