Impact of genetic variants on gene regulation and 3D genome organization in human diseases

遗传变异对人类疾病中基因调控和 3D 基因组组织的影响

• 批准号: 9981773
• 负责人: Feng Yue
• 金额: $ 39.5万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9981773
• 关键词: 3-Dimensional; Address; Base Pairing; Biological Assay; ChIP-seq; Chromatin; Chromatin Interaction Analysis by Paired-End Tag Sequencing; Chromatin Loop; Complex; Computer Models; DNase I hypersensitive sites sequencing; Data; Data Analyses; Disease; Distal; Enhancers; Epigenetic Process; Gene Expression Regulation; Genes; Genetic Variation; Genome; Genomics; Goals; Higher Order Chromatin Structure; Human Genome; Link; Methods; Molecular; Nucleic Acid Regulatory Sequences; Pathogenesis; Phenotype; Regulatory Element; Structure; Techniques; Training; Transcript; Untranslated RNA; Variant; Work; base; causal variant; chromosome conformation capture; epigenome; experimental study; genetic variant; genome wide association study; genome-wide; high throughput screening; human disease; multidisciplinary; three dimensional structure

PROJECT SUMMARY / ABSTRACT Genome-wide association studies (GWAS) have discovered thousands of genetic variations that are associated with hundreds of complex human diseases. However, the underlying mechanisms of how these variants contribute to disease pathogenesis remain obscure. One of the main hurdles is that the majority of disease-associated variants identified are located in the non-coding regions, whose annotations and functions are traditionally poorly understood. Thanks to recent efforts by the ENCODE and Epigenome Roadmap projects, we have identified millions of potential non-coding regulatory elements in the human genome, mainly based on high-throughput assays such as DNase-Seq or ChIP-Seq data. More importantly, it has been shown that 77% of the disease-associated SNPs are located within a potential regulatory region. However, there have been very few studies in which functional experiments were properly performed to elucidate how SNPs can disrupt the function of a distal regulatory element and influence the phenotypes. Another daunting task is how to identify target genes for the distal regulatory elements that harbor the disease-associated SNP. This is a challenging problem because enhancers can work from either upstream or downstream of their target genes, and can be located as far as 1 million base pairs away and still function through chromatin looping. High-throughput methods based on Chromatin Conformation Capture (3C) have emerged (such as Hi-C and ChIA-PET, and Capture Hi-C) and represent an unprecedented opportunity to study higher-order chromatin structure genome-wide. However, data analysis and interpretation for 3C types of data are still in their early stages, and the complex relationship between chromatin interactions and gene regulation has just started to be unraveled. The mechanisms of how TADs, sub-TADs and domain boundaries are formed remains unclear. On the other hand, the impact of 3D structure on gene transcript and epigenetic landscape is also largely unknown and whether they are the cause or the consequence of 3D genome structure is yet to be explored as well. Given the aforementioned challenges and my unique multi-disciplinary training, my long-term goal is to use a combination of high throughput genomic experiments, computational modeling, and functional assays to address the following fundamental questions: 1) How to identify non-coding causal variants for human diseases? 2) What is the molecular mechanism for the formation of 3D genome organization? 3) What is the impact of 3D genome organization on gene regulation and human diseases? The proposed work will deepen our understanding on how genetic variants contribute to gene regulation, 3D genome organization and molecular mechanisms underlying human diseases.

项目摘要 /摘要 全基因组关联研究（GWAS）发现了数千种遗传变异 与数百种复杂的人类疾病有关。但是，这些方式的基本机制 变体导致疾病发病机理仍然晦涩。主要障碍之一是大多数 鉴定出疾病相关的变体位于非编码区域，其注释和功能 传统上是鲜为人知的。多亏了编码和表观基因组路线图的最新努力 项目，我们已经确定了人类基因组中数百万个潜在的非编码调节元素，主要是 基于高通量测定法，例如DNase-seq或chip-seq数据。更重要的是，它已显示 与疾病相关的SNP的77％位于潜在的调节区域内。但是，那里有 很少有研究正确执行功能实验以阐明SNP的方式 破坏远端调节元件的功能并影响表型。 另一个艰巨的任务是如何识别带有远端调节元素的目标基因 与疾病相关的SNP。这是一个具有挑战性的问题，因为增强剂可以从上游起作用或 其目标基因的下游，可以位于100万个碱基对远处，并且仍然运行 通过染色质循环。基于染色质构象捕获（3C）的高通量方法具有 出现（例如Hi-C和Chia-Pet，捕获HI-C），代表了前所未有的机会 研究更高阶的染色质结构全基因组。但是，3C类型的数据分析和解释 数据仍处于早期阶段，染色质相互作用与基因之间的复杂关系 法规刚刚开始解散。 TAD，子塔和域边界的机制 被形成尚不清楚。另一方面，3D结构对基因转录和表观遗传学的影响 景观在很大程度上也是未知的，它们是3D基因组的原因还是后果 结构也尚待探索。 鉴于上述挑战和我独特的多学科培训，我的长期目标是 使用高通量基因组实验，计算建模和功能测定的组合 解决以下基本问题：1）如何确定人类的非编码因果变体 疾病？ 2）形成3D基因组组织的分子机制是什么？ 3）什么是 3D基因组组织对基因调节和人类疾病的影响？拟议的工作将加深 我们对遗传变异的理解如何对基因调节，3D基因组组织和 人类疾病的分子机制。