BAPS 2: enhanced possibilities for the analysis of genetic population structure.
Bayesian statistical methods based on simulation techniques have recently been shown to provide powerful tools for the analysis of genetic population structure. We have previously developed a Markov chain Monte Carlo (MCMC) algorithm for characterizing genetically divergent groups based on molecular markers and geographical sampling design of the dataset. However, for large-scale datasets such algorithms may get stuck to local maxima in the parameter space. Therefore, we have modified our earlier algorithm to support multiple parallel MCMC chains, with enhanced features that enable considerably faster and more reliable estimation compared to the earlier version of the algorithm. We consider also a hierarchical tree representation, from which a Bayesian model-averaged structure estimate can be extracted. The algorithm is implemented in a computer program that features a user-friendly interface and built-in graphics. The enhanced features are illustrated by analyses of simulated data and an extensive human molecular dataset.


BACKGROUND
Freely available at http://www.rni.helsinki.fi/~jic/bapspage.html.
Corander
Jukka
J
Rolf Nevanlinna Institute, P.O. Box 4, Fin-00014 University of Helsinki, Finland. jukka.corander@rni.helsinki.fi
Waldmann
Patrik
P
Marttinen
Pekka
P
Sillanpää
Mikko J
MJ
D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D005787:Gene Frequency; D005819:Genetic Markers / Q000235:genetics; D005820:Genetic Testing / Q000379:methods*; D014644:Genetic Variation; D005828:Genetics, Population; D006801:Humans; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software; D014584:User-Computer Interface
D005819:Genetic Markers