Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA.
Am J Hum Genet. 2009 Dec;85(6):862-72. doi: 10.1016/j.ajhg.2009.11.001.
Data from multiple genome-wide association studies are often analyzed together for the purposes of combining information from several studies of the same disease or comparing results across different disorders. We provide a valid and efficient approach to such meta-analysis, allowing for overlapping study subjects. The available data may contain individual participant records or only meta-analytic summary results. Simulation studies demonstrate that failure to account for overlapping subjects can greatly inflate type I error when combining results from multiple studies of the same disease and can drastically reduce power when comparing results across different disorders. In addition, the proposed approach can be substantially more powerful than the simple approach of splitting the overlapping subjects among studies, especially for comparing results across different disorders. The advantages of the new approach are illustrated with empirical data from two sets of genome-wide association studies.
多组全基因组关联研究的数据通常会被合并分析，以便整合同一疾病的多项研究的信息，或比较不同疾病之间的结果。我们提供了一种有效的方法来进行这种元分析，同时考虑了重叠的研究对象。可用的数据可能包含个体参与者记录，或者只有元分析的汇总结果。模拟研究表明，如果不考虑重叠的研究对象，在合并来自同一疾病的多项研究的结果时，可能会极大地增加Ⅰ类错误，并在比较不同疾病的结果时极大地降低功效。此外，与将重叠的研究对象分配到不同研究中的简单方法相比，所提出的方法可以显著提高功效，特别是在比较不同疾病的结果时。我们用来自两组全基因组关联研究的实际数据说明了新方法的优势。
