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: 10115944
• 负责人: Chun Jimmie Ye
• 金额: $ 13.27万
• 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/10115944
• 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; 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.

项目摘要摘要 在这里，我们建议制定两步计算策略，以提高 通过整合功能性基因组，鉴定自身免疫性疾病的非编码变体因果关系 数据。这里开发的计算方法解决了疾病生物学的重要问题： 精确指出了全基因组关联研究涉及的确切致病突变（GWAS） 并了解其作用的生物学机制。我们将使用激活的 CD4+ T细胞作为模型系统，因为它们与自身免疫性疾病有关 功能性基因组数据以及实验操纵原代T细胞和相关细胞系的能力。 三个重叠的目标是： 1。利用等位基因特异性读取以增加检测功能基因组定量的功能 特质基因座（FGQTL）。我们将（i）开发一种准确量化等位基因特异性读取的方法 基因组测序数据在考虑测序和映射偏差时，（ii）开发线性混合 模型（LMM）的方法来执行功能性基因组特征的相感关联测试，并且（iii）应用 在〜100个个体中激活的CD4+ T细胞中识别表达和染色质可及性QTL的方法。 2。在自身免疫性疾病相关基因座中提名的因果非编码变体。我们将（i） 开发一种利用功能性基因组QTL的方法，以在一个基因座中的细微地图引起疾病的变体，（ii） 应用该方法将AIM 1的表达和染色质可访问性与三个自动免疫性集成 风湿病GWAS数据集识别最有可能与CD4+ T细胞相关的引起疾病的变体 激活，（iii）使用正交功能基因组数据在计算上完善和注释因果变体 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 平行记者测定（MPRA）在激活的Jurkats中进行测试，约有500个携带的合成结构 AIMS 1和2优先考虑GWAS基因座的因果变体，并使用CRISPR/CAS9到（ii）淘汰25 具有因果变异的增强剂（MPRA命中的一个子集）和CD4+原代T细胞以及（iii） 敲入10预测CD4+原代T细胞中的因果变异。我们将观察 基因组通过在CD4+ T细胞激活和分化过程中分析分子和细胞表型来编辑。