Integrative Approaches for Identifying Causal Gene-Cell Type Pairs of Complex Disease

项目摘要 确定致病基因和细胞类型的疾病病因是必不可少的设计靶向 诊断和治疗策略。全基因组关联研究（GWAS）、DNA测序和RNA- 测序研究已经确定了多种人类疾病的潜在致病基因。虽然这些方法 提供疾病相关的“基因列表”，由于缺乏细胞类型，它们存在重大缺陷。 信息.首先，每个组织由多种细胞类型组成，对疾病的贡献不同 表型，并因此研究使用散装组织数据单独导致的因果细胞群体的模糊性。 其次，来自罕见细胞类型的致病基因信号可能在大量组织中被掩盖。最后，理解 在设计下游功能研究所需的细胞类型中，哪些基因受到干扰。到 确定驱动人类疾病的基因-细胞对，系统性方法将患者队列数据与 迫切需要细胞类型特异性数据。我的研究项目旨在确定致病基因和细胞类型 使用多组学方法驱动人类疾病。我们的中心假设是失调的基因 映射到特定的细胞类型驱动疾病病因。以前，我们开发的算法， 常见和罕见的基因组变异，表观基因组，转录组和蛋白质组的大规模数据，以确定 组织中的致病基因影响特定的细胞类型，为基因治疗提供了强大的生物学和技术基础。 项目此外，所提出的方法通过快速扩增细胞特异性表观基因组和 使用分选的细胞群体或单细胞谱分析来分析转录组学数据。在未来五年内，我们将 专门开发算法，将来自患者队列的基因组发现与细胞特异性 转录组学数据，解决两个主要问题：（1）基因-细胞类型对有助于什么 疾病的病因？(2)疾病相关基因的表达是如何在单细胞中调节的 水平？拟议的项目将通过发现与广泛的遗传病相关的基因-细胞对来强烈影响该领域。 用于下游调查的疾病范围。这一研究成果将为生物医学领域提供新的方法， 和单细胞转录组数据，以扩大大规模患者基因组队列的发现。从长远 从长远来看，成功鉴定的基因-细胞对可以转化为诊断标记或治疗靶点 人类疾病。

Project Summary Identifying causal genes and cell types underlying disease etiologies are essential for designing targeted diagnostic and treatment strategies. Genome-wide association study (GWAS), DNA-sequencing, and RNA- sequencing studies have identified potentially causal genes in multiple human diseases. While these methods provide disease-associated “gene lists”, they suffer from major shortcomings given the lack of cell-type information. First, each tissue is composed of multiple cell types with diverse contributions to disease phenotypes, and thus studies using bulk-tissue data alone result in the ambiguity of the causal cell populations. Secondly, causal gene signals from rare cell types may be masked in bulk tissues. Finally, understanding which genes are perturbed in which cell types is required for designing downstream functional studies. To identify the gene-cell pairs driving human disease, systematic approaches to integrate patient-cohort data with cell-type-specific data are urgently needed. My research program aims to identify causal genes and cell types driving human diseases using multi-omics approaches. Our central hypothesis is that dysregulated genes mapped to specific cell types drive disease etiologies. Previously, we developed algorithms that integrate large-scale data of common and rare genomic variants, epigenomes, transcriptomes, and proteomes to identify causal genes in tissue affecting specific cell types, providing strong biological and technical foundations for the project. Further, the proposed approaches are empowered by rapidly-expanding cell-specific epigenomic and transcriptomic data using sorted cell populations or single-cell profiling. In the next 5-year period, we will specifically develop algorithms that integrate genomic findings from patient cohorts with cell-specific transcriptomic data, addressing two major questions: (1) What are the gene-cell type pairs contributing to disease etiologies? (2) How are expressions of disease-associated genes regulated at a single-cell level? The proposed project will strongly impact the field by discovering gene-cell pairs associated with a wide range of diseases for downstream investigation. The development will afford new methods to integrate purified and single-cell transcriptome data to expand on findings from large-scale patient genomic cohorts. In the long term, the successfully identified gene-cell pairs can be translated into diagnostic markers or treatment targets of human disease.