Comprehensive characterization of variants underlying heart and blood diseases with CRISPR base editing

项目摘要 基因组变异如何影响细胞功能是一个根本性的问题，对 人类疾病。虽然传统上很难研究特定序列变体在 以实验控制的方式，精确的基因组编辑技术，如CRISPR碱基编辑使 将与性状相关的变异“写入”细胞，以解开它们的功能。在这份提案中，我们将执行多式联运 基于基因组编辑的总共72,000个与之相关的基因组变异的功能表征 心血管疾病(CVD)和血液学特征。心血管疾病和血液特征是唯一适合功能性的 因心血管疾病(冠状动脉疾病、高血压、血脂异常)和血液特性而进行的解剖 在所有性状中，都是最强大的多种族GWA之一，并且是性状变异的重要组成部分 可在细胞检测中捕获，可按比例进行高通量筛选。 我们组建了一支由世界级专家组成的跨学科团队，为 通过整合：(1)丰富和血统多样化的人类，揭示心血管疾病和血液特征变异的功能影响 基因发现，(2)目标广泛的CRISPR碱基编辑和高效地传递到原代人类细胞， (3)高含量分析，在染色质、基因表达和细胞功能水平上分析表型， 以及(4)设计、解释、可视化和共享实验结果的计算方法。 在目标1中，我们将采用一个强大的、三级不同的优先排序方案，该方案结合了疾病的证据 来自大型、多种族GWA的关联性以及提名功能变体的因果关系的概率 评估。通过这个方案，我们将选择与红细胞和中性粒细胞特性相关的变体， 冠状动脉疾病、血压、高密度脂蛋白和低密度脂蛋白胆固醇跨越一系列等位基因频率 以及可能在高通量细胞分析中测试的因果关系。 在目标2中，我们将使用基本编辑器来安装候选对象，以执行基于细胞表型的系统筛选 以及CRISPR表观遗传抑制和激活，以探索包含变体的调控元件。 我们将使用8个已建立的、可扩展的细胞表型读数，每个读数都将使我们能够评估 12,000个变异体和以变异体为中心的元件中的哪一个会改变心血管疾病和血液性状相关细胞 表型。我们还将采用高通量、基因组整合的染色质可及性分析来 评估哪些变异改变了性状相关细胞系中染色质的可及性。我们将跟进有针对性的单曲 来自不同性别和种族捐赠者的原代细胞中5,600个变异体的细胞RNA序列。 在目标3中，我们将制作一份经过验证的变体目录，以及它们与每个 建议的屏幕。我们将与其他IGVF小组合作，利用这些数据来优化预测模型 功能变异、调控元件和致病生物机制，最终导致更多 全面了解心血管和血液疾病风险的遗传基础。

Project Summary How genomic variation influences cellular function is a fundamental problem with tremendous importance for human disease. While it has traditionally been difficult to study the effects of specific sequence variants in an experimentally controlled manner, precise genome editing technologies such as CRISPR base editing enable “writing” of trait-associated variants to cells to unravel their function. In this proposal, we will perform multi-modal genome editing-based functional characterization of a total of 72,000 genomic variants associated with cardiovascular diseases (CVDs) and hematological traits. CVD and blood traits are uniquely suited to functional dissection because cardiovascular (coronary artery disease, high blood pressure, dyslipidemia) and blood traits have among the best-powered multi-ethnic GWAS of any traits, and a substantial component of trait variability can be captured in cellular assays that can be scaled to perform high-throughput screening. We have assembled an interdisciplinary team of world-class experts to provide a generalizable pipeline to unravel the functional impact of CVD and blood trait variants by integrating: (1) rich and ancestry-diverse human genetic discoveries, (2) broadly targetable CRISPR base editors and efficient delivery to primary human cells, (3) high-content assays to profile phenotypes at the levels of chromatin, gene expression and cellular function, and (4) computational methods to design, interpret, visualize, and share experimental results. In Aim 1, we will employ a robust, three-tiered variant prioritization scheme that incorporates evidence for disease association from large, multi-ethnic GWAS as well as probability of causality to nominate variants for functional assessment. Through this scheme, we will select variants associated with red blood cell and neutrophil traits, coronary artery disease, blood pressure, and HDL and LDL cholesterol that span a range of allelic frequencies and likely causality to test in high-throughput cellular assays. In Aim 2, we will perform systematic cellular phenotype-based screens using base editors to install candidate variants as well as CRISPR epigenetic inhibition and activation to explore variant-containing regulatory elements. We will use eight established, scalable cellular phenotypic readouts, each of which will enable us to assess which of 12,000 variants and variant-centered elements alter CVD and blood trait-associated cellular phenotypes. We will additionally employ a high-throughput, genome-integrated chromatin accessibility assay to assess which variants alter chromatin accessibility in trait-relevant cell lines. We will follow up with targeted single cell RNA-seq of 5,600 variants in primary cells from donors of different sex and ethnicity. In Aim 3, we will produce a catalog of validated variants and their association with phenotypes for each of the proposed screens. We will collaborate with other IGVF groups to utilize these data to optimize models that predict functional variants, regulatory elements and disease-causing biological mechanisms, ultimately leading to more complete understanding of the genetic underpinnings of cardiovascular and blood disease risk.