Defining the genomic architecture of expression quantitative traits

描述（由申请人提供）：数量性状的基因组结构是什么？尽管在人类和模式生物中做出了大量的努力来回答这个问题，但我们仍然远远不了解大多数多基因特征（如人类疾病）背后有多少基因、基因-基因相互作用以及基因-环境相互作用。全球基因表达研究将基因组中的每个转录本视为数量性状，为了解个体之间性状差异的遗传基础提供了重要见解。特别是，由Leonid Kruglyak实验室（我目前是该实验室的博士后）完成的BY实验室菌株和RM葡萄酒芽殖酵母酿酒酵母菌的杂交研究，揭示了两个人之间表达差异背后的遗传复杂性。然而，即使在这个杂交中，基因组中大多数转录本的遗传变异仍然不完全定位，检测到的连锁效应的总和通常只能解释转录本表达变异的一小部分。我正在开发一种新方法，对于许多多基因结构，将促进基因组中所有联系的定位，这些联系是单一环境下两个酵母菌株之间转录物差异的基础，可能具有基因水平的定位分辨率。这种方法利用了最近开发的合成遗传阵列（SGA）技术的各个方面，从单个杂交中创建极大的重组MATa单倍体池（~10'^5到10'^7）。这些大群体的整体分离分析（BSA）可以通过使用携带翻译融合荧光报告的亲本和分离池上的细胞分选/重捕来完成，将有助于绘制目标转录本的基因组结构。一旦起作用，这种方法可以扩展到多种环境，其他可选择的特征（例如耐药性）和新的背景。目标1：开发一种强大的方法来绘制大分离池中表达数量性状的基因组结构。目的2：将该方法应用于25个转录本，这些转录本先前已被证明在葡萄糖限制的环境中跨BY X RM杂交的分离区表现出遗传变异。目的3：通过在by和RM背景下进行所有必要的等位基因替换来验证一个转录本的基因组结构。

DESCRIPTION (provided by applicant): What is the genomic architecture of a quantitative trait? Despite substantial effort to answer this question in humans and model organisms, we remain far from understanding how many genes, gene-gene interactions, and gene-environment interactions underlie most polygenic traits, such as human disease. Global gene expression studies, which treat each transcript in the genome as a quantitative trait, have provided crucial insights into the genetic basis of trait differences between individuals. In particular, a cross of the BY lab strain and the RM wine strain of the budding yeast Saccharomyces cerevisiae that was done by the lab of Leonid Kruglyak, where I am presently a postdoctoral fellow, has illuminated the genetic complexity underlying expression differences between two individuals. However, even within this cross, the genetic variance for the majority of the transcripts in the genome remains incompletely mapped, with the summed effects of detected linkages often explaining only a small fraction of a transcript's expression variance. I am developing a new method that, for many polygenic architectures, will facilitate the mapping of all linkages in the genome that underlie a transcript difference between two yeast strains in a single environment, potentially with a gene-level mapping resolution. This approach exploits aspects of the recently developed Synthetic Genetic Array (SGA) technology to create extremely large pools (~10'^5 to 10'^7) of recombinant MATa haploids from a single cross. Bulk segregant analysis (BSA) on these large populations, which can be done by using parents that harbor translational fusion fluorescent reporters and cell sorting/recapture on the segregant pool, will facilitate the mapping of the genomic architecture of target transcripts. Once working, this approach can be extended to multiple environments, other selectable traits (e.g. drug resistance), and new backgrounds. AIM 1: To develop a robust metholodogy for mapping the genomic architecture of expression quantitative traits in large pools of segregants. AIM2: To apply this method to 25 transcripts that have previously been shown to exhibit heritable variation across segregants of a BY X RM cross in a glucose-limited environment. AIM3: To validate the genomic architecture of one transcript by doing all necessary allele replacements in both the BY and RM backgrounds. Public Health Relevance: Many diseases are influenced by multiple genes, with the number of carried risk alleles varying from person- to-person. Understanding how many genes contribute to risk for a particular disease remains a major challenge for medical genetics. The experiments I propose on yeast gene expression can provide critical information about how many genes underlie trait variation, such as disease risk.