Becker Tim, Knapp Michael
Institute for Medical Biometry, Informatics and Epidemiology University of Bonn, Bonn, Germany.
Hum Hered. 2005;59(4):185-9. doi: 10.1159/000086696. Epub 2005 Jul 7.
In the context of haplotype association analysis of unphased genotype data, methods based on Monte-Carlo simulations are often used to compensate for missing or inappropriate asymptotic theory. Moreover, such methods are an indispensable means to deal with multiple testing problems. We want to call attention to a potential trap in this usually useful approach: The simulation approach may lead to strongly inflated type I errors in the presence of different missing rates between cases and controls, depending on the chosen test statistic. Here, we consider four different testing strategies for haplotype analysis of case-control data. We recommend to interpret results for data sets with non-comparable distributions of missing genotypes with special caution, in case the test statistic is based on inferred haplotypes per individual. Moreover, our results are important for the conduction and interpretation of genome-wide association studies.
在对未分型基因型数据进行单倍型关联分析的背景下，基于蒙特卡罗模拟的方法常被用于弥补缺失或不适用的渐近理论。此外，此类方法是处理多重检验问题不可或缺的手段。我们想提醒大家注意这种通常很有用的方法中一个潜在的陷阱：在病例组和对照组存在不同缺失率的情况下，根据所选检验统计量，模拟方法可能会导致I型错误大幅膨胀。在此，我们考虑了病例对照数据单倍型分析的四种不同检验策略。我们建议，对于基因型缺失分布不可比的数据集，如果检验统计量基于每个个体推断的单倍型，则在解释结果时要格外谨慎。此外，我们的结果对于全基因组关联研究的实施和解释具有重要意义。
