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, Ontario, K7L 2N8, Canada.
Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, U.S.A.
Res Sq. 2024 May 23:rs.3.rs-4392123. doi: 10.21203/rs.3.rs-4392123/v1.
Epistasis, the phenomenon where the effect of one gene (or variant) is masked or modified by one or more other genes, can significantly contribute to the observed phenotypic variance of complex traits. To date, it has been generally assumed that genetic interactions can be detected using a Cartesian, or multiplicative, interaction model commonly utilized in standard regression approaches. However, a recent study investigating epistasis in obesity-related traits in rats and mice has identified potential limitations of the Cartesian model, revealing that it only detects some of the genetic interactions occurring in these systems. By applying an alternative approach, the exclusive-or (XOR) model, the researchers detected a greater number of epistatic interactions and identified more biologically relevant ontological terms associated with the interacting loci. This suggests that the XOR model may provide a more comprehensive understanding of epistasis in these species and phenotypes. To further explore these findings and determine if different interaction models also make up distinct epistatic networks, we leverage network science to provide a more comprehensive view into the genetic interactions underlying BMI in this system.
Our comparative analysis of networks derived from Cartesian and XOR interaction models in rats () uncovers distinct topological characteristics for each model-derived network. Notably, we discover that networks based on the XOR model exhibit an enhanced sensitivity to epistatic interactions. This sensitivity enables the identification of network communities, revealing novel trait-related biological functions through enrichment analysis. Furthermore, we identify triangle network motifs in the XOR epistatic network, suggestive of higher-order epistasis, based on the topology of lower-order epistasis.
These findings highlight the XOR model's ability to uncover meaningful biological associations as well as higher-order epistasis from lower-order epistatic networks. Additionally, our results demonstrate that network approaches not only enhance epistasis detection capabilities but also provide more nuanced understandings of genetic architectures underlying complex traits. The identification of community structures and motifs within these distinct networks, especially in XOR, points to the potential for network science to aid in the discovery of novel genetic pathways and regulatory networks. Such insights are important for advancing our understanding of phenotype-genotype relationships.
上位性是指一个基因（或变异）的效应被一个或多个其他基因掩盖或修饰的现象，它可显著影响复杂性状的表型变异。迄今为止，人们普遍认为可使用标准回归方法中常用的笛卡尔积（或乘法）相互作用模型来检测基因相互作用。然而，最近一项针对大鼠和小鼠肥胖相关性状上位性的研究发现了笛卡尔积模型的潜在局限性，表明该模型只能检测这些系统中发生的部分基因相互作用。通过应用另一种方法——异或（XOR）模型，研究人员检测到了更多的上位性相互作用，并确定了与相互作用位点相关的更多生物学相关本体术语。这表明XOR模型可能会让我们对这些物种和表型中的上位性有更全面的理解。为了进一步探究这些发现，并确定不同的相互作用模型是否也构成不同的上位性网络，我们利用网络科学对该系统中体重指数（BMI）潜在的基因相互作用提供更全面的视角。
我们对大鼠中源自笛卡尔积和XOR相互作用模型的网络进行的比较分析，揭示了每个模型衍生网络的独特拓扑特征。值得注意的是，我们发现基于XOR模型的网络对上位性相互作用表现出更高的敏感性。这种敏感性能够识别网络群落，通过富集分析揭示与性状相关的新生物学功能。此外，基于低阶上位性的拓扑结构，我们在XOR上位性网络中识别出三角形网络基序，提示存在高阶上位性。
这些发现突出了XOR模型从低阶上位性网络中揭示有意义的生物学关联以及高阶上位性的能力。此外，我们的结果表明，网络方法不仅增强了上位性检测能力，还能让我们对复杂性状潜在的遗传结构有更细致入微的理解。在这些不同网络中，尤其是在XOR网络中识别出群落结构和基序，表明网络科学有助于发现新的遗传途径和调控网络。这些见解对于深化我们对表型-基因型关系的理解非常重要。
