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Bioinformatics Advance Access published online on September 29, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn509
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Smarter Clustering Methods for SNP Genotype Calling

Yan Lin 1,3,*, George Tseng 1,2, Soo Yeon Cheong 1, Lora J.H. Bean 4, Stephanie L. Sherman 4 and Eleanor Feingold 1,2

1Department of Biostatistics, University of Pittsburgh.
2Department of Human Genetics, University of Pittsburgh.
3Department of Medicine, University of Pittsburgh.
4Department of Human Genetics, Emory University.

*To whom correspondence should be addressed. Dr. Yan Lin, E-mail: yal14{at}pitt.edu


  Abstract

Motivation: Most genotyping technologies for single nucleotide polymorphism (SNP) markers use standard clustering methods to "call" the SNP genotypes. These methods are not always optimal in distinguishing the genotype clusters of a SNP because they do not take advantage of specific features of the genotype calling problem. In particular, when family data are available, pedigree information is ignored. Furthermore, prior information about the distribution of the measurements for each cluster can be used to choose an appropriate model-based clustering method and can significantly improve the genotype calls. One special genotyping problem that has never been discussed in the literature is that of genotyping of trisomic individuals, such as individuals with Down syndrome. Calling trisomic genotypes is a more complicated problem, and the addition of external information becomes very important.

Results: In this article, we discuss the impact of incorporating external information into clustering algorithms to call the genotypes for both disomic and trisomic data. We also propose two new methods to call genotypes using family data. One is a modification of the K-means method and uses the pedigree information by updating all members of a family together. The other is a likelihood-based method that combines the Gaussian or beta mixture model with pedigree information. We compare the performance of these two methods and some other existing methods using simulation studies. We also compare the performance of these methods on a real dataset generated by the Illumina platform (www.illumina.com).

Availability: The R code for the family-based genotype calling methods (SNPCaller) is available to be downloaded from the following website: http://watson.hgen.pitt.edu/register.

Contact: yal14{at}pitt.edu

Associate Editor: Prof. Dmitrij Frishman

Received on May 8, 2008; revised on September 23, 2008; accepted on September 23, 2008

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