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:
- 金额:$ 80.39万
- 依托单位:
- 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:
- 关键词:AddressAffectAllelesBiologicalBiological AssayBiopsy SpecimenCardiovascular DiseasesCholesterol HomeostasisChromatinClinicalClinical MedicineCodeCollaborationsComplexCoronary ArteriosclerosisDataData SetDiffuseDiseaseElementsEnvironmentEpigenetic ProcessEtiologyEventFamilyFollow-Up StudiesFutureGene ExpressionGene TargetingGenesGeneticGenetic VariationGenomeGenome engineeringGenomicsGenotypeGoalsGrowthHealthHepatocyteHeritabilityHistonesHumanIndividualInstitutesInterventionJointsLightLinear RegressionsLinkLinkage DisequilibriumLiverMapsMeta-AnalysisMethodsMindModelingModernizationMolecularMolecular ConformationMolecular Mechanisms of ActionMolecular ProfilingMyocardial InfarctionOutcomePathway interactionsPhenotypePlayPopulationProteinsRecording of previous eventsRegulationRegulator GenesRegulatory ElementReporterReproducibilityResearchResearch DesignResolutionRiskRisk FactorsRoleSample SizeSamplingSignal TransductionSingle Nucleotide PolymorphismStatistical MethodsStatistical ModelsTissue SampleTissuesTransplant SurgeonTransplantationUntranslated RNAValidationVariantWorkanalytical toolbaseblood lipidcardiovascular disorder riskclinical practiceclinical riskdesigndisorder riskexperimental studygenetic variantgenome editinggenome wide association studygenomic predictorshigh dimensionalityhistone modificationimprovedinduced pluripotent stem cellinsightlipid metabolismliver biopsymodel developmentmolecular phenotypemortalitynovelpleiotropismrisk varianttherapeutic developmenttooltraittranscriptomicstranslational 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.
人类肝脏心脏代谢疾病位点的表观遗传精细定位
概括
心血管疾病 (CVD) 是全球死亡的主要原因:估计有 17 人;
2012 年,全世界都有人死于 CVD 相关疾病。而他汀类药物等改变疾病的疗法
对健康产生了巨大的影响,许多人对治疗没有反应或未得到诊断
此外,临床危险因素和家族史具有显着的预测性。
必须改进CVD风险、风险预测和早期临床干预,以降低CVD的致死率
科学研究发现超过 182 种不同的遗传基因存在共同的遗传变异。
导致 CVD、冠状动脉疾病 (CAD)、心肌梗塞 (MI) 风险变异的基因座,
以及包括血脂在内的相关代谢物。然而,一些关键的限制限制了它。
重要的是,这些研究发现对临床医学的转化影响。
基因组和细胞背景因人而异,目前的 GWAS 研究假设静态
事实上,正是这种动态背景将揭示特定的情况。
遗传变异会影响分子特征,进而调节主要组织的疾病风险。
与 CVD 相关的是人类肝脏,由于难以深入研究其表型
获取肝脏样本。
在本提案中,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节信号,并利用这些预测来驱动实验验证
通过报告分析和基因组编辑来分析调节信号,以研究调节信号的机制
在目标 1 中,PI 与宾夕法尼亚大学的两名移植外科医生合作,研究了 CVD 风险的基因调控。
旨在构建 200 个人类肝脏活检样本的遗传和表观遗传特征的综合图谱。
在目标 2 中,PI 建议开发统计方法,使用配对来识别调控遗传变异
样本设计以共享多个表观遗传特征的强度,而这种类型的研究数据是当前的。
我们预计此类研究将大幅增长,并且我们的分析方法将得到广泛使用,这一点实属罕见。
在目标 3 中,PI 建议开发实验方法来验证功能性机制
SNP 尤其会影响 CVD 风险,我们将开发大规模并行 CRE 报告基因检测和基因组。
在 iPSC 衍生的肝细胞中进行工程设计,以表征多种 CVD 风险变异的精确机制
在整个提案中,PI 将开发、评估并公开新的分析工具。
充分利用多核计算环境的优势,并将公开所有的遗传和
从肝脏样本中生成的表观遗传数据。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Christopher David Brown其他文献
Christopher David Brown的其他文献
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{{ truncateString('Christopher David Brown', 18)}}的其他基金
The Transmission Biology of Mycobacterium Tuberculosis
结核分枝杆菌的传播生物学
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$ 80.39万 - 项目类别:
The Transmission Biology of Mycobacterium Tuberculosis
结核分枝杆菌的传播生物学
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$ 80.39万 - 项目类别:
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结核分枝杆菌的传播生物学
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8878357 - 财政年份:2013
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8586116 - 财政年份:2013
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$ 80.39万 - 项目类别:
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通过贝叶斯模型识别和验证细胞特异性 eQTL
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8708217 - 财政年份:2013
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