A simulated annealing approach to define the genetic structure of populations

We present a new approach for defining groups of populations that are geographically homogeneous and maximally differentiated from each other. As a by-product, it also leads to the identification of genetic barriers between these groups. The method is based on a simulated annealing procedure that aims to maximize the proportion of total genetic variance due to differences between groups of populations (spatial analysis of molecular variance; SAMOVA). Monte Carlo simulations were used to study the performance of our approach and, for comparison, the behaviour of the Monmonier algorithm, a procedure commonly used to identify zones of sharp genetic changes in a geographical area. Simulations showed that the SAMOVA algorithm indeed finds maximally differentiated groups, which do not always correspond to the simulated group structure in the presence of isolation by distance, especially when data from a single locus are available. In this case, the Monmonier algorithm seems slightly better at finding predefined genetic barriers, but can often lead to the definition of groups of populations not differentiated genetically. The SAMOVA algorithm was then applied to a set of European roe deer populations examined for their mitochondrial DNA (mtDNA) HVRI diversity. The inferred genetic structure seemed to confirm the hypothesis that some Italian populations were recently reintroduced from a Balkanic stock, as well as the differentiation of groups of populations possibly due to the postglacial recolonization of Europe or the action of a specific barrier to gene flow.