Allele age estimators designed for whole genome datasets show only a modest decrease in accuracy when applied to whole exome datasets.

Personalized genomics in the healthcare system is becoming increasingly accessible as the costs of sequencing decreases. With the increase in number of genomes, larger numbers of rare variants are being discovered and much work is being done to identify their functional impacts in relation to disease phenotypes. One way to characterize these variants is to estimate the time the mutation entered the population.

Analyses of allele age and fitness impact reveal human beneficial alleles to be older than neutral controls.

A classic population genetic prediction is that alleles experiencing directional selection should swiftly traverse allele frequency space, leaving detectable reductions in genetic variation in linked regions. However, despite this expectation, identifying clear footprints of beneficial allele passage has proven to be surprisingly challenging. We addressed the basic premise underlying this expectation by estimating the ages of large numbers of beneficial and deleterious alleles in a human population genomic data set.

An estimator of first coalescent time reveals selection on young variants and large heterogeneity in rare allele ages among human populations.

Allele age has long been a focus of population genetic research, primarily because it can be an important clue to the fitness effects of an allele. By virtue of their effects on fitness, alleles under directional selection are expected to be younger than neutral alleles of the same frequency. We developed a new coalescent-based estimator of a close proxy for allele age, the time when a copy of an allele first shares common ancestry with other chromosomes in a sample not carrying that allele.