Bayesian Model Adequacy and Choice in Phylogenetics
Open accessAbstract
Bayesian inference is becoming a common statistical approach to phylogenetic estimation because, among other reasons, it allows for rapid analysis of large data sets with complex evolutionary models. Conveniently, Bayesian phylogenetic methods use currently available stochastic models of sequence evolution. However, as with other model-based approaches, the results of Bayesian inference are conditional on the assumed model of evolution: inadequate models (models that poorly fit the data) may result in erroneous inferences. In this article, I present a Bayesian phylogenetic method that evaluates the adequacy of evolutionary models using posterior predictive distributions. By evaluating a model's posterior predictive performance, an adequate model can be selected for a Bayesian phylogenetic study. Although I present a single test statistic that assesses the overall (global) performance of a phylogenetic model, a variety of test statistics can be tailored to evaluate specific features (local performance) of evolutionary models to identify sources failure. The method presented here, unlike the likelihood-ratio test and parametric bootstrap, accounts for uncertainty in the phylogeny and model parameters.
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Sequence similarity is strictly an empirical observation, whereas homology is a hypothesis intended to explain the similarity.
“Nonetheless, as I and many others have argued [1,32-40], sequence similarity is strictly an empirical observation; homology, on the other hand, is a hypothesis intended to explain the similarity.”
From On universal common ancestry, sequence similarity, and phylogenetic structure: the sins of P-values and the virtues of Bayesian evidence (2011) · cites this paper as [33]
The Introduction discusses whether models of DNA sequence evolution fit observed data, and the Models of Sequence Evolution section describes substitution models. Neither passage addresses the claimed distinction between sequence similarity as an observation and homology as an explanatory hypothesis. Searches for that distinction and a paraphrase found no supporting passage in our copy; page numbers were not provided. (LLM, conf=0.76)
Experimental — accuracy not yet measuredAn adequate modeling of the evolutionary process that generated the data is fundamental to the recovery of accurate phylogenies.
“An adequate modeling of the evolutionary process that generated the data is fundamental to the recovery of accurate phylogenies [16-20].”
From Improved Modeling of Compositional Heterogeneity Supports Sponges as Sister to All Other Animals (2017) · cites this paper as [17]
If the best-fitting model does not describe the evolutionary process that generated the data adequately, there can still be a high probability of recovering a tree with relationships representing systematic error.
“Yet, if the best-fitting model does not describe the evolutionary process that generated the data adequately, there can still be a high probability of recovering a tree with relationships representing systematic error [16-20].”
From Improved Modeling of Compositional Heterogeneity Supports Sponges as Sister to All Other Animals (2017) · cites this paper as [17]
Porifera-sister better represents the phylogenetic signal in the data.
“As phylogenetic methods are less likely to recover artifactual topologies when the models of evolution moreadequatelydescribethedata[16-20],weconcludethatPorifera-sister better represents the phylogenetic signal in the data.”
From Improved Modeling of Compositional Heterogeneity Supports Sponges as Sister to All Other Animals (2017) · cites this paper as [17]
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Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879 · see citing works
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033 · see citing works
Kimura, M. (1980) · Journal of Molecular Evolution · doi:10.1007/bf01731581 · Cited by 29,778 · see citing works
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) · doi:10.1201/b16018 · Cited by 27,276 · see citing works
MRBAYES: Bayesian inference of phylogenetic trees.
Huelsenbeck, J. P., & Ronquist, F. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.8.754 · Cited by 21,937 · see citing works
Evolutionary trees from DNA sequences: A maximum likelihood approach.
Felsenstein, J. (1981) · Journal of Molecular Evolution · doi:10.1007/bf01734359 · Cited by 14,571 · see citing works
Evolution of Protein Molecules
JUKES, T. H., & CANTOR, C. R. (1969) · Mammalian Protein Metabolism · doi:10.1016/b978-1-4832-3211-9.50009-7 · Cited by 11,032 · see citing works
Dating of the human–ape splitting by a molecular clock of mitochondrial DNA.
