Detection of Copy Number Variants using Targeted Next-Generation Sequencing Data from the Inherited Disease Panel

Abstract

Background: Copy number variants (CNVs) are linked to numerous genomic disorders, especially those involving dosage-sensitive genes. While chromosomal microarrays have been the standard method for CNV detection, next-generation sequencing (NGS) allows for CNV detection with improved resolution. The Inherited Disease Panel (IDP) is a custom Ion AmpliSeq™ panel targeting 500 genes associated with a range of single-gene disorders. This study aimed to develop a baseline for a CNV-detection workflow specific to samples sequenced using the IDP and to assess the workflow’s utility as a complementary analysis tool. Methods: This study used NGS data derived from patient samples that were negative for single nucleotide variants. Three custom read depth baselines were created in Ion ReporterTM, namely: a 10-sample baseline, a 48-sample baseline, and a uniform-read-depth baseline using 18 samples with mean read depths of between 200x and 400x. Their performance was tested by incorporating each into a workflow and analysing five samples with known CNVs. The best-performing workflow was further applied to 78 samples. The resulting CNV calls were filtered by genes and confidence scores. Candidate CNVs were then classified according to the American College of Medical Genetics and ClinGen guidelines. Results: The known CNVs were detected in all five validation samples. The workflow incorporating the uniform-read-depth baseline was selected for all subsequent analyses. Analysis of the IDP samples identified four potentially clinically relevant CNVs, including pathogenic deletions in the TCOF1 and NIPBL genes, as well as a whole X-chromosome duplication. CNVs of interest all had high confidence and matching precision. Analyses of samples with mean read depths 200–400x showed improved specificity compared to samples outside this range. Conclusions: This CNV-detection method demonstrated potential as a supplementary analysis tool, with a diagnostic yield of ~4.3% (3/70). A few limitations were identified, particularly with samples exhibiting aberrant CNV calls and genes with recurrent CNVs across multiple samples, although manual prioritization strategies were able to counteract these. This method may be useful as a screening tool; however, all clinically relevant CNVs require validation before reporting.