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Bioinformatics Advance Access originally published online on January 18, 2005
Bioinformatics 2005 21(9):1958-1963; doi:10.1093/bioinformatics/bti275
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Dynamic model based algorithms for screening and genotyping over 100K SNPs on oligonucleotide microarrays

Xiaojun Di *, Hajime Matsuzaki , Teresa A. Webster , Earl Hubbell , Guoying Liu , Shoulian Dong , Dan Bartell , Jing Huang , Richard Chiles , Geoffrey Yang , Mei-mei Shen , David Kulp , Giulia C. Kennedy , Rui Mei , Keith W. Jones and Simon Cawley

Affymetrix, Inc. 3380 Central Expressway, Santa Clara, CA 95051, USA

*To whom correspondence should be addressed.

Motivation: A high density of single nucleotide polymorphism (SNP) coverage on the genome is desirable and often an essential requirement for population genetics studies. Region-specific or chromosome-specific linkage studies also benefit from the availability of as many high quality SNPs as possible. The availability of millions of SNPs from both Perlegen and the public domain and the development of an efficient microarray-based assay for genotyping SNPs has brought up some interesting analytical challenges. Effective methods for the selection of optimal subsets of SNPs spanning the genome and methods for accurately calling genotypes from probe hybridization patterns have enabled the development of a new microarray-based system for robustly genotyping over 100 000 SNPs per sample.

Results: We introduce a new dynamic model-based algorithm (DM) for screening over 3 million SNPs and genotyping over 100 000 SNPs. The model is based on four possible underlying states: Null, A, AB and B for each probe quartet. We calculate a probe-level log likelihood for each model and then select between the four competing models with an SNP-level statistical aggregation across multiple probe quartets to provide a high-quality genotype call along with a quality measure of the call. We assess performance with HapMap reference genotypes, informative Mendelian inheritance relationship in families, and consistency between DM and another genotype classification method. At a call rate of 95.91% the concordance with reference genotypes from the HapMap Project is 99.81% based on over 1.5 million genotypes, the Mendelian error rate is 0.018% based on 10 trios, and the consistency between DM and MPAM is 99.90% at a comparable rate of 97.18%. We also develop methods for SNP selection and optimal probe selection.

Availability: The DM algorithm is available in Affymetrix's Genotyping Tools software package and in Affymetrix's GDAS software package. See http://www.affymetrix.com for further information. 10K and 100K mapping array data are available on the Affymetrix website.

Contact: xiaojun_di{at}affymetrix.com


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