GWA for Gene-Environment Interaction Effects Influencing CHD
GWA for Gene-Environment Interaction Effects Influencing CHD
批准号:
7479363
负责人:
ERIC A. BOERWINKLE
金额:
$84.16万
依托单位国家:
美国
项目类别:
财政年份:
2007
资助国家:
美国
项目状态:
已结题
起止时间:
2007-08-06 至 2011-05-31
关键词:
Acute myocardial infarctionAfrican AmericanAlgorithmsAmericanArtsAtherosclerosisBioinformaticsBiologicalBloodBlood Coagulation FactorBlood PressureBody SizeCandidate Disease GeneCase-Control StudiesChronicChronic Obstructive Airway DiseaseClinic VisitsCohort StudiesCollaborationsCollectionCommunitiesConfidentialityCoronary heart diseaseDataData AnalysesData SetDepthDeveloped CountriesDeveloping CountriesDietDiseaseDisease OutcomeEnd PointEnvironmentEnvironmental Risk FactorEuropeanFamily StudyFramingham Heart StudyFundingGene FamilyGenesGeneticGenetic PolymorphismGenetic VariationGenomeGenotypeGrantHaplotypesHealthHeartHeart RateHuman GenomeIndividualLightLinkage DisequilibriumLipidsLungMeasuresMethodsMorbidity - disease rateObesityParticipantPathway interactionsPhenotypePhysical activityPlayPolymorphism AnalysisPopulationPopulation GeneticsPrevention programPrivacyPublicationsResearchResearch PersonnelResourcesRespiratory physiologyRiskRisk FactorsRoleSamplingScientistSingle Nucleotide PolymorphismSlideSmokingStatistical MethodsUnited StatesVariantagedbasecohortcomputer sciencecostcost effectivedisorder riskenvironmental changefollow-upgene environment interactiongenome wide association studygenotyping technologyheart disease riskimprovedinnovationmortalitynovelprogramssuccess
中文摘要
描述(由申请人提供):我们建议利用社区动脉粥样硬化风险(ARIC)研究的全部范围来研究遗传变异和可改变环境之间的相互作用,以确定冠心病的发生。我们目前获得资助(HL087641),在队列随机样本(CRS)中对600例冠心病病例和1,400名个体进行50万个标签snp的分类。在拟议的研究中,我们将增加额外的1100例冠心病病例(总共1700例冠心病病例)和额外的1000例CRS个体(总共2400例CRS个体)进行相同的全基因组snp收集,以确定影响冠心病事件的环境特异性遗传效应。已经获得资助的赠款仅用于主要效果;这一新的机会使研究能够为相互作用提供动力。我们将结合计算机科学和滑动窗口单倍型分析,直接结合基因-环境相互作用效应,以确定预测冠心病事件的snp -环境组合。通过调查来自ARIC的前10,000个(~2%)snp与Framingham心脏研究中基因-环境相互作用效应分析的前10,000个snp之间的“统计显著性和效应方向”的一致性来复制初始发现。对于那些始终复制的snp,我们将对ARIC队列的其余部分(-11,900人)进行基因分型,以便在最初的随访分析中代表环境和表型变异的全谱。CRS为使用GWA方法研究其他心脏、肺和血液表型的基因-环境相互作用的初步研究提供了理想的成本效益机会。我们将开始使用CRS和500,000个SNP数据集识别影响其他心脏、肺和血液表型的基因。最后,我们将通过及时与其他科学家分享数据和分析算法来支持进一步的分析和发现。该项目将汇集适当的基于人群的样本、良好的表型、环境措施的细节和最先进的分析,以确定影响冠心病以及其他心脏、肺和血液表型的环境特异性遗传效应。识别这些环境特异性遗传效应将改善风险预测,并阐明连接健康和疾病的新生物学途径。
英文摘要
DESCRIPTION (provided by applicant): We propose to leverage the full scope of the Atherosclerosis Risk in Communities (ARIC) study to investigate the interaction between genetic variation and modifiable environments in the determination of incident CHD. We are currently funded (HL087641) to type 600 incident CHD cases and 1,400 individuals in a cohort random sample (CRS) for 500,000 TagSNPs. For the proposed research, we will add an additional 1,100 incident CHD cases (a total of 1,700 CHD cases) and an additional 1,000 CRS individuals (a total of 2,400 CRS individuals) for the same genome-wide collection of SNPs to identify environment-specific genetic effects influencing incident CHD. The already-funded grant is only powered for main effects; this new opportunity allows the study to be powered for interaction effects. We will perform a combination of computer science and sliding window haplotype analyses which directly incorporate gene-environment interaction effects to identify SNP-environmental combinations that predict incident CHD. Replication of initial findings will be done by investigating the concordance of "statistical significance and direction of effect" between the top 10,000 (~2%) SNPs from ARIC and the top 10,000 SNPs from analyses of gene-environment interaction effects in the Framingham Heart Study. For those SNPs that consistently replicate throughout, we will genotype the remainder of the ARIC cohort (-11,900 individuals) so that the full spectrum of environmental and phenotypic variation is represented in initial follow-up analyses. The CRS provides an ideal cost effective opportunity for initial studies to use GWA methods to investigate gene-environment interactions for other heart, lung and blood phenotypes. We will begin to identify genes influencing other heart, lung and blood phenotypes using the CRS and the 500,000 SNP dataset. Finally, we will support further analysis and discovery by sharing the data and analysis algorithms with other scientists in a timely fashion. This project will bring together appropriate population-based samples, good phenotyping, details on environmental measures and state-of-the art analyses to identify environment-specific genetic effects influencing CHD, along with other heart, lung and blood phenotypes. Identification of these environment-specific genetic effects will improve risk prediction and shed light on novel biological pathways bridging health and disease.
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