Testing for treeness: lateral gene transfer,phylogenetic inference,and model selection |
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Authors: | Joel D Velasco Elliott Sober |
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Institution: | (1) Department of Philosophy, Cornell University, Ithaca, NY, USA;(2) Department of Philosophy, University of Wisconsin, Madison, Madison, WI, USA |
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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 to considering 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 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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