title: Bayesian analysis of an admixture model with mutations and arbitrarily linked markers.
abstract: We introduce here a Bayesian analysis of a classical admixture model in which all parameters are simultaneously estimated. Our approach follows the approximate Bayesian computation (ABC) framework, relying on massive simulations and a rejection-regression algorithm. Although computationally intensive, this approach can easily deal with complex mutation models and partially linked loci, and it can be thoroughly validated without much additional computation cost. Compared to a recent maximum-likelihood (ML) method, the ABC approach leads to similarly accurate estimates of admixture proportions in the case of recent admixture events, but it is found superior when the admixture is more ancient. All other parameters of the admixture model such as the divergence time between parental populations, the admixture time, and the population sizes are also well estimated, unlike the ML method. The use of partially linked markers does not introduce any particular bias in the estimation of admixture, but ML confidence intervals are found too narrow if linkage is not specifically accounted for. The application of our method to an artificially admixed domestic bee population from northwest Italy suggests that the admixture occurred in the last 10-40 generations and that the parental Apis mellifera and A. ligustica populations were completely separated since the last glacial maximum.
authors.affiliation: Institut National de la Recherche Agronomique, Centre de Biologie et de Gestion des Populations (CBGP), Campus International de Baillarguet, Montferrier-sur-Lez, France. laurent.excoffier@zoo.unibe.ch
authors.forename: Laurent
authors.initials: L
authors.lastname: Excoffier
authors.forename: Arnaud
authors.initials: A
authors.lastname: Estoup
authors.forename: Jean-Marie
authors.initials: JM
authors.lastname: Cornuet
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D001516:Bees / Q000235:genetics; D002874:Chromosome Mapping; D003198:Computer Simulation; D003710:Demography; D004171:Diploidy; D005819:Genetic Markers; D007558:Italy; D018895:Microsatellite Repeats; D008957:Models, Genetic; D009154:Mutation
chemical_list: D005819:Genetic Markers