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

项目总结/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， 来自数百名受试者的47个组织部位的基因表达数据。它产生了巨大的影响， 组织水平的基因表达和表达数量性状位点（eQTL）超过7,000出版物。然而，在这方面， 组织是无数细胞的混合物，组织水平的基因调控受细胞组成的影响。到 为了获得细胞类型特异性（CTS）效应，GTEx开始收集单核RNA测序（snRNA-seq） 数据来自八种组织类型。单细胞数据收集是极其昂贵和劳动密集型的，因此 snRNA-seq数据仅收集自16个供体的25个组织样品，其可能不代表群体。 为了充分利用现有的数据集，迫切需要更多的成本和劳动力效率高的方法。事实证明， 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- 以HuBMAP单细胞数据为参考，通过开发计算效率高的 方法.作为GTEx和其他单小区参考的补充，HuBMAP单小区参考 使我们能够将47种GTEx组织分解为200多种细胞类型。除了细胞碎片，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个组织部位的超过200种细胞类型的部分; 2）计算这些细胞的CTS-eQTL 数以百计的细胞类型，具有统计上的严谨性和力量。我们将进一步考虑潜在的选择偏差， eQTL分析表明GTEx仅收集正常组织。该项目的成功完成将 最大限度地利用NIH共同基金GTEx和HuBMAP项目， 细胞类型分辨率。它将在下游分析中发挥强大作用，例如通过连接 全基因组关联研究（GWAS）和CTS全转录组关联研究（TWAS）， 预测遗传调节的CTS基因表达。总之，该项目将提供一个全球性的图片， 以高分辨率绘制人体健康和复杂疾病的细胞图。

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.