Assessment of an automated approach for variant interpretation in screening for monogenic disorders: A single‐center study. Issue 12 (5th November 2022) - Gall, Bryan J.; Smart, Trevor B.; Munch, Robin; Kolluri, Supraja; Tadepally, Hamsa; Lim, Karen Phaik Har; Demko, Zachary P.; Benn, Peter; Souter, Vivienne; Sanapareddy, Nina; Keen‐Kim, Dianne
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. Abstract : 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.