Survey of Branch Support Methods Demonstrates Accuracy, Power, and Robustness of Fast Likelihood-based Approximation Schemes

By Maria Anisimova, Manuel Gil, Jean-François Dufayard, Christophe Dessimoz, Olivier Gascuel. Systematic Biology, 2011

1,123 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/28ee96a33f59541d515391889a157ef3a9ac4393

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Environmental Engineering

Citations in the corpus

Ultrafast Approximation for Phylogenetic Bootstrap (2013, methodology): “Bayesian MCMC methods, however, tend to overestimate the true probabilities in case of model misspeciﬁcation or polytomies (Suzuki et al. 2002; Douady et al. 2003; Lewis et al. 2005; Anisimova et al. 2011).”
From primordial clocks to circadian oscillators (2023, methodology): “To enable determination of branch support, an aBayes test 48 , a SH-aLRT test (20,000 bootstrap replicates 49 ) and an ultrafast bootstrap (20,000 bootstrap replicates 50 ) were performed (Supplementary Dataset 1; branch supports in order: SH-aLRT support (%)/aBayes support/ultrafast bootstrap…”
A tale of worldwide success: Behind the scenes of Carex (Cyperaceae) biogeography and diversification (2019, methodology): “Finally, the SH ‐ aLRT value (nonparametric Shimodaira ‐ Hasegawa implementation of the approximate likelihood ‐ ratio test; Anisimova & Gascuel, 2006; Anisimova et al., 2011) was used to evaluate query tree branch support with RAxML.”
IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era (2019, neutral): “2010), and the aBayes test (Anisimova et al. 2011).”
Tracing the origin of a cryptic invader: phylogeography of the Euwallacea fornicatus (Coleoptera: Curculionidae: Scolytinae) species complex (2017, methodology): “Branch support was assessed using a nonparametric approximate likelihood-ratio test [aLTR (SH-Like)] (Anisimova & Gascuel, 2006; Guindon et al., 2010; Anisimova et al., 2011).”
W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis (2016, methodology): “W-IQ-TREE provides a number of methods to assess the reliability of internal branches: standard bootstrap (22), the SH-aLRT (4), aBayes test (8) and the ultrafast bootstrap (7) (UFBoot).”
A phylogeny and revised classification of Squamata, including 4161 species of lizards and snakes (2013, methodology): “a 15% chance that a branch i s "incorrect") as strong support [41,212,213].”
Renewing Felsenstein’s Phylogenetic Bootstrap in the Era of Big Data (2018, methodology): “However, RAxML was more accurate than FastTree, with higher branch supports, as generally observed with rapid bootstrap 16 (Fig.”