Hasegawa, M., Kishino, H., & Yano, T.-A. (1985) · Journal of Molecular Evolution · doi:10.1007/bf02101694 · Cited by 8,687 · see citing works
Markov Chain Monte Carlo in Practice
Gilks, W. R., Richardson, S. T., & Spiegelhalter, D. J. (1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835 · Cited by 7,927 · see citing works
The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses
Wilks, S. S. (1938) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177732360 · Cited by 3,719 · see citing works
Cases in which Parsimony or Compatibility Methods Will be Positively Misleading
Felsenstein, J. (1978) · Systematic Biology · doi:10.1093/sysbio/27.4.401 · Cited by 3,221 · see citing works
(1964) · Elsevier eBooks · doi:10.1016/b978-1-4832-3210-2.50006-6 · Cited by 3,130 · see citing works
Yang, Z. (1994) · Journal of Molecular Evolution · doi:10.1007/bf00160154 · Cited by 2,960 · see citing works
Bayesian Inference of Phylogeny and Its Impact on Evolutionary Biology
Huelsenbeck, J. P., Ronquist, F., Nielsen, R., & Bollback, J. P. (2001) · Science · doi:10.1126/science.1065889 · Cited by 2,737 · see citing works
Self-citationCases in which parsimony or compatibility methods will be positively misleading.
Felsenstein, J. (1978) · Systematic Zoology · doi:10.2307/2412923 · Cited by 2,230 · see citing works
Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference
Strawderman, R. L., & Gamerman, D. (2000) · Journal of the American Statistical Association · doi:10.2307/2669581 · Cited by 1,957 · see citing works
Probability Distribution of Molecular Evolutionary Trees: A New Method of Phylogenetic Inference
Rannala, B. H., & Yang, Z. (1996) · Journal of Molecular Evolution · doi:10.1007/bf02338839 · Cited by 1,751 · see citing works
Markov Chasin Monte Carlo Algorithms for the Bayesian Analysis of Phylogenetic Trees
Larget, B. R., & Simon, D. L. (1999) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a026160 · Cited by 1,624 · see citing works
Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician
Rubin, D. (1984) · The Annals of Statistics · doi:10.1214/aos/1176346785 · Cited by 1,503 · see citing works
Bayesian phylogenetic inference using DNA sequences: a Markov Chain Monte Carlo Method
Yang, Z., & Rannala, B. (1997) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a025811 · Cited by 1,274 · see citing works
Bayesian Model Choice Via Markov Chain Monte Carlo Methods
Carlin, B., & Chib, S. (1995) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1995.tb02042.x · Cited by 1,137 · see citing works
Smith, J. Q., Bernardo, J. M., Berger, J. O., Dawid, A. P., & Smith, A. F. M. (1993) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2982740 · Cited by 1,098 · see citing works
Yang, Z. (1993) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a040082 · Cited by 1,040 · see citing works
Selecting the Best-Fit Model of Nucleotide Substitution
Posada, D., & Crandall, K. A. (2001) · Systematic Biology · doi:10.1080/10635150118469 · Cited by 937 · see citing works
Bayesian Selection of Continuous-Time Markov Chain Evolutionary Models
Suchard, M. A., Weiss, R. E., & Sinsheimer, J. S. (2001) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a003872 · Cited by 791 · see citing works
Success of Phylogenetic Methods in the Four-Taxon Case
Huelsenbeck, J. P., & Hillis, D. M. (1993) · Systematic Biology · doi:10.1093/sysbio/42.3.247 · Cited by 784 · see citing works
Statistical tests of models of DNA substitution
Goldman, N. (1993) · Journal of Molecular Evolution · doi:10.1007/bf00166252 · Cited by 736 · see citing works
Model Determination using Predictive Distributions with Implementation via Sampling-Based Methods
Gelfand, A. E., Dey, D. K., & Chang, H. (1992) · doi:10.1093/oso/9780198522669.003.0009 · Cited by 660 · see citing works
Handbook of Statistical Genetics
Balding, D., Bishop, M., & Cannings, C. (2004) · Wiley eBooks · doi:10.1002/0470022620 · Cited by 619 · see citing works
Bayesian Phylogenetic Inference via Markov Chain Monte Carlo Methods
Mau, B., Newton, M. A., & Larget, B. R. (1999) · Biometrics · doi:10.1111/j.0006-341x.1999.00001.x · Cited by 541 · see citing works
Mapping Mutations on Phylogenies
Nielsen, R. (2002) · Systematic Biology · doi:10.1080/10635150290102393 · Cited by 413 · see citing works
Are Guinea Pigs Rodents? The Importance of Adequate Models in Molecular Phylogenetics
Sullivan, J. M., & Swofford, D. L. (1997) · Journal of Mammalian Evolution · doi:10.1023/a:1027314112438 · Cited by 283 · see citing works
Success of maximum likelihood phylogeny inference in the four-taxon case.
