Oct 13, 2026
PLOS (Public Library of Science)
Author summary Repeated calculation of full-genome population genomic measurements for biodiversity monitoring across time and space remains expensive due to the need for high-coverage sequencing and genome assemblies. Genome skimming is a viable, low-coverage alternative for obtaining genomic distances, and alignment- and assembly-free methods exist for analyzing nuclear data from skimming data to estimate the distance between samples. However, existing methods fail to model within-sample heterozygosity, expected for diploid organisms. Given the dominance of diploidy among species of interest to ecologists, the implications of these simplifying assumptions warrant further study. Here, we present a mathematical model of the k-mer sets sampled from two diploid genomes under Kingman’s coalescent model. We use the model to develop DipSkmer, a k-mer-based, reference-free method for estimating genetic diversity that, unlike its predecessors, models within-sample heterozygosity. Benchmarking shows more accurate genomic diversity estimates compared to existing reference-free, genome-skimming methods and comparable performance to the popular high-coverage, reference-based method, ANGSD. Thus, DipSkmer enables accessible, less expensive population monitoring through genetic diversity estimates.