Testing single-sample estimators of effective population size in genetically structured populations

Abstract

The effective population size (N e) is a key parameter in evolutionary and population genetics. Single-sample N e estimation provides an alternative to traditional approaches requiring two or more samples. Single-sample methods assume that the study population has no genetic sub-structure, which is unlikely to be true in wild populations. Here we empirically investigated two single-sample estimators (onesamp and L d N e) in replicated and controlled genetically structured populations of Drosophila melanogaster. Using experimentally controlled population parameters, we calculated the Wright-Fisher expected N e for the structured population (Total N e) and demonstrated that the loss of heterozygosity did not significantly differ from Wright's model. We found that disregarding the population substructure resulted in Total N e estimates with a low coefficient of variation but these estimates were systematically lower than the expected values, whereas hierarchical estimates accounting for population structure were closer to the expected values but had a higher coefficient of variation. Analysis of simulated populations demonstrated that incomplete sampling, initial allelic diversity and balancing selection may have contributed to deviations from the Wright-Fisher model. Overall the approximate-Bayesian onesamp method performed better than L d N e (with appropriate priors). Both methods performed best when dispersal rates were high and the population structure was approaching panmixia. Show more