Gene expression in the mouse eye: an online resource for genetics using 103 strains of mice
Gene expression in the mouse eye: an online resource for genetics using 103 strains of mice
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DOI：
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发表时间：
2009-08
期刊：
影响因子：
2.2
通讯作者：
E. Geisert;Lu Lu-Lu;N. Freeman-Anderson;Justin P. Templeton;M. Nassr;Xusheng Wang;W. Gu;Y. Jiao;Robert W. Williams
中科院分区：
文献类型：
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作者：
E. Geisert;Lu Lu-Lu;N. Freeman-Anderson;Justin P. Templeton;M. Nassr;Xusheng Wang;W. Gu;Y. Jiao;Robert W. Williams
目的 基因表达模式的个体差异在很大程度上解释了眼部表型的多样性和疾病风险的变化。我们研究了表达差异的原因，以及它们与序列变异、功能差异和眼部病理生理学的联系。方法将来自年轻成人眼睛的 mRNA 与寡聚物微阵列 (Affymetrix M430v2) 杂交。数据嵌入 GeneNetwork 中，其中包含数百万个单核苷酸多态性、定制阵列注释以及有关互补细胞、功能和行为特征的信息。数据包括来自 28 个常见品系、68 个 BXD 重组自交系以及几个突变体和敲除品系的雄性和雌性样本。结果我们提供完全集成的资源来绘制、绘制图表、分析和测试眼睛基因表达差异的原因和相关性。 mRNA 表达的协方差可用于推断基因功能、提取不同细胞或组织的特征、定义分子网络以及绘制产生表达差异的数量性状基因座。这些数据还可用于将疾病表型与序列变异联系起来。我们证明视紫红质表达的变化可以有效预测八种非克隆视网膜疾病的候选基因，包括人类 RP29 基因座的 WDR17。结论 基因表达的高水平菌株变异是一个强大的工具，可用于探索和测试结构、功能和疾病易感性变异背后的分子网络。将这些数据整合到 GeneNetwork 中，为用户提供了一个工作台来测试序列差异与眼睛结构和功能之间的联系。
Purpose Individual differences in patterns of gene expression account for much of the diversity of ocular phenotypes and variation in disease risk. We examined the causes of expression differences, and in their linkage to sequence variants, functional differences, and ocular pathophysiology. Methods mRNAs from young adult eyes were hybridized to oligomer microarrays (Affymetrix M430v2). Data were embedded in GeneNetwork with millions of single nucleotide polymorphisms, custom array annotation, and information on complementary cellular, functional, and behavioral traits. The data include male and female samples from 28 common strains, 68 BXD recombinant inbred lines, as well as several mutants and knockouts. Results We provide a fully integrated resource to map, graph, analyze, and test causes and correlations of differences in gene expression in the eye. Covariance in mRNA expression can be used to infer gene function, extract signatures for different cells or tissues, to define molecular networks, and to map quantitative trait loci that produce expression differences. These data can also be used to connect disease phenotypes with sequence variants. We demonstrate that variation in rhodopsin expression efficiently predicts candidate genes for eight uncloned retinal diseases, including WDR17 for the human RP29 locus. Conclusions The high level of strain variation in gene expression is a powerful tool that can be used to explore and test molecular networks underlying variation in structure, function, and disease susceptibility. The integration of these data into GeneNetwork provides users with a workbench to test linkages between sequence differences and eye structure and function.