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Testing for Treeness: Lateral Gene Transfer, Phylogenetic Inference, and Model Selection

Velasco, Joel D. and Sober, Elliott (2010) Testing for Treeness: Lateral Gene Transfer, Phylogenetic Inference, and Model Selection. [Preprint]

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    Abstract

    A phylogeny that allows for lateral gene transfer (LGT) can be thought of as a strictly branching tree (all of whose branches are vertical) to which lateral branches have been added. Given that the goal of phylogenetics is to depict evolutionary history, we should look for the best supported phylogenetic network and not restrict ourselves just to trees. However, the obvious extensions of popular tree-based methods such as Maximum Parsimony and Maximum Likelihood face a serious problem – if we judge networks by fit to data alone, networks that have lots of lateral branches will always fit the data at least as well as any network that restricts itself to vertical branches. This is analogous to the well-studied problem of overfitting data in the curve-fitting problem. Analogous problems often have analogous solutions and we propose to treat network inference as a case of model selection and use the Akaike Information Criterion (AIC). Strictly tree-like networks are more parsimonious than those that postulate lateral as well as vertical branches. This leads to the conclusion that we should not always infer LGT events whenever it would improve our fit-to-data, but should do so only when the improved fit is larger than the penalty for adding extra lateral branches.


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    Item Type: Preprint
    Additional Information: This paper is forthcoming in Biology and Philosophy
    Keywords: Akaike Information Criterion, Lateral gene transfer, Model selection, Parsimony, Phylogenetic networks
    Subjects: Specific Sciences > Biology > Evolutionary Theory
    Specific Sciences > Biology > Systematics
    Depositing User: Joel Velasco
    Date Deposited: 17 Jun 2010
    Last Modified: 07 Oct 2010 11:19
    Item ID: 5420
    URI: http://philsci-archive.pitt.edu/id/eprint/5420

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