Related Experiment Video
Updated: Aug 23, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
Published on: April 4, 2018
Assessment of an automated approach for variant interpretation in screening for monogenic disorders: A single-center
Bryan J Gall1, Trevor B Smart1, Robin Munch1
1Natera Inc., Austin, Texas, USA.
Background:
Automation has been introduced into variant interpretation, but it is not known how automated variant interpretation performs on a stand-alone basis. The purpose of this study was to evaluate a fully automated computerized approach.
Method:
We reviewed all variants encountered in a set of carrier screening panels over a 1-year interval. Observed variants with high-confidence ClinVar interpretations were included in the analysis; those without high-confidence ClinVar entries were excluded.
Results:
Discrepancy rates between automated interpretations and high-confidence ClinVar entries were analyzed. Of the variants interpreted as positive (likely pathogenic or pathogenic) based on ClinVar information, 22.6% were classified as negative (variants of uncertain significance, likely benign or benign) variants by the automated method. Of the ClinVar negative variants, 1.7% were classified as positive by the automated software. On a per-case basis, which accounts for variant frequency, 63.4% of cases with a ClinVar high-confidence positive variant were classified as negative by the automated method.
Conclusion:
While automation in genetic variant interpretation holds promise, there is still a need for manual review of the output. Additional validation of automated variant interpretation methods should be conducted.
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