Assessing the Accuracy of Variant Detection in Cost-Effective Gene Panel Testing by Next-Generation Sequencing

Abstract

There is significant debate within the diagnostics community regarding the accuracy of variant identification by next-generation sequencing and the necessity of confirmatory testing of detected variants. Because the quality threshold to discriminate false positives depends on the workflow, no regulatory standard regarding this matter has yet been published. The goal of this study was to empirically determine the threshold to perform additional Sanger sequencing and to reduce the experimental cost to a practical level. Using 278 model genes, a hybridization capture-based protocol was examined to meet the clinical requirements of low cost, high efficiency, and high-quality data. To reduce excessive false-positive detection, filtering processes were introduced to remove mismapped reads and strand-biased detection to a published best-practices pipeline. With seven samples from the 1000 Genomes Project, 2750 single-nucleotide polymorphisms and 142 insertions/deletions were identified by our designed workflow. Compared with variants registered in the single nucleotide polymorphism database (dbSNP), a zero false-positive threshold value was determined (quality score > 1000). The variants satisfying these criteria accounted for 95.6% of single-nucleotide polymorphisms and 50.7% of insertions/deletions. Except for deletions located within the highly repeated sequences, the workflow achieved 100% sensitivity. The established threshold allowed us to discriminate between convincing variants and those requiring validation, a design that reconciles the competing objectives of cost minimization and quality maximization of clinical gene panel testing.