Mapping dynamic functional networks across environments and genetic backgrounds

描述(由申请人提供)：全基因组测序项目正在提供有关人类遗传变异的前所未有的信息。人类基因组在编码区和非编码区都存在大量的多态性，但将基因组变异与功能结果联系起来仍然是一个重大挑战。人们越来越意识到，遗传相互作用，包括多态等位基因组合，必须在决定表型方面发挥主要作用。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。解决这一具有挑战性的问题的关键之一肯定是理解管理遗传网络的一般规则，以及它们如何重新连接以应对环境或遗传扰动。萌芽酵母酿酒酵母已经成为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一种独特的形式。我们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，并使遗传相互作用的系统分析成为可能。在上一次资助期间，我们使用SGA方法完成了酵母在标准生长条件下的参考遗传互作图谱。全球网络具有丰富的功能信息，绘制了一张多效性的蜂窝接线图。我们的分析还显示，该网络的一部分是不可映射的，大约35%的查询基因突变体表现出弱的双基因遗传交互作用。这些观察强调，需要以特定条件的方式调查遗传相互作用，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。目标1：在全基因组范围内绘制特定条件的遗传网络图。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见、基因组规模的地图。我们的系统方法将产生同类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。目标2：高阶遗传互作网络的全球定位。我们将绘制一个由涉及两个以上基因的复杂遗传交互作用组成的网络。我们将关注HUB基因，这些基因在遗传网络中高度相连，可能作为一般的遗传修饰物。对涉及两个以上基因的复杂遗传交互作用进行建模，将使我们能够得出控制遗传稳健性以及基因型和表型之间关系的一般规则。目的3：量化和分析特定条件和高阶遗传交互作用。我们将开发一个跨环境和遗传背景测量遗传交互作用的计算框架，这将为解决关于遗传网络的可塑性和高阶遗传交互作用调节复杂表型的能力等几个基本问题提供基础。

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.