Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

6 Issues per year


IMPACT FACTOR 2016: 0.646
5-year IMPACT FACTOR: 1.191

CiteScore 2016: 0.94

SCImago Journal Rank (SJR) 2016: 0.625
Source Normalized Impact per Paper (SNIP) 2016: 0.596

Mathematical Citation Quotient (MCQ) 2016: 0.06

Online
ISSN
1544-6115
See all formats and pricing
More options …
Volume 11, Issue 4 (Jul 2012)

Issues

Volume 10 (2011)

Volume 9 (2010)

Volume 6 (2007)

Volume 5 (2006)

Volume 4 (2005)

Volume 2 (2003)

Volume 1 (2002)

An Integrated Hierarchical Bayesian Model for Multivariate eQTL Mapping

Marie Pier Scott-Boyer / Gregory C. Imholte / Arafat Tayeb / Aurelie Labbe / Christian F. Deschepper / Raphael Gottardo
Published Online: 2012-07-12 | DOI: https://doi.org/10.1515/1544-6115.1760

Abstract

Recently, expression quantitative loci (eQTL) mapping studies, where expression levels of thousands of genes are viewed as quantitative traits, have been used to provide greater insight into the biology of gene regulation. Originally, eQTLs were detected by applying standard QTL detection tools (using a “one gene at-a-time” approach), but this method ignores many possible interactions between genes. Several other methods have proposed to overcome these limitations, but each of them has some specific disadvantages. In this paper, we present an integrated hierarchical Bayesian model that jointly models all genes and SNPs to detect eQTLs. We propose a model (named iBMQ) that is specifically designed to handle a large number G of gene expressions, a large number S of regressors (genetic markers) and a small number n of individuals in what we call a ``large G, large S, small n'' paradigm. This method incorporates genotypic and gene expression data into a single model while 1) specifically coping with the high dimensionality of eQTL data (large number of genes), 2) borrowing strength from all gene expression data for the mapping procedures, and 3) controlling the number of false positives to a desirable level. To validate our model, we have performed simulation studies and showed that it outperforms other popular methods for eQTL detection, including QTLBIM, R-QTL, remMap and M-SPLS. Finally, we used our model to analyze a real expression dataset obtained in a panel of mice BXD Recombinant Inbred (RI) strains. Analysis of these data with iBMQ revealed the presence of multiple hotspots showing significant enrichment in genes belonging to one or more annotation categories.

Keywords: Bayesian multiple regression; eQTL mapping; Markov chain Monte Carlo; multiple testing; sparse modeling; variable selection

About the article

Published Online: 2012-07-12


Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, DOI: https://doi.org/10.1515/1544-6115.1760.

Export Citation

©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston. Copyright Clearance Center

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Greg C. Imholte, Marie-Pier Scott-Boyer, Aurélie Labbe, Christian F. Deschepper, and Raphael Gottardo
Bioinformatics, 2013, Volume 29, Number 21, Page 2797
[2]
Saloua Jeidane, Marie-Pier Scott-Boyer, Nicolas Tremblay, Sophie Cardin, Sylvie Picard, Martin Baril, Daniel Lamarre, and Christian F. Deschepper
Cell Reports, 2016, Volume 17, Number 2, Page 425
[3]
Chaitanya R. Acharya, Janice M. McCarthy, Kouros Owzar, and Andrew S. Allen
BMC Bioinformatics, 2016, Volume 17, Number 1
[4]
Alex Lewin, Habib Saadi, James E. Peters, Aida Moreno-Moral, James C. Lee, Kenneth G. C. Smith, Enrico Petretto, Leonardo Bottolo, and Sylvia Richardson
Bioinformatics, 2016, Volume 32, Number 4, Page 523

Comments (0)

Please log in or register to comment.
Log in