Accurate clinical detection of exon copy number variants in a targeted NGS panel using DECoN

Abstract

Background: Targeted next generation sequencing (NGS) panels are increasingly being used in clinical genomics to increase capacity, throughput and affordability of gene testing. Identifying whole exon deletions or duplications (termed exon copy number variants, ‘exon CNVs’) in exon-targeted NGS panels has proved challenging, particularly for single exon CNVs. Methods: We developed a tool for the Detection of Exon Copy Number variants (DECoN), which is optimised for analysis of exon-targeted NGS panels in the clinical setting. We evaluated DECoN performance using 96 samples with independently validated exon CNV data. We performed simulations to evaluate DECoN detection performance of single exon CNVs and to evaluate performance using different coverage levels and sample numbers. Finally, we implemented DECoN in a clinical laboratory that tests BRCA1 and BRCA2 with the TruSight Cancer Panel (TSCP). We used DECoN to analyse 1,919 samples, validating exon CNV detections by multiplex ligation-dependent probe amplification (MLPA). Results: In the evaluation set, DECoN achieved 100% sensitivity and 99% specificity for BRCA exon CNVs, including identification of 8 single exon CNVs. DECoN also identified 14/15 exon CNVs in 8 other genes. Simulations of all possible BRCA single exon CNVs gave a mean sensitivity of 98% for deletions and 95% for duplications. DECoN performance remained excellent with different levels of coverage and sample numbers; sensitivity and specificity was >98% with the typical NGS run parameters. In the clinical pipeline, DECoN automatically analyses pools of 48 samples at a time, taking 24 minutes per pool, on average. DECoN detected 24 BRCA exon CNVs, of which 23 were confirmed by MLPA, giving a false discovery rate of 4%. Specificity was 99.7%. Conclusions: DECoN is a fast, accurate, exon CNV detection tool readily implementable in research and clinical NGS pipelines. It has high sensitivity and specificity and acceptable false discovery rate. DECoN is freely available at www.icr.ac.uk/decon.

Benchmark of tools for CNV detection from NGS panel data in a genetic diagnostics context

“…Five tools were tested in the benchmark: CoNVaDING v1.2.0 (Johansson et al, 2016), DECoN v1.0.1 (Fowler et al, 2016), panelcn.MOPS v1.0.0 (Povysil et al, 2017), ExomeDepth v1.1.10 (Plagnol et al, 2012) and CODEX2 v1.2.0 (Jiang et al, 2018).…”

“…The regions of interest (ROIs) were dependent on the dataset. For TruSight based datasets, ICR96 and panelcnDataset, we used the targets bed file published elsewhere (Fowler et al, 2016) with some modifications: the fourth column was removed, the gene was added and file was sorted by chromosome and start position (Supplementary File 2). For in-house datasets, we generated a target bed file containing all coding exons from all protein-coding transcripts of genes in the I2HCP panel v2.1 (Supplementary File 3).…”

“…After a literature search process, we selected five CNV calling tools to be evaluated ( Table 2), all of them based on depth-of-coverage analysis. Three tools have been reported to perform well on NGS panel data at single-exon resolution: CoNVaDING v1.2.0 (Johansson et al, 2016), DECoN v1.0.1 (Fowler et al, 2016) and panelcn.MOPS v1.0.0 (Povysil et al, 2017). ExomeDepth v1.1.10 (Plagnol et al, 2012) was included due to its high performance in benchmarks on WES data (de Ligt et al, 2013;Sadedin et al, 2018) and because the developers reported good performance with panel data (https://github.com/vplagnol/ExomeDepth).…”

“…Other benchmarks of CNV calling tools on targeted NGS panel data have been published. However, they were performed by the authors of the tools and executed against a single dataset (Johansson et al, 2016;Fowler et al, 2016;Povysil et al, 2017;Kim et al, 2017;Chiang et al, 2019), or used mainly simulated data with a small number of validated CNVs (Roca et al, 2019). The aim of this work is to perform an independent benchmark of multiple CNV calling tools, optimizing and evaluating them against multiple datasets generated in diagnostics settings, to identify the most suitable tools to be used for genetic diagnostics (Figure 1).…”

Atlas-CNV: a validated approach to call Single-Exon CNVs in the eMERGESeq gene panel

“…The ability to detect CNVs accurately is critical for both genetic diagnostics and to advance understanding their impact on gene function. Next-generation sequencing (NGS) based targeted gene panels are commonly used in clinical genetic testing and various methods [5][6][7][8][9][10][11][12][13] have been developed to identify exonic CNVs in gene panel sequence data. Gene panels afford a qualitatively different opportunity to assess small CNVs due to their typically deeper sequence coverage when compared with Whole Genome or Exome Sequencing (WES).…”