Empirically calibrated allele frequency thresholds for ACMG BA1, BS1 and PM2 evidence criteria
- 1Bitscopic
Abstract
Allele frequency (AF) is among the most frequently applied lines of evidence in variant classification, yet the ACMG/AMP criteria that use it (BA1, BS1, PM2) are still applied at fixed defaults while computational predictors have been systematically recalibrated. Population frequencies are shaped by selection, ascertainment, and gene-level demography at once, and few genes carry enough classified variants to set a threshold directly. Extending the calibration approach applied to computational predictors, we used inheritance mode and gene-level missense constraint as stratification axes and pooled variants within each stratum. ClinVar missense variants annotated against gnomAD v4.1.1 were stratified along both, and gene-normalized kernel density estimates were fit to pathogenic and benign variants within a sliding window along the constraint axis. Thresholds were placed where the likelihood ratio crossed ACMG/AMP evidence strengths at a prior of 0.0441. Derived thresholds varied systematically with constraint and differed between inheritance modes, departing from the fixed defaults in both directions. On held-out genes, stratified cutoffs reached 96.7% accuracy against 90.1% unstratified. Restricted to the 73 ClinGen expert panel genes with autosomal dominant or recessive inheritance, the derived cutoffs reached 91.0% accuracy at 69.5% variant coverage, against 88.8% accuracy at 86.2% coverage for the panel-specified cutoffs. AF thresholds for these criteria are not constant across genes, and inheritance mode and missense constraint capture much of that variation. The resulting cutoffs are empirically derived, carry explicit uncertainty, and deploy as a lookup table across thousands of genes no expert panel currently covers.
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BibTeX
@misc{dubey2026empirically,
title = {{Empirically calibrated allele frequency thresholds for ACMG BA1, BS1 and PM2 evidence criteria}},
author = {V. Dubey and C. E. Eisenhart},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.07.26362456},
url = {https://www.medrxiv.org/content/10.64898/2026.09.07.26362456v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Empirically calibrated allele frequency thresholds for ACMG BA1, BS1 and PM2 evidence criteria
AU - V. Dubey
AU - C. E. Eisenhart
PY - 2026
DA - 2026/09/07
PB - medRxiv
DO - 10.64898/2026.09.07.26362456
UR - https://www.medrxiv.org/content/10.64898/2026.09.07.26362456v1
AB - Allele frequency (AF) is among the most frequently applied lines of evidence in variant classification, yet the ACMG/AMP criteria that use it (BA1, BS1, PM2) are still applied at fixed defaults while computational predictors have been systematically recalibrated. Population frequencies are shaped by selection, ascertainment, and gene-level demography at once, and few genes carry enough classified variants to set a threshold directly. Extending the calibration approach applied to computational predictors, we used inheritance mode and gene-level missense constraint as stratification axes and pooled variants within each stratum. ClinVar missense variants annotated against gnomAD v4.1.1 were stratified along both, and gene-normalized kernel density estimates were fit to pathogenic and benign variants within a sliding window along the constraint axis. Thresholds were placed where the likelihood ratio crossed ACMG/AMP evidence strengths at a prior of 0.0441. Derived thresholds varied systematically with constraint and differed between inheritance modes, departing from the fixed defaults in both directions. On held-out genes, stratified cutoffs reached 96.7% accuracy against 90.1% unstratified. Restricted to the 73 ClinGen expert panel genes with autosomal dominant or recessive inheritance, the derived cutoffs reached 91.0% accuracy at 69.5% variant coverage, against 88.8% accuracy at 86.2% coverage for the panel-specified cutoffs. AF thresholds for these criteria are not constant across genes, and inheritance mode and missense constraint capture much of that variation. The resulting cutoffs are empirically derived, carry explicit uncertainty, and deploy as a lookup table across thousands of genes no expert panel currently covers.
ER -