Defining the genomic architecture of expression quantitative traits

定义表达数量性状的基因组结构

• 批准号: 7748265
• 负责人: Ian Michael Ehrenreich
• 金额: $ 4.52万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7748265
• 关键词: Affect; Agriculture; Alleles; Animal Model; Architecture; Biology; Cell Separation; Chromosome Mapping; DNA; Disease; Drug resistance; Environment; Exhibits; Fluorescence-Activated Cell Sorting; Fungal Genome; Gene Expression; Genes; Genetic; Genetic Crosses; Genome; Genomics; Genotype; Glucose; Green Fluorescent Proteins; Haploidy; Health; Human; Individual; Laboratories; Maps; Medical Genetics; Methods; Parents; Persons; Phenotype; Polygenic Traits; Population; Postdoctoral Fellow; Quantitative Genetics; Quantitative Trait Loci; R7 Virus; Recombinants; Reporter; Resolution; Risk; Running; Saccharomyces cerevisiae; Saccharomycetales; Sum; Technology; Transcript; Variant; Wine; Work; Yeasts; base; disorder risk; gene environment interaction; gene interaction; human disease; insight; population based; public health relevance; research study; simulation; trait

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.

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为回答这个问题付出了巨大的努力，但我们仍然远未了解有多少基因、基因-基因相互作用以及基因-环境相互作用构成了大多数多基因性状（例如人类疾病）。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的见解。特别是，由 Leonid Kruglyak 实验室（我目前是该实验室的博士后研究员）进行的 BY 实验室菌株和酿酒酵母 RM 葡萄酒菌株的杂交，阐明了两个个体之间表达差异背后的遗传复杂性。然而，即使在这种杂交中，基因组中大多数转录本的遗传方差仍然不完全映射，检测到的连锁的总效应通常只能解释转录本表达方差的一小部分。我正在开发一种新方法，对于许多多基因结构，将有助于对基因组中所有连接进行映射，这些连接是单一环境中两种酵母菌株之间转录差异的基础，可能具有基因水平的映射分辨率。这种方法利用最近开发的合成遗传阵列 (SGA) 技术的各个方面，通过单次杂交创建极大的重组 MATa 单倍体库（~10'^5 至 10'^7）。对这些大群体的批量分离分析（BSA）可以通过使用带有翻译融合荧光报告基因的亲本和分离池上的细胞分选/重新捕获来完成，这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量分离子库中表达数量性状的基因组结构。目标 2：将此方法应用于 25 个转录物，这些转录物先前已被证明在葡萄糖限制环境中的 BY X RM 杂交分离子之间表现出可遗传变异。目标 3：通过在 BY 和 RM 背景中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：许多疾病受多种基因影响，携带风险等位基因的数量因人而异。了解有多少基因会导致特定疾病的风险仍然是医学遗传学的主要挑战。我提出的关于酵母基因表达的实验可以提供有关有多少基因导致性状变异（例如疾病风险）的关键信息。