Oncology

Detection of structural variation using target captured next-generation sequencing data for genetic diagnostic testing

Abstract

Purpose

Structural variation (SV) is associated with inherited diseases. Next-generation sequencing (NGS) is an efficient method for SV detection because of its high-throughput, low cost, and base-pair resolution. However, due to lack of standard NGS protocols and a limited number of clinical samples with pathogenic SVs, comprehensive standards for SV detection, interpretation, and reporting are to be established.

Methods

We performed SV assessment on 60,000 clinical samples tested with hereditary cancer NGS panels spanning 48 genes. To evaluate NGS results, NGS and orthogonal methods were used separately in a blinded fashion for SV detection in all samples.

Results

A total of 1,037 SVs in coding sequence (CDS) or untranslated regions (UTRs) and 30,847 SVs in introns were detected and validated. Across all variant types, NGS shows 100% sensitivity and 99.9% specificity. Overall, 64% of CDS/UTR SVs were classified as pathogenic/likely pathogenic, and five deletions/duplications were reclassified as pathogenic using breakpoint information from NGS.

Conclusion

The SVs presented here can be used as a valuable resource for clinical research and diagnostics. The data illustrate NGS as a powerful tool for SV detection. Application of NGS and confirmation technologies in genetic testing ensures delivering accurate and reliable results for diagnosis and patient care.

  • Authors: Wenbo Mu; Bing Li; Sitao Wu; Jefferey Chen; Divya Shah; Dong Xu; Mary Helen Black; Rachid Karam; Katrina Gillespie; Kelly D. Farwell Hagman; Lucia Guiduglia; Melissa Pronold; Aaron Elliott; Hsiao-Mei Lu
  • Journal: Genetic Medicine
  • Date: 2018 - Dec

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