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Bioinformatics Advance Access originally published online on January 9, 2009
Bioinformatics 2009 25(4):552-554; doi:10.1093/bioinformatics/btn665
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Correcting for ascertainment bias in the inference of population structure

Gilles Guillot 1,2,3,* and Matthieu Foll 4,5

1Centre for Ecological and Evolutionary Synthesis, Department of Biology, University of Oslo, P.O. Box 1066, Blindern 0316, Oslo Norway, 2Gothenburg Stochastic Centre, Chalmers University of Technology, Gothenburg, Sweden, 3Department of Applied Mathematics and Computer Science, INRA/Agro-ParisTech, Paris, France, 4Computational and molecular population genetics lab, Institute of Ecology and Evolution, University of Bern, 3012 Bern, Switzerland and 5Swiss Institute of Bioinformatics, Berne, Switzerland

*To whom correspondence should be addressed.


  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.

Method: 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).

Result: 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.

Availability: Further details and a computer program implementing the method are available from http://folk.uio.no/gillesg/AscB/

Contact: gilles.guillot{at}bio.uio.no

Associate Editor: Martin Bishop

Received on September 19, 2008; revised on December 29, 2008; accepted on December 29, 2008

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