Allele Specific Regulation of Context Specific GRN

项目摘要 基因调控网络(GRN)的精确理解是现代定量研究的主要目标之一 和统计遗传学。系统级模型将GRN与环境联系起来，提供了一个对洞察至关重要的框架 转化为复杂的特征。了解复杂的疾病需要我们了解GRN中的点 最容易受到扰动，以及GRN内部如何发生调控失调。GRN可能是如何的问题 受到导致疾病的环境和基因扰动的影响，进化问题大约 系统的健壮性。生物系统在进化过程中会被选为健壮的吗？在什么情况下 健壮性被破坏了？回答这些问题是我们寻求通过这项提案解决的挑战。基因组 广谱关联研究在统计上将基因类型与表型联系在一起，而没有解释分子 互动。分子生物学通过基因调控网络将基因功能与表型直接联系起来 (GRN)，通常通过使用大效应(敲除/过表达)等位基因。多态现象的影响 在“野生型”等位基因中，以及它们对网络的影响往往是未知的。GWA和GRN方法 可以合并到单个框架中，结构方程建模(SEM-GRN)。这种方法充分利用了 自然种群中无数的多态性来阐明和量化分子途径 这是表型变异的基础。该框架可用于评估GRN的稳健性。当务之急是 GRN的模型允许在条件之间进行正式比较，并能够预测 一组自然等位基因之间的等位基因替换。一旦GRN建模考虑了条件的影响， 可用于阐明GRN与表型变异的关系。个体等位基因如何扰乱 GRN，GRN的监管组成部分；GRN在多大程度上相似或不同 条件；以及哪些等位基因以特定条件扰乱GRN的识别位于心脏 这项提议。果蝇性别决定(SD)GRN概括了所有这些复杂性。可持续发展署- 对GRN进行了很好的研究，建立了一个转录调控级联。已知的差异是在 雄性和雌性之间以及物种之间的GRN连接。性别/物种内的“野生型”等位基因 已被归类在几个对表型有数量影响的基因座上。然而，仍然有许多 监管投入；下游目标；以及未知的环境影响。我们用这个系统来测试 并对本文提出的新型扫描电子显微镜-GRN方法进行了验证。我们比较了我们的新方法 以eQTL为基础的方法和确保方法的广泛适用性通过广泛的模拟和 对果蝇INR/Tor通路的额外数据分析和对人类GTEx数据的重新分析。全 这里的方法与包括人类在内的自然人口直接相关。我们培养未来一代的 研究人员将同样出色地处理分子、数量、遗传和统计研究与实践。

Project Abstract Precision understanding of gene regulatory networks (GRN) is one of the major goals of modern quantitative and statistical genetics. Systems-level models contextualize GRNs providing a framework critical for insights into complex traits. Understanding complex disease requires that we understand the points in GRNs that are most susceptible to perturbation and how dysregulation within GRNs occurs. Questions of how GRNs may be compromised by environmental and genetic perturbations leading to disease are evolutionary questions of about robustness in the system. Are biological systems evolutionarily selected to be robust? Under what conditions is robustness violated? Answering these questions is a challenge we seek to address with this proposal. Genome wide association studies (GWAS) statistically connect genotypes to phenotypes, without explaining molecular interactions. Molecular biology directly ties gene function to phenotype through gene regulatory networks (GRNs), usually through the use of large effect (knock out /overexpression) alleles. The effect of polymorphisms among `wild type' alleles and how they impact the network are often unknown. GWAS and GRN approaches can be merged into a single framework, Structural Equation Modeling (SEM-GRN). This approach leverages the myriad of polymorphisms in natural populations to elucidate and quantitate the molecular pathways that underlie phenotypic variation. This framework can be used to evaluate GRN robustness. It is imperative that models of GRNs allow for a formal comparison between conditions and have the ability to predict the effect of allelic substitutions among a set of natural alleles. Once GRN modeling accounts for the effects of conditions it can be used to elucidate the relationships between GRN and phenotype variation. How individual alleles perturb the GRN, the regulatory components of GRNs; the degree to which GRNs are similar or different among conditions; and the identification of which alleles perturb the GRN in a condition specific manner lie at the heart of this proposal. The Drosophila sex determination (SD) GRN encapsulates all of these complexities. The SD- GRN is well studied with an established transcriptional regulatory cascade. There are known differences in the wiring of the GRN between males and females and between species. Within a sex/species `wild type' alleles have been categorized at several loci that have a quantitative effect on phenotype. Yet, there are still many regulatory inputs; downstream targets; and environmental effects that are unknown. We use this system to test and validate the novel SEM-GRN methods proposed to be developed here. We compare our novel approaches to eQTL based approaches and ensure broad applicability of the methods through extensive simulation and additional data analysis of the InR/Tor pathway in Drosophila and a reanalysis of the GTeX data in humans. All the methods here are directly relevant to natural populations including humans. We train future generation of researchers that will equally well tackle molecular quantitative genetic and statistical research and practice.