Mapping dynamic functional networks across environments and genetic backgrounds

绘制跨环境和遗传背景的动态功能网络

• 批准号: 8631143
• 负责人: Brenda Jean ANDREWS
• 金额: $ 52.78万
• 依托单位: UNIVERSITY OF TORONTO
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-26 至 2017-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8631143
• 关键词: Accounting; Address; Affect; Alleles; Animal Model; Awareness; Biological; Buffers; Cataloging; Catalogs; Cells; Chromosome Mapping; Code; Communities; Complex; Computing Methodologies; Data; Data Set; Disease; Drug effect disorder; Environment; Essential Genes; Eukaryota; Exhibits; Failure; Functional RNA; Genes; Genetic; Genetic Polymorphism; Genetic Variation; Genome; Genotype; Goals; Grant; Growth; Health; Hereditary Disease; Heritability; Human Genetics; Human Genome; Individual; Lead; Link; Maps; Measures; Methods; Metric; Modeling; Other Genetics; Phenotype; Play; Property; Research; Resolution; Resources; Role; Saccharomyces cerevisiae; Saccharomycetales; Stress; Structure; Surveys; System; Temperature; Translating; Variant; Yeasts; base; computer framework; fitness; functional genomics; gene discovery; gene function; genetic analysis; genetic variant; genome sequencing; genome wide association study; genome-wide; human disease; insight; interest; mutant; pleiotropism; research study; response; tool; trait; yeast genetics

DESCRIPTION (provided by applicant): Whole-genome sequencing projects are providing unprecedented information about human genetic variation. Polymorphisms abound in the human genome, in both coding and non-coding regions, but it remains a major challenge to associate genome variation with a functional consequence. There is growing awareness that genetic interactions, involving combinations of polymorphic alleles, must play a major role in determining phenotype. Yet, we have a limited understanding of how genetic variation translates into genetic interactions that affect an individual. One of the keys to solving this challenging problem will most certainly be an understanding of the general rules governing genetic networks, and how they are rewired in response to environmental or genetic perturbation. The budding yeast Saccharomyces cerevisiae has served as the pioneer model organism for virtually all genome-scale methods, and offers a unique format for exploring genetic networks. Our group developed the Synthetic Genetic Array (SGA) method, which automates yeast genetics and enables systematic analysis of genetic interactions. In the last grant period, we used the SGA method to complete a reference genetic interaction map for yeast, in standard growth conditions. The global network is rich in functional information, mapping a cellular wiring diagram of pleiotropy. Our analysis also revealed that a portion of the network was not mappable, with ~35% of query gene mutants exhibiting weak digenic genetic interaction profiles. These observations emphasize the need to survey genetic interactions in a condition-specific manner, to understand how genetic networks respond to genetic and other insults that may lead to disease states. AIM 1: Mapping condition-specific genetic networks on a genome-wide scale. We will use the SGA method to generate unbiased, genome-scale maps of genetic interactions across diverse conditions. Our systematic approach will generate the largest dynamic biological network of its kind, and will provide a resource to quantify environmental influences on genetic network structure. AIM 2: Global mapping of higher-order genetic interaction networks. We will map a network comprised of complex genetic interactions involving more than two genes. We will focus on hub genes, which are highly connected in the genetic network, and may act as general genetic modifiers. Modeling complex genetic interactions involving more than two genes will allow us to derive general rules governing genetic robustness and the relationship between genotype and phenotype. AIM 3: Quantification and analysis of condition-specific and higher-order genetic interactions. We will develop a computational framework for measuring genetic interactions across environments and genetic backgrounds, which will provide the basis for addressing several fundamental questions regarding the plasticity of genetic networks and the ability of higher-order genetic interactions t modulate complex phenotypes.

描述（由申请人提供）：全基因组测序项目提供了前所未有的人类遗传变异信息。在人类基因组中，在编码区和非编码区都存在大量多态性，但将基因组变异与功能后果相关联仍然是一个重大挑战。越来越多的人认识到，遗传相互作用，包括多态等位基因的组合，必须发挥决定表型的主要作用。然而，我们对遗传变异如何转化为影响个体的遗传相互作用的理解有限。解决这个具有挑战性的问题的关键之一肯定是理解遗传网络的一般规则，以及它们如何响应环境或遗传干扰而重新连接。芽殖酵母酿酒酵母（Saccharomyces cerevisiae）是几乎所有基因组规模方法的先驱模式生物，并为探索遗传网络提供了独特的形式。我们的团队开发了合成遗传阵列（SGA）方法，该方法使酵母遗传学自动化，并能够系统地分析遗传相互作用。在上一个资助期，我们使用SGA方法在标准生长条件下完成了酵母的参考遗传相互作用图谱。全球网络包含丰富的功能信息，映射出多效性的细胞接线图。我们的分析还显示，网络的一部分是不可映射的，约35%的查询基因突变体表现出弱的双基因遗传相互作用谱。这些观察结果强调了以特定条件的方式调查遗传相互作用的必要性，以了解遗传网络如何应对可能导致疾病状态的遗传和其他侮辱。目的1：在全基因组范围内绘制条件特异性遗传网络。我们将使用SGA方法来生成不同条件下遗传相互作用的无偏的基因组规模图。我们的系统方法将产生最大的动态生物网络，并将提供一个资源来量化环境对遗传网络结构的影响。目的2：高阶遗传相互作用网络的全局映射。我们将绘制一个由涉及两个以上基因的复杂遗传相互作用组成的网络。我们将重点放在枢纽基因，这是高度连接在遗传网络，并可能作为一般的遗传修饰剂。对涉及两个以上基因的复杂遗传相互作用进行建模，将使我们能够推导出控制遗传鲁棒性和基因型与表型之间关系的一般规则。目的3：条件特异性和高阶遗传相互作用的定量和分析。我们将开发一个计算框架来测量跨环境和遗传背景的遗传相互作用，这将为解决有关遗传网络的可塑性和高阶遗传相互作用调节复杂表型的能力的几个基本问题提供基础。