title: A comparison of rarefaction and bayesian methods for predicting the allelic richness of future samples on the basis of currently available samples.
abstract: Rarefaction methods have been introduced into population genetics (from ecology) for predicting and comparing the allelic richness of future samples (or sometimes populations) on the basis of currently available samples, possibly of different sizes. Here, we focus our attention on one such problem: Predicting which population is most likely to yield the future sample having the highest allelic richness. (This problem can arise when we want to construct a core collection from a larger germplasm collection.) We use extensive simulations to compare the performance of the Monte Carlo rarefaction (repeated random subsampling) method with a simple Bayesian approach we have developed-which is based on the Ewens sampling distribution. We found that neither this Bayesian method nor the (Monte Carlo) rarefaction method performed uniformly better than the other. We also examine briefly some of the other motivations offered for these methods and try to make sense of them from a Bayesian point of view.
authors.affiliation: Laboratoire Génome, Populations, Interactions, Adaptation, UM2-IFREMER-CNRS UMR 5171, Université de Montpellier II, 34095 Montpellier, Cedex 5, France. belkhir@univ-montp2.fr
authors.forename: Khalid
authors.initials: K
authors.lastname: Belkhir
authors.forename: Kevin J
authors.initials: KJ
authors.lastname: Dawson
authors.forename: François
authors.initials: F
authors.lastname: Bonhomme
mesh_terms: D000483:Alleles; D001499:Bayes Theorem; D005787:Gene Frequency; D014644:Genetic Variation; D005828:Genetics, Population / Q000379:methods*; D009010:Monte Carlo Method; D018401:Sample Size