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 expansion and background selection models. We found that each provides usable estimates of allele age from whole-exome datasets. Relate performs the best amongst all three estimators with Pearson coefficients of 0.83 and 0.73 (with respect to true simulated values, for neutral constant and expansion population model, respectively) with a 12 percent and 20 percent decrease in correlation between whole genome and whole exome estimations. Of the three estimators, Relate is best able to parallelize to yield quick results with little resources, however, Relate is currently only able to scale to thousands of samples making it unable to match the hundreds of thousands of samples being currently released. While more work is needed to expand the capabilities of current methods of estimating allele age, these methods show a modest decrease in performance in the estimation of the age of mutations. Article SummaryIncreasing availability of whole exome sequencing yields large numbers of rare variants that have direct impact on disease phenotypes. Many methods of identifying the functional impact of mutations exist including the estimation of the time a mutation entered a population. Popular methods of estimating this time assume whole genome data in the estimate of the allele age based on haplotypes. We simulated genome and exome data under a constant and expansion population demography model and found that there is a decrease in performance in all three methods on exome data of 15-30% depending on the method. Testing the robustness of the best performing method, Relate, further simulations introducing background selection and varying the sample size were also undertaken with similar results.