The Equidistance Index of Population Structure

Measures of population differentiation, such as _{FST}, are traditionally derived from the ratio of genetic diversity within and between populations. However, the emergence of population clusters from multilocus analysis is a function of genetic structure (departures from panmixia) rather than diversity. If the populations are close to panmixia, slight differences between the mean pairwise distance within and between populations (low _{FST}) can manifest as strong separation between the populations, thus population clusters are often evident even when the vast majority of diversity is partitioned within populations rather than between them. Moreover, because _{FST} utilizes the mean rather than deviations from the mean, it does not directly reflect the strength of separation between population clusters. For any given _{FST} value, clusters can be tighter (more panmictic) or looser (more stratified), and in this respect higher _{FST} does not always imply stronger differentiation. In this study we propose substituting the mean in the _{FST} equation with the standard deviation, thereby deriving a novel measure of population separability, denoted _{EST} , which is more consistent with clustering and classification. To assess the utility of this metric, we ranked various human (HGDP) population pairs based on _{FST} and _{EST} and found substantial differences in ranking order. In some cases examined, most notably among isolated Amazonian tribes, _{EST} ranking seems more consistent with demographic, phylogeographic and linguistic measures of classification compared to _{FST}. Thus, _{EST} may at times outperform _{FST} in identifying evolutionarily significant differentiation.