Fine mapping rheumatic disease variants using functional genomic sequencing

使用功能基因组测序精细绘制风湿病变异图谱

• 批准号: 9906757
• 负责人: Chun Jimmie Ye
• 金额: $ 34.87万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9906757
• 关键词: ATAC-seq; Accounting; Address; Alleles; Antiviral Response; Autoimmune Process; Awareness; Bayesian Method; Biological; Biological Assay; Biological Models; Biology; CD4 Positive T Lymphocytes; CRISPR/Cas technology; Cell Line; Cell model; Cells; Cellular biology; Chromatin; Clustered Regularly Interspaced Short Palindromic Repeats; Computer Models; Computer software; Computing Methodologies; DNA; Data; Data Set; Dendritic Cells; Development; Disease; Emerging Technologies; Enhancers; Etiology; European; Genes; Genetic; Genetic Determinism; Genome; Genome engineering; IL2RA gene; Individual; Intervention; Knock-in; Knock-out; Lead; Linkage Disequilibrium; Maps; Methods; Modeling; Molecular; Molecular Mechanisms of Action; Molecular Profiling; Outcome; Pathway interactions; Phase; Phenotype; Process; Public Health; Publishing; Quantitative Trait Loci; RNA Splicing; Reporter; Resolution; Rheumatism; Rheumatoid Arthritis; Scheme; Science; Signal Transduction; Single Nucleotide Polymorphism; Sjogren' s Syndrome; System; Systemic Lupus Erythematosus; T cell differentiation; T-Cell Activation; T-Lymphocyte; Testing; Untranslated RNA; Validation; Variant; Writing; XCL1 gene; base; candidate validation; causal variant; cell type; computerized tools; disease-causing mutation; functional genomics; genome editing; genome wide association study; genomic data; improved; programs; synthetic biology; synthetic construct; trait; transcriptomics

PROJECT SUMMARY ABSTRACT Here, we propose to develop a two-step computational strategy to improve the power and resolution of identifying non-coding variants causal for autoimmune rheumatic disease by integrating functional genomic data. The computational methods developed here address an important problem in disease biology: pinpointing the precise disease-causing mutations implicated by genome-wide association studies (GWAS) and understanding the biological mechanisms by which they act. We will develop our program using activated CD4+ T cells as a model system because of their relevance to autoimmune rheumatic disease, the availability of functional genomic data, and the ability to experimentally manipulate primary T cells and related cell lines. The three overlapping aims are: 1. Leveraging allele-specific reads to increase the power of detecting functional genomic quantitative trait loci (fgQTLs). We will (i) develop an approach to accurately quantify allele-specific reads from functional genomic sequencing data while accounting for sequencing and mapping biases, (ii) develop a linear mixed model (LMM) method to perform phase-aware association tests for functional genomic traits, and (iii) apply the method to identify expression and chromatin accessibility QTLs in activated CD4+ T cells in ~100 individuals. 2. Nominate causal non-coding variants in autoimmune rheumatic disease-associated loci. We will (i) develop a method that leverages functional genomic QTLs to fine map disease-causing variants in a locus, (ii) apply the method to integrate expression and chromatin accessibility QTLs from Aim 1 with three autoimmune rheumatic disease GWAS datasets to identify disease-causing variants most likely associated with CD4+ T cell activation, (iii) computationally refine and annotate causal variants using orthogonal functional genomic data in CD4+ T cells. 3. Validate predictions using synthetic biology and genome engineering. We will (i) use massively parallel reporter assays (MPRAs) to test in activated Jurkats, ~500 synthetic constructs harboring predicted causal variants from Aims 1 and 2 prioritized for GWAS loci, and use CRISPR/Cas9 to (ii) knock out 25 enhancers harboring causal variants (a subset of the MPRA hits) in Jurkats and CD4+ primary T cells and (iii) knock-in 10 predicted causal variants in CD4+ primary T cells. We will observe the endogenous effects of genome edits by profiling molecular and cellular phenotypes during CD4+ T cell activation and differentiation.

项目概要 摘要 在这里，我们建议开发一种两步计算策略来提高 通过整合功能基因组来识别导致自身免疫性风湿病的非编码变异 数据。这里开发的计算方法解决了疾病生物学中的一个重要问题： 查明全基因组关联研究 (GWAS) 所涉及的精确致病突变 并了解它们发挥作用的生物机制。我们将使用激活的程序来开发我们的程序 CD4+ T 细胞作为模型系统，因为它们与自身免疫性风湿病相关，可用性 功能基因组数据，以及通过实验操作原代 T 细胞和相关细胞系的能力。 这三个重叠的目标是： 1. 利用等位基因特异性读数来提高功能基因组定量检测的能力 性状位点（fgQTL）。我们将 (i) 开发一种方法来准确量化功能性等位基因特异性读数 基因组测序数据，同时考虑测序和作图偏差，(ii) 开发线性混合 模型（LMM）方法对功能基因组特征进行相感知关联测试，并且（iii）应用 方法鉴定约 100 个个体中激活的 CD4+ T 细胞的表达和染色质可及性 QTL。 2. 指定自身免疫性风湿病相关基因座中的因果非编码变异。我们将（一） 开发一种利用功能基因组 QTL 精细定位基因座中致病变异的方法，(ii) 应用该方法整合来自 Aim 1 的表达和染色质可及性 QTL 与三个自身免疫 风湿病 GWAS 数据集，用于识别最有可能与 CD4+ T 细胞相关的致病变异 激活，（iii）使用正交功能基因组数据计算细化和注释因果变异 CD4+ T 细胞。 3. 使用合成生物学和基因组工程验证预测。我们将 (i) 大量使用 平行报告基因检测 (MPRA) 在激活的 Jurkats 中进行测试，约 500 个合成结构包含预测的 目标 1 和 2 的因果变异优先考虑 GWAS 位点，并使用 CRISPR/Cas9 来 (ii) 敲除 25 个 Jurkats 和 CD4+ 原代 T 细胞中含有因果变异（MPRA 命中的一个子集）的增强子和 (iii) 在 CD4+ 原代 T 细胞中敲入 10 个预测的因果变异。我们将观察内生效应 通过分析 CD4+ T 细胞激活和分化过程中的分子和细胞表型进行基因组编辑。