title: Bayesian identification of admixture events using multilocus molecular markers.
abstract: Bayesian statistical methods for the estimation of hidden genetic structure of populations have gained considerable popularity in the recent years. Utilizing molecular marker data, Bayesian mixture models attempt to identify a hidden population structure by clustering individuals into genetically divergent groups, whereas admixture models target at separating the ancestral sources of the alleles observed in different individuals. We discuss the difficulties involved in the simultaneous estimation of the number of ancestral populations and the levels of admixture in studied individuals' genomes. To resolve this issue, we introduce a computationally efficient method for the identification of admixture events in the population history. Our approach is illustrated by analyses of several challenging real and simulated data sets. The software (baps), implementing the methods introduced here, is freely available at http://www.rni.helsinki.fi/~jic/bapspage.html.
authors.affiliation: Department of Mathematics and Statistics, PO Box 68, Fin-00014 University of Helsinki, Finland. jukka.corander@helsinki.fi
authors.forename: Jukka
authors.initials: J
authors.lastname: Corander
authors.forename: Pekka
authors.initials: P
authors.lastname: Marttinen
mesh_terms: D000483:Alleles; D001499:Bayes Theorem; D030541:Databases, Genetic; D005819:Genetic Markers / Q000235:genetics*; D006801:Humans; D020407:Internet; D044382:Population Groups; D012984:Software
chemical_list: D005819:Genetic Markers