Screen-VarCal: An Interpretable Probabilistic Framework for Recalibrating ACMG Rule-Based Variant Classification in Preventive Medicine
Bibliographic record
Abstract
Abstract Motivation hole-genome sequencing (WGS) is increasingly used for preventive genomics, yet rule-based ACMG engines such as InterVar were tuned for high pre-test probability diagnostics. In screening contexts, these heuristics can inflate pathogenic/likely pathogenic (P/LP) calls, prompting unnecessary follow-up. We sought an interpretable, data-driven recalibration tailored to proactive use. Results Across 20 WGS cases, InterVar flagged 109 variants as P/LP; only 18 (16.5%) were concordant with ClinVar P/LP assertions. The remaining 83.5% were largely absent from Clin-Var (n=68) or mapped to benign/likely benign (n=13). Nearly all flagged variants (96%) were heterozygous, predominantly in autosomal recessive genes (e.g., FAM20C, MTMR2 ), indicating a dominant carrier inflation effect; recurrent loci (e.g., ATXN3, FAM20C ) further amplified yield. We introduce Screen-VarCal , an interpretable probabilistic framework that combines logistic regression with isotonic adjustment to align InterVar outputs with observed ClinVar P/LP distributions. Screen-VarCal reduced false positives by ∼ 60% while retaining all ClinVar-concordant findings and yields calibrated probabilities with coefficients that transparently link ACMG evidence categories (PVS/PS/PM/PP/BS/BP) and zygosity to risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".