Integration of GTEx and HuBMAP data to gain population-level cell-type-specific insights

整合 GTEx 和 HuBMAP 数据以获得群体水平的细胞类型特异性见解

• 批准号: 10575440
• 负责人: Jiebiao Wang
• 金额: $ 31.47万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2024-09-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10575440
• 关键词: Affect; Bioinformatics; Biological; Blood Cell Count; Brain; Catalogs; Cell Count; Cell Nucleus; Cells; Collection; Communities; Complex; Computational algorithm; Computer software; Data; Data Collection; Data Set; Disease; Flow Cytometry; Funding; Gene Expression; Gene Expression Profiling; Gene Expression Regulation; General Population; Genetic; Genomics; Genotype; Genotype-Tissue Expression Project; Goals; Health; Human; Human BioMolecular Atlas Program; Human Biology; Human Genetics; Human body; Maps; Masks; Measures; Methods; Modeling; Normal tissue morphology; Pilot Projects; Population; Publications; Quantitative Trait Loci; Regulation; Research; Research Design; Resolution; Resources; Selection Bias; Site; Small Nuclear RNA; Solid; Statistical Methods; System; Techniques; Testing; Tissue Sample; Tissue-Specific Gene Expression; Tissues; Uncertainty; United States National Institutes of Health; cell type; cost; genome sequencing; genome wide association study; human tissue; improved; innovation; insight; large scale data; multidisciplinary; novel; programs; public database; response; statistics; theories; tool; transcriptome; transcriptome sequencing; user-friendly; whole genome

PROJECT SUMMARY/ABSTRACT The NIH Common Fund Genotype-Tissue Expression project (GTEx) collected whole-genome sequencing and gene expression data from 47 tissues sites of hundreds of subjects. It generated a huge impact by providing tissue-level gene expression and expression quantitative trait loci (eQTLs) for over 7,000 publications. However, tissues are mixtures of myriad cells, and tissue-level gene regulation is affected by cellular compositions. To obtain cell-type-specific (CTS) effects, GTEx started to collect single-nucleus RNA-sequencing (snRNA-seq) data from eight tissue types. The single-cell data collection is extremely expensive and labor-intensive, and thus snRNA-seq data are only collected from 25 tissue samples of 16 donors that may not represent the population. More cost and labor-efficient methods are urgently needed to use existing datasets fully. It turns out that with another NIH Common Fund project, Human BioMolecular Atlas Program (HuBMAP), we can gain population- level insights with HuBMAP single-cell data as a reference by developing computationally efficient methods. Complementary to GTEx and other single-cell references, the HuBMAP single-cell reference allows us to deconvolve the 47 GTEx tissues into over 200 cell types. In addition to the cellular fractions, we will calculate CTS eQTLs for those cell types at a population scale. Specifically, we will: 1) estimate cellular fractions of over 200 cell types from 47 tissue sites across the human body; 2) calculate CTS-eQTLs for those hundreds of cell types with statistical rigor and power. We will further consider the potential selection bias in the eQTL analysis that GTEx collected only normal tissues. The successful completion of this project will maximize the usage of NIH Common Fund GTEx and HuBMAP projects to provide a new eQTL resource at cell-type resolution. It will be powerful in downstream analyses such as CTS colocalization by connecting with genome-wide association studies (GWAS) and CTS transcriptome-wide association studies (TWAS) by predicting genetically regulated CTS gene expression. Altogether, this project will provide a global picture of the human body at high resolution to map cells to health and complex diseases.

项目概要/摘要 NIH 共同基金基因型组织表达项目 (GTEx) 收集了全基因组测序和 来自数百名受试者 47 个组织位点的基因表达数据。它通过提供 超过 7,000 份出版物的组织水平基因表达和表达数量性状位点 (eQTL)。然而， 组织是无数细胞的混合物，组织水平的基因调控受到细胞组成的影响。到 获得细胞类型特异性（CTS）效应，GTEx开始收集单核RNA测序（snRNA-seq） 来自八种组织类型的数据。单细胞数据收集极其昂贵且劳动密集型，因此 snRNA-seq 数据仅从 16 名捐赠者的 25 个组织样本中收集，可能不代表总体。 迫切需要更具成本效益和劳动力效率的方法来充分利用现有数据集。事实证明，随着 另一个 NIH 共同基金项目，人类生物分子图谱计划 (HuBMAP)，我们可以获得人口- 通过开发高效的计算能力，以 HuBMAP 单细胞数据作为参考来提高洞察力 方法。 HuBMAP 单细胞参考是 GTEx 和其他单细胞参考的补充 使我们能够将 47 种 GTEx 组织解卷积为 200 多种细胞类型。除了细胞部分之外，我们 将在群体规模上计算这些细胞类型的 CTS eQTL。具体来说，我们将：1）估计细胞 来自人体 47 个组织部位的 200 多种细胞类型的片段； 2) 计算那些的CTS-eQTL 数百种具有统计严谨性和功效的细胞类型。我们将进一步考虑潜在的选择偏差 GTEx仅收集正常组织的eQTL分析。该项目的顺利完成将 最大限度地利用 NIH 共同基金 GTEx 和 HuBMAP 项目，在以下位置提供新的 eQTL 资源： 细胞类型分辨率。通过连接，它将在下游分析（例如 CTS 共定位）中发挥强大作用 全基因组关联研究（GWAS）和 CTS 转录组全关联研究（TWAS） 预测基因调控的 CTS 基因表达。总而言之，该项目将提供全球概况 以高分辨率绘制人体细胞图，以绘制健康和复杂疾病的图谱。