Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix
Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix
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DOI：
10.1038/sj.hdy.6800717
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发表时间：
2005-09-01
期刊：
影响因子：
3.8
通讯作者：
Ji, L
中科院分区：
文献类型：
--
作者：
Li, J;Ji, L
相关多重检验广泛应用于遗传学研究，尤其是复杂疾病的多位点分析。如果不能对多重测试的效果进行适当的控制，要么会导致大量的假阳性声明，要么会导致真正的命中被忽视。切弗鲁德提出了根据独立测试的有效数量(M-ef)来调整相关测试的想法，就好像它们是独立的一样。然而，我们的经验表明，切弗鲁德对M-ef的估计过大，将导致过于保守的结果。我们提出了一种更准确的M-Eff估计，并设计了基于M-Eff的程序来控制实验意义水平和错误发现率。在基于真实和模拟数据的评估中，基于M-Eff的程序能够准确地控制错误率，从而导致功率增加，特别是在多点分析中。结果表明，M-ff在相关检验的误码率控制中是一个有用的概念。由于其效率和精度，M-Eff方法提供了一种替代计算密集型方法(如置换检验)的方法。
Correlated multiple testing is widely performed in genetic research, particularly in multilocus analyses of complex diseases. Failure to control appropriately for the effect of multiple testing will either result in a flood of false-positive claims or in true hits being overlooked. Cheverud proposed the idea of adjusting correlated tests as if they were independent, according to an 'effective number' (M-eff) of independent tests. However, our experience has indicated that Cheverud's estimate of the M-eff is overly large and will lead to excessively conservative results. We propose a more accurate estimate of the M-eff, and design M-eff-based procedures to control the experiment-wise significant level and the false discovery rate. In an evaluation, based on both real and simulated data, the M-eff-based procedures were able to control the error rate accurately and consequently resulted in a power increase, especially in multilocus analyses. The results confirm that the M-eff is a useful concept in the error-rate control of correlated tests. With its efficiency and accuracy, the M-eff method provides an alternative to computationally intensive methods such as the permutation test.