Sha Zhendong, Freda Philip J, Bhandary Priyanka, Ghosh Attri, Matsumoto Nicholas, Moore Jason H, Hu Ting
School of Computing, Queen's University, 557 Goodwin Hall, 21-25 Union St, Kingston, K7L 2N8, Ontario, Canada.
Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, 90069, CA, USA.
BioData Min. 2024 Dec 28;17(1):61. doi: 10.1186/s13040-024-00413-w.
Epistasis, the phenomenon where the effect of one gene (or variant) is masked or modified by one or more other genes, significantly contributes to the phenotypic variance of complex traits. Traditionally, epistasis has been modeled using the Cartesian epistatic model, a multiplicative approach based on standard statistical regression. However, a recent study investigating epistasis in obesity-related traits has identified potential limitations of the Cartesian epistatic model, revealing that it likely only detects a fraction of the genetic interactions occurring in natural systems. In contrast, the exclusive-or (XOR) epistatic model has shown promise in detecting a broader range of epistatic interactions and revealing more biologically relevant functions associated with interacting variants. To investigate whether the XOR epistatic model also forms distinct network structures compared to the Cartesian model, we applied network science to examine genetic interactions underlying body mass index (BMI) in rats (Rattus norvegicus).
Our comparative analysis of XOR and Cartesian epistatic models in rats reveals distinct topological characteristics. The XOR model exhibits enhanced sensitivity to epistatic interactions between the network communities found in the Cartesian epistatic network, facilitating the identification of novel trait-related biological functions via community-based enrichment analysis. Additionally, the XOR network features triangle network motifs, indicative of higher-order epistatic interactions. This research also evaluates the impact of linkage disequilibrium (LD)-based edge pruning on network-based epistasis analysis, finding that LD-based edge pruning may lead to increased network fragmentation, which may hinder the effectiveness of network analysis for the investigation of epistasis. We confirmed through network permutation analysis that most XOR and Cartesian epistatic networks derived from the data display distinct structural properties compared to randomly shuffled networks.
Collectively, these findings highlight the XOR model's ability to uncover meaningful biological associations and higher-order epistasis derived from lower-order network topologies. The introduction of community-based enrichment analysis and motif-based epistatic discovery emphasize network science as a critical approach for advancing epistasis research and understanding complex genetic architectures.
上位性是指一个基因（或变体）的效应被一个或多个其他基因掩盖或修饰的现象，它对复杂性状的表型变异有显著贡献。传统上，上位性是使用笛卡尔上位性模型来建模的，这是一种基于标准统计回归的乘法方法。然而，最近一项关于肥胖相关性状上位性的研究发现了笛卡尔上位性模型的潜在局限性，表明它可能只检测到自然系统中发生的一部分基因相互作用。相比之下，异或（XOR）上位性模型在检测更广泛的上位性相互作用以及揭示与相互作用变体相关的更多生物学相关功能方面显示出前景。为了研究与笛卡尔模型相比，XOR上位性模型是否也形成独特的网络结构，我们应用网络科学来研究大鼠（褐家鼠）体重指数（BMI）背后的基因相互作用。
我们对大鼠中XOR和笛卡尔上位性模型的比较分析揭示了不同的拓扑特征。XOR模型对笛卡尔上位性网络中发现的网络群落之间的上位性相互作用表现出更高的敏感性，通过基于群落的富集分析有助于识别与性状相关的新生物学功能。此外，XOR网络具有三角形网络基序，表明存在高阶上位性相互作用。本研究还评估了基于连锁不平衡（LD）的边修剪对基于网络的上位性分析的影响，发现基于LD的边修剪可能导致网络碎片化增加，这可能会阻碍用于上位性研究的网络分析的有效性。我们通过网络置换分析证实，与随机打乱的网络相比，从数据中得出的大多数XOR和笛卡尔上位性网络显示出不同的结构特性。
总体而言，这些发现突出了XOR模型揭示有意义的生物学关联以及从低阶网络拓扑中得出的高阶上位性的能力。基于群落的富集分析和基于基序的上位性发现的引入强调了网络科学作为推进上位性研究和理解复杂遗传结构的关键方法。
