Background During the most recent decade many Bayesian statistical models and software for answering questions related to the genetic structure underlying population samples have appeared in the scientific literature. Most of these methods utilize molecular markers for the inferences, while some are also capable of handling DNA sequence data. In a number of earlier works, we have introduced an array of statistical methods for population genetic inference that are implemented in the software BAPS.

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However, the complexity of biological problems related to genetic structure analysis keeps increasing such that in many cases the current methods may provide either inappropriate or insufficient solutions. Results We discuss the necessity of enhancing the statistical approaches to face the challenges posed by the ever-increasing amounts of molecular data generated by scientists over a wide range of research areas and introduce an array of new statistical tools implemented in the most recent version of BAPS. With these methods it is possible, e.g., to fit genetic mixture models using user-specified numbers of clusters and to estimate levels of admixture under a genetic linkage model. Also, alleles representing a different ancestry compared to the average observed genomic positions can be tracked for the sampled individuals, and a priori specified hypotheses about genetic population structure can be directly compared using Bayes' theorem.

In general, we have improved further the computational characteristics of the algorithms behind the methods implemented in BAPS facilitating the analyses of large and complex datasets. In particular, analysis of a single dataset can now be spread over multiple computers using a script interface to the software. Background The past decade has provided an upsurge of methods and software enabling Bayesian statistical analyses of the ancestry and the current genetic structure of natural populations using a variety of molecular information sources, such as microsatellite and single-nucleotide polymorphism markers, as well as mitochondrial and house-keeping DNA sequences, to name a few. Reviews of the existing methods (see e.g. [-]) illustrate the jungle of software that can be in general exploited to infer ancestral patterns, migration and genetic isolation of subgroups of samples using Bayesian inference, e.g., BAPS [-], BAYES [,], BayesAss+ [], GENECLUST, TESS [], GENELAND [-], InStruct [], NEWHYBRIDS [], PARTITION [], STRUCTURAMA [], STRUCTURE [,]. Most of these methods rely on Markov chain Monte Carlo (MCMC)-computation in ways that have become more or less standard for modern Bayesian analysis [].

We have earlier demonstrated the versatility of enhancing Bayesian computation through incorporation of analytical integration techniques into the stochastic search over the space of putative models, in the current applied context [,,], in more general bioinformatics pattern recognition problems [], as well as from a more theoretical statistical perspective [,]. It is apparent from the generic developments within molecular biology, that the statistical and computational methods used for the analyses of molecular datasets must evolve to meet the challenge stated by continuously increasing sizes of samples and the amount of molecular information characterized for them. With the cost of DNA sequencing decreasing rapidly and with the increasing number of molecular markers available for non-model organisms, a large number of research areas will face the need of feasibly applicable statistical tools to the myriad of questions related to the genetic population structure.