Survey of branch support methods demonstrates accuracy, power, and robustness of fast likelihood-based approximation schemes.

Details

Serval ID
serval:BIB_9E20AFF0611F
Type
Article: article from journal or magazin.
Collection
Publications
Title
Survey of branch support methods demonstrates accuracy, power, and robustness of fast likelihood-based approximation schemes.
Journal
Systematic biology
Author(s)
Anisimova M., Gil M., Dufayard J.F., Dessimoz C., Gascuel O.
ISSN
1076-836X (Electronic)
ISSN-L
1063-5157
Publication state
Published
Issued date
10/2011
Peer-reviewed
Oui
Volume
60
Number
5
Pages
685-699
Language
english
Notes
Publication types: Evaluation Study ; Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
Phylogenetic inference and evaluating support for inferred relationships is at the core of many studies testing evolutionary hypotheses. Despite the popularity of nonparametric bootstrap frequencies and Bayesian posterior probabilities, the interpretation of these measures of tree branch support remains a source of discussion. Furthermore, both methods are computationally expensive and become prohibitive for large data sets. Recent fast approximate likelihood-based measures of branch supports (approximate likelihood ratio test [aLRT] and Shimodaira-Hasegawa [SH]-aLRT) provide a compelling alternative to these slower conventional methods, offering not only speed advantages but also excellent levels of accuracy and power. Here we propose an additional method: a Bayesian-like transformation of aLRT (aBayes). Considering both probabilistic and frequentist frameworks, we compare the performance of the three fast likelihood-based methods with the standard bootstrap (SBS), the Bayesian approach, and the recently introduced rapid bootstrap. Our simulations and real data analyses show that with moderate model violations, all tests are sufficiently accurate, but aLRT and aBayes offer the highest statistical power and are very fast. With severe model violations aLRT, aBayes and Bayesian posteriors can produce elevated false-positive rates. With data sets for which such violation can be detected, we recommend using SH-aLRT, the nonparametric version of aLRT based on a procedure similar to the Shimodaira-Hasegawa tree selection. In general, the SBS seems to be excessively conservative and is much slower than our approximate likelihood-based methods.
Keywords
Amino Acids/genetics, Animals, Bayes Theorem, Classification/methods, Computer Simulation, Likelihood Functions, Models, Genetic, Models, Statistical, Orchidaceae/genetics, Phylogeny, Statistics, Nonparametric
Pubmed
Web of science
Open Access
Yes
Create date
02/09/2015 8:16
Last modification date
06/03/2024 10:17
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