Quantitative Epistasis Analysis and Pathway Inference from Genetic Interaction Data

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dc.contributor.authorPhenix, Hilary
dc.contributor.authorMorin, Katy
dc.contributor.authorBatenchuk, Cory
dc.contributor.authorParker, Jacob
dc.contributor.authorAbedi, Vida
dc.contributor.authorYang, Liu
dc.contributor.authorTepliakova, Lioudmila
dc.contributor.authorPerkins, Theodore J.
dc.contributor.authorKærn, Mads
dc.date.accessioned2011-05-25T19:01:05Z
dc.date.available2011-05-25T19:01:05Z
dc.date.created2011
dc.date.issued2011-05-25
dc.identifier.urihttp://hdl.handle.net/10393/20021
dc.description.abstractInferring regulatory and metabolic network models from quantitative genetic interaction data remains a major challenge in systems biology. Here, we present a novel quantitative model for interpreting epistasis within pathways responding to an external signal. The model provides the basis of an experimental method to determine the architecture of such pathways, and establishes a new set of rules to infer the order of genes within them. The method also allows the extraction of quantitative parameters enabling a new level of information to be added to genetic network models. It is applicable to any system where the impact of combinatorial loss-of-function mutations can be quantified with sufficient accuracy. We test the method by conducting a systematic analysis of a thoroughly characterized eukaryotic gene network, the galactose utilization pathway in Saccharomyces cerevisiae. For this purpose, we quantify the effects of single and double gene deletions on two phenotypic traits, fitness and reporter gene expression. We show that applying our method to fitness traits reveals the order of metabolic enzymes and the effects of accumulating metabolic intermediates. Conversely, the analysis of expression traits reveals the order of transcriptional regulatory genes, secondary regulatory signals and their relative strength. Strikingly, when the analyses of the two traits are combined, the method correctly infers ~80% of the known relationships without any false positives.
dc.language.isoen
dc.titleQuantitative Epistasis Analysis and Pathway Inference from Genetic Interaction Data
dc.typeArticle
dc.identifier.doi10.1371/journal.pcbi.1002048
CollectionMédecine cellulaire et moléculaire // Cellular and Molecular Medicine
Publications en libre accès financées par uOttawa // uOttawa financed open access publications

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