PURPOSE: Internationally adopted variant interpretation guidelines from the 
American College of Medical Genetics and Genomics (ACMG) are generic and require 
disease-specific refinement. Here we developed CardioClassifier ( 
http://www.cardioclassifier.org ), a semiautomated decision-support tool for 
inherited cardiac conditions (ICCs).
METHODS: CardioClassifier integrates data retrieved from multiple sources with 
user-input case-specific information, through an interactive interface, to 
support variant interpretation. Combining disease- and gene-specific knowledge 
with variant observations in large cohorts of cases and controls, we refined 14 
computational ACMG criteria and created three ICC-specific rules.
RESULTS: We benchmarked CardioClassifier on 57 expertly curated variants and 
show full retrieval of all computational data, concordantly activating 87.3% of 
rules. A generic annotation tool identified fewer than half as many clinically 
actionable variants (64/219 vs. 156/219, Fisher's P = 1.1  ×  10-18), with 
important false positives, illustrating the critical importance of disease and 
gene-specific annotations. CardioClassifier identified putatively 
disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with 
leading ICC laboratories. Through addition of manually curated data, variants 
found in over 40% of cardiomyopathy cases are fully annotated, without requiring 
additional user-input data.
CONCLUSION: CardioClassifier is an ICC-specific decision-support tool that 
integrates expertly curated computational annotations with case-specific data to 
generate fast, reproducible, and interactive variant pathogenicity reports, 
according to best practice guidelines.