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Jun Liu Seminar 02/11/2009
1 abstract & title
| speaker: | Jun Liu, Harvard, Professor of Statistics |
|---|---|
| title: | A Bayesian Partition Method for Detecting eQTL Modules |
| time: | Friday, February 13, 2009, 2:00 pm |
| room: | RRI 101 |
| Host: | Fengzhu Sun |
Abstract:
Studies of the relationship between DNA variation and gene expression variation, often referred to as "expression quantitative trait loci (eQTL) mapping", has been conducted in many species and resulted in many significant findings. Because of the large number of genes and genetic markers in such analyses, it is extremely challenging to discover how a small number of eQTLs interact with each other to affect mRNA expression levels for a set of (most likely co-regulated) genes. We present here a novel Bayesian method to facilitate the task, in which genes with similar expression profiles and mapped to a common set of markers are treated as a module characterized by latent indicator variables. For each module, individuals are further clustered based on the similarities of the genotypes and gene expression profiles for the markers and genes in the module. A Markov chain Monte Carlo algorithm is designed to search simultaneously for the module genes and their linked markers. We show by simulation studies that this method achieves significantly improved power for detecting true eQTLs, their interactions, and their target gene clusters compared to the traditional QTL mapping methods. We applied the procedure to a data set consisting of gene expression and genotypes for 112 segregants from a cross between laboratory (BY) and wild (RM) strains of S. cerevisiae. We identified several modules containing genes mapped to single loci previously reported as eQTL hot spots. Our method dissected these large eQTL hot spots into several modules that correspond to different causal regulators or primary and secondary responses to causal perturbations. In addition, we identified nine modules associated with two eQTLs, two of which have been previously reported. However, the remaining seven are novel. We detected strong epitstatic interactions between two loci for three of the nine modules. We further demonstrated that one of the modules consisting of many daughter-cell expressed genes is regulated by AMN1 and BPH1.
Gene Regulatory Networks Special Feature (PNAS)