Distinct network patterns emerge from Cartesian and XOR epistasis models: a comparative network science analysis

作者：Zhendong Sha, Philip J. Freda, Priyanka Bhandary, Attri Ghosh, Nicholas Matsumoto, Jason H. Moore, Ting Hu · 发表于：BioData Mining · 年份：2024 · DOI：10.1186/s13040-024-00413-w · 被引用次数：2 · 研究领域：Bioinformatics and Genomic Networks、Genetic Associations and Epidemiology、Mental Health Research Topics

BACKGROUND: 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). RESULTS: 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 mo...