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.
Bioinformatics. 2013 Nov 1;29(21):2797-8. doi: 10.1093/bioinformatics/btt485. Epub 2013 Aug 19.
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.
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.
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.
christian.deschepper@ircm.qc.ca or rgottard@fhcrc.org.
Supplementary data are available at Bioinformatics online.
最近，表达数量性状基因座 (eQTL) 的映射研究（其中基因表达水平被视为定量性状）为基因调控的生物学提供了深入了解。贝叶斯方法为分析 eQTL 研究提供了自然的建模框架，其中跨标记和/或基因共享的信息可以提高检测 eQTL 的能力。贝叶斯方法往往计算量大，需要专门的软件。因此，大多数 eQTL 研究使用独立处理每个基因的单变量方法，导致结果不理想。
我们提出了一种强大的、计算优化的、免费的开源 R 包 iBMQ。我们的软件包实现了一个联合层次贝叶斯模型，其中所有基因和 SNP 都同时进行建模。使用马尔可夫链蒙特卡罗算法估计模型参数。免费且广泛使用的 openMP 并行库加速了计算。使用小鼠心脏数据集，我们表明 iBMQ 与其他用于 eQTL 分析的最先进的软件包相比，提高了大跨 eQTL 热点的检测能力。
R 包 iBMQ 可从 Bioconductor 网站获得，网址为 http://bioconductor.org，可在 Linux、Windows 和 MAC OS X 上运行。它根据艺术许可 2.0 条款分发。
christian.deschepper@ircm.qc.ca 或 rgottard@fhcrc.org。
补充数据可在 Bioinformatics 在线获得。
