Convergence of genetic distances in a migration matrix model

Abstract

A recurring problem with the use of migration matrix models of genetic differentiation has to do with their convergence properties. In practice, predictions can be drawn from these models only at equilibrium; but in the case of the standard predictors (most of which are modifications of Wright's F_ST), it can take an unrealistically large number of generations to approach equilibrium. An alternative set of predictors, the set of all pairwise genetic distances among the populations that define the rows and columns of the migration matrix, is investigated here. These distances are shown analytically to converge much more rapidly than the more commonly used predictors. In an application of the model to migration data on a human population from Papua New Guinea, it takes only about three to four generations for the pairwise distances to converge, in contrast to more than 100 generations for one of the standard predictors. In this case, moreover, the distances predicted by the model at equilibrium are similar to those calculated from the available genetic data.