Beyond thresholds: a fully Bayesian framework for quantifying allele count evidence for variant pathogenicity

Abstract

Allele count data from affected individuals and population controls are central to variant interpretation, yet their evidential meaning is often mediated by discrete thresholds and implicit assumptions. This work introduces a fully quantitative Bayesian framework for dominant rare disease genetics in which all allele count evidence is summarized by a single quantity, the Bayes factor, that evaluates the probability of observing the same data under two explicitly defined competing models. Rather than replacing individual ACMG/AMP pathogenicity criteria, the Bayes factor provides a unified measure that naturally incorporates evidence in both the pathogenic and benign directions. The framework accounts for variation in affected cohort size, penetrance, disease prevalence, and assay error rates, allowing these biologically and technically meaningful quantities to be specified directly instead of absorbed into fixed cutoffs. Application to a non-Finnish European population shows that the dependence of the Bayes factor on observed allele counts is strongly shaped by how the affected cohort is defined and by false positive rates in control datasets. Across representative scenarios, Bayes factor values are broadly compatible with established allele count criteria combinations expressed on odds-ratio scales under typical parameterizations, while remaining tunable beyond these defaults.