The ten year evolution of Sherloc, a points-based framework for genetic variant classification

ImportanceVariant classification underpins clinical genetics. We examine the impact of classification framework updates and the utility of semi-quantitative scores for interpreting variants of uncertain significance (VUS). ObjectiveEvaluate the impact of Sherloc on variant classification and VUS reduction in a large dataset. DesignCohort study using variant classification data generated between 2016 to 2025. Setting: A laboratory providing testing for hereditary genetic disorders. Participants5.5 million patients referred for genetic testing. Main Outcome(s) and Measure(s)We hypothesized that framework revisions would decrease the rate of VUS and improve accuracy. Outcome measures included changes in variant classification over time and in simulations. We further hypothesized that variant classification scores could predict the probability of pathogenicity and VUS reclassification rates. Outcome measures included the correlation of variant classification scores with the direction and rate of reclassification. ResultsSherloc has been used to classify 2.6 million variants across 19 successive versions (v4.2 to v7.1). Tracking 32,241 variants classified using v4.2, we found that 3,834 VUS had been reclassified by the end of the study period; 2801 (73%) of those relied on evidence criteria introduced after v4.2. In a simulation that removed various post-v4.2 evidence categories from 615,341 recent classifications, removing AI-related criteria alone changed 129,459 (21.04%) classifications. Classification reversals declined from 13/2909 (0.45%) in v4.2 to 7/61587 (0.011%) in v7.1, indicating improved accuracy concurrent with VUS reduction. The cumulative Sherloc score (pathogenic points minus benign points) strongly correlated with the log ratio of upgrades to downgrades (r{superscript 2} = 0.95), but less with the overall reclassification rate (r{superscript 2} = 0.49). Although VUS reclassified using RNA or cascade segregation analysis tended to have higher scores, the majority of reclassified variants had intermediate scores. Conclusion and RelevanceAdaptive, continuously updated classification systems that rapidly incorporate advances in clinical genetics, such as AI tools, reduce VUS and improve accuracy. Systematic performance monitoring, including reclassification tracking, enables data-driven improvements. A points-based framework like Sherloc represents progress toward probabilistic variant evaluation, which may lead to improved medical management.