Lin Honghuang, Wang Min, Brody Jennifer A, Bis Joshua C, Dupuis Josée, Lumley Thomas, McKnight Barbara, Rice Kenneth M, Sitlani Colleen M, Reid Jeffrey G, Bressler Jan, Liu Xiaoming, Davis Brian C, Johnson Andrew D, O'Donnell Christopher J, Kovar Christie L, Dinh Huyen, Wu Yuanqing, Newsham Irene, Chen Han, Broka Andi, DeStefano Anita L, Gupta Mayetri, Lunetta Kathryn L, Liu Ching-Ti, White Charles C, Xing Chuanhua, Zhou Yanhua, Benjamin Emelia J, Schnabel Renate B, Heckbert Susan R, Psaty Bruce M, Muzny Donna M, Cupples L Adrienne, Morrison Alanna C, Boerwinkle Eric
Circ Cardiovasc Genet. 2014 Jun;7(3):335-43. doi: 10.1161/CIRCGENETICS.113.000350.
Genome-wide association studies have identified thousands of genetic variants that influence a variety of diseases and health-related quantitative traits. However, the causal variants underlying the majority of genetic associations remain unknown. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Targeted Sequencing Study aims to follow up genome-wide association study signals and identify novel associations of the allelic spectrum of identified variants with cardiovascular-related traits.
The study included 4231 participants from 3 CHARGE cohorts: the Atherosclerosis Risk in Communities Study, the Cardiovascular Health Study, and the Framingham Heart Study. We used a case-cohort design in which we selected both a random sample of participants and participants with extreme phenotypes for each of 14 traits. We sequenced and analyzed 77 genomic loci, which had previously been associated with ≥1 of 14 phenotypes. A total of 52 736 variants were characterized by sequencing and passed our stringent quality control criteria. For common variants (minor allele frequency ≥1%), we performed unweighted regression analyses to obtain P values for associations and weighted regression analyses to obtain effect estimates that accounted for the sampling design. For rare variants, we applied 2 approaches: collapsed aggregate statistics and joint analysis of variants using the sequence kernel association test.
We sequenced 77 genomic loci in participants from 3 cohorts. We established a set of filters to identify high-quality variants and implemented statistical and bioinformatics strategies to analyze the sequence data and identify potentially functional variants within genome-wide association study loci.
全基因组关联研究已经鉴定出数千种影响多种疾病和健康相关数量性状的基因变异。然而，大多数遗传关联背后的因果变异仍然未知。基因组流行病学心脏与衰老研究队列（CHARGE）联盟靶向测序研究旨在跟进全基因组关联研究信号，并确定已鉴定变异的等位基因谱与心血管相关性状的新关联。
该研究纳入了来自3个CHARGE队列的4231名参与者：社区动脉粥样硬化风险研究、心血管健康研究和弗雷明汉心脏研究。我们采用了病例-队列设计，为14种性状中的每一种既选择了参与者的随机样本，也选择了具有极端表型的参与者。我们对77个基因组位点进行了测序和分析，这些位点先前已与14种表型中的至少1种相关。通过测序共鉴定出52736个变异，并通过了我们严格的质量控制标准。对于常见变异（次要等位基因频率≥1%），我们进行了未加权回归分析以获得关联的P值，并进行了加权回归分析以获得考虑抽样设计的效应估计值。对于罕见变异，我们应用了两种方法：合并汇总统计和使用序列核关联检验对变异进行联合分析。
我们对来自3个队列的参与者中的77个基因组位点进行了测序。我们建立了一套筛选标准以识别高质量变异，并实施了统计和生物信息学策略来分析序列数据，并在全基因组关联研究位点内识别潜在的功能性变异。
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