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Allele Specific Regulation of Context Specific GRN

背景特异性 GRN 的等位基因特异性调控

基本信息

批准号：
10254258
负责人：
Lauren M. MCINTYRE
金额：
$ 36.27万
依托单位：
UNIVERSITY OF FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10254258
关键词：
Accounting Address Affect Alleles Allelic Imbalance Amyotrophic Lateral Sclerosis Bayesian Modeling Binding Biological Cells Code Complex Data Data Analyses Development Disease Drosophila genus Encapsulated Ensure Environment Equation Female Future Generations Galaxy Gene Expression Gene Expression Regulation Genes Genetic Genetic Polymorphism Genetic Transcription Genetic Variation Genomics Genotype Genotype-Tissue Expression Project Goals Hand Heart Human Individual Knock-out Knowledge Methods Modeling Modernization Molecular Molecular Biology Motor Neurons Muscle Muscular Dystrophies Mutation Pathway interactions Phenotype Population Purkinje Cells Quantitative Genetics Regulation Regulator Genes Research Research Personnel Residual state Sex Differences Source Spinocerebellar Ataxias Statistical Models Statistical Study Structure System TIE gene Testing Tissues Training Validation Variant Work base behavior test biological systems flexibility gene function gene interaction genome wide association study human data insight male method development network models novel novel strategies open source overexpression sex sex determination simulation statistics trait

项目摘要

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.

项目摘要精确理解基因调控网络（GRN）是现代定量分析的主要目标之一。和统计遗传学。系统级模型将GRN置于情境中，为洞察力提供关键框架转化为复杂的特征了解复杂的疾病需要我们了解GRNs中的点，最容易受到干扰以及GRNs内的失调如何发生。关于GRNs如何受到环境和遗传干扰的影响而导致疾病的进化问题，系统中的鲁棒性。生物系统是进化选择的吗？在什么条件下是否违反了鲁棒性？解决这些问题是我们在本提案中寻求解决的一个挑战。基因组广泛关联研究（GWAS）在统计学上将基因型与表型联系起来，但没有解释分子交互.分子生物学通过基因调控网络直接将基因功能与表型联系起来在一些实施方案中，GRN通常通过使用大效应（敲除/过表达）等位基因来表达。多态性的影响在“野生型”等位基因之间，以及它们如何影响网络往往是未知的。GWAS和GRN方法可以合并到一个单一的框架，结构方程模型（SEM-GRN）。这种方法利用了自然群体中的无数多态性，以阐明和定量的分子途径，是表型变异的基础。该框架可用于评估GRN的鲁棒性。当务之急是 GRNs模型允许条件之间的正式比较，并有能力预测一组天然等位基因之间的等位基因取代。一旦GRN建模考虑了条件的影响，可用于阐明GRN与表型变异之间的关系。个体等位基因如何干扰 GRN，GRN的调节成分; GRN之间相似或不同的程度条件;以及确定哪些等位基因以条件特异性方式干扰GRN位于核心位置这一提议。果蝇性别决定（SD）GRN包含了所有这些复杂性。SD- GRN是很好的研究与建立转录调控级联。已知的差异在于雄性和雌性之间以及物种之间GRN的连接。在性别/物种“野生型”等位基因内已经在对表型具有定量影响的几个基因座上进行了分类。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验证了本文提出的SEM-GRN新方法。我们将我们的新方法 eQTL为基础的方法，并确保广泛的适用性的方法，通过广泛的模拟和对果蝇中InR/Tor途径的额外数据分析和对人类中GTeX数据的重新分析。所有这里的方法与包括人类在内的自然群体直接相关。我们培养下一代研究人员将同样很好地解决分子定量遗传和统计研究和实践。