Cardiac genetic effects across HLBS phenotypes
Cardiac genetic effects across HLBS phenotypes
批准号:
9521873
负责人:
ARAVINDA CHAKRAVARTI
金额:
$43.81万
依托单位国家:
美国
项目类别:
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-05-01 至 2020-04-30
关键词:
ATAC-seqAffectAlgorithmsBase PairingBindingBloodCardiacCardiovascular DiseasesChromatinCodeCollaborationsComplexComputing MethodologiesDNADNase I hypersensitive sites sequencingDataDiseaseEnhancersFamilyGene ExpressionGene FrequencyGene TargetingGenesGeneticGenetic TranscriptionGenetic VariationGenomeGenomicsGenotypeGenotype-Tissue Expression ProjectHeartHeart DiseasesHematological DiseaseIndividualLeadLinkLungLung diseasesMachine LearningMapsMinorModelingMolecularPeripheralPhenotypePhysiologyPublic HealthPublishingQuality ControlRegulator GenesRegulatory ElementResearch PersonnelResourcesRoleSample SizeSleepSleep DisordersTestingTissuesTrans-Omics for Precision MedicineUnited States National Institutes of HealthUntranslated RNAVariantWeightbaseepigenomicsfunctional genomicsgene discoverygenetic variantgenome wide association studygenome-widegenome-wide analysisgenomic datagenomic variationhistone modificationimprovednovelnovel strategiesprogramsrare variantreverse geneticssuccesstraittranscription factortranscriptome sequencingwhole genome
中文摘要
正向遗传全基因组关联研究已经成功地定位了数千个基因座
心、肺、血和睡眠的调节紊乱(HLBS),涉及广泛的序列变异
在非编码基因组中。然而,它们的功能、作用机制以及它们如何影响疾病是
仍不清楚。为了解决这个新的和重要的GWA瓶颈,我们使用了功能基因组学启发的反向
识别“转录机制”(转录因子(Tf)、顺式调控元件)的遗传学策略
(Cre),靶基因)控制HLBS相关组织功能及其DNA变异如何影响HLBS
疾病。利用我们在复杂的心血管疾病方面的长期专业知识和成功经验,
和我们最近开发的新计算方法,我们提出了新的基因组学分析
精准医学(TOPMed)项目表型及其全基因组序列,
结合公开的表观基因组学数据,鉴定HLBS病的分子基础。我们将首先
先关注控制心脏生理及其疾病的转录机制,然后再探索其他
与TOPMed其他研究人员合作的与HLBS相关的组织和疾病。我们的具体目标是:
(1)识别心脏和其他HLBS相关组织中的转录机制;和(2)连接
HLBS性状转录机制中的基因组变异。我们的方法将使识别
控制HLBS组织的核心分子成分以及它们在HLBS疾病中是如何受到损害的。
英文摘要
Forward genetic genome-wide association studies (GWAS) have successfully mapped thousands of loci
regulating disorders of the heart, lung, blood and sleep (HLBS), implicating widespread sequence variation
within the non-coding genome. However, their functions, mechanisms of action and how they impact disease is
still unclear. To solve this new and important GWAS bottleneck, we use a functional genomics-inspired reverse
genetics strategy to identify the `transcriptional machinery' (transcription factors (TF), cis-regulatory elements
(CRE), target genes) controlling HLBS-relevant tissue functions and how DNA variants in them affect HLBS
diseases. Taking advantage of our long-standing expertise and successes in complex, cardiovascular disorders,
and novel computational methods we have recently developed, we propose novel genomics analyses of the
Trans-Omics for Precision Medicine (TOPMed) Program phenotypes and their whole genome sequences,
together with publicly available epigenomics data, to identify the molecular bases of HLBS disease. We will first
focus on the transcriptional machinery controlling heart physiology and its disorders before exploring other
HLBS-relevant tissues and disorders in collaboration with other TOPMed investigators. Our specific aims are:
(1) Identifying the transcriptional machinery in the heart and other HLBS relevant tissues; and, (2) Connecting
genomic variation in the transcriptional machinery to HLBS traits. Our approach will enable identification of
the core molecular components that control HLBS tissues and how they are compromised in HLBS disorders.
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