Background Multifactor dimensionality reduction (MDR) is a powerful method for analysis of gene-gene interactions and has been successfully applied to many genetic studies of complex diseases. However, the main application of MDR has been limited to binary traits, while traits having ordinal features are commonly observed in many genetic studies (e.g., obesity classification - normal, pre-obese, mild obese and severe obese). Methods We propose ordinal MDR (OMDR) to facilitate gene-gene interaction analysis for ordinal traits. As an alternative to balanced accuracy, the use of tau-b, a common ordinal association measure, was suggested to evaluate interactions. Also, we generalized cross-validation consistency (GCVC) to identify multiple best interactions. GCVC can be practically useful for analyzing complex traits, especially in large-scale genetic studies. Results and conclusions In simulations, OMDR showed fairly good performance in terms of power, predictability and selection stability and outperformed MDR. For demonstration, we used a real data of body mass index (BMI) and scanned 1~4-way interactions of obesity ordinal and binary traits of BMI via OMDR and MDR, respectively. In real data analysis, more interactions were identified for ordinal trait than binary traits. On average, the commonly identified interactions showed higher predictability for ordinal trait than binary traits. The proposed OMDR and GCVC were implemented in a C/C++ program, executables of which are freely available for Linux, Windows and MacOS upon request for non-commercial research institutions.
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机译：背景技术多因素降维（MDR）是一种分析基因与基因相互作用的有力方法，已成功应用于许多复杂疾病的遗传研究。然而，MDR的主要应用仅限于二元性状，而在许多遗传学研究中通常观察到具有序数特征的性状（例如，肥胖分类-正常，肥胖前，轻度肥胖和重度肥胖）。方法我们提出序数MDR（OMDR），以促进序性状的基因-基因相互作用分析。作为平衡精度的替代方法，建议使用tau-b（一种常见的序数关联度量）来评估交互作用。此外，我们对交叉验证一致性（GCVC）进行了概括，以识别多个最佳交互。 GCVC在分析复杂性状上尤其实用，特别是在大规模遗传研究中。结果与结论在仿真中，OMDR在功能，可预测性和选择稳定性方面表现出相当不错的性能，并且表现优于MDR。为了进行演示，我们使用了体重指数（BMI）的真实数据，并分别通过OMDR和MDR扫描了BMI的肥胖有序和二元性状的1〜4向相互作用。在实际数据分析中，序数特征比二元特征识别出更多的交互。平均而言，通常识别的交互作用对序性状的预测性高于对二元性状的预测性。拟议的OMDR和GCVC在C / C ++程序中实现，应非商业研究机构的要求，其可执行文件可免费用于Linux，Windows和MacOS。
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