MOM overview heat plot

eQTL Mapping via Mixture Over Markers (MOM) in R/eqtlM


Traditional genetic mapping has largely focused on the identification of loci affecting one, or at most a few, complex traits. Microarrays allow for measurement of thousands of gene expression abundances, themselves complex traits, and a number of recent investigations have considered these measurements as phenotypes in mapping studies. These expression quantitative trait loci (eQTL) mapping studies have demonstrated utility for identifying candidate genes, inferring not only correlative but also causal relationships between modulator and modulated genes, identifying regulatory networks, and elucidating subclasses of clinical phenotypes. As a result of these early successes, a number of eQTL mapping efforts are now underway to localize the genetic basis of gene expression. Kendziorski and colleagues (2006) proposed a mixture over markers (MOM) model to facilitate localization of eQTL. The approach calculates transcript specific posterior probabilities that quantify evidence in favor of mapping, while accounting for multiplicities across both markers and transcripts. Details of the approach can be found in Kendziorski et al., 2006.

eqtlM_1.4.0.tar.gz contains the first formal implementation of MOM as an add-on package for R. R/eqtlM contains numerous tools for pre- and post-fit analysis and visualization, as well as functions for hot spot identification and generation of an FDR-controlled list of transcripts. The package also contains two data sets for illustration. The first is a simulation with 5000 transcripts, 100 subjects, and 23 markers on 2 chromosomes, generated using the approach described in Kendziorski et al. (2006). The second is from an F2 cross containing genotypic and phenotypic information on 60 mice from the Attie Lab at the University of Wisconsin-Madison. The original data set** involved 45,265 transcripts and 293 markers genotyped across 20 chromosomes. Since the data presented here are meant to be used as an example, we have included only the 5000 most variable expression traits.

A comprehensive vignette is also available for this package.

Contact: John A. Dawson (Maintainer): jadawson@wisc.edu

Overview and zoomed-in heat plots of the posterior probabilites are shown below from the second data set. The hot spot at D6Mit224 was anticipated, since the 60 F2 mice were leptin knockouts (Kendziorski et al. 2006). The corresponding locus for leptin is at 10.5 cM, which is between the first (D6Mit86) and second (D6Mit224) markers on chromosome 6.

OverviewZoom-in on 6


Kendziorski, C., M. Chen, M. Yuan, H. Lan, and A.D. Attie. Statistical Methods for Expression Quantitative Trait Loci (eQTL) Mapping. Biometrics 62, 19-27, 2006 .

**The F2 data set used in the manuscript is available at GEO, accession number GSE3330.



Last Modified July 2009