Fine mapping rheumatic disease variants using functional genomic sequencing

项目摘要 在这里，我们建议开发一个两步计算策略，以提高功率和分辨率的 通过整合功能基因组鉴定导致自身免疫性风湿病的非编码变体 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）所涉及的致病突变 并了解它们的生物学机制。我们将使用激活的 由于CD 4 + T细胞与自身免疫性风湿病的相关性， 功能基因组数据，以及实验操作原代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段来增加检测功能性基因组定量的能力 性状基因座（fgQTL）。我们将（i）开发一种方法来准确地定量来自功能基因组的等位基因特异性读段， 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）方法来执行功能基因组性状的相位感知关联测试，以及（iii）应用 方法鉴定约100个个体中活化的CD 4 + T细胞中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中的非编码变异。我们将（一） 开发利用功能基因组QTL来精细定位基因座中的致病变体的方法，（ii） 应用该方法整合来自Aim 1的表达和染色质可及性QTL， 风湿性疾病GWAS数据集，以确定最可能与CD 4 + T细胞相关的致病变体 激活，（iii）使用正交功能基因组数据在计算上精炼和注释因果变体， CD 4 + T细胞。 3.利用合成生物学和基因组工程进行预测。我们将（i）大量使用 平行报告基因测定（MPRA），以测试活化的Jurkats，约500个合成的构建体， 来自GWAS基因座优先化的目标1和2的致病变体，并使用CRISPR/Cas9来（ii）敲除25 在Jurkats和CD 4+原代T细胞中携带因果变体（MPRA命中的子集）的增强子，和（iii） 在CD 4+原代T细胞中敲入10个预测的因果变体。我们将观察的内源性影响， 通过分析CD 4 + T细胞活化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.