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.

项目总结/文摘