Saba Laura, Hoffman Paula L, Hornbaker Cheryl, Bhave Sanjiv V, Tabakoff Boris
Department of Pharmacology, University of Colorado-Denver School of Medicine, Aurora, Colorado.
Alcohol Res Health. 2008;31(3):272-4.
Researchers from a wide variety of backgrounds and with a broad range of goals have utilized high-throughput screening technologies (i.e., microarray technologies) to identify candidate genes that may be associated with an observable characteristic or behavior (i.e., phenotype) of interest. However, the initial microarray analyses typically also yield many genes that are not related to the phenotype of interest. Therefore, additional analyses are necessary to select the most likely candidates and eventually identify one or more genes that actually underlie that phenotype. After briefly explaining how microarray data are generated, this article describes one approach to narrowing down the resulting candidate genes and a database that can help in this analysis.
来自各种背景、有着广泛目标的研究人员利用高通量筛选技术（即微阵列技术）来识别可能与感兴趣的可观察特征或行为（即表型）相关的候选基因。然而，最初的微阵列分析通常也会产生许多与感兴趣的表型无关的基因。因此，需要进行额外的分析来选择最有可能的候选基因，并最终确定一个或多个实际构成该表型基础的基因。在简要解释微阵列数据是如何生成之后，本文描述了一种缩小候选基因范围的方法以及一个有助于此分析的数据库。
