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

项目摘要/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来提高功率 以及通过整合功能基因组识别导致1型糖尿病的非编码变异的解决方案 和高密度基因分型数据。我的建议解决了一个重要的问题，即如何理解 疾病相关的遗传变异影响人类初级免疫细胞亚群的功能，特别是 炎性CD4+T细胞，从而参与1型糖尿病的发病过程。我们选择发展 我们的项目是从健康和健康人群中产生和分析炎性CD4+T细胞的实验数据 因为1型糖尿病患者捐赠者的这个亚群与1型糖尿病的病理相关，所以准备好了吗？ 糖尿病(NPOD)胰腺器官捐献者通过网络获得匹配样本的情况 和我们实验室以前生成和分析功能基因组数据的经验 原代T细胞及相关细胞类型。 这两个目的是：1)分析遗传变异(基因分型)、染色质状态(atac-seq)和基因 1型糖尿病患者和对照献血员外周血中CD4+T细胞的表达(RNA-seq)，2)整合 对功能基因组和疾病遗传数据的分析以解释1型糖尿病相关变异 中等功能基因组表型。该提案将为以下项目提供基础的实验数据集 研究与1型糖尿病相关的免疫细胞亚群的遗传变异的贡献。使用这些 数据集，我们将应用利用功能基因组数据的个体间差异的模型 改进了非编码变体的注释。将该策略应用于生成的数据将(I) 确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异和(Ii) 描述与疾病相关的变异如何结合在一起影响疾病风险。

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.