Prediction of Cardiovascular Disease Outcomes and Established Cardiovascular Risk Factors by Genome-Wide Association Markers
Prediction of Cardiovascular Disease Outcomes and Established Cardiovascular Risk Factors by Genome-Wide Association Markers
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DOI：
10.1161/circgenetics.108.833392
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发表时间：
2009-02-01
影响因子：
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通讯作者：
Ioannidis, John P. A.
中科院分区：
文献类型：
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作者：
Ioannidis, John P. A.
背景-全基因组关联（GWA）平台已经产生了数量迅速增加的新遗传标记。这些标志物的能力，以提高预测临床上重要的outcomes.Methods和Results-A系统的审查进行GWA衍生的标志物与心血管疾病的结果或其他表型，代表常见的心血管疾病的风险因素。信息来源包括国家人类基因组研究所已发表的GWA研究目录，以及对符合条件的GWA文章的细读，对各自关联的荟萃分析，以及关于GWA时代常见变异的增量预测性能的文章。截至2008年9月，从国家人类基因组研究所发表的GWA研究目录中共检索到95个符合条件的关联。其中36例具有P < 10（-7）的统计学支持。对相关文章的深入评价显示了28种具有统计学支持的独立相关性，涉及冠状动脉疾病、心肌梗死、房颤/房扑、QT间期延长以及2型糖尿病、体重指数、高密度脂蛋白水平、低密度脂蛋白水平和尼古丁依赖。研究间异质性通常不被考虑，但它似乎很常见，这将对这些标志物在不同人群中的普遍性构成挑战。在非白人人口中，可获得的数据仍然有限。效应量很小，在随后的重复和荟萃分析中可能更小。由于风险等位基因的频率很高，因此人群归因分数很高。然而，个性化的风险度量通常非常小（解释的方差比例
Background-Genome-wide association (GWA) platforms have yielded a rapidly increasing number of new genetic markers. The ability of these markers to improve prediction of clinically important outcomes is debated.Methods and Results-A systematic review was performed of GWA-derived markers associated with cardiovascular outcomes or other phenotypes that represent common established risk factors for cardiovascular outcomes. Sources of information included the National Human Genome Research Institute catalog of published GWA studies, and perusal of the eligible GWA articles, meta-analyses on the respective associations, and articles on the incremental predictive performance of common variants in the GWA era. A total of 95 eligible associations were retrieved from the National Human Genome Research Institute catalogue of published GWA studies as of September 2008. Of those 36 have statistical support of P < 10(-7). In depth evaluation of the respective articles shows 28 independent associations with such statistical support, pertaining to coronary artery disease, myocardial infarction, atrial fibrillation/flutter, prolongation of QT interval, as well as type 2 diabetes, body mass index, high-density lipoprotein levels, low-density lipoprotein levels, and nicotine dependence. Between-study heterogeneity is not taken into account usually, but it seems common and it would pose a challenge to generalizability across different populations for these markers. Still limited data are available in non-white populations. Effect sizes are small and may be even smaller in subsequent replications and meta-analysis. Population attributable fractions are substantial, given the large frequency of the risk alleles. However, individualized risk measures are typically very small (proportion of variance explained