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.

项目摘要对基因调节网络（GRN）的精确理解是现代定量的主要目标之一和统计遗传学。系统级别模型将GRN上下文化，为洞察力提供了一个至关重要的框架进入复杂的特征。了解复杂疾病要求我们了解GRN中的观点最容易受到扰动以及GRN内部的失调。关于格伦的问题因环境和遗传扰动而妥协导致疾病的疾病是进化问题系统中的鲁棒性。生物系统是否在进化上选择是健壮的？在什么条件下鲁棒性侵犯了？回答这些问题是我们寻求解决此建议的挑战。基因组广泛的关联研究（GWAS）从统计上将基因型与表型联系起来，而无需解释分子互动。分子生物学通过基因调节网络将基因功能直接与表型联系起来（grns）通常通过使用大效应（敲出 /过表达）等位基因。多态性的影响在“野生型”等位基因以及它们如何影响网络中，通常是未知的。 GWAS和GRN方法可以合并为一个框架，结构方程建模（SEM-GRN）。这种方法利用自然种群中的无数多态性，以阐明和量化分子途径基础表型变异。该框架可用于评估GRN的鲁棒性。当务之急 GRN的模型允许在条件之间进行正式比较，并能够预测一组天然等位基因之间的等位基因取代。一旦GRN建模说明条件的影响可用于阐明GRN和表型变化之间的关系。单个等位基因如何扰动 GRN，GRN的调节组成部分； GRN与GRN相似或不同的程度状况;以及在特定方式上识别其等位基因的grn扰动grn的心脏该提议。果蝇性别确定（SD）GRN封装了所有这些复杂性。 SD- GRN通过已建立的转录调节级联对GRN进行了很好的研究。有已知差异男性和女性之间以及物种之间的grn接线。在性别/物种“野生型”等位基因已经在几个基因座分类，这些基因座对表型具有定量作用。但是，还有很多监管输入；下游目标；和未知的环境影响。我们使用此系统测试并验证此处提议开发的新型SEM-GRN方法。我们比较我们的新方法通过基于EQTL的方法，并通过广泛的模拟确保该方法的广泛适用性果蝇中INR/TOR途径的其他数据分析以及人类GTEX数据的重新分析。全部这里的方法与包括人类在内的自然种群直接相关。我们训练未来的一代将同样可以很好地解决分子定量遗传和统计研究和实践的研究人员。