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Genet Med. 2019 Sep;21(9):2135-2144. doi: 10.1038/s41436-019-0475-4. Epub 2019 Mar 20.

Atlas-CNV: a validated approach to call single-exon CNVs in the eMERGESeq gene panel.

Author information

1
Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA. tchiangbcm@gmail.com.
2
Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
3
Helix OpCo LLC, San Francisco, CA, USA.
4
Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
5
University of Washington Medical Center, Seattle, WA, USA.
6
Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA.
7
Children's Hospital of Philadelphia, Philadelphia, PA, USA.
8
Department of Medicine, Division of Nephrology, Columbia University, New York, NY, USA.
9
Irving Institute for Clinical and Translational Research, Columbia University, New York, NY, USA.
10
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
11
Baylor Genetics Laboratories, Houston, TX, USA.
12
Veritas Genetics, Danvers, MA, USA.
13
UTHealth School of Public Health, Houston, TX, USA.

Abstract

PURPOSE:

To provide a validated method to confidently identify exon-containing copy-number variants (CNVs), with a low false discovery rate (FDR), in targeted sequencing data from a clinical laboratory with particular focus on single-exon CNVs.

METHODS:

DNA sequence coverage data are normalized within each sample and subsequently exonic CNVs are identified in a batch of samples, when the target log2 ratio of the sample to the batch median exceeds defined thresholds. The quality of exonic CNV calls is assessed by C-scores (Z-like scores) using thresholds derived from gold standard samples and simulation studies. We integrate an ExonQC threshold to lower FDR and compare performance with alternate software (VisCap).

RESULTS:

Thirteen CNVs were used as a truth set to validate Atlas-CNV and compared with VisCap. We demonstrated FDR reduction in validation, simulation, and 10,926 eMERGESeq samples without sensitivity loss. Sixty-four multiexon and 29 single-exon CNVs with high C-scores were assessed by Multiplex Ligation-dependent Probe Amplification (MLPA).

CONCLUSION:

Atlas-CNV is validated as a method to identify exonic CNVs in targeted sequencing data generated in the clinical laboratory. The ExonQC and C-score assignment can reduce FDR (identification of targets with high variance) and improve calling accuracy of single-exon CNVs respectively. We propose guidelines and criteria to identify high confidence single-exon CNVs.

KEYWORDS:

Atlas-CNV; CNV; copy-number variation; single-exon deletion duplication; targeted gene panel clinical sequencing

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