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Detecting Copy Number Variation via Next Generation Technology

  • Cytogenetics (CL Martin and E Williams, Section Editors)
  • Published:
Current Genetic Medicine Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

Copy number variants (CNVs), gains and losses of segments of genomic DNA associated with normal phenotypic variation and disease states, are traditionally detected using chromosomal microarrays. Recent bioinformatic advances now allow for the detection of CNVs using next generation sequencing (NGS) data, greatly increasing the clinical utility of NGS tests.

Recent Findings

Though not widespread, clinical diagnostic laboratories have started to implement CNV detection from targeted NGS gene panels and whole exome sequencing data, despite some limitations. Multiple tools have been designed to overcome these limitations, with some promising results. However, no single tool yet enables the high sensitivity and specificity needed to make it more than a supplementary assay for clinical laboratories.

Summary

As sequencing costs drop and sequencing technologies improve, some of these shortcomings may be overcome by whole genome sequencing or long-read sequencing technologies. Here, we review methods used to detect CNVs from NGS data, including studies comparing their performance.

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Acknowledgments

The authors acknowledge Birgit Funke and Fei Dong for their helpful discussions. MSL is funded in part by the National Human Genome Research Institute (NHGRI; U01 HG006500 and U01 HG008676-01).

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  1. Departments of Pathology, Harvard Medical School and Brigham and Women’s Hospital, 75 Francis Str, Boston, MA, 02115, USA

    Heather Mason-Suares, Latrice Landry & Matthew S. Lebo

  2. Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, 65 Landsdowne Str., Cambridge, MA, 02115, USA

    Heather Mason-Suares, Latrice Landry & Matthew S. Lebo

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  1. Heather Mason-Suares
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Correspondence to Heather Mason-Suares.

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Mason-Suares, H., Landry, L. & S. Lebo, M. Detecting Copy Number Variation via Next Generation Technology. Curr Genet Med Rep 4, 74–85 (2016). https://doi.org/10.1007/s40142-016-0091-4

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