Cardiovascular Disease Pathogenicity Predictor (CVD-PP): A tissue-specific tool for discriminating pathogenicity cardiovascular disease gene variants

Abstract

Introduction. The interpretation of variants of uncertain significance (VUS) remains a challenge in the care of patients with established or familial cardiovascular diseases. 56% of potential variants within known cardiovascular risk genes are characterized as VUS and unbiased machine learning algorithms trained upon large data resources can stratify VUS into higher vs. lower probability of contributing to a cardiovascular disease phenotype. Methods. ClinVar pathogenic or likely pathogenic (P/LP) and benign or likely benign (B/LB) from 47 genes previously associated with monogenic cardiovascular diseases (MCVDs) were identified. A random forest model was trained using six-fold cross validation on these variants to build a predictive model of variant pathogenicity using measures of evolutionary constraint, deleteriousness, splicogenicity, local pathogenic variation, cardiac-specific exon expression, and population allele frequency. Predicted pathogenicity was computed as a linear outcome coupled with a naïve Bayes classifier to determine an optimal cut-off to distinguish VUS of pathogenic interest versus VUS with low likelihood of pathogenicity. Performance of our model was validated using variants for which ClinVar pathogenicity assignment changed between 2014 to 2022. As a proof-of-concept we demonstrated the utility of our model in the (CATHeterization GENetics [CATHGEN]) cohort. Results. Random forest identified a top-ranked model using ClinVar known P/LP and B/LB variants that weighted evolutionary constraint (CADD score) most heavily. The model accurately prioritized variants for which ClinVar clinical significance had changed from 2014 to 2022 (precision recall AUC = 0.97) and had equal or greater performance when compared to conventional in-silico methods for predicting variant pathogenicity. In the CATHGEN cohort, there was a higher burden of VUS of pathogenic interest in individuals with DCM as compared to controls without DCM (p = 8.2x10− 15). Individuals in CATHGEN who harbored model predicted and ACMG/AMP reviewed pathogenic VUSs demonstrated that 27.6% had clinical evidence of the relevant disease. Lastly, variant prioritization using this model provided genetic diagnosis in 11.9% of CATHGEN patients diagnosed with HCM clinically who did not harbor a HCM genetic P/LP variant by initial ACMG/AMP review. Conclusion. We have developed a cardiac-specific model for prioritizing variants underlying familial cardiovascular disease syndromes. CVD-PP proves to have high performance in discriminating pathogenicity of VUS in MCVD genes. ACMG/AMP review and phenotyping of individuals carrying VUS of pathogenic interest in a large cardiovascular cohort support the clinical utility of this model.