Integrated omics analysis of clonal hematopoiesis and cardiovascular disease risk in TOPMed

TOPMed 中克隆造血和心血管疾病风险的综合组学分析

• 批准号: 10753891
• 负责人: Karen Conneely
• 金额: $ 66.21万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-19 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10753891
• 关键词: Adult; Age; Aging; Alternative Splicing; Atherosclerosis; Biological Process; Blood; Blood Cells; Blood specimen; Cardiovascular Diseases; Cells; DNA; DNA Methylation; DNMT3a; Data; Data Discovery; Data Set; Development; Disease; Epigenetic Process; Gene Expression; Gene Expression Profile; Genes; Genetic Transcription; Hematologic Neoplasms; Hematopoiesis; Hematopoietic Neoplasms; Hematopoietic stem cells; Immune; Individual; JAK2 gene; Link; Literature; Malignant neoplasm of prostate; Mediating; Mediator; Mendelian randomization; Methylation; Modality; Modeling; Multiomic Data; Mutate; Mutation; National Heart, Lung, and Blood Institute; PPM1D gene; Pattern; Phenotype; Population; Prevalence; Process; Protein Isoforms; RNA Splicing; Risk; Role; Signal Transduction; Site; Somatic Mutation; TP53 gene; Testing; Tissues; Trans-Omics for Precision Medicine; Validation; Whole Blood; Work; atherosclerosis risk; cardiovascular disorder risk; cell type; cohort; data resource; deep learning; deep learning model; deep neural network; disorder risk; epigenome; epigenomics; genome sequencing; genome wide association study; genome-wide; interest; methylomics; mortality; mutational status; network models; neural network architecture; novel; patient stratification; predictive modeling; programs; response; risk prediction; risk stratification; statistics; tool; transcriptome; transcriptome sequencing; transcriptomics; whole genome

PROJECT SUMMARY Clonal hematopoiesis of indeterminate potential (CHIP) involves the gain of somatic mutations that confer a selective advantage to hematopoietic stem cells and lead to the expansion of a clonal population of blood cells. CHIP is common in older individuals and is associated with increased risk of blood cancers and atherosclerotic cardiovascular disease (ASCVD). Genes commonly mutated in CHIP include known regulators of DNA methylation (DNAm) and RNA splicing. In this application, we propose to take advantage of a novel omics data resource being generated by the TOPMed program to investigate the roles of DNAm, gene expression, and differential splicing as mechanisms potentially mediating the relationship between CHIP and risk for ASCVD. In Aim 1 we will use genome-wide DNAm and RNA-seq data collected from blood samples of tens of thousands of individuals to investigate whether methylomic and transcriptomic patterns differ in individuals with CHIP. We will consider individual genes and subsets of the genes most commonly mutated in CHIP separately, since each of these genes is likely to have different functional consequences given their different regulatory roles. In Aim 2, we will test for the presence of CHIP-associated methylomic and transcriptomic differences that are driven by specific immune cell types. In Aim 3, we will use Mendelian randomization to identify CpG sites or genes that may causally mediate the relationship between CHIP mutations and ASCVD risk. In Aim 4, we will integrate the multiple omics data in a biologically informed deep learning model to investigate how CHIP mutations and methylomic and transcriptomic changes may collectively contribute to risk for ASCVD and identify key biological processes through which these changes influence disease risk. By characterizing the epigenomic and transcriptomic consequences of CHIP mutations in specific genes and within specific cell types and examining the potential of TOPMed trans-omic data to predict disease risk, this work will advance current understanding of CHIP and risk for ASCVD.

项目摘要 不确定电势（CHIP）的克隆造血作用涉及赋予A的体细胞突变 造血干细胞的选择性优势，并导致血细胞克隆种群的扩张。 芯片在老年人中很常见，与血液癌和动脉粥样硬化的风险增加有关 心血管疾病（ASCVD）。芯片中通常突变的基因包括已知的DNA调节剂 甲基化（DNAM）和RNA剪接。在此应用程序中，我们建议利用新型的OMIC数据 最高程序生成的资源来研究DNAM，基因表达和 差异剪接作为机制，可能介导芯片与ASCVD风险之间的关系。在 AIM 1我们将使用全基因组DNAM和RNA-seq数据，这些数据从数万的血液样本中收集 个体研究芯片个体的甲基甲基组学和转录组模式是否有所不同。我们将 考虑到芯片中最常见的基因的单个基因和亚集，因为每个基因 鉴于其不同的调节作用，这些基因可能会产生不同的功能后果。在AIM 2中， 我们将测试由芯片相关的甲基甲基和转录组差异的存在 特异性免疫细胞类型。在AIM 3中，我们将使用Mendelian随机化来识别CpG位点或基因 可能会导致芯片突变与ASCVD风险之间的关系。在AIM 4中，我们将整合 在生物学知情的深度学习模型中，多个OMICS数据研究芯片突变和如何 甲基组和转录组的变化可能会统称有助于ASCVD的风险，并确定关键的生物学 这些变化会影响疾病风险的过程。通过表征表观基因组和 特定基因和特定细胞类型中芯片突变的转录组后果并检查 最高的跨词数据预测疾病风险的潜力，这项工作将提高当前对 芯片和ASCVD的风险。