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

Area of Science:

• Genomics
• Molecular Diagnostics
• Bioinformatics

Background:

• Debate exists on next-generation sequencing (NGS) variant identification accuracy and confirmatory testing needs.
• Current diagnostic workflows lack a standardized quality threshold for discriminating false positives.
• Confirmatory testing adds cost and complexity to clinical gene panel testing.

Purpose of the Study:

• To empirically determine a quality threshold for Sanger sequencing validation.
• To reduce experimental costs in clinical gene panel testing.
• To establish a reliable workflow for accurate variant identification.

Main Methods:

• A hybridization capture-based protocol was developed and tested using 278 model genes.
• Filtering processes were implemented to remove mismapped reads and strand-biased data.
• Seven samples from the 1000 Genomes Project were analyzed to identify single-nucleotide polymorphisms (SNPs) and insertions/deletions (indels).

Main Results:

• A zero false-positive threshold was determined at a quality score > 1000.
• The workflow identified 2750 SNPs and 142 indels with high accuracy.
• Sensitivity reached 100% for most variants, except for deletions in repetitive sequences.
• 95.6% of SNPs and 50.7% of indels met the established criteria.

Conclusions:

• The established quality threshold effectively discriminates reliable variants from those needing validation.
• This approach reconciles cost minimization with quality maximization in clinical gene panel testing.
• The workflow provides a practical solution for accurate and efficient variant detection in diagnostics.