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iBMQ: a R/Bioconductor package for integrated Bayesian modeling of eQTL data.

Greg C Imholte1, Marie-Pier Scott-Boyer, Aurélie Labbe

  • 1Department 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 (Oxford, England)
|August 21, 2013
PubMed
Summary

We developed iBMQ, a free R package for expression quantitative loci (eQTL) analysis. It uses a joint hierarchical Bayesian model to improve the detection of trans-eQTL hotspots, outperforming existing methods.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Expression quantitative loci (eQTL) studies reveal gene regulation mechanisms.
  • Bayesian methods enhance eQTL analysis by sharing information across genes and markers.
  • Current univariate methods for eQTL analysis are suboptimal due to independent gene treatment.

Purpose of the Study:

  • To introduce iBMQ, a novel, computationally optimized, and free open-source R package for eQTL analysis.
  • To implement a joint hierarchical Bayesian model for concurrent analysis of all genes and single nucleotide polymorphisms (SNPs).
  • To improve the detection power for trans-eQTL hotspots.

Main Methods:

  • Implementation of a joint hierarchical Bayesian model.
  • Estimation of model parameters using a Markov chain Monte Carlo (MCMC) algorithm.
  • Leveraging the OpenMP parallel library for accelerated computation.

Main Results:

  • The iBMQ package demonstrates improved detection of large trans-eQTL hotspots.
  • Performance comparison on a mouse cardiac dataset shows superiority over state-of-the-art eQTL analysis packages.
  • The package is computationally optimized and free for academic use.

Conclusions:

  • iBMQ offers a powerful and efficient solution for eQTL analysis.
  • The joint modeling approach enhances the discovery of complex genetic regulatory relationships.
  • iBMQ is readily available and compatible with major operating systems.