GENES AND GENETIC INTERACTIONS UNDERLYING PHARMACOLOGICAL VARIATION IN YEAST

描述（由申请人提供）：即使是一个单一的基因，有助于一个复杂的性状的鉴定提供了深入了解其分子基础。然而，如果不同的基因通过不同的生物过程或途径影响一个性状，则需要鉴定多个基因。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已被证明是分析复杂性状的有效工具，但它们很少在每次研究中识别出多于一个或少量的基因，并且每个基因都需要相当大的努力来精细作图。本研究的目的是利用S.酿酒酵母作为一个模型系统，以了解不同的基因影响菌落颜色，药物依赖性的数量性状的分子机制的多样性。该研究的第一个目的是确定是否可以通过使用酵母缺失收集的定量非互补筛选来鉴定多个基因。将使用互反半合子分析来估计由于缺失集合内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菌落颜色等位基因之间的关系。将基于与菌落颜色相关的基因表达变化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检测该模型预测遗传背景影响的能力。总之，这些目标将提供深入了解不同基因影响性状的分子机制的多样性，以及它们的影响如何被遗传背景改变。 公共卫生相关性：复杂的性状是多个基因的产物，它们彼此之间的相互作用，以及它们与环境的相互作用。虽然有许多方法可以用来鉴定一个或几个基因参与一个复杂的性状，它一直很难系统地确定每一个基因的性状和机制，它们影响性状。拟议的研究将开发和评估一种识别有助于复杂性状的多个基因的方法，以及一种使用基因表达数据预测其对遗传背景依赖性的模型。

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.