Key result
The disease-specific variant classifier CardioBoost achieved high discrimination accuracy (precision recall AUC 0.91 for cardiomyopathies; 0.96 for arrhythmias), outperforming existing tools.
Why the study?
Existing machine learning variant prioritization tools are imprecise and ignore gene-disease parameters such as gain- versus loss-of-function effects when interpreting rare variants.
Does CardioBoost improve pathogenicity prediction for rare missense variants in inherited cardiac conditions compared to existing genome-wide tools?
Does CardioBoost improve pathogenicity prediction for rare missense variants in inherited cardiac conditions compared to existing genome-wide tools?
A disease-specific machine learning classifier, CardioBoost, significantly improves the accuracy of pathogenicity prediction for rare missense variants in inherited cardiomyopathies and arrhythmias compared to existing genome-wide tools.
May aid variant interpretation in inherited cardiac conditions; leaves open prospective clinical validation before practice change.
PURPOSE: Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning variant prioritization tools are imprecise and ignore important parameters defining gene-disease relationships, e.g., distinct consequences of gain-of-function versus loss-of-function variants. We hypothesized that incorporating disease-specific information would improve tool performance. METHODS: We developed a disease-specific variant classifier, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias. We assessed CardioBoost's ability to discriminate known pathogenic from benign variants, prioritize disease-associated variants, and stratify patient outcomes. RESULTS: CardioBoost has high global discrimination accuracy (precision recall area under the curve [AUC] 0.91 for cardiomyopathies; 0.96 for arrhythmias), outperforming existing tools (4-24% improvement). CardioBoost obtains excellent accuracy (cardiomyopathies 90.2%; arrhythmias 91.9%) for variants classified with >90% confidence, and increases the proportion of variants classified with high confidence more than twofold compared with existing tools. Variants classified as disease-causing are associated with both disease status and clinical severity, including a 21% increased risk (95% confidence interval [CI] 11-29%) of severe adverse outcomes by age 60 in patients with hypertrophic cardiomyopathy. CONCLUSIONS: A disease-specific variant classifier outperforms state-of-the-art genome-wide tools for rare missense variants in inherited cardiac conditions ( https://www.cardiodb.org/cardioboost/ ), highlighting broad opportunities for improved pathogenicity prediction through disease specificity.
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Zhang et al. (2020) studied Inherited cardiomyopathies and arrhythmias. CardioBoost (disease-specific variant classifier) vs. Existing state-of-the-art genome-wide tools was evaluated on Global discrimination accuracy (precision recall area under the curve [AUC]). The disease-specific variant classifier CardioBoost achieved high discrimination accuracy (precision recall AUC 0.91 for cardiomyopathies; 0.96 for arrhythmias), outperforming existing tools.
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