title: Correcting for ascertainment bias in the inference of population structure.
abstract: BACKGROUND
  The ascertainment process of molecular markers amounts to disregard loci carrying alleles with low frequencies. This can result in strong biases in inferences under population genetics models if not properly taken into account by the inference algorithm. Attempting to model this censoring process in view of making inference of population structure (i.e.identifying clusters of individuals) brings up challenging numerical difficulties.
  
  
  METHODS
  These difficulties are related to the presence of intractable normalizing constants in Metropolis-Hastings acceptance ratios. This can be solved via an Markov chain Monte Carlo (MCMC) algorithm known as single variable exchange algorithm (SVEA).
  
  
  RESULTS
  We show how this general solution can be implemented for a class of clustering models of broad interest in population genetics that includes the models underlying the computer programs STRUCTURE, GENELAND and GESTE. We also implement the method proposed for a simple example and show that it allows us to reduce the bias substantially.
  
  
  BACKGROUND
  Further details and a computer program implementing the method are available from http://folk.uio.no/gillesg/AscB/.
authors.affiliation: Centre for Ecological and Evolutionary Synthesis, Department of Biology, University of Oslo, P.O. Box 1066, Blindern 0316, Oslo, Norway. gilles.guillot@bio.uio.no
authors.forename: Gilles
authors.initials: G
authors.lastname: Guillot
authors.forename: Matthieu
authors.initials: M
authors.lastname: Foll
mesh_terms: D000465:Algorithms; D000483:Alleles; D003198:Computer Simulation; D005787:Gene Frequency; D005828:Genetics, Population / Q000379:methods*; D008390:Markov Chains; D009010:Monte Carlo Method; D012984:Software