Bayesian clustering algorithms ascertaining spatial population structure-a new computer program and a comparison study
Bayesian clustering algorithms have recently emerged as a prominent computational tool for inferring population structure in population genetics and in molecular ecology (Beaumont & Rannala 2004). Bayesian clustering methods use genetic information to ascertain population membership of individuals without assuming predefined populations. They can assign either the individuals or a fraction of their genome to a number of clusters based on multilocus genotypes. The methods operate by minimizing Hardy–Weinberg and linkage disequilibria, and the assignment of each individual genotype to its population of origin is carried out probabilistically. The assignment can generally be achieved by using Markov chain Monte Carlo (MCMC) approaches. These particular assignment methods are useful when genetic data for potential source populations are not available, and they offer a powerful tool to answer questions of ecological, evolutionary, or conservation relevance
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