Abstract
BackgroundThe inference of the hidden structure of a population is an essential issue in population genetics. Recently, several methods have been proposed to infer population structure in population genetics.MethodsIn this study, a new method to infer the number of clusters and to assign individuals to the inferred populations is proposed. This approach does not make any assumption on Hardy-Weinberg and linkage equilibrium. The implemented criterion is the maximisation (via a simulated annealing algorithm) of the averaged genetic distance between a predefined number of clusters. The performance of this method is compared with two Bayesian approaches: STRUCTURE and BAPS, using simulated data and also a real human data set.ResultsThe simulations show that with a reduced number of markers, BAPS overestimates the number of clusters and presents a reduced proportion of correct groupings. The accuracy of the new method is approximately the same as for STRUCTURE. Also, in Hardy-Weinberg and linkage disequilibrium cases, BAPS performs incorrectly. In these situations, STRUCTURE and the new method show an equivalent behaviour with respect to the number of inferred clusters, although the proportion of correct groupings is slightly better with the new method. Re-establishing equilibrium with the randomisation procedures improves the precision of the Bayesian approaches. All methods have a good precision for FST ≥ 0.03, but only STRUCTURE estimates the correct number of clusters for FST as low as 0.01. In situations with a high number of clusters or a more complex population structure, MGD performs better than STRUCTURE and BAPS. The results for a human data set analysed with the new method are congruent with the geographical regions previously found.ConclusionThis new method used to infer the hidden structure in a population, based on the maximisation of the genetic distance and not taking into consideration any assumption about Hardy-Weinberg and linkage equilibrium, performs well under different simulated scenarios and with real data. Therefore, it could be a useful tool to determine genetically homogeneous groups, especially in those situations where the number of clusters is high, with complex population structure and where Hardy-Weinberg and/or linkage equilibrium are present.
Highlights
The inference of the hidden structure of a population is an essential issue in population genetics
When the modal value was the comparison criterion, both STRUCTURE and MGD had an optimal behaviour in the simulated scenarios since they always yielded the true number of subpopulations
BAPS overestimated the number of populations when a reduced number of molecular information was available
Summary
The inference of the hidden structure of a population is an essential issue in population genetics. Traditional population genetic analyses deal with the distribution of allele frequencies between and within populations. From these frequencies several measures of population structure can be estimated, the most widely used being the Wright F statistics [1]. To calculate these estimators of population structure an a priori definition of the population is needed. The genetic structure of a population is not always reflected in the geographical proximity of individuals. In groups of individuals with different geographical locations, behavioural patterns or phenotypes are not necessarily genetically differentiated [2]. An inappropriate a priori grouping of individuals into populations may diminish the power of the analyses to elucidate biological processes, potentially leading to unsuitable conservation or management strategies
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