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Bioinformatics Advance Access originally published online on November 29, 2005
Bioinformatics 2006 22(3):341-345; doi:10.1093/bioinformatics/bti803
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Comparison of Bayesian and maximum-likelihood inference of population genetic parameters

Peter Beerli

School of Computational Science and Department of Biological Sciences, Florida State University Tallahassee, FL 32306-4120, USA

*To whom correspondence should be addressed.

Comparison of the performance and accuracy of different inference methods, such as maximum likelihood (ML) and Bayesian inference, is difficult because the inference methods are implemented in different programs, often written by different authors. Both methods were implemented in the program MIGRATE, that estimates population genetic parameters, such as population sizes and migration rates, using coalescence theory. Both inference methods use the same Markov chain Monte Carlo algorithm and differ from each other in only two aspects: parameter proposal distribution and maximization of the likelihood function. Using simulated datasets, the Bayesian method generally fares better than the ML approach in accuracy and coverage, although for some values the two approaches are equal in performance.

Motivation: The Markov chain Monte Carlo-based ML framework can fail on sparse data and can deliver non-conservative support intervals. A Bayesian framework with appropriate prior distribution is able to remedy some of these problems.

Results: The program MIGRATE was extended to allow not only for ML(-) maximum likelihood estimation of population genetics parameters but also for using a Bayesian framework. Comparisons between the Bayesian approach and the ML approach are facilitated because both modes estimate the same parameters under the same population model and assumptions.

Availability: The program is available from http://popgen.csit.fsu.edu/

Contact: beerli{at}csit.fsu.edu

Received on September 8, 2005; revised on November 23, 2005; accepted on November 25, 2005

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