Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations

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. 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...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Jukka Corander, Pekka Marttinen, Jukka Sirén, Jing Tang
• Keywords: BAPS, Bayesian Statistical Models, BMCBI 2007, BMCBI 2008, Genetic Structure