Statistical Applications in Genetics and Molecular Biology
Editor-in-Chief: Stumpf, Michael P.H.
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Most Downloaded Articles
- A General Framework for Weighted Gene Co-Expression Network Analysis by Zhang, Bin and Horvath, Steve
- Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments by Smyth, Gordon K
- Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates by Lund, Steven P./ Nettleton, Dan/ McCarthy, Davis J. and Smyth, Gordon K.
- A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics by Schäfer, Juliane and Strimmer, Korbinian
- Normalization, bias correction, and peak calling for ChIP-seq by Diaz, Aaron/ Park, Kiyoub/ Lim, Daniel A. and Song, Jun S.
An Integrated Hierarchical Bayesian Model for Multivariate eQTL Mapping
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
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, Pages –, ISSN (Online) 1544-6115, DOI: 10.1515/1544-6115.1760, July 2012
- Published Online:
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.