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ABSTRACT
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.
Keywords
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Article info
Publication history
Accepted:
March 12,
2023
Received in revised form:
March 9,
2023
Received:
February 8,
2022
Publication stage
In Press Accepted ManuscriptIdentification
Copyright
© 2023 Published by Elsevier Inc. on behalf of American College of Medical Genetics and Genomics.
