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Bioinformatics Vol. 19 no. 18 2003
pages 2397-2403
© 2003 Oxford University Press

Algorithms for large-scale genotyping microarrays

Wei-min Liu *, Xiaojun Di , Geoffrey Yang , Hajime Matsuzaki , Jing Huang , Rui Mei , Thomas B. Ryder , Teresa A. Webster , Shoulian Dong , Guoying Liu , Keith W. Jones , Giulia C. Kennedy and David Kulp

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

Received on March 18, 2003 ; revised on May 30, 2003 ; accepted on June 6, 2003

Motivation: Analysis of many thousands of single nucleotide polymorphisms (SNPs) across whole genome is crucial to efficiently map disease genes and understanding susceptibility to diseases, drug efficacy and side effects for different populations and individuals. High density oligonucleotide microarrays provide the possibility for such analysis with reasonable cost. Such analysis requires accurate, reliable methods for feature extraction, classification, statistical modeling and filtering.

Results: We propose the modified partitioning around medoids as a classification method for relative allele signals. We use the average silhouette width, separation and other quantities as quality measures for genotyping classification. We form robust statistical models based on the classification results and use these models to make genotype calls and calculate quality measures of calls. We apply our algorithms to several different genotyping microarrays. We use reference types, informative Mendelian relationship in families, and leave-one-out cross validation to verify our results. The concordance rates with the single base extension reference types are 99.36% for the SNPs on autosomes and 99.64% for the SNPs on sex chromosomes. The concordance of the leave-one-out test is over 99.5% and is 99.9% higher for AA, AB and BB cells. We also provide a method to determine the gender of a sample based on the heterozygous call rate of SNPs on the X chromosome. See http://www.affymetrix.com for further information. The microarray data will also be available from the Affymetrix web site.

Availability: The algorithms will be available commercially in the Affymetrix software package.

Contact: wliu{at}cs.iupui.edu

* To whom correspondence should be addressed at Department of Computer, Information Science, Indiana University Purdue University Indianapolis, 723 W. Michigan St., Indianapolis, IN 46202-5132, USA.


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