Guo Yingjie, Cheng Honghong, Yuan Zhian, Liang Zhen, Wang Yang, Du Debing
School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China.
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
Front Genet. 2021 Dec 8;12:801261. doi: 10.3389/fgene.2021.801261. eCollection 2021.
Unexplained genetic variation that causes complex diseases is often induced by gene-gene interactions (GGIs). Gene-based methods are one of the current statistical methodologies for discovering GGIs in case-control genome-wide association studies that are not only powerful statistically, but also interpretable biologically. However, most approaches include assumptions about the form of GGIs, which results in poor statistical performance. As a result, we propose gene-based testing based on the maximal neighborhood coefficient (MNC) called gene-based gene-gene interaction through a maximal neighborhood coefficient (GBMNC). MNC is a metric for capturing a wide range of relationships between two random vectors with arbitrary, but not necessarily equal, dimensions. We established a statistic that leverages the difference in MNC in case and in control samples as an indication of the existence of GGIs, based on the assumption that the joint distribution of two genes in cases and controls should not be substantially different if there is no interaction between them. We then used a permutation-based statistical test to evaluate this statistic and calculate a statistical -value to represent the significance of the interaction. Experimental results using both simulation and real data showed that our approach outperformed earlier methods for detecting GGIs.
导致复杂疾病的不明原因遗传变异通常由基因-基因相互作用（GGIs）引起。基于基因的方法是目前在病例对照全基因组关联研究中发现GGIs的统计方法之一，这些方法不仅在统计上具有强大的功效，而且在生物学上也具有可解释性。然而，大多数方法都包含关于GGIs形式的假设，这导致了较差的统计性能。因此，我们提出了基于最大邻域系数（MNC）的基于基因的检验，即通过最大邻域系数进行基于基因的基因-基因相互作用检验（GBMNC）。MNC是一种用于捕捉两个具有任意但不一定相等维度的随机向量之间广泛关系的度量。我们基于这样的假设建立了一个统计量，即如果两个基因之间没有相互作用，那么病例组和对照组中两个基因的联合分布不应有显著差异，利用病例组和对照组中MNC的差异作为GGIs存在的指标。然后，我们使用基于置换的统计检验来评估这个统计量，并计算一个统计P值来表示相互作用的显著性。使用模拟数据和真实数据的实验结果表明，我们的方法在检测GGIs方面优于早期方法。
