A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies.

ABSTRACT: Gene expression measurements are influenced by a wide range of factors, such as the state of the cell, experimental conditions and variants in the sequence of regulatory regions. To understand the effect of a variable of interest, such as the genotype of a locus, it is important to account for variation that is due to confounding causes. Here, we present VBQTL, a probabilistic approach for mapping expression quantitative trait loci (eQTLs) that jointly models contributions from genotype as well as known and hidden confounding factors. VBQTL is implemented within an efficient and flexible inference framework, making it fast and tractable on large-scale problems. We compare the performance of VBQTL with alternative methods for dealing with confounding variability on eQTL mapping datasets fro

SUBMITTER: Stegle O 

PROVIDER: S-EPMC2865505 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature