A comparison of allele age estimators on WGS and WES datasets
"Alyssa Pivirotto (1); Noah Peles (1); Jody Hey, PhD (1) Dept of Biology, Temple University"
Poster # 48
With the rapid improvement in sequencing technology, increasing amounts of whole genome and whole exome sequencing data are being generated. While allele age estimators have been found to estimate the age of mutations with little bias in whole genome sequencing data, no work has been done to examine whether these methods can be sufficiently utilized with whole exome data. Whole exome sequencing (WES) data allows researchers to identify protein coding mutations and examine the phenotypic effects of these mutations in clinical settings. In this study, we compared the allele age estimates from three different estimators: Relate, GEVA (Genealogical Estimation of Variant Age), and time of coalescence. Using simulated data, we compared the estimates of mutation age estimated from both whole genome datasets and whole exome datasets. While all three estimators do reasonably well at estimating known allele ages from whole genome data. Of the three estimators, we found that in both simple models and more complex models there is bias towards overestimation for young mutations and underestimation for old mutations. In comparing whether there was a significant difference in accuracy when looking across the frequency spectrum, it was found that more common alleles were more accurately estimated than rare alleles.