Yu Wenbao, Lee Seungyeoun, Park Taesung
Department of Statistics, Seoul National University, Shilim-Dong, Kwanak-Gu, Seoul 151-742, Korea.
Department of Mathematics and Statistics, Sejong University, Seoul 143-747, Korea.
Bioinformatics. 2016 Sep 1;32(17):i605-i610. doi: 10.1093/bioinformatics/btw424.
Gene-gene interaction (GGI) is one of the most popular approaches for finding and explaining the missing heritability of common complex traits in genome-wide association studies. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGI effects. However, there are several disadvantages of the existing MDR-based approaches, such as the lack of an efficient way of evaluating the significance of multi-locus models and the high computational burden due to intensive permutation. Furthermore, the MDR method does not distinguish marginal effects from pure interaction effects.
We propose a two-step unified model based MDR approach (UM-MDR), in which, the significance of a multi-locus model, even a high-order model, can be easily obtained through a regression framework with a semi-parametric correction procedure for controlling Type I error rates. In comparison to the conventional permutation approach, the proposed semi-parametric correction procedure avoids heavy computation in order to achieve the significance of a multi-locus model. The proposed UM-MDR approach is flexible in the sense that it is able to incorporate different types of traits and evaluate significances of the existing MDR extensions.
The simulation studies and the analysis of a real example are provided to demonstrate the utility of the proposed method. UM-MDR can achieve at least the same power as MDR for most scenarios, and it outperforms MDR especially when there are some single nucleotide polymorphisms that only have marginal effects, which masks the detection of causal epistasis for the existing MDR approaches.
UM-MDR provides a very good supplement of existing MDR method due to its efficiency in achieving significance for every multi-locus model, its power and its flexibility of handling different types of traits.
A R package "umMDR" and other source codes are freely available at http://statgen.snu.ac.kr/software/umMDR/ CONTACT: tspark@stats.snu.ac.kr
Supplementary data are available at Bioinformatics online.
基因-基因相互作用（GGI）是全基因组关联研究中寻找和解释常见复杂性状缺失遗传力的最常用方法之一。多因素降维（MDR）方法已被广泛用于检测GGI效应。然而，现有的基于MDR的方法存在几个缺点，例如缺乏评估多位点模型显著性的有效方法以及由于大量置换导致的高计算负担。此外，MDR方法无法区分边际效应和纯相互作用效应。
我们提出了一种基于两步统一模型的MDR方法（UM-MDR），在该方法中，即使是高阶模型，通过带有控制I型错误率的半参数校正程序的回归框架，也可以轻松获得多位点模型的显著性。与传统的置换方法相比，所提出的半参数校正程序避免了繁重的计算，从而实现了多位点模型的显著性。所提出的UM-MDR方法具有灵活性，因为它能够纳入不同类型的性状并评估现有MDR扩展的显著性。
提供了模拟研究和一个实际例子的分析，以证明所提出方法的实用性。在大多数情况下，UM-MDR至少可以达到与MDR相同的功效，并且在存在一些仅具有边际效应的单核苷酸多态性时，它优于MDR，而这些边际效应会掩盖现有MDR方法对因果上位性的检测。
UM-MDR为现有MDR方法提供了很好的补充，因为它在实现每个多位点模型的显著性方面具有效率、功效以及处理不同类型性状的灵活性。
一个R包“umMDR”和其他源代码可在http://statgen.snu.ac.kr/software/umMDR/免费获取。联系方式：tspark@stats.snu.ac.kr
补充数据可在《生物信息学》在线获取。
