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

单细胞图谱作为解释复杂疾病中人类遗传变异的路线图

• 批准号: 10179368
• 负责人: Karthik Anand Jagadeesh
• 金额: $ 6.64万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10179368
• 关键词: Affect; Amaze; Area; Asthma; Atlases; Automobile Driving; Biological; Cell physiology; Cells; Colon; Complex; Data; Data Set; Detection; Disease; Disease Outcome; Disease model; Foundations; Gene Expression; Genes; Genetic; Genetic Variation; Genome; Goals; Grouping; Health; Human; Human Genetics; Individual; Inflammation; Inflammatory Bowel Diseases; Learning; Link; Measures; Meta-Analysis; Methodology; Methods; Modality; Molecular; Mutation; Non-Insulin-Dependent Diabetes Mellitus; Organ; Participant; Pathway interactions; Patient Care; Pattern; Phenotype; Physiology; Population; Research Proposals; Resources; Schizophrenia; Signal Pathway; Signal Transduction; Therapeutic; Thinking; Tissues; Translating; Variant; Work; biobank; burden of illness; causal variant; cell type; cohort; design; disease phenotype; disorder risk; endoplasmic reticulum stress; exome; exome sequencing; falls; genome wide association study; genomic locus; heterogenous data; improved; machine learning method; personalized medicine; phenotypic data; rare variant; response; single cell analysis; single cell mRNA sequencing; single-cell RNA sequencing; statistical and machine learning; therapeutic target; trait; transcriptomics; unsupervised learning

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.

项目摘要 全基因组关联研究（GWAS）已经成功地鉴定了数千个可能影响人类遗传多样性的基因座。 健康为了将这些发现转化为治疗靶点和疾病治疗，我们需要了解 细胞环境和潜在的生物学机制，通过这些机制，每种疾病相关的变体破坏了 功能正在跨多种模态生成大规模、信息丰富的数据集，包括 来自单细胞RNA-seq研究的转录组学，来自英国生物库和种系的性状和表型 外显子组测序研究的遗传变异。在这里，我们建议开发方法来整合这些 惊人的资源，以了解识别生物和细胞机制，导致 疾病。这些目标将通过以下具体目标来实现： 1)整合群体规模生物数据集，包括英国生物库和单细胞转录组学数据， 构建基因模块，以重现生物学途径为目标。 2)开发一个统计框架，以测量每个细胞类型特异性基因的突变负荷 模块。总之，这项研究提案将增加解释人类遗传变异的能力， 帮助我们更好地理解他们的行为机制。 这些方法正在围绕IBD数据集开发，并将获得大量的分子信息 关于IBD的发病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背景。