VarSight: Prioritizing Clinically Reported Variants with Binary Classification Algorithms

Motivation: In genomic medicine for rare disease patients, the primary goal is to identify one or more variants that cause their disease. Typically, this is done through filtering and then prioritization of variants for manual curation. However, prioritization of variants in rare disease patients remains a challenging task due to the high degree of variability in phenotype presentation and molecular source of disease. Thus, methods that can identify and/or prioritize variants to be clinically reported in the presence of such variability are of critical importance. Results: We tested the application of classification algorithms that ingest variant annotations along with phenotype information for predicting whether a variant will ultimately be clinically reported and returned to a patient. To test the classifiers, we performed a retrospective study on variants that were clinically reported to 237 patients in the Undiagnosed Diseases Network. We treated the classifiers as variant prioritization systems and compared them to four variant prioritization algorithms and two single-measure controls. We showed that these classifiers outperformed the other methods with the best classifiers ranking 72% of all reported variants and 94% of reported pathogenic variants in the top 20. Availability: The scripts used to generate results presented in this paper are available at https://github.com/HudsonAlpha/VarSight release v1.1. Contact: jholt@hudsonalpha.org