Mapping dynamic functional networks across environments and genetic backgrounds

全基因组测序提供了有关人类遗传变异的前所未有的信息。的确有 越来越多的人意识到变异体之间的相互作用在决定表型方面发挥着重要作用。然而，我们缺乏 对遗传变异如何转化为影响个体的遗传互动的理解。一把钥匙 解决这个问题需要了解管理遗传网络的规则。 在我们目前的融资期间，我们使用了合成基因阵列方法，我们开发该方法是为了实现自动化 酵母遗传学，完成酵母的参考遗传互作图谱。该网络提供了全球视野 细胞的功能组织，揭示了细胞功能的层次模型。参考网络 使我们能够努力探索生物网络动态，以响应环境和遗传 扰动，包括遗传抑制和三重突变相互作用。我们的工作突出了这一潜力 影响从基因组序列信息推断表型的遗传交互作用。虽然基因 编辑方法提供了在其他系统中加速类似研究的承诺，许多类型的重要 基因的相互作用仍然相对未被探索，并且只能在基因组范围内被绘制在酵母中。因此， 我们建议继续分析酵母中复杂的遗传相互作用网络，并将其用作模型 用于设计信息丰富的实验，以探索人类细胞中的基因相互作用。 目标1：一种迭代的计算-实验方法来定位特定条件的遗传网络。 我们将测试特定的基因条件组合，预计会产生许多遗传交互作用。基于 初步分析，我们估计，对特定条件相互作用的系统分析将扩大 全球遗传互作网络扩大了~3倍，为探索环境对环境的影响提供了资源 遗传网络布线。 目的2：大规模绘制酵母抑制子遗传网络图。我们会绘制出外源和剂量图 酵母必需基因的抑制遗传相互作用网络。这一分析将揭示新的关系 并为更复杂系统中的类似研究提供了一个模板。 目的3：阐明复杂遗传相互作用的背景和原理。我们将绘制复杂的地图 二倍体品系必需基因杂合等位基因之间的单倍性不足(CHI)互作 测试涉及自然发生的遗传变异以改变表型的遗传相互作用的可能性。这些 研究将提供关于倍性和自然变异如何塑造突变表型和外显的洞察力。 复杂的特征。 目标4：将来自全球酵母遗传相互作用网络的见解转化为人类细胞。施药 我们的酵母参考网络的知识，我们将选择和筛选一组信息丰富的查询基因突变体 为了有效地绘制人类细胞的支架遗传网络。这个网络将识别遗传网络的属性 它们通常是保守的，并为注释人类基因功能提供了资源。

Whole genome sequencing has provided unprecedented information about human genetic variation. There is growing awareness that interactions between variants play a major role in determining phenotype. Yet, we lack an understanding of how genetic variation translates into genetic interactions that affect an individual. A key to solving this problem requires an understanding of the rules governing genetic networks. During our current funding period, we used the Synthetic Genetic Array method, which we developed to automate yeast genetics, to complete a reference genetic interaction map for yeast. This network provides a global view of the functional organization of a cell and reveals a hierarchical model of cell function. The reference network enabled our efforts to explore biological network dynamics in response to environmental and genetic perturbations, including genetic suppression and triple mutant interactions. Our work underscores the potential of genetic interactions to impact the inference of phenotype from genome sequence information. Although gene editing approaches offer the promise to accelerate similar studies in other systems, many types of important genetic interactions remain relatively unexplored, and can only be mapped on a genome-scale in yeast. Thus, we propose continued analysis of complex genetic interaction networks in yeast, which we will use as a model for designing informative experiments to explore genetic interactions in human cells. Aim 1: An iterative computational-experimental approach to map a condition-specific genetic network. We will test specific gene-condition combinations expected to yield many genetic interactions. Based on preliminary analyses, we estimate that a systematic analysis of condition-specific interactions will expand the global genetic interaction network by ~3-fold, providing a resource to explore the influence of environment on genetic network wiring. Aim 2: Large-scale mapping of suppressor genetic networks in yeast. We will map extragenic and dosage suppression genetic interaction networks for yeast essential genes. This analysis will uncover novel relationships between genes and provide a template for similar studies in more complex systems. Aim 3: Elucidating the landscape and principles of complex genetic interactions. We will map Complex HaploInsufficiency (CHI) interactions between heterozygous alleles of essential genes in a diploid strain and also test the potential of genetic interactions involving naturally occurring genetic variants to modify phenotype. These studies will offer insights into how ploidy and natural variation shape the penetrance of mutant phenotypes and complex traits. Aim 4: Translating insights from the global yeast genetic interaction network to human cells. Applying our knowledge of the yeast reference network, we will select and screen an informative set of query gene mutants to efficiently map a scaffold genetic network for a human cell. This network will identify genetic network properties that are generally conserved and provide a resource for annotating human gene function.