Clinical Validation of Copy Number Variant Detection from Targeted Next-Generation Sequencing Panels.

Area of Science:

• Clinical Genetics
• Genomic Medicine
• Molecular Diagnostics

Background:

• Next-generation sequencing (NGS) is standard for genetic variation analysis but struggles with copy number variations (CNVs) >50 bp.
• Detecting CNVs often requires additional labor-intensive and costly methods like microarray or MLPA.
• CNVs are significant mutational drivers in many genetic disorders, necessitating accurate detection.

Purpose of the Study:

• To clinically validate a novel algorithm for detecting CNVs using targeted NGS gene panel data.
• To establish NGS as a standalone, first-tier method for both sequence variants and CNVs.
• To reduce labor, cost, and turnaround time in clinical genetic testing.

Main Methods:

• Application of a novel CNV detection algorithm to targeted clinical NGS gene panel data.
• Retrospective analysis of 391 samples and prospective analysis of 2375 samples.
• Validation across nine distinct targeted NGS gene panels.

Main Results:

• Achieved 100% sensitivity (95% CI, 89%-100%) for 37 unique CNV events.
• Demonstrated high specificity in detecting CNVs across multiple NGS panels.
• Successfully applied the pipeline to complex genomic regions and mitochondrial heteroplasmy.

Conclusions:

• The validated NGS CNV pipeline offers a stand-alone, efficient first-tier assessment for CNVs and sequence variants.
• This approach obviates the need for parallel traditional CNV detection methods, streamlining clinical workflows.
• The pipeline's versatility extends to complex genomic regions and mitochondrial DNA analysis.