Integrative genomic and functional genomic studies to connect variant to function for CAD GWAS loci

整合基因组和功能基因组研究，将 CAD GWAS 位点的变异与功能联系起来

• 批准号: 10639274
• 负责人: Wei Li
• 金额: $ 80.25万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10639274
• 关键词: Acceleration; Address; Affect; Alleles; Apoptosis; Apoptotic; Atherosclerosis; Automobile Driving; Biological; Biological Assay; Biology; Blood Vessels; CRISPR interference; CRISPR screen; Catalogs; Cause of Death; Cells; Cellular Assay; Chemotaxis; Chromatin; Clinical Trials; Clustered Regularly Interspaced Short Palindromic Repeats; Complex; Coronary Arteriosclerosis; Data; Data Set; Disease; Disease susceptibility; Drug Targeting; Encyclopedias; Endothelial Cells; Etiology; Foam Cells; Foamy Macrophage; Genes; Genetic; Genetic Predisposition to Disease; Genetic Variation; Genomics; Human; Immune; Knock-out; Link; Lipids; Lipoproteins; Machine Learning; Macrophage; Maps; Metabolic; Nature; Outcome; Pathway interactions; Patients; Performance; Phagocytes; Phenotype; Regulatory Element; Reporter; Risk Factors; Role; Smooth Muscle Myocytes; Stimulus; Testing; Therapeutic; Tissues; Training; Translations; Untranslated RNA; Validation; Variant; Vascular Smooth Muscle; candidate identification; causal variant; cell type; computational pipelines; disorder risk; effective therapy; epigenomics; experimental study; follow-up; functional genomics; genome wide association study; genome-wide analysis; genomic data; improved; in vivo Model; induced pluripotent stem cell; innovation; insight; machine learning model; machine learning prediction; monocyte; new therapeutic target; novel; overexpression; risk variant; screening; single-cell RNA sequencing; success; therapeutic target; transcriptomics; uptake

PROJECT SUMMARY Atherosclerotic coronary artery disease (CAD) remains the leading cause of death globally, despite effective therapies for many known risk factors. The majority of CAD loci identified by genome-wide association studies (GWAS) are not associated with traditional risk factors, providing opportunities to discover novel mechanisms and therapies. Encouragingly, many new therapies targeting causal genes and pathways inspired by GWASs have received approval or fared favorably in clinical trials. Despite the success, the bottleneck remains the lack of experimental studies systematically linking CAD variants to the causal genes and pathways at scale in a cell type-specific manner. To overcome these barriers and define tissue/cell type-specific contribution to the genetic predisposition of CAD risks, we leverage the STARNET eQTL data in nine CAD-relevant tissue/cell type from >1300 subjects and public eQTL data of immune and vascular cells. Aim 1 will apply advanced computational pipelines to nominate candidate causal CAD variants, cis-regulatory elements (cREs), and their target genes and related pathways at CAD loci in CAD-relevant tissue and cell types, providing tissue/cell type- specific mechanistic and therapeutic insights for CAD. Initial analysis supports that ~30% CAD risk loci are most strongly associated with eQTLs in macrophages or foamy macrophages compared with the other seven tissues in STARNET data. This is in line with the major role of circulating monocyte-derived macrophages in driving atherosclerosis. Leveraging the rich functional genomic datasets available for monocytes/macrophages, Aim 2 will apply mid-throughput functional assays, including single-cell CRISPR screening, massively parallel reporter assay, and arrayed knockout or overexpression cellular assays, to experimentally define the genetic contributions of monocytes/macrophages to CAD by connecting variants to genes and phenotypic roles, and build machine learning models predicting functional cREs and their target genes. Our study addresses unmet needs in the functional follow-up of CAD GWASs by performing integrative genomic analysis of CAD loci at an unprecedented scale, experimentally connecting variant to function in cell types critical for the genetic predisposition of CAD, building machine learning models for predicting functional cREs and their target genes for improved prioritization workflow, and providing a generalizable framework for extended discoveries in other CAD-relevant cell types. With the MPI’s expertise in statistical genetics, machine learning, macrophage biology, and functional genomics, our study has significant and broad impacts by providing (1) novel insights into tissue/cell type-specific contributions to the genetic predisposition of CAD that inform new biological mechanisms and therapeutic targets; (2) a catalog of phenotypically tested target genes highly likely to be causal for CAD, allowing immediate refocus to the most promising targets for accelerated translation, and (3) an innovative computational and experimental framework for systematic variant-to-function discoveries.

项目摘要 动脉粥样硬化性冠状动脉疾病（CAD）仍然是全球死亡的主要原因， 治疗许多已知的风险因素。通过全基因组关联研究确定的大多数CAD位点 （GWAS）与传统的风险因素无关，为发现新机制提供了机会 和治疗。令人鼓舞的是，许多针对致病基因和途径的新疗法受到GWAS的启发， 已获得批准或在临床试验中表现良好。尽管取得了成功，但瓶颈仍然是缺乏 实验研究系统地将CAD变体与细胞中的因果基因和途径联系起来 特定类型的方式。为了克服这些障碍，并确定组织/细胞类型对遗传的特异性贡献， 我们利用9种CAD相关组织/细胞类型的STARNET eQTL数据， 来自>1300名受试者和免疫和血管细胞的公开eQTL数据。目标1将适用于高级 计算管道，以提名候选的因果CAD变体，顺式调控元件（克雷斯）， CAD相关组织和细胞类型中CAD基因座的靶向基因和相关途径，提供组织/细胞类型- 对CAD的具体机制和治疗见解。初步分析支持约30%的CAD风险位点是最常见的 与其他7种组织相比，在巨噬细胞或泡沫巨噬细胞中与eQTL强相关 在STARNET数据中。这与循环单核细胞衍生的巨噬细胞在驱动细胞增殖中的主要作用是一致的。 动脉粥样硬化利用单核细胞/巨噬细胞丰富的功能基因组数据集，目标2 将应用中通量功能分析，包括单细胞CRISPR筛选，大规模平行报告基因， 分析和阵列敲除或过表达细胞分析，以实验性地确定遗传学上的差异。 单核细胞/巨噬细胞通过将变体与基因和表型作用联系起来而对CAD的贡献，以及 建立机器学习模型预测功能性克雷斯及其靶基因。我们的研究解决未满足的 通过对CAD基因座进行整合基因组分析， 前所未有的规模，实验性地将变异与对遗传至关重要的细胞类型中的功能联系起来， CAD的易感性，建立机器学习模型来预测功能性克雷斯及其靶基因 改进优先级工作流程，并为其他领域的扩展发现提供可推广的框架 CAD相关细胞类型。凭借MPI在统计遗传学，机器学习，巨噬细胞生物学， 和功能基因组学，我们的研究具有重大和广泛的影响，提供（1）新的见解， 组织/细胞类型特异性对CAD遗传易感性的贡献，为新的生物学机制提供信息 和治疗靶点;（2）表型测试的靶基因的目录，其高度可能是CAD的病因， 允许立即重新聚焦于最有希望的目标，以加速翻译，以及（3）创新的 计算和实验框架的系统变量功能的发现。