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

基本信息

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
项目类别：
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

项目摘要

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.

人肝脏中心脏代谢疾病基因座的表观遗传图映射概括心血管疾病（CVD）是世界上死亡率的主要原因：估计17； 500; 000 全球人在2012年因与CVD相关的疾病而死亡。对健康的影响很大，许多人对治疗没有反应或未诊断直到发生致命事件。此外，虽然临床风险因素和家族史具有显着预测性必须改善CVD风险，风险预测和早期临床干预措施，以降低疾病。科学研究已经发现了超过182个单独遗传的常见遗传变异导致CVD，冠状动脉疾病（CAD），心肌梗塞（MI）风险的可变性的基因座，以及包括血脂在内的相关代谢产物。但是，几个关键局限性限制了这些研究发现对临床医学的翻译影响。重要的是，虽然我们知道那个暂时的基因组和细胞环境各个个体动态各种，当前的GWAS研究假设静态所有样本的上下文。确实，正是这种动态环境将阐明特定方式遗传变异会影响分子特征，进而调节疾病风险。此外，主组织参与CVD的是人类肝脏，由于困难获取肝样品。在此提案中，PI将通过创建深层分子表型来解决这些关键局限性通过开发基本的统计工具来预测基因组的200人肝活检样品的地图这些丰富的肝脏数据中的调节信号，并通过使用这些预测来推动实验验证通过记者测定和基因组编辑的监管信号，以研究 CVD风险的遗传调节。在AIM 1中，与Penn的两名移植外科医生合作，PIS Pro- 姿势构建200个人肝活检样本的遗传和表观遗传特征的综合图。在AIM 2中，PIS提出了开发统计方法以使用配对来识别调节性遗传变异的统计方法样品设计可在多个表观遗传特征上共享强度。虽然这种类型的研究数据是我们非常罕见，我们预计这种类型的研究和我们的分析方法的广泛使用会大幅增长。在AIM 3中，PIS提出了开发实验方法以验证功能性的机制 SNP会影响CVD风险。特别是，我们将开发大量平行的CRE报告基因组和基因组 IPSC衍生的肝细胞的工程以表征多重CVD风险变化的精确机制蚂蚁。通过该建议，PI将开发，评估和制造公开采用新的分析工具多核计算环境的优势，将使所有遗传和从肝样品产生的表观遗传数据。