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Statistical Applications in Genetics and Molecular Biology

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An Integrated Hierarchical Bayesian Model for Multivariate eQTL Mapping

Marie Pier Scott-Boyer1 / Gregory C. Imholte2 / Arafat Tayeb3 / Aurelie Labbe4 / Christian F. Deschepper5 / Raphael Gottardo6

1Institut de recherches cliniques de Montréal (IRCM) and Université de Montréal

2Fred Hutchinson Cancer Research Center

3Institut de recherches cliniques de Montréal (IRCM) and Université de Montréal

4University McGill

5Institut de recherches cliniques de Montréal (IRCM) and Université de Montréal

6Fred Hutchinson Cancer Research Center

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 11, Issue 4, ISSN (Online) 1544-6115, DOI: 10.1515/1544-6115.1760, July 2012

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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

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