An integrated hierarchical Bayesian model for multivariate eQTL mapping.

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 marker

SUBMITTER: Scott-Boyer MP 

PROVIDER: S-EPMC4627701 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature