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Pair-wise multifactor dimensionality reduction method to detect gene-gene interactions in a case-control study.

机译：作用多因素维数减少方法检测病例对照研究中基因基因相互作用。

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OBJECTIVE: The identification of gene-gene interactions has been limited by small sample size and large number of potential interactions between genes. To address this issue, Ritchie et al. [2001] have proposed multifactor dimensionality reduction (MDR) method to detect polymorphisms associated with the disease risk. The MDR reduces the dimension of the genetic factors by classifying them into high-risk and low-risk groups. The strong point in favor of MDR is that it can detect interactions in absence of significant main effects. However, it often suffers from the sparseness of the cells in high-dimensional contingency tables, since it cannot classify an empty cell as high risk or low risk. METHOD: We propose a pair-wise multifactor dimensionality reduction (PWMDR) approach to address the issue of MDR in classifying sparse or empty cells. Instead of looking at the higher dimensional contingency table, we score each pair-wise interaction of the genetic factors involved and combine the scores of all such pairwise interactions. RESULTS: Simulation studies showed that the PWMDR generally had greater power than MDR to detect third order interactions for polymorphisms with low allele frequencies. The PWMDR also outperformed the MDR in detecting gene-gene interaction on a kidney transplant dataset. CONCLUSION: The PWMDR outperformed the MDR to detect polymorphisms with low frequencies.
机译：目的：鉴定基因 - 基因相互作用的鉴定受到小的样品大小和大量基因之间的潜在相互作用的限制。要解决这个问题，Ritchie等人。 [2001]已经提出了多因素维度减少（MDR）方法来检测与疾病风险相关的多态性。 MDR通过将它们分类为高风险和低风险群体来减少遗传因素的维度。有利于MDR的强点是它可以检测在没有显着的主要效果的情况下的相互作用。然而，它通常存在高维应变表中细胞的稀疏性，因为它不能将空的细胞作为高风险或低风险分类。方法：我们提出了一种成对的多因素维数减少（PWMDR）方法，以解决分类稀疏或空单元格中的MDR问题。我们不考虑较高的维度依次表，我们得分涉及的遗传因子的每对一对相互作用，并结合所有这些成对相互作用的分数。结果：仿真研究表明，PWMDR通常具有比MDR更大的功率，以检测具有低等位基因频率的多态性的三阶相互作用。 PWMDR还优于MDR检测肾移植数据集上的基因基因相互作用。结论：PWMDR优于MDR以检测低频率的多态性。

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