With genetic testing advancements, the burden of incidental cardiac gene variants is rising. Cardiovascular disease-associated variants carry a risk of sudden cardiac death (SCD), highlighting the need for accurate diagnostic interpretation. We sought to identify pathogenic hotspots in SCD-associated genes using amino acid-level signal-to-noise (S:N) and to develop a web-based precision medicine tool, DiscoVari, to improve variant evaluation. The minor allele frequency (MAF) of putatively pathogenic variants was derived from cohort-based cardiomyopathy and channelopathy studies in the literature. We normalized disease-associated MAFs to rare variants in an ostensibly healthy population (gnomAD) to calculate amino acid-level S:N. Amino acids with S:N above the gene-specific threshold were defined as hotspots. We validated the ability of DiscoVari to identify pathogenic variants using variants from ClinVar and individuals clinically evaluated at Duke with cardiac genetic testing. DiscoVari was built using JavaScript ES6 and utilizing open-source JavaScript library ReactJS, web development framework Next.js, and JavaScript runtime NodeJS. A higher proportion of ClinVar likely pathogenic/pathogenic (LP/P) variants localized to DiscoVari hotspots (43.1%) than likely benign/benign (LB/B) variants (17.8%, P < 0.0001). Further, 75.3% of ClinVar variants reclassified to LP/P were in hotspots, compared to 41.3% of those reclassified as variants of uncertain significance (P < 0.0001) and 23.4% of those reclassified as LB/B (P < 0.0001). Of the clinical cohort variants, 72.9% of LP/P were in hotspots, compared to 19.6% of LB/B (P < 0.0001). DiscoVari reliably identifies disease-susceptible amino acid residues to evaluate variants by searching amino acid-specific S:N ratios.