Vandenbergh David J, Heron Kathrine, Peterson Ryan, Shpargel Karl B, Woodroffe Abigail, Blizard David A, McClearn Gerald E, Vogler George P
Center for Developmental and Health Genetics, Department of Biobehavioral Health, and Life Sciences Consortium, The Pennsylvania State University, University Park, PA 16802, USA.
J Biomol Tech. 2003 Mar;14(1):9-16.
With the advent of high-density DNA marker data sets for the mouse and other model systems, 100 or more genotype are routinely generated from large groups of mice. Issues of the accuracy and reliability of the genotyping are extremely important but often not addressed until genetic analysis is conducted. Simple tests that rely on the robust predictions arising from Mendelian genetics can be made quickly in the molecular laboratory as the data are generated, and require only a spreadsheet program. In this report, genotype data from 392 mice tested at 96 marker sites were analyzed for errors that are typical when handling large volumes of data generated in a repetitive process. The testing consisted of: (1) repeating the genotyping of approximately 1% of the samples; (2) examining the deviation from the expected segregation ratio ( 1:2:1 ) on a marker-by-marker basis; and (3) testing the correlation of the genotype at one marker with that at neighboring genetic markers on a chromosome. These three steps allowed analysis at the level of the microtiter plate, where errors are most likely to occur. A set of 96 dinucleotide repeat markers that are polymorphic between the C57BL/6J and DBA/2J mouse strains and can be multiplexed is reported for use in other genotyping projects.
随着小鼠和其他模型系统的高密度DNA标记数据集的出现,通常可以从大量小鼠中生成100个或更多的基因型。基因分型的准确性和可靠性问题极其重要,但在进行遗传分析之前往往未得到解决。在分子实验室生成数据时,可以根据孟德尔遗传学的可靠预测快速进行简单测试,并且只需要一个电子表格程序。在本报告中,分析了在96个标记位点测试的392只小鼠的基因型数据,以查找在处理重复过程中生成的大量数据时常见的错误。测试包括:(1)对约1%的样本重复进行基因分型;(2)逐个标记地检查与预期分离比(1:2:1)的偏差;(3)测试一个标记处的基因型与染色体上相邻遗传标记处的基因型的相关性。这三个步骤允许在微量滴定板水平进行分析,因为此处最容易出现错误。本文报道了一组96个二核苷酸重复标记,它们在C57BL/6J和DBA/2J小鼠品系之间具有多态性并且可以进行多重分析,可用于其他基因分型项目。