Integrative modelling of single-cell data to elucidate the genetic architecture of complex disease

单细胞数据的综合建模以阐明复杂疾病的遗传结构

• 批准号: 10889304
• 负责人: ALEXANDER GUSEV
• 金额: $ 40万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-21 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10889304
• 关键词: Alleles; Benchmarking; Biological; Biology; Cells; Cellular biology; Chromatin; Collaborations; Collection; Communities; Complex; Computer software; Data; Data Set; Disease; Epigenetic Process; Gene Expression; Genes; Genetic Transcription; Genome; Heritability; Immune; Individual; Maps; Measurement; Modality; Modeling; Molecular; Multiomic Data; Nature; Phenotype; Population; Principal Component Analysis; Process; Publications; Publishing; Quantitative Trait Loci; Regulatory Element; Research; Resolution; Statistical Models; Study models; Tissues; Transcription Process; Untranslated RNA; Variant; Work; causal variant; cell type; data integration; effective intervention; epigenome; epigenomics; genetic architecture; genome wide association study; improved; multiple omics; neuropsychiatry; novel; novel strategies; single cell sequencing; single-cell RNA sequencing; tool; trait; transcriptomics

PROJECT SUMMARY/ABSTRACT Leveraging Genome Wide Association Studies (GWAS) to understand disease has proven challenging, as the underlying biological mechanisms are often poorly captured by bulk tissues. Recent advances in single-cell sequencing have led to a torrent of data across multiple modalities, contexts, and individuals, which provide an unprecedented opportunity to understand disease biology at high resolution. We hypothesize that the fine- scale cellular contexts captured by single-cell data will be effective at explaining disease heritability and fine- mapping disease mechanisms. However, current approaches to integrate single-cell data with GWAS largely rely on off-the-shelf approaches developed for bulk sequencing, which obscure the rich phenotypic diversity present in individual cells within and across canonical cell types. The sparse and highly variable nature of single-cell data has additionally posed challenges for robustly identifying single-cell quantitative trait loci (QTL). Single-cell data continues to increase in size and complexity, emphasizing the need for scalable integrative modeling. Here, we propose a 5 year research plan to develop novel approaches for integrating single-cell data with GWAS by modeling complex cellular phenotypes not captured by existing bulk approaches. Our proposal will identify novel disease-relevant cell states; leverage multiple single-cell modalities to fine-map disease variants and their target genes; and discover novel single-cell QTLs associated with disease. Our specific aims are: Aim 1: Leveraging single-cell epigenetic data to identify heritable components of disease; Aim 2: Leveraging single-cell data to fine-map disease variants and their mechanisms; Aim 3: Defining the regulatory effects of disease variants using population-scale scRNA-seq. While our proposed approaches are broadly applicable to common diseases, we will benchmark them on immune-related traits and neuropsychiatric traits which we have studied extensively with bulk datasets in published work and where we have now aggregated a large collection of relevant single-cell datasets. Our collaboration has multiple strengths: our focus on functional data integration across multiple single-cell modalities; our broad statistical and computational expertise; and our extensive, data-driven publication record on common disease.

项目概要/摘要 事实证明，利用全基因组关联研究 (GWAS) 来了解疾病具有挑战性，因为 大量组织通常很难捕获潜在的生物学机制。单细胞研究的最新进展 测序产生了跨多种模式、背景和个人的大量数据，这提供了 以高分辨率了解疾病生物学的前所未有的机会。我们假设罚款 由单细胞数据捕获的规模细胞环境将有效地解释疾病的遗传性和精细化 绘制疾病机制。然而，目前将单细胞数据与 GWAS 集成的方法很大程度上 依赖于为批量测序开发的现成方法，这掩盖了丰富的表型多样性 存在于典型细胞类型内部和之间的单个细胞中。稀疏且高度可变的性质 单细胞数据还对稳健识别单细胞数量性状基因座（QTL）提出了挑战。 单细胞数据的规模和复杂性不断增加，强调了对可扩展集成的需求 造型。在这里，我们提出了一项为期 5 年的研究计划，以开发整合单细胞的新方法 通过对现有批量方法无法捕获的复杂细胞表型进行建模，利用 GWAS 来获取数据。我们的 该提案将确定新的与疾病相关的细胞状态；利用多种单细胞模式进行精细绘制 疾病变异及其靶基因；并发现与疾病相关的新型单细胞 QTL。我们的 具体目标是： 目标 1：利用单细胞表观遗传数据来识别疾病的遗传成分； 目标 2：利用单细胞数据精细绘制疾病变异及其机制；目标 3：定义 使用群体规模的 scRNA-seq 来调节疾病变异的效应。虽然我们提出的方法是 广泛适用于常见疾病，我们将根据免疫相关特征对它们进行基准测试 我们在已发表的作品中使用大量数据集广泛研究了神经精神特征 现在已经聚集了大量相关的单细胞数据集。我们的合作有多个 优势：我们专注于跨多种单细胞模式的功能数据集成；我们广泛的统计 和计算专业知识；以及我们关于常见疾病的广泛的、数据驱动的出版记录。