Psaty Bruce M, O'Donnell Christopher J, Gudnason Vilmundur, Lunetta Kathryn L, Folsom Aaron R, Rotter Jerome I, Uitterlinden André G, Harris Tamara B, Witteman Jacqueline C M, Boerwinkle Eric
Departments of Medicine, Epidemiology, and Health Services, University of Wash, Seattle, USA.
Circ Cardiovasc Genet. 2009 Feb;2(1):73-80. doi: 10.1161/CIRCGENETICS.108.829747.
The primary aim of genome-wide association studies is to identify novel genetic loci associated with interindividual variation in the levels of risk factors, the degree of subclinical disease, or the risk of clinical disease. The requirement for large sample sizes and the importance of replication have served as powerful incentives for scientific collaboration. Methods- The Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium was formed to facilitate genome-wide association studies meta-analyses and replication opportunities among multiple large population-based cohort studies, which collect data in a standardized fashion and represent the preferred method for estimating disease incidence. The design of the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium includes 5 prospective cohort studies from the United States and Europe: the Age, Gene/Environment Susceptibility-Reykjavik Study, the Atherosclerosis Risk in Communities Study, the Cardiovascular Health Study, the Framingham Heart Study, and the Rotterdam Study. With genome-wide data on a total of about 38 000 individuals, these cohort studies have a large number of health-related phenotypes measured in similar ways. For each harmonized trait, within-cohort genome-wide association study analyses are combined by meta-analysis. A prospective meta-analysis of data from all 5 cohorts, with a properly selected level of genome-wide statistical significance, is a powerful approach to finding genuine phenotypic associations with novel genetic loci.
The Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium and collaborating non-member studies or consortia provide an excellent framework for the identification of the genetic determinants of risk factors, subclinical-disease measures, and clinical events.
全基因组关联研究的主要目的是识别与个体间风险因素水平、亚临床疾病程度或临床疾病风险差异相关的新基因位点。对大样本量的要求以及重复验证的重要性有力地推动了科学合作。方法——基因组流行病学心脏与衰老研究队列联盟的成立是为了促进全基因组关联研究的荟萃分析以及多个大型人群队列研究之间的重复验证机会,这些队列研究以标准化方式收集数据,是估计疾病发病率的首选方法。基因组流行病学心脏与衰老研究队列联盟的设计包括来自美国和欧洲的5项前瞻性队列研究:年龄、基因/环境易感性-雷克雅未克研究、社区动脉粥样硬化风险研究、心血管健康研究、弗雷明汉心脏研究和鹿特丹研究。这些队列研究总共约有38000人的全基因组数据,有大量以相似方式测量的与健康相关的表型。对于每个经过协调的性状,通过荟萃分析将队列内的全基因组关联研究分析结果合并。对所有5个队列的数据进行前瞻性荟萃分析,并适当选择全基因组统计学显著性水平,是发现与新基因位点真正表型关联的有力方法。
基因组流行病学心脏与衰老研究队列联盟以及合作的非成员研究或联盟为识别风险因素、亚临床疾病指标和临床事件的遗传决定因素提供了一个极好的框架。