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

Theodore Chiang, Xiuping Liu, Tsung Jung Wu, Jianhong Hu, Fritz J. Sedlazeck, Simon White, Daniel Schaid, Mariza de Andrade, Gail P. Jarvik, David Crosslin, Ian Stanaway, David S. Carrell, John J. Connolly, Hakon Hakonarson, Emily E. Groopman, Ali G. Gharavi, Alexander Fedotov, Weimin Bi, Magalie S. Leduc, David R. MurdockYunyun Jiang, Linyan Meng, Christine M. Eng, Shu Wen, Yaping Yang, Donna M. Muzny, Eric Boerwinkle, William Salerno, Eric Venner, Richard A. Gibbs

Research output: Contribution to journalArticle

1 Citation (Scopus)

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 log 2 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.

Original languageEnglish (US)
JournalGenetics in Medicine
DOIs
StatePublished - Jan 1 2019

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Atlases
Exons
Genes
Ligation
Software
Guidelines

Keywords

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

ASJC Scopus subject areas

  • Genetics(clinical)

Cite this

Chiang, T., Liu, X., Wu, T. J., Hu, J., Sedlazeck, F. J., White, S., ... Gibbs, R. A. (2019). Atlas-CNV: a validated approach to call single-exon CNVs in the eMERGESeq gene panel. Genetics in Medicine. https://doi.org/10.1038/s41436-019-0475-4

Atlas-CNV : a validated approach to call single-exon CNVs in the eMERGESeq gene panel. / Chiang, Theodore; Liu, Xiuping; Wu, Tsung Jung; Hu, Jianhong; Sedlazeck, Fritz J.; White, Simon; Schaid, Daniel; Andrade, Mariza de; Jarvik, Gail P.; Crosslin, David; Stanaway, Ian; Carrell, David S.; Connolly, John J.; Hakonarson, Hakon; Groopman, Emily E.; Gharavi, Ali G.; Fedotov, Alexander; Bi, Weimin; Leduc, Magalie S.; Murdock, David R.; Jiang, Yunyun; Meng, Linyan; Eng, Christine M.; Wen, Shu; Yang, Yaping; Muzny, Donna M.; Boerwinkle, Eric; Salerno, William; Venner, Eric; Gibbs, Richard A.

In: Genetics in Medicine, 01.01.2019.

Research output: Contribution to journalArticle

Chiang, T, Liu, X, Wu, TJ, Hu, J, Sedlazeck, FJ, White, S, Schaid, D, Andrade, MD, Jarvik, GP, Crosslin, D, Stanaway, I, Carrell, DS, Connolly, JJ, Hakonarson, H, Groopman, EE, Gharavi, AG, Fedotov, A, Bi, W, Leduc, MS, Murdock, DR, Jiang, Y, Meng, L, Eng, CM, Wen, S, Yang, Y, Muzny, DM, Boerwinkle, E, Salerno, W, Venner, E & Gibbs, RA 2019, 'Atlas-CNV: a validated approach to call single-exon CNVs in the eMERGESeq gene panel', Genetics in Medicine. https://doi.org/10.1038/s41436-019-0475-4
Chiang, Theodore ; Liu, Xiuping ; Wu, Tsung Jung ; Hu, Jianhong ; Sedlazeck, Fritz J. ; White, Simon ; Schaid, Daniel ; Andrade, Mariza de ; Jarvik, Gail P. ; Crosslin, David ; Stanaway, Ian ; Carrell, David S. ; Connolly, John J. ; Hakonarson, Hakon ; Groopman, Emily E. ; Gharavi, Ali G. ; Fedotov, Alexander ; Bi, Weimin ; Leduc, Magalie S. ; Murdock, David R. ; Jiang, Yunyun ; Meng, Linyan ; Eng, Christine M. ; Wen, Shu ; Yang, Yaping ; Muzny, Donna M. ; Boerwinkle, Eric ; Salerno, William ; Venner, Eric ; Gibbs, Richard A. / Atlas-CNV : a validated approach to call single-exon CNVs in the eMERGESeq gene panel. In: Genetics in Medicine. 2019.
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T2 - a validated approach to call single-exon CNVs in the eMERGESeq gene panel

AU - Chiang, Theodore

AU - Liu, Xiuping

AU - Wu, Tsung Jung

AU - Hu, Jianhong

AU - Sedlazeck, Fritz J.

AU - White, Simon

AU - Schaid, Daniel

AU - Andrade, Mariza de

AU - Jarvik, Gail P.

AU - Crosslin, David

AU - Stanaway, Ian

AU - Carrell, David S.

AU - Connolly, John J.

AU - Hakonarson, Hakon

AU - Groopman, Emily E.

AU - Gharavi, Ali G.

AU - Fedotov, Alexander

AU - Bi, Weimin

AU - Leduc, Magalie S.

AU - Murdock, David R.

AU - Jiang, Yunyun

AU - Meng, Linyan

AU - Eng, Christine M.

AU - Wen, Shu

AU - Yang, Yaping

AU - Muzny, Donna M.

AU - Boerwinkle, Eric

AU - Salerno, William

AU - Venner, Eric

AU - Gibbs, Richard A.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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 log 2 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.

AB - 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 log 2 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.

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KW - single-exon deletion duplication

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