Epigenetic fine-mapping of cardiometabolic disease loci in the human liver

人类肝脏心脏代谢疾病位点的表观遗传精细定位

• 批准号: 9309707
• 负责人: Christopher David Brown
• 金额: $ 80.39万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2021-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9309707
• 关键词: Address; Affect; Alleles; Biological; Biological Assay; Biopsy Specimen; Cardiovascular Diseases; Cholesterol Homeostasis; Chromatin; Clinical; Clinical Medicine; Code; Collaborations; Complex; Coronary Arteriosclerosis; Data; Data Set; Diffuse; Disease; Elements; Environment; Epigenetic Process; Etiology; Event; Family; Follow-Up Studies; Future; Gene Expression; Gene Targeting; Genes; Genetic; Genetic Variation; Genome; Genome engineering; Genomics; Genotype; Goals; Growth; Health; Hepatocyte; Heritability; Histones; Human; Individual; Institutes; Intervention; Joints; Light; Linear Regressions; Link; Linkage Disequilibrium; Liver; Maps; Meta-Analysis; Methods; Mind; Modeling; Modernization; Molecular; Molecular Conformation; Molecular Mechanisms of Action; Molecular Profiling; Myocardial Infarction; Outcome; Pathway interactions; Phenotype; Play; Population; Proteins; Recording of previous events; Regulation; Regulator Genes; Regulatory Element; Reporter; Reproducibility; Research; Research Design; Resolution; Risk; Risk Factors; Role; Sample Size; Sampling; Signal Transduction; Single Nucleotide Polymorphism; Statistical Methods; Statistical Models; Tissue Sample; Tissues; Transplant Surgeon; Transplantation; Untranslated RNA; Validation; Variant; Work; analytical tool; base; blood lipid; cardiovascular disorder risk; clinical practice; clinical risk; design; disorder risk; experimental study; genetic variant; genome editing; genome wide association study; genomic predictors; high dimensionality; histone modification; improved; induced pluripotent stem cell; insight; lipid metabolism; liver biopsy; model development; molecular phenotype; mortality; novel; pleiotropism; risk variant; therapeutic development; tool; trait; transcriptomics; translational impact

Epigenetic fine-mapping of cardiometabolic disease loci in the human liver Summary Cardiovascular disease (CVD) is the leading cause of mortality in the world: an estimated 17; 500; 000 people worldwide died from CVD-related illness in 2012. While disease altering therapies such as statins have had a tremendous health impact, many individuals are unresponsive to treatment or go undiagnosed until a fatal event occurs. Moreover, while clinical risk factors and family history are significantly predictive of CVD risk, risk prediction and early clinical intervention must be improved to diminish the lethality of the disease. Scientific studies have uncovered common genetic variation at more than 182 separate genetic loci that contribute to variability in CVD, coronary artery disease (CAD), myocardial infarction (MI) risk, and associated metabolites including blood lipids. However, several critical limitations have restricted the translational impact of these study findings on clinical medicine. Importantly, while we know that temporal, genomic, and cellular context varies dramatically across individuals, current GWAS studies assume a static context across all samples. Indeed, it is precisely this dynamic context that will shed light on how a specific genetic variant impacts molecular traits, which, in turn, modulate disease risk. Furthermore, a primary tissue involved in CVD is the human liver, which has been difficult to deeply phenotype because of the difficulty of acquiring liver samples. In this proposal, the PIs will address these critical limitations by creating a deep molecular phenotype map of 200 human liver biopsy samples, by developing essential statistical tools to predict the genomic regulatory signals in these rich liver data, and by using these predictions to drive experimental validation of regulatory signals through reporter assays and genome editing in order to study the mechanisms of the genetic regulation of CVD risk. In Aim 1, in collaboration with two transplant surgeons at Penn, the PIs pro- pose to build a comprehensive map of the genetic and epigenetic traits of 200 human liver biopsy samples. In Aim 2, the PIs propose to develop statistical methods to identify regulatory genetic variants using paired sample design to share strength across the multiple epigenetic traits. While study data of this type is cur- rently rare, we anticipate substantial growth in studies of this type and broad use of our analytic approaches. In Aim 3, the PIs propose to develop experimental methods to validate the mechanisms by which functional SNPs impact CVD risk. In particular, we will develop massively parallel CRE reporter assays and genome engineering in iPSC derived hepatocytes to characterize the precise mechanism of multiple CVD risk vari- ants. Throughout this proposal, the PIs will develop, evaluate, and make public new analytic tools that take advantage of many-core computing environments, and will make publicly available all of the genetic and epigenetic data generated from the liver samples.

人肝脏心脏代谢性疾病基因座的表观遗传fiNe定位 摘要 心血管疾病(CVD)是世界上导致死亡的主要原因：估计有17500；000人 2012年，世界各地的人死于心血管疾病相关疾病。虽然疾病改变了他汀类药物等治疗方法 已经对健康产生了巨大的影响，许多人对治疗没有反应或没有得到诊断 直到发生致命事件。此外，虽然临床危险因素和家族史对fi有显著的预测作用 必须改进对心血管疾病风险的预测和早期临床干预，以降低心血管疾病的致命性。 这种疾病。科学研究中心的fic研究发现了182多个不同基因的共同遗传变异。 导致心血管疾病、冠状动脉疾病(CAD)、心肌梗死(MI)风险、 以及相关的代谢物，包括血脂。然而，几个关键的限制限制了 这些研究fi的翻译对临床医学的影响。重要的是，虽然我们知道时间， 基因组和细胞环境在不同的个体之间有很大的不同，目前的GWAS研究假设 所有样本的上下文。事实上，正是这种动态的背景将阐明一个特定的fic 基因变异会影响分子特征，而分子特征又会调节疾病风险。此外，初级组织 参与脑血管病的是人的肝脏，由于fifi的不同，它很难深入表型。 获取肝脏样本。 在这项提案中，PI将通过创建深层分子表型来解决这些关键限制 通过开发基本的统计工具来预测基因组，绘制了200个人类肝脏活检样本的图谱 这些丰富的肝脏数据中的调控信号，并通过使用这些预测来推动实验验证 通过记者分析和基因组编辑对调控信号进行分析，以研究 心血管疾病风险的遗传调控。在Aim 1中，与宾夕法尼亚大学的两名移植外科医生合作，Pis Pro- 建立200个人肝活检样本的遗传和表观遗传特征的综合图谱。 在目标2中，PIs建议开发统计方法，以使用配对来识别调节性遗传变异 样本设计，以分享多个表观遗传特征的力量。而这种类型的研究数据是连续的。 最近，我们预计这种类型的研究和我们的分析方法的广泛使用将大幅增长。 在目标3中，私人投资机构建议开发实验方法来验证 单核苷酸多态影响心血管疾病风险。特别是，我们将开发大规模并行的Cre报告分析和基因组 在IPSC来源的肝细胞中进行工程以表征多种心血管疾病风险变量的精确机制 蚂蚁。在整个提案中，PI将开发、评估和公开新的分析工具，这些工具需要 多核计算环境的优势，并将公开所有遗传和 从肝脏样本中产生的表观遗传学数据。