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.

Francisco M. De La Vega, Pavana Anur, Kelly Potts, Lewis Kraft, Raul Torres, and Peter Kang are or were employees and received stock from Tempus AI, Inc. Sean A. Irvine was a subcontractor to Tempus AI, Inc. through NetValue Ltd. Sean Truong, Yeonghun Lee, Shunhua Han, Vitor Onuchic, and James Han are employees and received stock from Illumina, Inc.

This study did not receive any funding

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

All data produced in the present study are available upon reasonable request to the authors