Single cell atlas as a roadmap for interpreting human genetic variation in complex disease

项目摘要 全基因组关联研究已成功识别出数千个可能影响人类的基因座 健康。为了将这些发现转化为治疗目标和疾病治疗，我们需要了解 细胞背景和潜在的生物学机制，每种疾病相关的变异通过这些机制来破坏 功能。正在跨多个医疗设备生成大规模、信息丰富的数据集，其中包括 来自英国生物库和生殖系的单细胞RNA-SEQ研究的转录组学、特征和表型 外显子组测序研究中的遗传变异。在这里，我们建议开发方法来集成这些 了解识别生物和细胞机制的惊人资源 为了疾病。这些目标将以以下具体目标实现： 1)整合种群规模的生物数据集，包括UK Biobank和单细胞转录数据，以 构建基因模块，目的是重述生物途径。 2)开发一个统计框架，以测量每个细胞类型特定基因的突变负担 模块。总而言之，这项研究提案将增加解释人类遗传变异和 帮助更好地理解他们采取行动的机制。 这些方法是围绕IBD数据集开发的，将获得大量的分子信息 关于IBD的驱动机制。这项工作的经验教训和方法进步将直接 适用于许多复杂的疾病环境。

Project Summary Genome wide association studies (GWAS) have successfully identified thousands of loci likely affecting human health. To translate these findings into therapeutic targets and disease treatments, we need to understand the cellular context and underlying biological mechanisms through which each disease associated variant disrupts function. Large scale, information rich datasets are being generated across multiple modalities including transcriptomics from single cell RNA-seq studies, traits and phenotypes from the UK Biobank and germline genetic variation from exome sequencing studies. Here, we propose to develop methods to integrate these amazing resources towards understanding the identifying biological and cellular mechanisms that are leading to disease. The objectives will be accomplished with the following specific aims: 1) Integrate population scale biological datasets including UK Biobank and single cell transcriptomics data to construct gene modules with the goal to recapitulate biological pathways. 2) Develop a statistical framework to measure mutational burden across each of the cell type specific gene modules. Together, this research proposal will increase the power in interpreting human genetic variation and help better understand the mechanism through which they act. These methods are being developed around an IBD dataset and will derive substantial molecular information about the mechanisms driving IBD. The lessons and methodological advances from this work will be directly applicable in many complex disease contexts.