Detecting gene-gene interactions in single-nucleotide polymorphism data is vital for understanding disease susceptibility. However, existing approaches may be limited by the sample size in case-control studies. Herein, we propose a balance approach for the multifactor dimensionality reduction (BMDR) method to increase the accuracy of estimates of the prediction error rate in small samples. BMDR explicitly selects the best model by evaluating the average of prediction error rates over k-fold cross-validation without cross-validation consistency selection. In this study, we used several epistatic models with and without marginal effects under different parameter settings (heritability and minor allele frequencies) to evaluate the performance of existing approaches. Using simulated data sets, BMDR successfully detected gene-gene interactions, particularly for data sets with small sample sizes. A large data set was obtained from the Wellcome Trust Case Control Consortium, and results indicated that BMDR could effectively detect significant gene-gene interactions.

中文翻译：

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类平衡多因素降维以检测基因-基因相互作用。

检测单核苷酸多态性数据中的基因-基因相互作用对于理解疾病的易感性至关重要。但是，在病例对照研究中，现有方法可能会受到样本量的限制。在这里，我们提出了一种平衡方法，用于多因素降维（BMDR）方法，以提高小样本中预测错误率的估计准确性。BMDR通过在不进行交叉验证一致性选择的情况下评估k倍交叉验证的预测错误率的平均值来明确选择最佳模型。在这项研究中，我们使用了几种在不同参数设置（遗传性和次要等位基因频率）下具有和不具有边际效应的上位性模型来评估现有方法的性能。BMDR使用模拟数据集成功检测了基因-基因相互作用，特别是对于样本量较小的数据集。从惠康信托案例控制协会获得了大量数据，结果表明BMDR可以有效检测重要的基因-基因相互作用。