Casas Juan P, Cooper Jackie, Miller George J, Hingorani Aroon D, Humphries Steve E
Centre for Clinical Pharmacology, Department of Medicine, BHF Laboratories at UCL, University College London, London, UK.
Ann Hum Genet. 2006 Mar;70(Pt 2):145-69. doi: 10.1111/j.1469-1809.2005.00241.x.
Coronary artery disease (CAD) has a polygenic basis, and identification of CAD susceptibility genes has the potential to aid the development of new treatments and enhance prediction of disease risk. Thus far, the strategy has firstly been to choose "candidate" genes coding for important "rate-limiting" proteins in the homeostatic systems involved in maintaining cardiovascular health; secondly to identify common variants in these candidate genes; thirdly to carry out genotyping and statistical analysis using genetic association studies; and finally to test the functional effects of the identified variants in vitro and in vivo. However, lack of reproducibility of genetic association studies has led to uncertainty about the nature and number of genes involved. In part this is because many of the studies conducted have not been adequately powered to detect small risk effects, or to permit adequate exploration of gene-gene or gene-environment interactions in a robust manner. Spurious positive and negative associations due to type I and type II statistical errors are likely to co-exist with real associations in the published literature. By utilising all available data to increase statistical power, meta-analysis of genetic association studies is increasingly being used to identify genotypic risk with a greater degree of precision. Though potentially powerful, this approach may be prone to publication bias. Therefore, very large genetic association studies will also be required to identify risk genes for CAD. This review lays out the framework for the candidate gene approach for CAD and illustrates this with published results from a UK prospective study of 3000 middle-aged men.
冠状动脉疾病（CAD）具有多基因基础，识别CAD易感基因有可能有助于开发新的治疗方法并提高疾病风险预测能力。到目前为止，策略首先是选择在维持心血管健康的稳态系统中编码重要“限速”蛋白的“候选”基因；其次是识别这些候选基因中的常见变异；第三是使用基因关联研究进行基因分型和统计分析；最后是在体外和体内测试已识别变异的功能效应。然而，基因关联研究缺乏可重复性导致了所涉及基因的性质和数量存在不确定性。部分原因是许多已开展的研究没有足够的效力来检测小的风险效应，或者无法以稳健的方式充分探索基因-基因或基因-环境相互作用。由于I型和II型统计错误导致的虚假阳性和阴性关联可能与已发表文献中的真实关联并存。通过利用所有可用数据来提高统计效力，基因关联研究的荟萃分析越来越多地被用于更精确地识别基因型风险。尽管这种方法可能很强大，但可能容易出现发表偏倚。因此，还需要开展非常大规模的基因关联研究来识别CAD的风险基因。本综述阐述了CAD候选基因方法的框架，并以一项针对3000名中年男性的英国前瞻性研究的已发表结果为例进行说明。
