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

项目摘要/摘要 全基因组关联研究已经发现了数以千计的遗传变异 与数百种复杂的人类疾病有关。然而，这些的潜在机制是如何 导致疾病的变异体的发病机制仍不清楚。主要障碍之一是，大多数人 已确定的疾病相关变体位于非编码区，其注释和功能 传统上人们对此了解甚少。由于Encode和Eigenome路线图最近的努力 项目中，我们已经确定了人类基因组中数百万个潜在的非编码调控元件，主要是 基于高通量分析，例如DNase-Seq或ChIP-Seq数据。更重要的是，它已经证明了 77%的与疾病相关的SNPs位于潜在的监管区域内。然而，有一些 很少有研究正确地进行功能实验来阐明SNPs如何 干扰远端调控元件的功能并影响表型。 另一个令人望而生畏的任务是如何识别含有 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或 位于它们的靶基因下游，可以位于100万个碱基对之外，仍然起作用 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(如Hi-C和Chia-PET，并捕获Hi-C)，并代表着前所未有的机会 全基因组研究高阶染色质结构。然而，对于3C类型的数据分析和解释 数据仍处于早期阶段，染色质相互作用和基因之间的复杂关系 监管才刚刚开始解体。TADS、亚TADS和域边界的机制 是否形成仍不清楚。另一方面，3D结构对基因转录和表观遗传学的影响 景观也在很大程度上是未知的，它们是3D基因组的原因还是结果 结构也有待探索。 鉴于上述挑战和我独特的多学科训练，我的长期目标是 使用高通量基因组实验、计算建模和功能分析的组合来 解决以下基本问题：1)如何识别人类的非编码因果变体 疾病？2)3D基因组组织形成的分子机制是什么？3)什么是 3D基因组组织对基因调控和人类疾病的影响？拟议的工作将深化 我们对基因变异如何有助于基因调控、3D基因组组织和 人类疾病背后的分子机制。

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.