Fuller Tova F, Ghazalpour Anatole, Aten Jason E, Drake Thomas A, Lusis Aldons J, Horvath Steve
Department of Human Genetics, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California, USA.
Mamm Genome. 2007 Jul;18(6-7):463-72. doi: 10.1007/s00335-007-9043-3. Epub 2007 Aug 1.
Systems-oriented genetic approaches that incorporate gene expression and genotype data are valuable in the quest for genetic regulatory loci underlying complex traits. Gene coexpression network analysis lends itself to identification of entire groups of differentially regulated genes-a highly relevant endeavor in finding the underpinnings of complex traits that are, by definition, polygenic in nature. Here we describe one such approach based on liver gene expression and genotype data from an F(2) mouse inter-cross utilizing weighted gene coexpression network analysis (WGCNA) of gene expression data to identify physiologically relevant modules. We describe two strategies: single-network analysis and differential network analysis. Single-network analysis reveals the presence of a physiologically interesting module that can be found in two distinct mouse crosses. Module quantitative trait loci (mQTLs) that perturb this module were discovered. In addition, we report a list of genetic drivers for this module. Differential network analysis reveals differences in connectivity and module structure between two networks based on the liver expression data of lean and obese mice. Functional annotation of these genes suggests a biological pathway involving epidermal growth factor (EGF). Our results demonstrate the utility of WGCNA in identifying genetic drivers and in finding genetic pathways represented by gene modules. These examples provide evidence that integration of network properties may well help chart the path across the gene-trait chasm.
整合基因表达和基因型数据的系统导向型遗传方法，对于探寻复杂性状背后的遗传调控位点具有重要价值。基因共表达网络分析有助于识别差异调控基因的整个群组——这在寻找复杂性状的基础方面是一项高度相关的工作，因为复杂性状从定义上讲本质上是多基因的。在此，我们描述一种基于F(2)小鼠杂交后代肝脏基因表达和基因型数据的方法，利用基因表达数据的加权基因共表达网络分析（WGCNA）来识别生理相关模块。我们描述了两种策略：单网络分析和差异网络分析。单网络分析揭示了一个在两个不同小鼠杂交组合中均可发现的具有生理意义的模块。发现了干扰该模块的模块数量性状位点（mQTL）。此外，我们报告了该模块的遗传驱动因子列表。差异网络分析揭示了基于瘦小鼠和肥胖小鼠肝脏表达数据的两个网络在连接性和模块结构上的差异。这些基因的功能注释表明存在一条涉及表皮生长因子（EGF）的生物学途径。我们的结果证明了WGCNA在识别遗传驱动因子以及发现由基因模块代表的遗传途径方面的实用性。这些例子提供了证据，表明网络特性的整合很可能有助于跨越基因-性状鸿沟绘制路径。
