title: An approximate Bayesian computation approach to overcome biases that arise when using amplified fragment length polymorphism markers to study population structure.
abstract: There is great interest in using amplified fragment length polymorphism (AFLP) markers because they are inexpensive and easy to produce. It is, therefore, possible to generate a large number of markers that have a wide coverage of species genomes. Several statistical methods have been proposed to study the genetic structure using AFLPs but they assume Hardy-Weinberg equilibrium and do not estimate the inbreeding coefficient, F(IS). A Bayesian method has been proposed by Holsinger and colleagues that relaxes these simplifying assumptions but we have identified two sources of bias that can influence estimates based on these markers: (i) the use of a uniform prior on ancestral allele frequencies and (ii) the ascertainment bias of AFLP markers. We present a new Bayesian method that avoids these biases by using an implementation based on the approximate Bayesian computation (ABC) algorithm. This new method estimates population-specific F(IS) and F(ST) values and offers users the possibility of taking into account the criteria for selecting the markers that are used in the analyses. The software is available at our web site (http://www-leca.ujf-grenoble.fr/logiciels.htm). Finally, we provide advice on how to avoid the effects of ascertainment bias.
authors.affiliation: Laboratoire d'Ecologie Alpine (LECA), CNRS UMR 5553, 38041 Grenoble Cedex 09, France.
authors.forename: Matthieu
authors.initials: M
authors.lastname: Foll
authors.forename: Mark A
authors.initials: MA
authors.lastname: Beaumont
authors.forename: Oscar
authors.initials: O
authors.lastname: Gaggiotti
mesh_terms: D000465:Algorithms; D054458:Amplified Fragment Length Polymorphism Analysis / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D015982:Bias; D001699:Biometry; D005819:Genetic Markers; D005828:Genetics, Population / Q000706:statistics & numerical data*; D008957:Models, Genetic; D015233:Models, Statistical; D012680:Sensitivity and Specificity; D012984:Software
chemical_list: D005819:Genetic Markers