Contemporary Reviews in Cardiovascular Medicine Genetic Cardiovascular Risk Prediction Will We Get There ?
Contemporary Reviews in Cardiovascular Medicine Genetic Cardiovascular Risk Prediction Will We Get There ?
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发表时间：
2010
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通讯作者：
G. Thanassoulis;Ramachandran S. Vasan
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作者：
G. Thanassoulis;Ramachandran S. Vasan
遗传学的重大进展，包括 20011 年人类基因组测序2 和 2005 年 HapMap 的出版3，为我们对包括心血管疾病 (CVD) 在内的复杂疾病的遗传学理解的革命铺平了道路。经过多年不一致的结果以及未能复制假定的候选基因关联之后，高通量技术（对超过 500 000 个称为单核苷酸多态性 [SNP] 的遗传标记进行基因分型）和新型统计工具导致与复杂人类疾病相关的新型遗传标记几乎呈爆炸式增长。在CVD方面，这些进展非常成功地揭示了许多与心肌梗塞（MI）和心血管危险因素（如血脂、血压、糖尿病和肥胖）之间的新遗传关联。这些研究的一个主要目标始终是为 CVD 生物学提供新的见解。然而，这些发现备受推崇的另一个目标是，通过将遗传信息纳入风险预测（包括心血管疾病的一级预防），利用这些遗传标记开创个性化医疗的新时代。事实上，尽管缺乏临床使用的证据，但对最近发现的遗传标记的直接面向消费者的测试已经激增。4与所有新兴技术一样，许多基本问题仍有待回答：遗传标记或基因评分能否超越经验证的风险算法（例如弗雷明汉风险评分和 CVD 家族史）改善 CVD 风险预测？有多少 SNP 与 CVD 的遗传成分有关，我们需要发现多少遗传标记才能可靠地改进风险预测？ CVD 和其他复杂疾病的等位基因结构对风险预测有何影响？最后，在将这些信息带给患者之前需要采取哪些步骤？在本综述中，我们将研究与一级预防环境中冠状动脉疾病（CAD）和 MI 风险预测有关的每个问题。
Major advances in genetics, including the sequencing of the human genome in 20011,2 and the publication of the HapMap in 2005,3 have paved the way for a revolution in our understanding of the genetics of complex diseases, including cardiovascular disease (CVD). After years of inconsistent results and failure to replicate putative candidate gene associations, high-throughput technologies (which genotype more than 500 000 genetic markers known as single-nucleotide polymorphisms [SNPs]) and novel statistical tools have led to a virtual explosion of novel genetic markers associated with complex human diseases. In the context of CVD, these advances have been remarkably successful in uncovering many novel genetic associations with myocardial infarction (MI) and cardiovascular risk factors such as lipids, blood pressure, diabetes, and obesity. A major objective of these studies has always been to provide new insights into the biology of CVD. However, a highly touted additional aim of these discoveries has been to use these genetic markers to usher in a new era of personalized medicine by incorporating genetic information into risk prediction (including for the primary prevention of CVD). In fact, direct-to-consumer testing of recently discovered genetic markers has proliferated despite a lack of evidence for clinical use.4 As with all nascent technologies, many fundamental questions remain to be answered: Can genetic markers or gene scores improve CVD risk prediction over and above validated risk algorithms such as the Framingham risk score and a family history of CVD? How many SNPs are responsible for the genetic component of CVD, and how many genetic markers will we need to discover to reliably improve risk prediction? What are the implications of the allelic architecture of CVD and other complex diseases for risk prediction? And, finally, what steps will be needed before this information is brought to patients? In the present review, we will examine each of these questions with regard to risk prediction of coronary artery disease (CAD) and MI in a primary prevention setting.