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Bioinformatics Advance Access published online on November 2, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti741
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received July 26, 2005
Revised September 28, 2005
Accepted October 21, 2005

Article

A genotype calling algorithm for Affymetrix SNP arrays

Nusrat Rabbee 1* and Terence P. Speed 2

1 Department of Statistics, University of California, Berkeley, CA, USA
2 Department of Statistics, University of California, Berkeley, CA, USA; Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia

* To whom correspondence should be addressed.
Nusrat Rabbee, E-mail: nrabbee{at}post.harvard.edu


  Abstract

Motivation: A classification algorithm, based on a multi-chip, multi-SNP approach is proposed for Affymetrix SNP arrays. Current procedures for calling genotypes on SNP arrays process all the features associated with one chip and one SNP at a time. Using a large training sample where the genotype labels are known, we develop a supervised learning algorithm to obtain more accurate classification results on new data. The method we propose, RLMM, is based on a robustly fitted, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variance is reduced through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as across thousands of SNPs for accurate classification. In this paper, we apply RLMM to Affymetrix 100K SNP array data, present classification results and compare them to genotype calls obtained from the Affymetrix procedure DM, as well as to the publicly available genotype calls from the HapMap project.

Availability: The RLMM software is implemented in R and is available from Bioconductor or from the first author at.


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