Computational approaches for functional annotations of non-coding sequences in immune disease

免疫疾病中非编码序列功能注释的计算方法

• 批准号: 9397451
• 负责人: Sasha Targ
• 金额: $ 3.6万
• 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: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9397451
• 关键词: ATAC-seq; Address; Affect; American; Biological Markers; CD4 Positive T Lymphocytes; Cells; Child; Chromatin; Chromosome Mapping; Data; Data Analyses; Data Set; Diabetes Mellitus; Disease; Foundations; Gene Expression; Gene Expression Regulation; Generations; Genes; Genetic; Genetic Determinism; Genetic Variation; Genome; Genotype; Hereditary Disease; Heritability; Histocompatibility; Human; IL2RA gene; Immune; Immune System Diseases; Incidence; Individual; Inflammatory; Insulin-Dependent Diabetes Mellitus; Introns; Laboratories; Measures; Mediating; Modeling; Molecular; Organ Donor; Pancreas; Pathology; Patients; Phenotype; Process; Public Health; Publishing; Quantitative Trait Loci; Regulator Genes; Resolution; Role; Sampling; T-Lymphocyte; Testing; Tissue-Specific Gene Expression; Tissues; Untranslated RNA; Variant; XCL1 gene; cell type; cohort; density; diabetic patient; disorder risk; experience; experimental analysis; functional genomics; genetic profiling; genetic variant; genome wide association study; genomic data; immunoregulation; improved; inter-individual variation; lymph nodes; novel; peripheral blood; programs; targeted treatment; trait; transcriptome sequencing

PROJECT SUMMARY/ABSTRACT Here, we propose to collect new experimental data and develop a computational strategy to improve the power and resolution of identifying non-coding variants causal for type 1 diabetes by integrating functional genomic and high-density genotyping data. My proposal addresses the important problem of understanding how disease-associated genetic variants affect the function of primary human immune cell subsets, specifically inflammatory CD4+ T cells, and thus contribute to type 1 diabetes disease processes. We choose to develop our project with generation and analysis of experimental data from inflammatory CD4+ T cells in healthy and type 1 diabetes patient donors because of the relevance of this subset to type 1 diabetes pathology, ready availability of matched samples through the Network for Pancreatic Organ Donors with Diabetes (nPOD) cohort, and our laboratory's previous experience generating and analyzing functional genomic data from primary T cells and related cell types. The two aims are: 1) Profile the genetic variation (genotyping), chromatin state (ATAC-seq) and gene expression (RNA-seq) from CD4+ T cells in type 1 diabetes patients and control donors, and 2) integrate analysis of functional genomic and disease genetic data to interpret type 1 diabetes-associated variants using intermediate functional genomic phenotypes. This proposal will deliver a foundational experimental dataset for studying the contribution of genetic variation in immune cell subsets relevant to type 1 diabetes. Using these datasets, we will apply models that make use of inter-individual variation in functional genomic data for improved annotation of non-coding variants. The application of the strategy to the generated data will (i) identify variants that contribute to disease via effects on chromatin accessibility or gene expression and (ii) characterize how disease-associated variants combine to influence disease risk.

项目总结/摘要 在这里，我们建议收集新的实验数据，并开发一种计算策略，以提高功率 和通过整合功能性基因组识别1型糖尿病病因的非编码变体的解析 和高密度基因分型数据。我的建议解决了一个重要问题，即理解如何 疾病相关的遗传变异影响原代人免疫细胞亚群的功能， 炎症性CD 4 + T细胞，从而导致1型糖尿病疾病过程。我们选择发展 我们的项目是生成和分析来自健康人和哺乳动物中炎性CD 4 + T细胞的实验数据， 1型糖尿病患者供体由于该亚群与1型糖尿病病理学的相关性， 通过糖尿病胰腺器官捐献者网络（nPOD）获得匹配样本 队列，以及我们实验室以前的经验，生成和分析功能基因组数据， 原代T细胞和相关细胞类型。 两个目的是：1）描绘遗传变异（基因分型）、染色质状态（ATAC-seq）和基因 1）从1型糖尿病患者和对照供体中的CD 4 + T细胞的RNA表达（RNA-seq），和2）整合 分析功能基因组和疾病遗传数据，以解释1型糖尿病相关变异， 中间功能基因组表型。该提案将提供一个基础实验数据集， 研究遗传变异在与1型糖尿病相关的免疫细胞亚群中的作用。使用这些 数据集，我们将应用模型，利用功能基因组数据中的个体间变异， 非编码变体的改进注释。将该策略应用于生成的数据将（i） 鉴定通过对染色质可及性或基因表达的影响而导致疾病的变体，和（ii） 描述疾病相关变异联合收割机如何影响疾病风险。