Lum Pek Yee, Chen Yanqing, Zhu Jun, Lamb John, Melmed Shlomo, Wang Susanna, Drake Tom A, Lusis Aldons J, Schadt Eric E
Rosetta Inpharmatics, LLC, Merck & Co., Inc., Seattle, WA 98109, USA.
J Neurochem. 2006 Apr;97 Suppl 1:50-62. doi: 10.1111/j.1471-4159.2006.03661.x.
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.
复杂生物系统最好被建模为高度模块化的流体系统，具有可塑性，使其能够适应大量不断变化的条件。在这里，我们重点介绍几种基于网络的新方法，以阐明复杂性状背后的遗传网络。这些整合基因组学方法结合大规模基因型和基因表达结果，在分离的小鼠群体中重建复杂性状（如疾病或药物反应）背后可靠的遗传网络。我们将这些新方法应用于在一个分离的小鼠群体中对全脑进行的最广泛的基因表达研究调查之一。在来自F2杂交群体的300多只小鼠的全脑样本中监测了超过23000个基因，并在均匀分布于整个基因组的1200多个单核苷酸多态性（SNP）标记处进行了基因分型。我们探索了大脑转录网络的拓扑特性，并强调了通过整合基因型和表达数据来推断基因间因果关联的不同方法。我们通过鉴定并实验验证预测对垂体肿瘤转化1基因（Pttg1）的强表达数量性状位点（eQTL）有反应的大脑基因表达性状，证明了这些方法的实用性，该eQTL与该基因的物理位置一致（顺式eQTL）。我们确定了构成小鼠大脑转录网络的核心功能模块，这些模块与包括肥胖和糖尿病在内的代谢疾病性状相关的核心生物学过程是一致的。
