Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America.
PLoS One. 2010 Feb 23;5(2):e9363. doi: 10.1371/journal.pone.0009363.
The initial presentation of multifactor dimensionality reduction (MDR) featured cross-validation to mitigate over-fitting, computationally efficient searches of the epistatic model space, and variable construction with constructive induction to alleviate the curse of dimensionality. However, the method was unable to differentiate association signals arising from true interactions from those due to independent main effects at individual loci. This issue leads to problems in inference and interpretability for the results from MDR and the family-based compliment the MDR-pedigree disequilibrium test (PDT). A suggestion from previous work was to fit regression models post hoc to specifically evaluate the null hypothesis of no interaction for MDR or MDR-PDT models. We demonstrate with simulation that fitting a regression model on the same data as that analyzed by MDR or MDR-PDT is not a valid test of interaction. This is likely to be true for any other procedure that searches for models, and then performs an uncorrected test for interaction. We also show with simulation that when strong main effects are present and the null hypothesis of no interaction is true, that MDR and MDR-PDT reject at far greater than the nominal rate. We also provide a valid regression-based permutation test procedure that specifically tests the null hypothesis of no interaction, and does not reject the null when only main effects are present. The regression-based permutation test implemented here conducts a valid test of interaction after a search for multilocus models, and can be applied to any method that conducts a search to find a multilocus model representing an interaction.
多因素维度降低（MDR）的初始表现具有交叉验证，以减轻过度拟合，有效地搜索遗传模型空间，以及使用构造归纳进行变量构建，以减轻维度的诅咒。然而，该方法无法区分来自真实相互作用的关联信号与来自单个基因座上独立主效应的信号。这个问题导致 MDR 和基于家族的互补多因素维度降低-系谱不平衡检验（PDT）的结果在推理和解释方面出现问题。以前的一项工作的建议是事后拟合回归模型，专门评估 MDR 或 MDR-PDT 模型没有相互作用的零假设。我们通过模拟表明，在与 MDR 或 MDR-PDT 分析相同数据上拟合回归模型不是对相互作用进行有效检验的方法。对于任何其他搜索模型然后对相互作用进行未校正检验的程序，情况可能也是如此。我们还通过模拟表明，当存在强烈的主效应并且没有相互作用的零假设为真时，MDR 和 MDR-PDT 的拒绝率远远高于名义拒绝率。我们还提供了一种有效的基于回归的置换检验程序，专门测试没有相互作用的零假设，并且仅当存在主效应时不会拒绝零假设。这里实现的基于回归的置换检验在搜索多基因座模型后进行有效的相互作用检验，并且可以应用于任何进行搜索以找到表示相互作用的多基因座模型的方法。
