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

项目摘要 ENCODE项目已经生成了控制细胞内转录因子的顺式调控元件（克雷斯）的全面图谱。 人类基因组中基因的转录。这些地图对我们的努力至关重要， 了解与人类特征和疾病相关的序列变异，因为这些变异中的大多数是非遗传性的。 编码调节变化而不是氨基酸取代。然而，即使我们知道 在数以千计的克雷斯中，我们对它们如何运作的理解来自一组相对较小的良好- 描述的例子。因此，我们计划直接表征基因组中ENCODE克雷斯的功能- 广泛应用于多种细胞类型。这将使功能基因组学领域从一个简单的基因图谱， 监管要素，以深入理解管理监管逻辑的基本规则， 碱基对分辨率。实现这一点将大大扩大ENCODE的效用，通过加强我们的能力， 解释自然人类变异对基因调控的影响。 我们建议直接测量超过3%的基因组的调节活性，追踪突出显示为 重要的是ENCODE和其他功能数据。我们将首先应用计算方法来确定最 生物信息克雷斯，代表了监管逻辑和架构的多样性，并将使用 机器学习技术，优先考虑与常见和罕见相关的表征功能变体 人类疾病、特征和适应。其中我们将选择20万个克雷斯和30万个变体， 代表100 Mb的基因组序列，并使用大规模平行报告基因测定对其进行表征 （MPRA），以了解每个元素的调节活动。为了补充MPRA的数据，我们将 使用基于CRISPR的非编码筛选来表征10个基因座上的额外的1 Mb区域，以构建 这些位点的全面图片。该策略利用了MPRA的吞吐量和灵活性， 在基于CRISPR的筛选中保持监管逻辑的连通性，这会扰乱 它们的内源性基因组背景。这将帮助我们判断ENCODE的准确性和完整性， 还提供了两种方法的数据，以解决各种各样的研究问题。这些方法 很难应用于疾病相关的原代细胞，但我们将使用我们的MPRA的结果， CRISPR筛选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。然后我们将 构建更小的靶向文库，以测试原代细胞中的疾病特异性变体，并使用特异性分析， 三种自身免疫性疾病中的每一种：1型糖尿病，炎症性肠病和狼疮。 这种方法将告知研究界关于管理克雷斯活动的规则， ENCODE项目，并将同时提供有关数百个 数千个与人类特征、健康和疾病相关的序列变异。

Project 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 200,000 CREs and 300,000 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 10 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.