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Statistical Applications in Genetics and Molecular Biology

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Modeling Read Counts for CNV Detection in Exome Sequencing Data

Michael I. Love1 / Alena Myšičková2 / Ruping Sun3 / Vera Kalscheuer4 / Martin Vingron5 / Stefan A. Haas6

1Max Planck Institute for Molecular Genetics

2Max Planck Institute for Molecular Genetics

3Max Planck Institute for Molecular Genetics

4Max Planck Institute for Molecular Genetics

5Max Planck Institute for Molecular Genetics

6Max Planck Institute for Molecular Genetics

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.

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 10, Issue 1, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.2202/1544-6115.1732, November 2011

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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

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