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

Greg C. Imholte | Zendy; MariePier ScottBoyer | Zendy; Aurélie Labbé | Zendy; Christian F. Deschepper | Zendy; Raphaël Gottardo | Zendy

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

Author(s) -

Greg C. Imholte,

MariePier ScottBoyer,

Aurélie Labbé,

Christian F. Deschepper,

Raphaël Gottardo

Publication year - 2013

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btt485

Subject(s) - bioconductor , expression quantitative trait loci , r package , computer science , bayesian probability , markov chain monte carlo , univariate , data mining , computational biology , biology , machine learning , artificial intelligence , single nucleotide polymorphism , gene , genetics , computational science , multivariate statistics , genotype

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.

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