Assessment of an automated approach for variant interpretation in screening for monogenic disorders: A single-center study

Abstract

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.

Keywords: automated variant classification; genetic testing; manual curation; monogenic disorder; variant classification; variant interpretation; variants of uncertain significance.