Clinical significance of genetic variation in hypertrophic cardiomyopathy: comparison of computational tools to prioritize missense variants

Pedro Barbosa; Marta Ribeiro; Maria Carmo-Fonseca; Alcides Fonseca

doi:10.3389/fcvm.2022.975478

Clinical significance of genetic variation in hypertrophic cardiomyopathy: comparison of computational tools to prioritize missense variants

Front Cardiovasc Med. 2022 Aug 18:9:975478. doi: 10.3389/fcvm.2022.975478. eCollection 2022.

Authors

Pedro Barbosa^{1

2}, Marta Ribeiro³, Maria Carmo-Fonseca², Alcides Fonseca^{1

4}

Affiliations

¹ LASIGE, Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal.
² Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal.
³ Department of Bioengineering and iBB-Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal.
⁴ GenoMed - Diagnósticos de Medicina Molecular, Lisboa, Portugal.

Abstract

Hypertrophic cardiomyopathy (HCM) is a common heart disease associated with sudden cardiac death. Early diagnosis is critical to identify patients who may benefit from implantable cardioverter defibrillator therapy. Although genetic testing is an integral part of the clinical evaluation and management of patients with HCM and their families, in many cases the genetic analysis fails to identify a disease-causing mutation. This is in part due to difficulties in classifying newly detected rare genetic variants as well as variants-of-unknown-significance (VUS). Multiple computational algorithms have been developed to predict the potential pathogenicity of genetic variants, but their relative performance in HCM has not been comprehensively assessed. Here, we compared the performance of 39 currently available prediction tools in distinguishing between high-confidence HCM-causing missense variants and benign variants, and we developed an easy-to-use-tool to perform variant prediction benchmarks based on annotated VCF files (VETA). Our results show that tool performance increases after HCM-specific calibration of thresholds. After excluding potential biases due to circularity type I issues, we identified ClinPred, MISTIC, FATHMM, MPC and MetaLR as the five best performer tools in discriminating HCM-associated variants. We propose combining these tools in order to prioritize unknown HCM missense variants that should be closely followed-up in the clinic.

Keywords: computational pathogenicity prediction; genetic testing; hypertrophic cardiomyopathy; missense variant interpretation; prediction tool comparison; variants-of-unknown-significance.