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努力来回答这个问题，但我们仍然远未了解有多少基因、基因-基因相互作用和基因-环境相互作用是大多数多基因特征(如人类疾病)的基础。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，为个体之间性状差异的遗传基础提供了至关重要的见解。特别是，由我目前是博士后研究员的莱昂尼德·克鲁格拉克的实验室完成的出芽酵母酿酒酵母的BY菌株和RM葡萄酒菌株的杂交，阐明了两个个体之间表达差异的遗传复杂性。然而，即使在这个杂交中，基因组中大多数转录本的遗传差异仍然没有完全绘制出来，检测到的连锁的总和效应往往只能解释转录本表达差异的一小部分。我正在开发一种新的方法，对于许多多基因体系结构，将有助于绘制基因组中所有连接的图谱，这些链接是单一环境中两个酵母菌株之间转录差异的基础，可能具有基因水平的图谱分辨率。这种方法利用最近发展的合成遗传阵列(SGA)技术的一些方面，从单个杂交中创造出极大的重组MATA单倍体池(~10‘^5到10’^7)。对这些大群体进行批量分离分析(BSA)将有助于绘制目标转录本的基因组结构图，这可以通过使用携带翻译融合荧光报告的亲本和分离体池上的细胞分类/重新捕获来完成。一旦奏效，这种方法可以扩展到多种环境、其他可选择的特征(例如抗药性)和新的背景。目的1：建立一种稳健的方法学，用于绘制大型分离体库中表达数量性状的基因组结构图。目的：将这种方法应用于25个转录本，这些转录本先前被证明在葡萄糖受限的环境中表现出可遗传的变异。目的：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。 公共卫生相关性：许多疾病受到多个基因的影响，携带的风险等位基因的数量因人而异。了解有多少基因对一种特定疾病的风险有贡献仍然是医学遗传学的一大挑战。我提出的关于酵母基因表达的实验可以提供关键信息，即有多少基因是导致性状变异的原因，例如疾病风险。

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.