Validation Study
Cohort 1: Pathogenic Variant Concordance Study
Pre-registered validation study with binary acceptance criteria. Twenty clinical cases with known pathogenic or likely pathogenic variants evaluated against an established clinical reference laboratory.
Study specification
Results against pre-registered acceptance criteria
| Metric | Result |
|---|---|
| P1 - Variant detection | 20/20 (100%) |
| P2 - Gene assignment | 20/20 (100%) |
| P3 - ACMG concordance (FULL) | 14/20 (70%) |
| P3 - ACMG concordance (CLINICAL, one-tier P/LP) | 6/20 (30%) |
| P3 - PARTIAL or DISCORDANT | 0/20 (0%) |
| P3 - Overall (FULL + CLINICAL) | 20/20 (100%) |
| S1 - HGVS notation match | 20/20 (100%) |
| S2 - Consequence match | 20/20 (100%) |
| S3 - Zygosity match | 20/20 (100%) |
| All primary criteria met | 20/20 (100%) |
Validation decision: GO
All 20 cases (100%) met all three primary concordance criteria (variant detection, gene assignment, ACMG concordance), exceeding the pre-defined GO threshold of 90% (>= 18/20). No PARTIAL or DISCORDANT results were observed. The six CLINICAL (one-tier P/LP) differences are within the expected range of inter-laboratory ACMG classification variability as documented in the peer-reviewed literature (Amendola 2016, Harrison 2017). The six CLINICAL differences fall in two patterns: cases where Helena applied more conservative ClinGen SVI 2023 thresholds (SpliceAI < 0.2 for PP3_splice), and cases where Helena applied ClinVar 2-star+ override or VCEP gene-specific rules to elevate Likely Pathogenic to Pathogenic.
The validation process additionally identified and resolved four classifier defects through iterative improvement (v3.6.0 to v3.6.4). The most significant was the systematic autosomal recessive loss-of-function gene curation expansion (approximately 150 genes with Definitive or Strong ClinGen Gene-Disease Validity for biallelic LoF disease mechanism), which prevents PVS1 misclassification of loss-of-function variants in autosomal recessive disease genes whose population constraint metrics are statistically underpowered.