Gene network inference via structural equation modeling in genetical genomics experiments
Gene network inference via structural equation modeling in genetical genomics experiments
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DOI：
10.1534/genetics.107.080069
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发表时间：
2008-03-01
期刊：
影响因子：
3.3
通讯作者：
Hoeschele, Ina
中科院分区：
文献类型：
--
作者：
Liu, Bing;de la Fuente, Alberto;Hoeschele, Ina
我们的目标是遗传基因组学或系统遗传学实验中的基因网络推理。对于可获得序列信息的物种，我们首先联合利用顺式、顺式反式和反式调节进行表达数量性状基因座（eQTL）作图。在使用局部结构模型识别每个 eQTL 的调节器-靶标对后，我们通过组装所有保留的调节器-靶标关系构建了一个包含定向网络 (EDN)。 EDN具有与表达基因和eQTL相对应的节点以及从eQTL到顺式调节靶基因、从顺式调节基因到顺反式调节靶基因、从反式调节基因到靶基因以及前反式eQTL到靶基因的定向边。对于 EDN 定义的强约束搜索空间内的网络推理，我们提出了结构方程建模（SEM），因为它可以对循环网络进行建模，并且 EDN 确实包含反馈关系。基于似然因式分解和受限搜索空间，我们的 SEM 算法推断出涉及数百个基因和 eQTL 的网络。结构推断基于惩罚似然比和奥卡姆窗模型选择的适应。 SEM 算法通过非线性常微分方程和已知的循环网络拓扑模拟上升数据进行评估，并应用于真实的酵母数据集。
Our goal is gene network inference in genetical genomics or systems genetics experiments. For species where sequence information is available, we first perform expression quantitative trait locus (eQTL) mapping by jointly utilizing cis-, cis-trans-, and trans-regulation. After using local structural models to identify regulator-target pairs for each eQTL, we construct an encompassing directed network (EDN) by assembling all retained regulator-target relationships. The EDN has nodes corresponding to expressed genes and eQTL and directed edges from eQTL to cis-regulated target genes, from cis-regulated genes to cis-trans-regulated target genes, from trans-regulator genes to target genes, and front trans-eQTL to target genes. For network inference within the strongly constrained search space defined by the EDN, we propose structural equation modeling (SEM), because it can model cyclic networks and the EDN indeed contains feedback relationships. On the basis of a factorization of the likelihood and the constrained search space, our SEM algorithm infers networks involving several hundred genes and eQTL. Structure inference is based on a penalized likelihood ratio and an adaptation of Occam's window model selection. The SEM algorithm was evaluated rising data simulated with nonlinear ordinary differential equations and known cyclic network topologies and was applied to a real yeast data set.