Abstract
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.
Highlights
Research in systems biology and genetics increasingly combines aspects of molecular biology with quantitative and statistical genetics
Understanding these data will require methods that combine the experimental power of molecular biology and the quantitative power of statistical genetics
We describe a novel approach that uses the complementary information encoded by multiple phenotypes in conjunction with genetic data to map genetic interaction networks in terms of quantitative variant-to-variant and variant-to-phenotype influences
Summary
Research in systems biology and genetics increasingly combines aspects of molecular biology with quantitative and statistical genetics. Studies of quantitative trait loci that affect gene expression (eQTL) identify multiple transcripts affected by interactions among genetic variants via cis and trans acting elements [21,22,23,24,25,26,27], providing insights into genetic influence on biological processes Interaction studies in this approach can be limited by the cost of assaying sufficiently large sample numbers and ambiguous connections to physiological phenotypes. In all of these cases, methods that directly infer the structure of genetic networks would provide a data-driven model of how the genetic variation in a population organizes to affect complex traits
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