Background: Actually, about 2000 sequence variations have been documented in the CFTR gene requiring extensive and multi-step genetic testing in the diagnosis of cystic fibrosis and CFTRrelated disorders. We present a two phases study, with validation and performance monitoring, of a single experiment methodology based on multiplex PCR and high throughput sequencing that allows detection of all variants, including large rearrangements, affecting the coding regions plus three deep intronic loci. Methods: A total of 340 samples, including 257 patients and 83 previously characterized control samples, were sequenced in 17 MiSeq runs and analyzed with two bioinformatic pipelines in routine diagnostic conditions. We obtained 100% coverage for all the target regions in every tested sample. Results: We correctly identified all the 87 known variants in the control samples and successfully confirmed the 62 variants identified among the patients without observing false positive results. Large rearrangements were identified in 18/18 control samples. Only 17 patient samples showed false positive signals (6.6%), 12 of which showed a borderline result for a single amplicon. We also demonstrated the ability of the assay to detect allele specific dropout of amplicons when a sequence variation occurs at a primer binding site thus limiting the risk for false negative results. Conclusions: We described here the first NGS workflow for CFTR routine analysis that demonstrated equivalent diagnostic performances compared to Sanger sequencing and multiplex ligation-dependent probe amplification. This study illustrates the advantages of NGS in term of scalability, workload reduction and cost-effectiveness in combination with an improvement of the overall data quality due to the simultaneous detection of SNVs and large rearrangements.
CITATION STYLE
Pagin, A., Devos, A., Figeac, M., Truant, M., Willoquaux, C., Broly, F., & Lalau, G. (2016). Applicability and efficiency of NGS in routine diagnosis: In-depth performance analysis of a complete workflow for CFTR mutation analysis. PLoS ONE, 11(2). https://doi.org/10.1371/journal.pone.0149426
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