Benchmarking of Germline Copy Number Variant Callers from Whole Genome Sequencing Data for Clinical Applications

Whole-genome sequencing (WGS) is increasingly favored over other genomic sequencing methods for clinical applications due to its comprehensive coverage and declining costs. WGS is particularly useful for the detection of copy number variants (CNVs), presumed to be more accurate than targeted sequencing assays such as WES or gene panels, because it can identify breakpoints in addition to changes in coverage depth. Recent advancements in bioinformatics tools, including those employing hardware acceleration and machine learning, have enhanced CNV detection. Although numerous benchmarking studies have been published, primarily focusing on open-source tools for short-read WGS CNV calling, systematic evaluations that encompass commercially available tools that meet the rigorous demands of clinical testing are still necessary. In clinical settings, where the confirmation of reported CNVs is often required, there is a higher priority on sensitivity over specificity/precision compared to research applications. Moreover, clinical gene panel reporting primarily concerns whether a CNV affects coding regions or, in some cases, promoters, rather than the precise detection of breakpoints. This study aims to benchmark the performance of various CNV detection tools tailored for clinical reporting from WGS using reference cell lines, providing insights critical for optimizing clinical diagnostics. Our results indicate that while different tools exhibit strengths in either sensitivity or precision and are better suited for certain classes and lengths of variants, few can deliver the balanced performance essential for clinical testing, where high sensitivity is imperative. Generally, callers demonstrate better performance for deletions than duplications, with the latter being poorly detected in events shorter than 5kb. We demonstrate that the DRAGEN™ v4.2 CNV caller, particularly with custom filters on its high sensitivity mode, offers a superior balance of sensitivity and precision compared to other available tools.