Evaluation of an Automated Genome Interpretation Model for Rare Disease Routinely Used in a Clinical Genetic Lab.

PURPOSE: The analysis of exome and genome sequencing data for the diagnosis of rare diseases is challenging and time-consuming. In this study, we evaluated a machine learning model for automating variant prioritization for diagnosing rare genetic diseases in the Baylor Genetics clinical laboratory.

METHODS: The automated analysis model was developed using a supervised learning approach based on thousands of manually curated variants. The model was evaluated on two cohorts. The model accuracy was determined using a retrospective cohort comprised of 180 randomly selected exome cases (57 singletons, 123 trios), all of which were previously diagnosed and solved by manual interpretation. Diagnostic yield with the modified workflow was estimated using a prospective "production" cohort of 334 consecutive clinical cases.

RESULTS: The model accurately pinpointed all manually reported variants as candidates. The reported variants were ranked in top-ten candidate variants in 98.4% (121/123) of trio cases, in 93.0% (53/57) of single proband cases, and 96.7% (174/180) of all cases. The accuracy of the model was reduced in some cases due to incomplete variant calling (e.g., copy number variants) or incomplete phenotypic description.

CONCLUSION: The automated model for case analysis assists clinical genetic laboratories in prioritizing candidate variants effectively. The use of such technology may facilitate the interpretation of genomic data for a large number of patients in the era of precision medicine.