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Bioinformatics Advance Access published online on April 25, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm131
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

A multi-array multi-SNP genotyping algorithm for Affymetrix SNP microarrays

Yuanyuan Xiao a, Mark R. Segal a, Y. H. Yang b and Ru-Fang Yeh a,*

aDepartment of Epidemiology and Biostatistics, Center for Bioinformatics and Molecular Biostatistics, University of California, 185 Berry Street, Lobby 4, Suite 5700, San Francisco, CA 94107, USA.
bSchool of Mathematics and Statistics, University of Sydney, NSW 2006, Australia.

*To whom correspondence should be addressed. Ru-Fang Yeh, E-mail: rufang{at}biostat.ucsf.edu


  Abstract

Motivation: Modern strategies for mapping disease loci require efficient genotyping of a large number of known polymorphic sites in the genome. The sensitive and high-throughput nature of hybridization-based DNA microarray technology provides an ideal platform for such an application by interrogating up to hundreds of thousands of single nucleotide polyphorphisms (SNPs) in a single assay. Similar to the development of expression arrays, these genotyping arrays pose many data analytic challenges that are often platform specific. Affymetrix SNP arrays, for example, use multiple sets of short oligonucleotide probes for each known SNP, and require effective statistical methods to combine these probe intensities in order to generate reliable and accurate genotype calls

Results: We developed an integrated multi-SNP, multi-array genotype calling algorithm for Affymetrix SNP arrays, MAMS, that combines single-array multi-SNP (SAMS) and multi-array single-SNP (MASS) calls to improve the accuracy of genotype calls, without the need for training data or computation-intensive normalization procedures as in other multi-array methods. The algorithm uses resampling techniques and model-based clustering to derive single array based genotype calls, which are subsequently refined by competitive genotype calls based on multi-array, single-SNP clustering. The resampling scheme caps computation for single-array analysis and hence is readily scaleable, important in view of expanding numbers of SNPs per array. The MASS update is designed to improve calls for typical SNPs, harboring allele-imbalanced binding affinities, that are difficult to genotype without information from other arrays. Using a publicly available data set of HapMap samples from Affymetrix, and independent calls by alternative genotyping methods from the HapMap project, we show that our approach performs competitively to existing methods.

Availability: R functions are available upon request from the authors.

Associate Editor: Dr. Chris Stoeckert

Received on August 1, 2006; revised on March 30, 2007; accepted on March 31, 2007

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