Journal Article

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

Long Yang Wu, Hugh A. Chipman, Shelley B. Bull, Laurent Briollais and Kesheng Wang

in Bioinformatics

Volume 25, issue 13, pages 1669-1679
Published in print July 2009 | ISSN: 1367-4803
Published online April 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp270
A Bayesian segmentation approach to ascertain copy number variations at the population level

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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@math.uwaterloo.ca

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  8701 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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