Allele age estimators designed for whole genome datasets show only a moderate reduction in performance when applied to whole exome datasets.

ABSTRACT: Personalized genomics in the healthcare system is becoming increasingly accessible as the costs of sequencing decreases. With the increase in the number of genomes, larger numbers of rare variants are being discovered, leading to important initiatives in identifying the functional impacts in relation to disease phenotypes. One way to characterize these variants is to estimate the time the mutation entered the population. However, allele age estimators such as those implemented in the programs Relate, Genealogical Estimator of Variant Age (GEVA), and Runtc, were developed based on the assumption that datasets include the entire genome. We examined the performance of each of these estimators on simulated exome data under a neutral constant population size model, as well as under population e

SUBMITTER: Pivirotto A 

PROVIDER: S-EPMC10871225 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature