Improving the inference of population genetic structure in the presence of related individuals.

Abstract

It is well known that the presence of related individuals can affect the inference of population genetic structure from molecular data. This has been verified, for example, on the unsupervised Bayesian clustering algorithm implemented in the software STRUCTURE. This methodology assumes, among others, Hardy-Weinberg and linkage equilibrium within subpopulations. The existence of groups of close relatives, such as full-sib families, may prevent these assumptions to be fulfilled, causing the algorithm to work suboptimally. The purpose of this study was to evaluate the effect of the presence of related individuals on a different methodology (implemented in CLUSTER_DIST) for population genetic structure inference. This approach arranges individuals to maximize the genetic distance between groups and does not make Hardy-Weinberg and linkage equilibrium assumptions. We study the robustness of this approach to the presence of close relatives in a sample using simulated scenarios involving combinations of several factors, including the number of subpopulations, the level of differentiation between them, the number, size and type (full or half-sibs) of families in a sample, and the type and number of molecular markers available for clustering analysis. Results indicate that the methodology that maximizes the genetic distance between subpopulations is less influenced by the presence of related individuals than the program STRUCTURE. Therefore, the former can be used, in combination with the program STRUCTURE, to analyse population genetic structure when related individuals are suspected to be present in a sample.