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

项目总结/摘要 全基因组关联研究（GWAS）已经发现了数千种遗传变异， 与数百种复杂的人类疾病有关。然而，这些疾病的潜在机制 导致疾病发病机制的变异仍然不清楚。其中一个主要障碍是， 所鉴定的疾病相关变体位于非编码区，其注释和功能 在传统上是很难理解的。由于ENCODE和表观基因组路线图最近的努力， 通过这些项目，我们已经确定了人类基因组中数百万个潜在的非编码调控元件，主要是 基于高通量测定，如DNase-Seq或ChIP-Seq数据。更重要的是， 77%的疾病相关SNP位于潜在的调控区域内。然而， 很少有研究正确地进行了功能实验来阐明SNP如何能够 破坏远端调节元件的功能并影响表型。 另一项艰巨的任务是如何识别携带这些基因的远端调控元件的靶基因。 疾病相关SNP。这是一个具有挑战性的问题，因为增强子可以从上游或下游起作用。 它们位于靶基因的下游，可以位于100万个碱基对之外， 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，以及Capture Hi-C），代表了前所未有的机会， 在全基因组范围内研究高级染色质结构。然而，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 监管才刚刚开始瓦解。TADs，sub-TADs和畴界的机制 形成的原因尚不清楚。另一方面，三维结构对基因转录和表观遗传的影响 景观也在很大程度上是未知的，无论他们是3D基因组的原因或后果 结构也有待探索。 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）如何识别人类非编码因果变异 疾病？2)3D基因组结构形成的分子机制是什么？3)是什么 三维基因组结构对基因调控和人类疾病的影响？拟议工作将深化 我们对遗传变异如何促进基因调控、3D基因组组织和 人类疾病的分子机制

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.