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          Institute: MPI für Entwicklungsbiologie     Collection: Abteilung 6 - Molecular Biology (D. Weigel)     Display Documents



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ID: 561639.0, MPI für Entwicklungsbiologie / Abteilung 6 - Molecular Biology (D. Weigel)
A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies
Authors:Stegle, O.; Parts, L.; Durbin, R.; Winn, J.
Date of Publication (YYYY-MM-DD):2010-05
Title of Journal:PLoS Computational Biology
Volume:6
Issue / Number:5
Sequence Number of Article:1000770
Review Status:not specified
Audience:Not Specified
Abstract / Description: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 from simulations, yeast, mouse, and human. Employing Bayesian complexity control and joint modelling is shown to result in more precise estimates of the contribution of different confounding factors resulting in additional associations to measured transcript levels compared to alternative approaches. We present a threefold larger collection of cis eQTLs than previously found in a whole-genome eQTL scan of an outbred human population. Altogether, 27% of the tested probes show a significant genetic association in cis, and we validate that the additional eQTLs are likely to be real by replicating them in different sets of individuals. Our method is the next step in the analysis of high-dimensional phenotype data, and its application has revealed insights into genetic regulation of gene expression by demonstrating more abundant cis-acting eQTLs in human than previously shown. Our software is freely available online at http://www.sanger.ac.uk/resources/software/peer/.
Free Keywords:Animals; *Bayes Theorem; Databases, Genetic; *Gene Expression; Humans; Internet; Markov Chains; Mice; *Models, Genetic; Models, Statistical; Phenotype; *Quantitative Trait Loci; Reproducibility of Results; *Software; Yeasts
External Publication Status:published
Document Type:Article
Affiliations:MPI für Entwicklungsbiologie/Abteilung 6 - Molekulare Biologie (Detlef Weigel)
External Affiliations:%G eng
Identifiers:ISSN:1553-7358 (Electronic) 1553-734X (Linking) %R 10.1... [ID No:1]
URL:http://www.ncbi.nlm.nih.gov/pubmed/20463871 [ID No:2]
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