Abstract
Testing for universal common ancestry.
Highlights
A phylogenetic model selection test to quantify the evidence for the Universal Common Ancestry (UCA) of life forms was proposed recently (Theobald 2010a), based on the comparison of the statistical support, using likelihoods, the Akaike Information Criterion (AIC), or Bayes factors, for two different phylogenetic models representing the UCA and the independent origins (IOs) hypotheses (Sober and Steel 2002)
In this point of view, we demonstrate a fundamental drawback of the original UCA test, which is the use of the same alignment to represent both the UCA and IO hypotheses
In order to make the competing model likelihoods comparable, they have to be based on the same data, which in the original UCA test translates into using a single, fixed global sequence alignment to represent both UCA and IO, even if the global alignment is later split for the calculation of the IO model likelihood
Summary
Received 14 January 2013; reviews returned 13 May 2013; accepted 28 May 2014 Associate Editor: Olivier Gascuel. A phylogenetic model selection test to quantify the evidence for the Universal Common Ancestry (UCA) of life forms was proposed recently (Theobald 2010a), based on the comparison of the statistical support, using likelihoods, the Akaike Information Criterion (AIC), or Bayes factors, for two different phylogenetic models representing the UCA and the independent origins (IOs) hypotheses (Sober and Steel 2002) In this test, the former is represented by a single phylogeny connecting all sequences, whereas the latter is depicted by several, independent phylogenetic trees (Fig. 1). We showed that under the representation of UCA and IO as one versus multiple phylogenies, we can distinguish sequences simulated under UCA versus IO by observing similarity measures, concluding that similarity should not be used to select which data sets are eligible for the UCA test In this point of view, we demonstrate a fundamental drawback of the original UCA test, which is the use of the same alignment to represent both the UCA and IO hypotheses. In phylogenetics, alignment algorithms try to optimize the data to conform to a common ancestry hypothesis, and many even use a guide tree, like ClustalW (Thompson et al 1994)
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