Fine mapping rheumatic disease variants using functional genomic sequencing

项目总结摘要 在这里，我们建议开发一个两步计算策略来提高功率和分辨率 整合功能基因组鉴定自身免疫性风湿病的非编码变异体 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： 精确定位全基因组关联研究(GWAS)所涉及的精确致病突变 并了解它们的生物学作用机制。我们将使用激活来开发我们的程序 CD4T细胞作为模型系统因其与自身免疫性风湿病的相关性而可用 功能基因组数据，以及实验操作原始T细胞和相关细胞系的能力。 这三个相互重叠的目标是： 1.利用等位基因特异性读数来提高检测功能基因组数量的能力 性状基因座(FgQTL)。我们将(I)开发一种方法来准确地量化功能基因的等位基因特异性读数 基因组测序数据在考虑测序和作图偏差的同时，(Ii)发展出线性混合 模型(LMM)方法执行功能基因组性状的阶段感知关联测试，以及(Iii)应用 方法检测100例受试者活化的CD4T细胞的表达和染色质可及性QTL。 2.命名自身免疫性风湿病相关基因座的因果非编码变异体。我们将(I) 开发一种利用功能基因组QTL精细定位致病基因变异的方法，(Ii) 应用该方法整合Aim 1和3个自身免疫的表达和染色质可及性QTL 风湿病GWAS数据集，以确定最可能与CD4T细胞相关的致病变异体 激活，(Iii)使用正交功能基因组数据对因果变异进行计算提炼和注释 CD4T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 平行报告分析(MPRA)在激活的Jurkat中进行测试，预测有约500个合成结构 AIMS 1和AIMS 2的因果变异优先用于GWAS基因座，并使用CRISPR/Cas9来(Ii)敲除25 Jurkat和CD4初级T细胞中含有因果变体(MPRA HITS的子集)的增强子和(Iii) 敲入10个基因预测了CD4初级T细胞的因果变异。我们将观察中国经济增长的内生效应 通过分析CD4T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。

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.