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Bioinformatics Advance Access originally published online on April 23, 2009
Bioinformatics 2009 25(13):1669-1679; doi:10.1093/bioinformatics/btp270
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A Bayesian segmentation approach to ascertain copy number variations at the population level

Long Yang Wu 1,*, Hugh A. Chipman 2, Shelley B. Bull 3,4, Laurent Briollais 4 and Kesheng Wang 5

1Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, 2Department of Mathematics and Statistics, Acadia University, Wolfville, Nova Scotia, 3Dalla Lana School of Public Health, University of Toronto, Toronto, 4Samuel Lunenfeld Research Institute, Toronto, Ontario and 5Department of Biostatistics and Epidemiology, College of Public Health, East Tennessee State University, Johnson City, TN, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Efficient and accurate ascertainment of copy number variations (CNVs) at the population level is essential to understand the evolutionary process and population genetics, and to apply CNVs in population-based genome-wide association studies for complex human diseases. We propose a novel Bayesian segmentation approach to identify CNVs in a defined population of any size. It is computationally efficient and provides statistical evidence for the detected CNVs through the Bayes factor. This approach has the unique feature of carrying out segmentation and assigning copy number status simultaneously—a desirable property that current segmentation methods do not share.

Results: In comparisons with popular two-step segmentation methods for a single individual using benchmark simulation studies, we find the new approach to perform competitively with respect to false discovery rate and sensitivity in breakpoint detection. In a simulation study of multiple samples with recurrent copy numbers, the new approach outperforms two leading single sample methods. We further demonstrate the effectiveness of our approach in population-level analysis of previously published HapMap data. We also apply our approach in studying population genetics of CNVs.

Availability: R programs are available at http://www.mshri.on.ca/mitacs/software/SOFTWARE.HTML

Contact: lwu{at}math.uwaterloo.ca

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Martin Bishop

Received on January 12, 2009; revised on March 28, 2009; accepted on April 18, 2009

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