Group for Research in Decision Analysis

An Integrated Hierarchical Bayesian Model for Multivariate expression-Quantitative Trait Locus (eQTL) genetic Mapping

Aurelie Labbé

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. Current data analysis and interpretation of eQTL studies involve the use of multiple methods and applications, the output of which is often fragmented. In this talk, 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 speci cally 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.