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PLoS genetics
Carter, G.W., The Jackson Laboratory, Bar Harbor, ME, United States
Hays, M., Institute for Systems Biology, Seattle, WA, United States
Sherman, A., Institute for Systems Biology, Seattle, WA, United States
Galitski, T., Institute for Systems Biology, Seattle, WA, United States, EMD Millipore, Billerica, MA, United States
Systems-level genetic studies in humans and model systems increasingly involve both high-resolution genotyping and multi-dimensional quantitative phenotyping. We present a novel method to infer and interpret genetic interactions that exploits the complementary information in multiple phenotypes. We applied this approach to a population of yeast strains with randomly assorted perturbations of five genes involved in mating. We quantified pheromone response at the molecular level and overall mating efficiency. These phenotypes were jointly analyzed to derive a network of genetic interactions that mapped mating-pathway relationships. To determine the distinct biological processes driving the phenotypic complementarity, we analyzed patterns of gene expression to find that the pheromone response phenotype is specific to cellular fusion, whereas mating efficiency was a combined measure of cellular fusion, cell cycle arrest, and modifications in cellular metabolism. We applied our novel method to global gene expression patterns to derive an expression-specific interaction network and demonstrate applicability to global transcript data. Our approach provides a basis for interpretation of genetic interactions and the generation of specific hypotheses from populations assayed for multiple phenotypes. © 2012 Carter et al.
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Use of Pleiotropy to Model Genetic Interactions in a Population
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Carter, G.W., The Jackson Laboratory, Bar Harbor, ME, United States
Hays, M., Institute for Systems Biology, Seattle, WA, United States
Sherman, A., Institute for Systems Biology, Seattle, WA, United States
Galitski, T., Institute for Systems Biology, Seattle, WA, United States, EMD Millipore, Billerica, MA, United States
Use of Pleiotropy to Model Genetic Interactions in a Population
Systems-level genetic studies in humans and model systems increasingly involve both high-resolution genotyping and multi-dimensional quantitative phenotyping. We present a novel method to infer and interpret genetic interactions that exploits the complementary information in multiple phenotypes. We applied this approach to a population of yeast strains with randomly assorted perturbations of five genes involved in mating. We quantified pheromone response at the molecular level and overall mating efficiency. These phenotypes were jointly analyzed to derive a network of genetic interactions that mapped mating-pathway relationships. To determine the distinct biological processes driving the phenotypic complementarity, we analyzed patterns of gene expression to find that the pheromone response phenotype is specific to cellular fusion, whereas mating efficiency was a combined measure of cellular fusion, cell cycle arrest, and modifications in cellular metabolism. We applied our novel method to global gene expression patterns to derive an expression-specific interaction network and demonstrate applicability to global transcript data. Our approach provides a basis for interpretation of genetic interactions and the generation of specific hypotheses from populations assayed for multiple phenotypes. © 2012 Carter et al.
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