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VariantInterpretationEngine: ACMG/AMP Classification, VUS Resolution, and Functional Evidence Integration
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Clinical variant interpretation requires systematic application of ACMG/AMP guidelines to classify variants as pathogenic, likely pathogenic, VUS, likely benign, or benign. We present VariantInterpretationEngine, a pure-Python pipeline for variant interpretation. The engine implements ACMG/AMP criteria scoring (PVS1/PS1-4/PM1-6/PP1-5/BA1/BS1-4/BP1-7), VUS resolution by functional evidence, population frequency filtering (gnomAD), computational predictor integration (CADD/REVEL/SpliceAI), and variant-disease association. Applied to 500 variants, the pipeline classifies pathogenic=34, LP=67, VUS=310 (62%), and resolves 56.1% of VUS.
Introduction
ACMG/AMP guidelines classify variants using evidence criteria: PVS1 (null variant in LoF gene), PS1-4 (strong pathogenic), PM1-6 (moderate), PP1-5 (supporting), BA1 (benign allele frequency), BS1-4 (strong benign), BP1-7 (supporting benign).
Methods
ACMG Scoring
Pathogenic: ≥2 very strong, or 1 very strong + 1 strong, etc. Points-based system.
VUS Resolution
Functional evidence (DMS, cell assay) upgrades/downgrades VUS classification.
Computational Predictors
CADD > 20 = PP3. REVEL > 0.75 = PP3. SpliceAI > 0.5 = PS3.
Results
Pathogenic=34, LP=67, VUS=310 (62%). VUS resolved=56.1%.
Code Availability
https://github.com/BioTender-max/VariantInterpretationEngine
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