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）：定量性状的基因组结构是什么？尽管在人类和模型生物体中回答了这个问题，但我们仍然远没有了解多少基因，基因 - 基因相互作用和基因环境相互作用是大多数多基因性状（例如人类疾病）的基础。全球基因表达研究将基因组中的每个转录本视为定量性状，已为个人之间性状差异的遗传基础提供了至关重要的见解。特别是，由实验室菌株的十字架和由Leonid Kruglyak的实验室完成的酿酒酵母的RM葡萄酒菌株（我目前是博后同胞）都阐明了两个个体之间表达差异的遗传复杂性。然而，即使在这个十字架中，基因组中大多数转录本的遗传差异仍然没有完全映射，而被检测到的联系的总结通常仅解释了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，将促进基因组中的所有链接的映射，这些链接是在单个环境中两个酵母菌菌株之间的转录本差异的基础，可能具有基因级映射分辨率。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，从而从一个十字架中创建了重组MATA单倍体的非常大的池（〜10'^5至10'^7）。这些较大人群的批量隔离分析（BSA）可以通过使用携带转化融合荧光记者的父母和在隔离池上的细胞分类/重新捕获来完成，这将促进目标转录基因组结构的映射。一旦工作，这种方法就可以扩展到多种环境，其他可选特征（例如耐药性）和新背景。目的1：开发一种可靠的甲膜词，以绘制大种群池中表达定量性状的基因组结构。 AIM2：将此方法应用于以前已显示出在葡萄糖有限的环境中x rm杂交的种植物之间表现出可遗传的变化的25种转录本。 AIM3：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成绩单的基因组结构。 公共卫生相关性：许多疾病受多种基因的影响，携带的风险等位基因的数量因人而异。了解有多少基因导致特定疾病的风险仍然是医学遗传学的主要挑战。我对酵母基因表达的实验可以提供有关特征变异的基因的关键信息，例如疾病风险。