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Accounting for Non-genetic Factors Improves the Power of eQTL Studies

Oliver Stegle, Anitha Kannan, Richard Durbin, and John M. Winn


The recent availability of large scale data sets profiling single nucleotide polymorphisms (SNPs) and gene expression across different human populations, has directed much attention towards discovering patterns of genetic variation and their association with gene regulation. The influence of environmental, developmental and other factors on gene expression can obscure such associations. We present a model that explicitly accounts for non-genetic factors so as to improve significantly the power of an expression Quantitative Trait Loci (eQTL) study. Our method also exploits the inherent block structure of haplotype data to further enhance its sensitivity. On data from the HapMap project, we find more than three times as many significant associations than a standard eQTL method.


Publication typeProceedings
Published inInternational Conference on Research in Computational Molecular Biology

Newer versions

Oliver Stegle, Leopold Parts, Richard Durbin, and John Winn. A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies, PLoS Computational Biology, PLoS Computational Biology (Public Library of Science Computational Biology), , 6 May 2010.

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