title: Analytical bayesian approach for assigning individuals to populations.
abstract: We propose a general formulation of the Bayesian method for assigning individuals to a population among a predetermined set of reference populations using molecular marker information. Compared to previously published methods, ours allows us to consider different types of prior information about allele frequencies by using a Dirichlet prior probability distribution. It also makes it possible to assign a set of individuals assumed to belong to the same population with increased accuracy using their pooled genotype data. The efficiency of the method is illustrated by application to a group of closely related coconut populations. An interesting feature of the Bayesian procedure is the way it handles imprecise information. With a poor or even incomplete dataset, assignment is still be possible and gives valid results: poor data quality is reflected in an ambiguous result rather than in a false conclusion.
authors.affiliation: Département Cultures Pérennes, TA 80/03, CIRAD, Avenue Agropolis, 34098 Montpellier CEDEX 5, France. Luc.baudouin@cirad.fr
authors.forename: L
authors.initials: L
authors.lastname: Baudouin
authors.forename: S
authors.initials: S
authors.lastname: Piry
authors.forename: J M
authors.initials: JM
authors.lastname: Cornuet
mesh_terms: D001499:Bayes Theorem; D003059:Cocos / Q000235:genetics; D005787:Gene Frequency; D005819:Genetic Markers; D005828:Genetics, Population; D005838:Genotype; D008954:Models, Biological; D012107:Research Design
chemical_list: D005819:Genetic Markers