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Automated SNP genotype clustering algorithm to improve data completeness in high-throughput SNP genotyping datasets from custom arrays

By Edward M. Smith, Jack Littrell and M. (Michael) Olivier

Abstract

High-throughput SNP genotyping platforms use automated genotype calling algorithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been optimized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be advisable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.\ud \u

Topics: QH426, QA76
Publisher: Elsevier Ltd
Year: 2007
OAI identifier: oai:wrap.warwick.ac.uk:3629

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Citations

  1. (2009). Highly multiplexed molecular inversion probe genotyping: over 10,000 targeted SNPs genotyped in a single tube assay. Genome Res 2005;15:269–275. [PubMed: 15687290] 9. Kissebah doi
  2. (2009). Genomics Proteomics Bioinformatics. Author manuscript; available in PMC

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