Integrated analysis of multi-omic QTLs at single cell resolution

项目概要 新颖的统计和计算工具使基因组学技术得到广泛采用并服务于 作为现代人类基因组学的基础。事实上，最近单细胞的出现和普及 基因组学平台——最著名的是单细胞 RNA 测序 (scRNA-seq)——导致了单细胞 RNA 测序的激增。 细胞数据处理、质量控制和分析框架。然而，要充分实现单细胞基因组学的前景 我们需要将细胞类型水平的调控表型与复杂疾病联系起来的方法。最有前途的 应对这一挑战的方法是通过绘制数量性状位点来识别功能相关的遗传变异 （QTL）。事实上，研究确定了与许多调控表型相关的调控变异， 包括但不限于基因表达 (eQTL)、DNA 甲基化 (meQTL)、染色质可及性 (caQTL) 和蛋白质 (pQTL) 已在大量样品中广泛进行。这些研究有 增进了我们对复杂疾病分子基础的理解，但缺乏粒度 批量分析提供的结果继续阻碍进展。当我们将这些方法转向细胞类型时 由单细胞基因组学实现的水平分析很明显，为批量样品开发的方法 不太适合处理单细胞数据的复杂性和具体特征；或者确实采取充分 利用单细胞数据的分辨率和丰富性。在这里，我们建议开发、验证和 部署使用单细胞组学技术数据绘制 QTL 的方法。我们认为这是至关重要的 使用从疾病状态下的原始人体组织获得的相关数据来构建这些方法非常重要。 因此，我们将联合收集来自两种组织类型的 scRNA-seq、scATAC-seq 和单细胞蛋白水平：肺 以及从肺纤维化（PF）患者或健康对照中收集的外周血。本次数据采集 将由我们现有的生物样本库提供便利，并以我们的专业知识为基础构建用于分析基因组的工具 数据，绘制调控表型的 QTL，并分析从肺组织中收集的 scRNA-seq 肺纤维化患者。使用这些数据，我们将构建单变量 scQTL 作图、多组学 scQTL 的方法 映射、上下文特异性 scQTL 的识别以及 scQTL 结果与结果的整合 GWAS 研究。这些方法将作为开源软件包发布，以便广泛采用 领域。我们的团队汇集了统计基因组学、计算生物学、功能学等方面的独特专业知识 基因组学、单细胞基因组学和 PF 疾病特定专业知识使我们特别适合携带 出这个作品。

Project Summary Novel statistical and computational tools have enabled the broad adoption of genomics technologies and served as the foundation for the modern age of human genomics. Indeed, the recent advent and popularity of single cell genomics platforms – most notably single cell RNA sequencing (scRNA-seq) – has led to a proliferation of single- cell data processing, QC, and analysis frameworks. However, to fully realize the promise of single cell genomics approaches we need to connect cell-type level regulatory phenotypes with complex disease. The most promising approach to this challenge is to identify functionally relevant genetic variation by mapping quantitative trait loci (QTLs). Indeed, studies identifying regulatory variation associated with a number of regulatory phenotypes, including but not limited to gene expression (eQTLs), DNA methylation (meQTLs), chromatin accessibility (caQTLs), and protein (pQTLs), have been carried out extensively in bulk samples. These studies have advanced our understanding of the molecular underpinnings of complex disease, but the lack of granularity provided by bulk analyses continues to hinder progress. As we move these approaches towards the cell-type level analyses enabled by single cell genomics it has become clear that the methods developed for bulk samples are not well suited to handle the complexity and specific characteristics of single cell data; or indeed to take full advantage of the resolution and richness of single cell data. Here we propose developing, validating, and deploying methods for mapping QTLs using data from single cell `omics technologies. We believe it is critically important to build these methods using relevant data obtained from primary human tissue in a disease state. Thus, we will jointly collect scRNA-seq, scATAC-seq and single cell protein levels from two tissue types: lung and peripheral blood collected from patients with pulmonary fibrosis (PF) or healthy controls. This data collection will be facilitated by our existing biorepository and build upon our expertise building tools for analyzing genomic data, mapping QTLs for regulatory phenotypes, and analyzing scRNA-seq collected from lung tissue from patients with PF. Using these data we will build methods for univariate scQTL mapping, multi-omic scQTL mapping, the identification of context specific scQTLs, and integration of scQTL results with the results from GWAS studies. These methods will be released as open-source software packages enabling broad adoption by the field. Our team brings together unique expertise in statistical genomics, computational biology, functional genomics, single cell genomics, and disease specific expertise in PF making us particularly well suited to carry out this work.