Bayesian analysis of population structure based on linked molecular information.

摘要: The Bayesian model-based approach to inferring hidden genetic population structures using multilocus molecular markers has become a popular tool within certain branches of biology. In particular, it been shown that heterogeneous data arising from genetically dissimilar latent groups individuals can be effectively modelled an unsupervised classification formulation. However, most currently employed models ignore potential linkage the information, and therefore lead biased inferences under circumstances. Utilizing general theory graphical models, we develop framework accounts for dependences both linked marker loci DNA sequence data. Due high level conservation among eukaryotic species, latter aspect is particularly relevant analyzing rapidly evolving microbial species. advantages incorporating dependence due in are illustrated by analyses simulated real samples Bacillus cereus.