Toward High-Throughput Genotyping: Dynamic and Automatic Software for Manipulating Large-Scale Genotype Data Using Fluorescently Labeled Dinucleotide Markers

  1. Jin-Long Li1,2,3,4,
  2. Hongyi Deng1,
  3. Dong-Bing Lai1,3,
  4. Fuhua Xu1,3,
  5. Jian Chen1,3,
  6. Guimin Gao1,
  7. Robert R. Recker1, and
  8. Hong-Wen Deng1,3,5,6
  1. 1Osteoporosis Research Center, 2Department of Mathematics and Computer Sciences, and 3Department of Biomedical Sciences, Creighton University, Omaha, Nebraska 68131, USA; 4Center for Hereditary Communication Disorders, Boys Town National Research Hospital, Omaha, Nebraska 68131, USA; 5Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, ChangSha, P.R. China 410081

Abstract

To efficiently manipulate large amounts of genotype data generated with fluorescently labeled dinucleotide markers, we developed a Microsoft Access database management system, namedGenoDB. GenoDB offers several advantages. First, it accommodates the dynamic nature of the accumulations of genotype data during the genotyping process; some data need to be confirmed or replaced by repeat lab procedures. By usingGenoDB, the raw genotype data can be imported easily and continuously and incorporated into the database during the genotyping process that may continue over an extended period of time in large projects. Second, almost all of the procedures are automatic, including autocomparison of the raw data read by different technicians from the same gel, autoadjustment among the allele fragment-size data from cross-runs or cross-platforms, autobinning of alleles, and autocompilation of genotype data for suitable programs to perform inheritance check in pedigrees. Third, GenoDB provides functions to track electrophoresis gel files to locate gel or sample sources for any resultant genotype data, which is extremely helpful for double-checking consistency of raw and final data and for directing repeat experiments. In addition, the user-friendly graphic interface ofGenoDB renders processing of large amounts of data much less labor-intensive. Furthermore, GenoDB has built-in mechanisms to detect some genotyping errors and to assess the quality of genotype data that then are summarized in the statistic reports automatically generated by GenoDB. The GenoDBcan easily handle >500,000 genotype data entries, a number more than sufficient for typical whole-genome linkage studies. The modules and programs we developed for the GenoDB can be extended to other database platforms, such as Microsoft SQL server, if the capability to handle still greater quantities of genotype data simultaneously is desired.

Footnotes

  • 6 Corresponding author.

  • E-MAIL deng{at}creighton.edu; FAX (402) 280-5034.

  • Article and publication are at http://www.genome.org/cgi/doi/10.1101/gr.159701.

    • Received August 10, 2000.
    • Accepted April 16, 2001.
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