Gaut, B. S., & Lewis, P. O. (1995) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a040183 · Cited by 245 · see citing works
Inferring the root of a phylogenetic tree.
Huelsenbeck, J. P., Bollback, J. P., & Levine, A. M. (2002) · Systematic Biology · doi:10.1080/106351502753475862 · Cited by 221 · see citing works
Self-citationPrimate η-globin DNA sequences and man's place among the great apes
Koop, B. F., Goodman, M., Xu, P.-L., Chan, K., & Slightom, J. L. (1986) · Nature · doi:10.1038/319234a0 · Cited by 219 · see citing works
Phylogenetic Inference for Binary Data on Dendograms Using Markov Chain Monte Carlo
Mau, B., & Newton, M. A. (1997) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1997.10474731 · Cited by 177 · see citing works
Topological bias and inconsistency of maximum likelihood using wrong models
Bruno, W., & Halpern, A. L. (1999) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a026137 · Cited by 147 · see citing works
Model checking and model improvement
Gilks, W. R., Richardson, S. T., & Spiegelhalter, D. (1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835-16 · Cited by 103 · see citing works
DETECTING POSITIVELY SELECTED AMINO ACID SITES USING POSTERIOR PREDICTIVE P -VALUES
Nielsen, R., & Huelsenbeck, J. P. (2001) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789812799623_0054 · Cited by 37 · see citing works
Newton, M. A., Mau, B., & Larget, B. R. (1999) · Lecture notes-monograph series · doi:10.1214/lnms/1215455551 · Cited by 36 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- [2]2. AKAIKE, H. 1974. A new look at statistical model identifica- tion. IEEE Trans. Autom. Contr. 19:716-723.
- [23]23. LI, S. 1996. Phylogenetic tree construction using Markov chain Monte Carlo. Doctoral dissertation, Ohio State University, Columbus.
- [36]36. SWOFFORD, D., G. OLSEN, P. WADDELL, and D. M. HILLIS. 1996. Phylogenetic inference. Pp. 407-511 in D. HILLIS, C. MORITZ, and B. MABLE, eds. Molecular systematics. 2nd edition. Sinauer, Sunderland, Mass.
- [37]37. TAVARE ´, S. 1986. Some probabilistic and statistical problems on the analysis of DNA sequences. Pp. 57-86 in Lectures in mathematics in the life sciences. Vol. 17[Please provide the publisher name and location].
Cited by
Sorted by citations
BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis
Bouckaert, R., Vaughan, T. G., Barido-Sottani, J., Duchêne, S., Fourment, M., Gavryushkina, A., Heled, J., Jones, G., Kühnert, D., De Maio, N., et al. (2019) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006650 · Cited by 4,556 · see citing works
Posada, D., & Buckley, T. R. (2004) · Systematic Biology · doi:10.1080/10635150490522304 · Cited by 3,927 · see citing works
Lartillot, N., Brinkmann, H., & Philippe, H. (2007) · BMC Evolutionary Biology · doi:10.1186/1471-2148-7-s1-s4 · Cited by 705 · see citing works
Computing Bayes factors using thermodynamic integration.
