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. 2019 Oct 15;20(1):496.
doi: 10.1186/s12859-019-3026-8.

VarSight: prioritizing clinically reported variants with binary classification algorithms

James M Holt  1 , Brandon Wilk  2 , Camille L Birch  2 , Donna M Brown  2 , Manavalan Gajapathy  2 , Alexander C Moss  2 , Nadiya Sosonkina  2   3 , Melissa A Wilk  2 , Julie A Anderson  2 , Jeremy M Harris  2 , Jacob M Kelly  2 , Fariba Shaterferdosian  2 , Angelina E Uno-Antonison  2 , Arthur Weborg  2 ; Undiagnosed Diseases Network; Elizabeth A Worthey  2
Collaborators, Affiliations

VarSight: prioritizing clinically reported variants with binary classification algorithms

James M Holt et al. BMC Bioinformatics. .

Abstract

Background: When applying genomic medicine to a rare disease patient, the primary goal is to identify one or more genomic variants that may explain the patient's phenotypes. Typically, this is done through annotation, 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.

Methods: 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.

Results: We treated the classifiers as variant prioritization systems and compared them to four variant prioritization algorithms and two single-measure controls. We showed that the trained classifiers outperformed all other tested methods with the best classifiers ranking 72% of all reported variants and 94% of reported pathogenic variants in the top 20.

Conclusions: We demonstrated how freely available binary classification algorithms can be used to prioritize variants even in the presence of real-world variability. Furthermore, these classifiers outperformed all other tested methods, suggesting that they may be well suited for working with real rare disease patient datasets.

Keywords: Binary classification; Clinical genome sequencing; Variant prioritization.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Receiver operator and precision-recall curves. These figures show the performance of the four classifiers on the testing set after hyperparameter tuning and fitting to the training set. On the left, we show the receiver operator curve (false positive rate against the true positive rate). On the right, we show the precision recall curve. Area under the curve (AUROC or AUPRC) is reported beside each method in the legend
See this image and copyright information in PMC

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