GENES AND GENETIC INTERACTIONS UNDERLYING PHARMACOLOGICAL VARIATION IN YEAST

描述(由申请人提供)：即使只有一个影响复杂性状的基因的鉴定也能提供对其分子基础的洞察。然而，如果不同的基因通过不同的生物学过程或途径影响一个性状，则需要识别多个基因。在药物治疗的背景下，每个基因都与获得有效治疗和避免药物不良反应有关。虽然连锁作图和关联研究都被证明是分析复杂性状的有效工具，但它们很少在每项研究中识别一个以上或少数基因，而且每个基因都需要相当大的努力来精细定位。这项研究的目的是以酿酒酵母为模型系统，了解不同基因影响菌落颜色的分子机制的多样性，菌落颜色是一种药物依赖的数量性状。拟议研究的第一个目标将确定是否可以通过使用酵母缺失集合的定量非互补筛查来识别多个基因。倒数半合度分析将被用来估计由于缺失集合中的单倍体不足和第二位点突变而导致的假阳性率。拟议研究的第二个目标将通过识别基因表达的下游变化来表征群体颜色等位基因之间的关系。将根据与菌落颜色相关的基因表达的变化来生成菌落颜色的定量模型，并将使用分离已知和未知菌落颜色等位基因的重组菌株来测试该模型预测遗传背景影响的能力。总之，这些目标将提供对不同基因影响特征的分子机制的多样性的洞察，以及它们的影响是如何被遗传背景改变的。 公共卫生相关性：复杂的性状是多个基因的产物，它们之间相互作用，以及它们与环境的相互作用。虽然有许多方法可以确定一个或几个与复杂性状有关的基因，但要系统地确定每个潜在性状的基因及其影响性状的机制一直是困难的。这项拟议的研究将开发和评估一种识别影响复杂性状的多个基因的方法，以及使用基因表达数据预测其对遗传背景的依赖性的模型。

DESCRIPTION (provided by applicant): 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.