Moharil Janhavi, May Paul, Gaile Daniel P, Blair Rachael Hageman
Stat Appl Genet Mol Biol. 2016 Mar;15(1):39-53. doi: 10.1515/sagmb-2015-0058.
Graphical models have proven to be a valuable tool for connecting genotypes and phenotypes. Structural learning of phenotype-genotype networks has received considerable attention in the post-genome era. In recent years, a dozen different methods have emerged for network inference, which leverage natural variation that arises in certain genetic populations. The structure of the network itself can be used to form hypotheses based on the inferred direct and indirect network relationships, but represents a premature endpoint to the graphical analyses. In this work, we extend this endpoint. We examine the unexplored problem of perturbing a given network structure, and quantifying the system-wide effects on the network in a node-wise manner. The perturbation is achieved through the setting of values of phenotype node(s), which may reflect an inhibition or activation, and propagating this information through the entire network. We leverage belief propagation methods in Conditional Gaussian Bayesian Networks (CG-BNs), in order to absorb and propagate phenotypic evidence through the network. We show that the modeling assumptions adopted for genotype-phenotype networks represent an important sub-class of CG-BNs, which possess properties that ensure exact inference in the propagation scheme. The system-wide effects of the perturbation are quantified in a node-wise manner through the comparison of perturbed and unperturbed marginal distributions using a symmetric Kullback-Leibler divergence. Applications to kidney and skin cancer expression quantitative trait loci (eQTL) data from different mus musculus populations are presented. System-wide effects in the network were predicted and visualized across a spectrum of evidence. Sub-pathways and regions of the network responded in concert, suggesting co-regulation and coordination throughout the network in response to phenotypic changes. We demonstrate how these predicted system-wide effects can be examined in connection with estimated class probabilities for covariates of interest, e.g. cancer status. Despite the uncertainty in the network structure, we demonstrate the system-wide predictions are stable across an ensemble of highly likely networks. A software package, geneNetBP, which implements our approach, was developed in the R programming language.
图形模型已被证明是连接基因型和表型的宝贵工具。在基因组时代之后，表型-基因型网络的结构学习受到了广泛关注。近年来，出现了十几种不同的网络推断方法，这些方法利用了特定遗传群体中出现的自然变异。网络本身的结构可用于基于推断出的直接和间接网络关系形成假设，但这只是图形分析的一个过早终点。在这项工作中，我们扩展了这个终点。我们研究了扰动给定网络结构并以节点方式量化对网络的全系统影响这一未被探索的问题。通过设置表型节点的值来实现扰动，这些值可能反映抑制或激活，并通过整个网络传播此信息。我们利用条件高斯贝叶斯网络（CG-BNs）中的信念传播方法，以便通过网络吸收和传播表型证据。我们表明，为基因型-表型网络采用的建模假设代表了CG-BNs的一个重要子类，其具有确保在传播方案中进行精确推断的属性。通过使用对称Kullback-Leibler散度比较扰动和未扰动的边际分布，以节点方式量化扰动的全系统影响。展示了对来自不同小家鼠群体的肾脏和皮肤癌表达数量性状基因座（eQTL）数据的应用。在一系列证据中预测并可视化了网络中的全系统影响。网络的子通路和区域协同响应，表明在整个网络中存在共同调节和协调以响应表型变化。我们展示了如何结合感兴趣的协变量（例如癌症状态）的估计类别概率来检查这些预测的全系统影响。尽管网络结构存在不确定性，但我们证明全系统预测在一组高度可能的网络中是稳定的。用R编程语言开发了一个实现我们方法的软件包geneNetBP。
