Division of Molecular Life Science, School of Medicine, Tokai University, Isehara, Kanagawa, Japan.
J Hum Genet. 2009 Nov;54(11):615-23. doi: 10.1038/jhg.2009.95. Epub 2009 Oct 23.
Meta-analysis is a useful tool to increase the statistical power to detect gene-disease associations by combining results from the original and subsequent replication studies. Recently, consortium-based meta-analyses of several genome-wide association (GWA) data sets have discovered new susceptibility genes of common diseases. We reviewed the process and the methods of meta-analysis of genetic association studies. To conduct and report a transparent meta-analysis, the search strategy, the inclusion or exclusion criteria of studies and the statistical procedures should be fully described. Assessing consistency or heterogeneity of the associations across studies is an important aim of meta-analysis. Random effects model (REM) meta-analysis can incorporate between-study heterogeneity. We illustrated properties of test for and measures of between-study heterogeneity and the effect of between-study heterogeneity on conclusions of meta-analyses through simulations. Our simulation shows that the power of REM meta-analysis of GWA data sets (total case-control sample size: 5000-20,000) to detect a small genetic effect (odds ratio (OR)=1.4 under dominant model) decreases as between-study heterogeneity increases and then the mean of OR of the simulated meta-analyses passing the genome-wide significance threshold would be upwardly biased (winner's curse phenomenon). Addressing observed between-study heterogeneity may be challenging but give a new insight into the gene-disease association.
荟萃分析是一种有用的工具，可以通过合并原始和后续复制研究的结果来增加检测基因-疾病关联的统计效力。最近，基于联盟的几项全基因组关联（GWA）数据集的荟萃分析发现了常见疾病的新易感基因。我们回顾了遗传关联研究荟萃分析的过程和方法。为了进行和报告透明的荟萃分析，应充分描述搜索策略、研究的纳入或排除标准以及统计程序。评估研究之间关联的一致性或异质性是荟萃分析的一个重要目标。随机效应模型（REM）荟萃分析可以包含研究之间的异质性。我们通过模拟说明了检验和衡量研究之间异质性的性质以及研究之间异质性对荟萃分析结论的影响。我们的模拟表明，随着研究之间异质性的增加，GWA 数据集（总病例对照样本量：5000-20000）的 REM 荟萃分析检测小遗传效应（显性模型下的优势比（OR）=1.4）的效力降低，然后通过全基因组显著性阈值的模拟荟萃分析的 OR 平均值将向上偏差（赢家诅咒现象）。解决观察到的研究之间的异质性可能具有挑战性，但为基因-疾病关联提供了新的见解。
