Work

Bayesian Population-Based Prevalence Estimation of KCNV2-Associated Retinopathy: A Comparative Analysis of Disease-Associated Variant Frequencies in Russian and Global Populations

Year: 2026

Type: article

Abstract: variants. Accurate prevalence data remain limited because current estimates rely on clinically ascertained cases. This study aimed to estimate its population prevalence by integrating curated variant evidence and large-scale population genomics resources. The key methodological feature of this study is a multi-tiered pathogenicity framework combined with estimation of a biologically plausible prevalence range: Known pathogenic variants define conservative estimates, whereas probably pathogenic variants and strong variants of uncertain significance define broader estimates. Allele frequencies were analyzed in 807,162 individuals from the gnomAD database and 144,127 Russian genomes from GDB and EvogenDB. A Bayesian framework was applied to calculate disease prevalence from aggregated carrier frequencies across predefined variant tiers. Conservative estimates based on known pathogenic variants, were 1 in 343,926 globally and 1 in 1,911,347 in Russia. Including broader tiers increased estimates to 1 in 14,620 and 1 in 32,764, respectively. Compared to previous clinically ascertained prevalence of 1 in 865,000, these estimates define a wider biologically plausible prevalence range and are consistent with possible clinical underascertainment. This tier-based approach accounts for uncertainty in variant classification when estimating rare disease prevalence.

Source: International Journal of Molecular Sciences

DOI: 10.3390/ijms27135911