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Bioinformatics Advance Access published online on November 29, 2005
Bioinformatics, 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
Received September 8, 2005
Revised November 23, 2005
Accepted November 25, 2005

Article

Comparison of Bayesian and maximum likelihood inference of population genetic parameters

Peter Beerli 1 *

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

* To whom correspondence should be addressed.
Peter Beerli, E-mail: beerli{at}csit.fsu.edu


  Abstract

Comparison of the performance and accuracy of different inference methods, such as maximum likelihood and a Bayesian inference, is difficult because the inference methods are implemented in different programs often written by different authors. I implemented both methods 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 from each other: parameter proposal distribution and maximization of the likelihood function. Using simulated data sets, 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 maximum likelihood 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 maximum likelihood based estimation of population genetics parameters but also to use 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 under the same assumptions.

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


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