Simple tests to detect errors in high-throughput genotype data in the molecular laboratory

David J. Vandenbergh, Kathrine Heron, Ryan Peterson, Karl B. Shpargel, Abigail Woodroffe, David A. Blizard, Gerald E. McClearn, George P. Vogler

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

With the advent of high-density DNA marker data sets for the mouse and other model systems,100 or more genotypes 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.

Original languageEnglish (US)
Pages (from-to)9-16
Number of pages8
JournalJournal of Biomolecular Techniques
Volume14
Issue number1
StatePublished - Mar 1 2003

All Science Journal Classification (ASJC) codes

  • Molecular Biology

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