Lartillot, N., & Philippe, H. (2006) · Systematic Biology · doi:10.1080/10635150500433722 · Cited by 668 · see citing works
Modeling compositional heterogeneity
Foster, P. G. (2004) · Systematic Biology · doi:10.1080/10635150490445779 · Cited by 474 · see citing works
Model Selection in Phylogenetics
Sullivan, J., & Joyce, P. (2005) · Annual Review of Ecology, Evolution, and Systematics · doi:10.1146/annurev.ecolsys.36.102003.152633 · Cited by 412 · see citing works
Bayesian Total-Evidence Dating Reveals the Recent Crown Radiation of Penguins
Gavryushkina, A., Heath, T. A., Ksepka, D. T., Stadler, T., Welch, D., & Drummond, A. J. (2016) · Systematic Biology · doi:10.1093/sysbio/syw060 · Cited by 372 · see citing works
The importance of proper model assumption in bayesian phylogenetics.
Lemmon, A. R., & Moriarty, E. C. (2004) · Systematic Biology · doi:10.1080/10635150490423520 · Cited by 356 · see citing works
Improved Modeling of Compositional Heterogeneity Supports Sponges as Sister to All Other Animals
Feuda, R., Dohrmann, M., Pett, W., Philippe, H., Rota-Stabelli, O., Lartillot, N., Wörheide, G., & Pisani, D. (2017) · Current Biology · doi:10.1016/j.cub.2017.11.008 · Cited by 291 · see citing works
Phylogenomics provides robust support for a two-domains tree of life
Williams, T. A., Cox, C. J., Foster, P. G., Szöllősi, G. J., & Embley, T. M. (2019) · Nature Ecology & Evolution · doi:10.1038/s41559-019-1040-x · Cited by 274 · see citing works
Rooting the Animal Tree of Life
Li, Y., Shen, X.-X., Evans, B., Dunn, C. W., & Rokas, A. (2021) · Molecular Biology and Evolution · doi:10.1093/molbev/msab170 · Cited by 146 · see citing works
Plastid establishment did not require a chlamydial partner
Domman, D., Horn, M., Embley, T. M., & Williams, T. A. (2015) · Nature Communications · doi:10.1038/ncomms7421 · Cited by 51 · see citing works
Ekman, S., & Blaalid, R. (2011) · Systematic Biology · doi:10.1093/sysbio/syr022 · Cited by 51 · see citing works
Assessment of substitution model adequacy using frequentist and Bayesian methods
Ripplinger, J., & Sullivan, J. (2010) · Molecular Biology and Evolution · doi:10.1093/molbev/msq168 · Cited by 50 · see citing works
Theobald, D. L. (2011) · Biology Direct · doi:10.1186/1745-6150-6-60 · Cited by 16 · see citing works
“Model selection methods, such as log likelihood ratios (LLR), the Akaike Information Criterion (AIC), and Bayes factors [47], are now used routinely in modern biological research, especially in phylogenetics, genetics, and bioinformatics [69,85-95].”
Method
- Authors
- Bollback, J. P.
- Affiliations
- University of Rochester (US)
- Corresponding author
- Bollback, Jonathan P.
- Venue
- Molecular Biology and Evolution
- Publisher
- Oxford University Press (OUP)
- Published
- 2002-07-01
- Volume
- 19
- Issue
- 7
- Pages
- 1171-1180
- ISSN
- 0737-4038 (ISSN-L)
- 1537-1719
- OpenAlex
- W2129928578
- Cited by
- 215
- References
- 40
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 10 · 2023: 8 · 2022: 14 · 2021: 11 · 2020: 9 · 2019: 14 · 2018: 8 · 2017: 6 · 2016: 5 · 2015: 13 · 2014: 15 · 2013: 11 · 2012: 3
- FWCI
- 3.59
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Molecular Biology and Evolution (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (0075404, 0075406)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Physical Sciences › Earth and Planetary Sciences › Paleontology › Evolution and Paleontology Studies
- Keywords
- Bayesian phylogenetics, model adequacy, model selection, posterior predictive distribution, evolutionary models, model misspecification, test statistics, parametric bootstrap, likelihood ratio test
- MeSH terms
- Animals, Bayes Theorem, Computer Simulation, DNA, Humans, Models, Genetic, Phylogeny, Probability, Models, Statistical, Likelihood Functions, Evolution, Molecular
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