Establishing an approach to reduce Sanger confirmation in NGS-based studies

Clinical diagnostic testing has been revolutionized by Next-Generation Sequencing (NGS). This powerful technology has been replacing Sanger sequencing, the gold standard methodology for clinical molecular diagnosis [1]. Currently, and given the complexity of NGS, no specific guidelines are addressing NGS quality parameters or providing concrete guidance for confirmatory analysis of NGS results [2]. Sanger sequencing is used as a broad methodology to validate each reported variant, however, these confirmations on all NGS-detected variants not only increase the test cost, but also delay the turnaround time [3]. Some studies [1-4] propose that independent confirmations of NGS findings are unnecessarily redundant and should not be required for high-quality variants. In this context, the aim of this work is to establish internal quality thresholds, above which Sanger confirmation of NGS variants will not be necessary.

A retrospective analysis of 500 Single Nucleotide Variants (SNVs), detected by NGS-based studies and further confirmed by Sanger sequencing, was conducted to determine quality thresholds based on specific variant metrics. Libraries were prepared using an oligonucleotide-based target capture chemistry by Agilent Technologies. Illumina next-generation sequencing was performed, and DRAGEN Enrichment was used for alignment and base calling.

References

1. Strom, S. P. et al. Assessing the necessity of confirmatory testing for exome-sequencing results in a clinical molecular diagnostic laboratory. Genet. Med. 16, 510–515 (2014);
2. Baudhuin, L. M. et al. Confirming Variants in Next-Generation Sequencing Panel Testing by Sanger Sequencing. J. Mol. Diagn. 17, 456–461 (2015);
3. Mu, W., Lu, H. M., Chen, J., Li, S. & Elliott, A. M. Sanger Confirmation Is Required to Achieve Optimal Sensitivity and Specificity in Next-Generation Sequencing Panel Testing. J. Mol. Diagn. 18, 923–932 (2016);
4. Bauer, P. et al. Development of an evidence-based algorithm that optimizes sensitivity and specificity in ES-based diagnostics of a clinically heterogeneous patient population. Genet. Med. 21, 53–61 (2019).