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

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. Here, we performed a comparative analysis of a fully automated curation of variant classification versus a process that included an additional manual component. The comparison was carried out for a set of variants where there was high confidence in their pathogenic classification, based on ClinVar entries. We found that a high proportion of automated interpretations (22.6% of positive and 1.7% of negative variants) were reclassified when there was a manual review. We conclude that manual review of the output from the automated variant classifier is currently essential.