Elucidating the murine brain transcriptional network in a segregating mouse population to identify core functional modules for obesity and diabetes
Elucidating the murine brain transcriptional network in a segregating mouse population to identify core functional modules for obesity and diabetes
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DOI：
10.1111/j.1471-4159.2006.03661.x
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发表时间：
2006-04-01
影响因子：
4.7
通讯作者：
Schadt, EE
中科院分区：
文献类型：
--
作者：
Lum, PY;Chen, YQ;Schadt, EE
复杂的生物系统最好建模为高度模块化的流体系统，该系统表现出可塑性，使其能够适应各种不断变化的条件。在这里，我们重点介绍几种基于网络的新颖方法来阐明复杂性状背后的遗传网络。这些综合基因组方法结合了大规模基因型和基因表达结果，分离小鼠群体，以重建疾病或药物反应等复杂性状背后的可靠遗传网络。我们将这些新颖的方法应用于有史以来在隔离小鼠群体的全脑中进行的最广泛的基因表达研究之一。对来自 F2 杂交群体的 300 多只小鼠的全脑样本中的 23,000 多个基因进行了监测，并对均匀分布在整个基因组中的 1200 多个 SNP 标记进行了基因分型。我们探索大脑转录网络的拓扑特性，并强调通过整合基因型和表达数据来推断基因之间因果关系的不同方法。我们通过识别和实验验证大脑基因表达性状来证明这些方法的实用性，这些性状预计会对垂体肿瘤转化1基因（Pttg1）的强表达数量性状基因座（eQTL）做出反应，该基因座与该基因的物理位置（顺式eQTL）一致。我们确定了构成小鼠大脑转录网络的核心功能模块，这些模块与肥胖和糖尿病等代谢疾病特征相关的核心生物过程是一致的。
Complex biological systems are best modeled as highly modular, fluid systems exhibiting a plasticity that allows them to adapt to a vast array of changing conditions. Here we highlight several novel network-based approaches to elucidate genetic networks underlying complex traits. These integrative genomic approaches combine large-scale genotypic and gene expression results in segregating mouse populations to reconstruct reliable genetic networks underlying complex traits such as disease or drug response. We apply these novel approaches to one of the most extensive surveys of gene expression studies ever undertaken in whole brain in a segregating mouse population. More than 23,000 genes were monitored in whole brain samples from more than 300 mice derived from an F2 intercross population and genotyped at over 1200 SNP markers uniformly spread over the entire genome. We explore the topological properties of the brain transcriptional network and highlight different approaches to inferring causal associations among genes by integrating genotypic and expression data. We demonstrate the utility of these approaches by identifying and experimentally validating brain gene expression traits predicted to respond to a strong expression quantitative trait locus (eQTL) for the pituitary tumor-transforming 1 gene (Pttg1) that coincides with the physical location of this gene (a cis eQTL). We identify core functional modules making up the brain transcriptional network in mice that are coherent for core biological processes associated with metabolic disease traits including obesity and diabetes.