Comprehensive functional characterization and dissection of noncoding regulatory elements and human genetic variation

非编码调控元件和人类遗传变异的综合功能表征和剖析

• 批准号: 10241056
• 负责人: Pardis Christine Sabeti
• 金额: $ 149.63万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-12 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10241056
• 关键词: Address; Amino Acid Substitution; Architecture; Autoimmune Diseases; Biological; Biological Assay; CRISPR screen; Cells; Clustered Regularly Interspaced Short Palindromic Repeats; Communities; Complement; Computing Methodologies; Data; Disease; Dissection; Elements; Gene Expression Regulation; Genetic Transcription; Genetic Variation; Genome; Genomics; Health; Human; Human Genetics; Human Genome; Inflammatory Bowel Diseases; Insulin-Dependent Diabetes Mellitus; Libraries; Link; Location; Logic; Lupus; Machine Learning; Maps; Measures; Methods; Modeling; Regulatory Element; Reporter; Research; Resolution; Resources; Techniques; Testing; Untranslated RNA; Variant; base; cell type; flexibility; functional genomics; genome-wide; human disease; improved; trait

Summary The ENCODE project has generated comprehensive maps of cis-regulatory elements (CREs) controlling the transcription of genes within the human genome. These maps have been crucial in our efforts to understand sequence variants linked to human traits and disease, as the majority of these variants are non-coding regulatory changes rather than amino acid substitutions. However, even though we know the locations of thousands of CREs, our understanding of how they operate is derived from a relatively small set of well-described examples. Therefore, we plan to directly characterize the function of ENCODE CREs at a genome-wide scale in multiple cell-types. This will transition the field of functional genomics from a simple map of regulatory elements towards a deep understanding of the fundamental rules governing regulatory logic down to the basepair resolution. Achieving this will dramatically expand ENCODE’s utility by strengthening our ability to interpret the effects of natural human variation on gene regulation. We propose to directly measure regulatory activity of over 3% of the genome, pursuing loci highlighted as important by ENCODE and other functional data. We will first apply computational methods to identify the most biologically informative CREs, representing a diversity of regulatory logic and architecture, and will use machine learning techniques to prioritize functional variants for characterization relevant to common and rare human diseases, traits, and adaptation. Of these we will select 100,000 CREs and 375,000 variants, representing ~100 Mb of genomic sequence, and characterize them using the massively parallel reporter assay (MPRA) to understand each element’s regulatory activity. Then, to complement data from the MPRA, we will characterize additional 1 Mb regions across 20 loci using CRISPR-based non-coding screens to build a comprehensive picture of these loci. This strategy leverages the throughput and flexibility of MPRA while maintaining the connectivity of regulatory logic in the CRISPR-based screens, which perturb elements within their endogenous genomic context. This will help us judge the accuracy and completeness of ENCODE, while also providing data from both approaches to address a wide-variety of research questions. These methods are difficult to apply to disease relevant primary cells at full scale, but we will use the results of our MPRA and CRISPR screens to inform our models and better predict the fundamental rules of regulatory logic. We will then construct smaller, targeted libraries to test disease-specific variants in primary cells and use assays specific for each of three autoimmune diseases: type 1 diabetes, inflammatory bowel disease, and lupus. This approach will inform the research community on the rules governing the activity of the CREs mapped by the ENCODE project, and will simultaneously provide concrete information about the function of hundreds of thousands of sequence variants relevant for human traits, health, and disease.

概括 编码项目已经生成了控制元素（CRE）的综合图 人类基因组中基因的转录。这些地图对于我们了解的努力至关重要 与人类性状和疾病相关的序列变体，因为这些变体中的大多数是非编码的 调节性变化而不是氨基酸取代。但是，即使我们知道 成千上万的CRE，我们对它们运作方式的理解是从一组相对较小的 描述的例子。因此，我们计划直接表征Ancode CRE的功能 多种细胞类型的全基因组量表。这将从简单的地图过渡功能基因组学领域 监管元素，深入了解有关监管逻辑的基本规则 到基地分辨率。实现这一目标将通过增强我们的能力来大大扩展编码的实用程序 解释自然人类变异对基因调节的影响。 我们建议直接测量超过3％的基因组调节活动，追求该基因座，强调为 通过编码和其他功能数据重要。我们将首先应用计算方法来识别最多 在生物学上有用的CRE，代表各种监管逻辑和建筑，并将使用 机器学习技术优先级功能变体，以表征与常见和稀有 人类疾病，特征和适应。其中，我们将选择100,000个CRE和375,000个变体， 代表〜100 MB的基因组序列，并使用大量平行报告基因表征它们 测定（MPRA）了解每个元素的监管活动。然后，为了完成MPRA的数据，我们 将使用基于CRISPR的非编码屏幕来构建20个基因座的其他1 MB区域 这些基因座的全面图片。该策略利用MPRA的吞吐量和灵活性 在基于CRISPR的屏幕中保持监管逻辑的连通性 他们的内源基因组环境。这将有助于我们判断编码的准确性和完整性，而 还提供两种方法的数据，以解决广泛的研究问题。这些方法是 很难全面应用于疾病相关的原代细胞，但我们将使用MPRA和 CRISPR屏幕为我们的模型提供信息，并更好地预测监管逻辑的基本规则。然后我们会 构建较小的针对性文库，以测试原代细胞中的疾病特异性变体，并使用特定于 三种自身免疫性疾病中的每一个：1型糖尿病，炎症性肠病和狼疮。 这种方法将向研究社区提供有关映射的CRE活动的规则 编码项目，只需提供有关数百个功能的具体信息 数千种与人类性状，健康和疾病有关的序列变体。