Long-Branch Attraction Bias and Inconsistency in Bayesian Phylogenetics

• Published in: PloS one Vol. 4; no. 12; p. e7891
• Main Authors: Kolaczkowski, Bryan, Thornton, Joseph W.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09.12.2009; Public Library of Science (PLoS)
• Subjects: Analysis; Attraction; Bayes Theorem; Bayesian analysis; Bias; Branches; Cladistic analysis; Computer Simulation; Data processing; Databases, Genetic; Datasets; Economic models; Empirical analysis; Evolution; Evolution (Biology); Evolutionary Biology; Evolutionary Biology/Genomics; Genetics and Genomics/Comparative Genomics; Humans; Likelihood Functions; Mathematical models; Models, Genetic; Numerical analysis; Phylogenetics; Phylogeny; Probabilistic inference; Probabilistic models; Robustness (mathematics); Simulation; Statistical analysis; Statistical inference; Topology; Trees; Oregon; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0007891

Bayesian inference (BI) of phylogenetic relationships uses the same probabilistic models of evolution as its precursor maximum likelihood (ML), so BI has generally been assumed to share ML's desirable statistical properties, such as largely unbiased inference of topology given an accurate model and increasingly reliable inferences as the amount of data increases. Here we show that BI, unlike ML, is biased in favor of topologies that group long branches together, even when the true model and prior distributions of evolutionary parameters over a group of phylogenies are known. Using experimental simulation studies and numerical and mathematical analyses, we show that this bias becomes more severe as more data are analyzed, causing BI to infer an incorrect tree as the maximum a posteriori phylogeny with asymptotically high support as sequence length approaches infinity. BI's long branch attraction bias is relatively weak when the true model is simple but becomes pronounced when sequence sites evolve heterogeneously, even when this complexity is incorporated in the model. This bias--which is apparent under both controlled simulation conditions and in analyses of empirical sequence data--also makes BI less efficient and less robust to the use of an incorrect evolutionary model than ML. Surprisingly, BI's bias is caused by one of the method's stated advantages--that it incorporates uncertainty about branch lengths by integrating over a distribution of possible values instead of estimating them from the data, as ML does. Our findings suggest that trees inferred using BI should be interpreted with caution and that ML may be a more reliable framework for modern phylogenetic analysis.