Nucleic Acids Research Advance Access originally published online on March 6, 2007
Nucleic Acids Research 2007 35(6):2013-2025; doi:10.1093/nar/gkm076
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Nucleic Acids Research, 2007, Vol. 35, No. 6 2013-2025
© 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.
Computational Biology |
QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data
1Genomics Laboratory and 4Bioinformatics, Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, 2Life Science Interface Doctoral Training Centre, Wolfson Building, Parks Road, Oxford OX1 3QD, 3Henry Wellcome Centre for Gene Function, Department of Statistics, University of Oxford, Oxford, OX1 3TG, 5Oxford Medical Genetics Laboratories, The Churchill Hospital, Oxford, OX3 7LJ, UK, 6Centre for Addiction & Mental Health, University of Toronto, 1001 Queen Street West, Toronto, Ontario M6J 1H4, Canada and 7MRC Mammalian Genetics Unit, Medical Research Council, Harwell, Oxford, OX11 0RD
*To whom correspondence should be addressed. Tel: +44-(0)1865 287526; Fax: +44-(0)1865 287533; Email: ioannis.ragoussis{at}well.ox.ac.uk
Correspondence may also be addressed to Christopher C. Holmes. Tel: +44 (0)1865 285368; Fax: +44 (0)1865 285384; Email: cholmes{at}stats.ox.ac.uk
Received December 20, 2006. Revised January 24, 2007. Accepted January 25, 2007.
Array-based technologies have been used to detect chromosomal copy number changes (aneuploidies) in the human genome. Recent studies identified numerous copy number variants (CNV) and some are common polymorphisms that may contribute to disease susceptibility. We developed, and experimentally validated, a novel computational framework (QuantiSNP) for detecting regions of copy number variation from BeadArrayTM SNP genotyping data using an Objective Bayes Hidden-Markov Model (OB-HMM). Objective Bayes measures are used to set certain hyperparameters in the priors using a novel re-sampling framework to calibrate the model to a fixed Type I (false positive) error rate. Other parameters are set via maximum marginal likelihood to prior training data of known structure. QuantiSNP provides probabilistic quantification of state classifications and significantly improves the accuracy of segmental aneuploidy identification and mapping, relative to existing analytical tools (Beadstudio, Illumina), as demonstrated by validation of breakpoint boundaries. QuantiSNP identified both novel and validated CNVs. QuantiSNP was developed using BeadArrayTM SNP data but it can be adapted to other platforms and we believe that the OB-HMM framework has widespread applicability in genomic research. In conclusion, QuantiSNP is a novel algorithm for high-resolution CNV/aneuploidy detection with application to clinical genetics, cancer and disease association studies.
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.
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