Comprehensive Genotype-transcript Association in human peripheral blood
Comprehensive Genotype-transcript Association in human peripheral blood
批准号:
8262855
负责人:
GREGORY C GIBSON
金额:
$23.69万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
--
资助国家:
美国
项目状态:
未结题
起止时间:
至
关键词:
15 year old3&apos Untranslated RegionsAccountingAddressAdultAgingAllelesAmericanBase SequenceBiological AssayBlood specimenCell LineCessation of lifeChildhoodChronic DiseaseCitiesCollaborationsComplexCoronaryCoronary ArteriosclerosisCrohn&aposs diseaseDNADNA SequenceDataDevelopmentDiseaseElderlyEnsureEnvironmentGenderGene ExpressionGene Expression ProfileGene Expression ProfilingGene TargetingGenesGeneticGenetic PolymorphismGenetic TranscriptionGenomeGenomicsGenotypeHealthHumanHuman GeneticsIndividualInflammationInflammatoryInstructionIntronsInvestigationJointsKnowledgeLeukocytesMeasurementMeasuresMethodsMethylationMolecular ProfilingMoroccoMyocardial InfarctionNucleic Acid Regulatory SequencesParticipantPatientsPhenotypePopulationPopulation ControlQuantitative GeneticsQuantitative Reverse Transcriptase PCRRNA SequencesRegulationRegulator GenesResearch PersonnelRiskSamplingSeveritiesSourceStatistical MethodsStressStructureTestingTranscriptUniversitiesVariantcohortdesigndiet and exercisegenetic variantgenome wide association studyimprovedlifestyle interventionlymphoblastnanoscaleperipheral bloodprogramstrait
中文摘要
项目总结(见说明);
这是一个经验性项目,旨在评估罕见和常见遗传变异对来自佐治亚州亚特兰大三个大型队列的人类外周血样本中转录本丰度变化的联合贡献。我们将对2,100个个体的96个基因的调控区进行多达20个DNA酶的深度测序,并通过纳米级定量RTPCR测量这些基因的转录丰度,以及通过靶向RNA测序测量等位基因特异性转录。将从健康成人队列(CHDWB)、冠状动脉疾病队列和儿童克罗恩病队列中各纳入700名个体,所有这些都是与埃默里大学的研究人员合作进行的。本计划其他项目中开发的统计方法将用于全面了解联合贡献
基因表达变异的罕见和常见变异。反过来,该项目产生的实验数据将支持统计方法的改进,以估计血统的同一性,控制罕见等位基因关联研究中的群体结构,推断环境基因型的存在。
互动和其他应用。所有三个队列均具有良好的临床表型,涵盖了人类多样性的几个重要方面,包括两种性别;美国大城市中存在的种族多样性;儿科,成人和老年人群;以及动脉粥样硬化和炎症性疾病患者。这将是迄今为止最大的基因型与基因表达相关性研究之一,第一次有意解决罕见变异对转录丰度调节的贡献,
采用纵向测量,以评估每年重复抽样的关联性的稳健性。此外,选择与冠状动脉和小儿克罗恩病相关的基因,确保我们也将评估与疾病相关的基因表达谱的发展,其潜在的效用预测健康的调节多态性的医学相关的贡献。
英文摘要
PROJECT SUMMARY (See instructions);
This is an empirical project designed to evaluate the joint contributions of rare and common genetic variants to variation in transcript abundance in human peripheral blood samples from three large cohorts in Atlanta, GA. We will deeply sequence up to 20 kilobases of DNA encompassing the regulatory regions of 96 genes from 2,100 individuals, and measure transcript abundance of these genes by nanoscale quantitative RTPCR as well as allele-specific transcription by targeted RNA sequencing. 700 individuals each will be included from a healthy adult cohort (CHDWB), a coronary artery disease cohort, and a pediatric Crohn's disease cohort, all in collaboration with investigators at Emory University. Statistical methods developed in the other projects of this Program will be used to generate a comprehensive picture of the joint contributions
of rare and common variants to gene expression variation. Reciprocally, the experimental data generated by this Project will support the refinement of statistical methods for estimation of identity-by-descent, controlling for population structure in rare allele association studies, inferring the existence of genotype-by-environment
interactions, and other applications. All three cohorts are clinically well-phenotyped and encompass several important aspects of human diversity, including both genders; the ethnic diversity present in a large American city; pediatric, adult, and aging populations; and including atherosclerotic and inflammatory disease patients. This will be one of the largest genotype association with gene expression studies to date, the first to deliberately address rare variant contributions to regulation of transcript abundance, and
incorporates longitudinal measurements to evaluate the robustness of associations in repeated sampling at yearly intervals. Furthermore, the selection of genes related to coronary and pediatric Crohn's disease ensures that we wilt also evaluate the medically relevant contribution of regulatory polymorphism to the development of disease-related gene expression profiles, and their potential utility for predictive health.
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