Inference of reticulate evolutionary histories by maximum likelihood: the performance of information criteria
Author
Park, Hyun Jung; Nakhleh, Luay
Date
2012Abstract
Background: Maximum likelihood has been widely used for over three decades to infer phylogenetic trees from
molecular data. When reticulate evolutionary events occur, several genomic regions may have conflicting
evolutionary histories, and a phylogenetic network may provide a more adequate model for representing the
evolutionary history of the genomes or species. A maximum likelihood (ML) model has been proposed for this
case and accounts for both mutation within a genomic region and reticulation across the regions. However, the
performance of this model in terms of inferring information about reticulate evolution and properties that affect
this performance have not been studied.
Results: In this paper, we study the effect of the evolutionary diameter and height of a reticulation event on its
identifiability under ML. We find both of them, particularly the diameter, have a significant effect. Further, we find
that the number of genes (which can be generalized to the concept of "non-recombining genomic regions") that
are transferred across a reticulation edge affects its detectability. Last but not least, a fundamental challenge with
phylogenetic networks is that they allow an arbitrary level of complexity, giving rise to the model selection
problem. We investigate the performance of two information criteria, the Akaike Information Criterion (AIC) and the
Bayesian Information Criterion (BIC), for addressing this problem. We find that BIC performs well in general for
controlling the model complexity and preventing ML from grossly overestimating the number of reticulation
events.
Conclusion: Our results demonstrate that BIC provides a good framework for inferring reticulate evolutionary
histories. Nevertheless, the results call for caution when interpreting the accuracy of the inference particularly for
data sets with particular evolutionary features.
Citation
Published Version
Type
Journal article
Publisher
Citable link to this page
https://hdl.handle.net/1911/70724Rights
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Link to License
https://creativecommons.org/licenses/by/2.0/Metadata
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