Identification of even a single gene that contributes to a complex trait provides insight into its molecular basis. However, multiple genes need to be identified if different genes affect a trait through different biological processes or pathways. In the context of pharmacological treatments, each gene is relevant to attaining effective therapy and avoiding adverse drug reactions. While both linkage mapping and association studies have proved to be effective tools for analysis of complex traits, they rarely identify more than one or a small number of genes per study and each gene requires considerable effort to fine-map. The goal of the proposed research is to use S. cerevisiae as a model system to understand the diversity of molecular mechanisms by which different genes affect colony color, a drug-dependent quantitative trait.
The first aim of the proposed research will determine whether multiple genes can be identified by a quantitative non-complementation screen using the yeast deletion collection. Reciprocal hemizygosity analysis will be used to estimate the rate of false positives due to haplo-insufficiency and second site mutations within the deletion collection.
The second aim of the proposed research will characterize the relationship between colony color alleles by identifying downstream changes in gene expression. A quantitative model of colony color will be generated based on changes in gene expression associated with colony color and the model's ability to predict the effects of genetic background will be tested using recombinant strains segregating both known and unknown colony color alleles. Together, these aims will provide insight into the diversity of molecular mechanisms by which different genes influence a trait and how their effects are modified by genetic background.

Public Health Relevance

Complex traits are the product of multiple genes, their interactions with each other, and their interactions with the environment. Although a number of methods are available to identify one or a few genes involved in a complex trait, it has been difficult to systematically identify every gene underlying a trait and the mechanisms by which they influence a trait. The proposed research will develop and evaluate a method of identifying multiple genes that contribute to complex traits and a model to predict their dependence on genetic background using gene expression data.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
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Genetic Variation and Evolution Study Section (GVE)
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Washington University
Schools of Medicine
Saint Louis
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