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Statistical Applications in Genetics and Molecular Biology

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1544-6115
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Volume 10, Issue 1 (Nov 2011)

Modeling Read Counts for CNV Detection in Exome Sequencing Data

Michael I. Love
  • Max Planck Institute for Molecular Genetics
/ Alena Myšičková
  • Max Planck Institute for Molecular Genetics
/ Ruping Sun
  • Max Planck Institute for Molecular Genetics
/ Vera Kalscheuer
  • Max Planck Institute for Molecular Genetics
/ Martin Vingron
  • Max Planck Institute for Molecular Genetics
/ Stefan A. Haas
  • Max Planck Institute for Molecular Genetics
Published Online: 2011-11-08 | DOI: https://doi.org/10.2202/1544-6115.1732

Varying depth of high-throughput sequencing reads along a chromosome makes it possible to observe copy number variants (CNVs) in a sample relative to a reference. In exome and other targeted sequencing projects, technical factors increase variation in read depth while reducing the number of observed locations, adding difficulty to the problem of identifying CNVs. We present a hidden Markov model for detecting CNVs from raw read count data, using background read depth from a control set as well as other positional covariates such as GC-content. The model, exomeCopy, is applied to a large chromosome X exome sequencing project identifying a list of large unique CNVs. CNVs predicted by the model and experimentally validated are then recovered using a cross-platform control set from publicly available exome sequencing data. Simulations show high sensitivity for detecting heterozygous and homozygous CNVs, outperforming normalization and state-of-the-art segmentation methods.

Keywords: exorne sequencing; targeted sequencing; CNV; copy number variant; HMM; hidden Markov model

We thank our collaborators on the XLID project, Prof. Dr. H.-Hilger Ropers, Wei Chen, Hao Hu, Reinhard Ullmann and the EUROMRX consortium for providing the XLID data, validation of CNVs and for helpful discussion. We also thank Ho-Ryun Chung for suggestions. Part of this work was financed by the European Union’s Seventh Framework Program under grant agreement number 241995, project GENCODYS.

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About the article

Published Online: 2011-11-08


Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.2202/1544-6115.1732. Export Citation

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