A powerful strategy to account for multiple testing in the context of haplotype analysis
A powerful strategy to account for multiple testing in the context of haplotype analysis
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DOI：
10.1086/424390
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发表时间：
2004-10-01
影响因子：
9.8
通讯作者：
Knapp, M
中科院分区：
文献类型：
--
作者：
Becker, T;Knapp, M
单倍型——即作为一个单位遗传的同一染色体上等位基因的线性排列——有望在复杂疾病的关联精细图谱中携带重要信息。考虑到一组紧密相连的标记，有大量不同的标记组合可以分析。因此，引入了一个严峻的多重测试问题。处理这个问题的一种方法是通过考虑的组合数进行Bonferroni校正。Bonferroni校正适用于独立试验，但在该区域存在联动不平衡时将导致功率损失。第二种方法是进行模拟。不幸的是，大多数单倍型分析方法已经需要模拟来获得特定标记组合的未校正P值。因此，似乎需要嵌套模拟来获得为多次测试校正的P值，由于计算机运行时间的限制，这显然限制了这种方法的适用性。这里，描述了一种避免这种嵌套模拟的算法。我们在两种疾病模型下检验了我们方法的有效性，用于家庭数据的单倍型分析。我们算法的真I型错误率对应于名义显著性水平。此外，我们观察到与计算全局P值的Bonferroni方法相比，我们的方法获得全局P值的能力有很大的提高。这里描述的方法已在我们的程序FAMHAP的最新更新中实现。
Haplotypes - that is, linear arrangements of alleles on the same chromosome that were inherited as a unit - are expected to carry important information in the context of association fine mapping of complex diseases. In consideration of a set of tightly linked markers, there is an enormous number of different marker combinations that can be analyzed. Therefore, a severe multiple-testing problem is introduced. One method to deal with this problem is Bonferroni correction by the number of combinations that are considered. Bonferroni correction is appropriate for independent tests but will result in a loss of power in the presence of linkage disequilibrium in the region. A second method is to perform simulations. It is unfortunate that most methods of haplotype analysis already require simulations to obtain an uncorrected P value for a specific marker combination. Thus, it seems that nested simulations are necessary to obtain P values that are corrected for multiple testing, which, apparently, limits the applicability of this approach because of computer running-time restrictions. Here, an algorithm is described that avoids such nested simulations. We check the validity of our approach under two disease models for haplotype analysis of family data. The true type I error rate of our algorithm corresponds to the nominal significance level. Furthermore, we observe a strong gain in power with our method to obtain the global P value, compared with the Bonferroni procedure to calculate the global P value. The method described here has been implemented in the latest update of our program FAMHAP.