Multiple testing in the context of haplotype analysis revisited:: Application to case-control data
Multiple testing in the context of haplotype analysis revisited:: Application to case-control data
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DOI：
10.1111/j.1529-8817.2005.00198.x
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发表时间：
2005-11-01
影响因子：
1.9
通讯作者：
Knapp, M
中科院分区：
文献类型：
--
作者：
Becker, T;Cichon, S;Knapp, M
我们最近提出了一种家系数据的测试程序，它解释了由一组紧密连锁的标记中可以分析的大量不同标记组合引起的多重测试问题。大多数基于单倍型的关联分析方法已经需要模拟来获得特定标记组合的未校正的P值。如前面所示，然而不必执行嵌套模拟来获得适当校正不同标记组合的多个测试的全局P值，而不忽略测试的依赖性。我们现在已经在我们的程序FAMHAP中为病例对照数据实现了这种方法，因为这种数据结构目前在该领域发挥着主导作用。我们考虑了不同的方法来处理相位模糊，并对潜在的单标记组合进行了两种不同的统计检验，以获得未校正的P值。一个检验统计量是基于卡方的，另一个是单倍型趋势回归。在一个大型的模拟研究中，研究了这些不同测试在多个测试情况下的性能。我们用我们的全球P值获得了相当大的功率增长，而不是针对所有建议的测试统计数据进行Bonferroni校正的P值。无论是采用单倍型趋势回归方法，还是采用更简单的卡方检验，都获得了良好的效果。此外，我们得出结论，处理阶段歧义的更好策略是为每个个体分配其加权单倍型解释列表，而不是为每个个体分配其最可能的单倍型解释。最后，我们通过一个真实的数据实例说明了该方法的有效性。
We have lately presented a testing procedure for family data which accounts for the multiple testing problem that is induced by the enormous number of different marker combinations that can be analyzed in a set of tightly linked markers. Most methods of haplotype based association analysis already require simulations to obtain an uncorrected P value for a specific marker combination. As shown before, it is nevertheless not necessary to carry out nested simulations to obtain a global P value that properly corrects for the multiple testing of different marker combinations without neglecting the dependency of the tests. We have now implemented this approach for case-control data in our program FAMHAP, as this data structure currently plays a dominant role in the field. We consider different ways to deal with phase ambiguities and two different statistical tests for the underlying single marker combinations to obtain uncorrected P values. One test statistic is chi-square based, the other is a haplotype trend regression. The performance of these different tests in the multiple testing situation is investigated in a large simulation study. We obtain a considerable gain in power with our global P values as opposed to Bonferroni corrected P values for all suggested test statistics. Good power was obtained both with the haplotype trend regression approach as well as with the simpler chi-square based test. Furthermore, we conclude that the better strategy to deal with phase ambiguities is to assign to each individual its list of weighted haplotype explanations, rather than to assign to each individual its most likely haplotype explanation. Finally, we demonstrate the usefulness of our approach by a real data example.