Bayesian selection of continuous-time Markov chain evolutionary models
- PMID: 11371589
- DOI: 10.1093/oxfordjournals.molbev.a003872
Bayesian selection of continuous-time Markov chain evolutionary models
Abstract
We develop a reversible jump Markov chain Monte Carlo approach to estimating the posterior distribution of phylogenies based on aligned DNA/RNA sequences under several hierarchical evolutionary models. Using a proper, yet nontruncated and uninformative prior, we demonstrate the advantages of the Bayesian approach to hypothesis testing and estimation in phylogenetics by comparing different models for the infinitesimal rates of change among nucleotides, for the number of rate classes, and for the relationships among branch lengths. We compare the relative probabilities of these models and the appropriateness of a molecular clock using Bayes factors. Our most general model, first proposed by Tamura and Nei, parameterizes the infinitesimal change probabilities among nucleotides (A, G, C, T/U) into six parameters, consisting of three parameters for the nucleotide stationary distribution, two rate parameters for nucleotide transitions, and another parameter for nucleotide transversions. Nested models include the Hasegawa, Kishino, and Yano model with equal transition rates and the Kimura model with a uniform stationary distribution and equal transition rates. To illustrate our methods, we examine simulated data, 16S rRNA sequences from 15 contemporary eubacteria, halobacteria, eocytes, and eukaryotes, 9 primates, and the entire HIV genome of 11 isolates. We find that the Kimura model is too restrictive, that the Hasegawa, Kishino, and Yano model can be rejected for some data sets, that there is evidence for more than one rate class and a molecular clock among similar taxa, and that a molecular clock can be rejected for more distantly related taxa.
Similar articles
-
Testing a molecular clock without an outgroup: derivations of induced priors on branch-length restrictions in a Bayesian framework.Syst Biol. 2003 Feb;52(1):48-54. doi: 10.1080/10635150390132713. Syst Biol. 2003. PMID: 12554439
-
Bayesian analysis of correlated evolution of discrete characters by reversible-jump Markov chain Monte Carlo.Am Nat. 2006 Jun;167(6):808-25. doi: 10.1086/503444. Epub 2006 May 9. Am Nat. 2006. PMID: 16685633
-
Bayesian phylogenetic model selection using reversible jump Markov chain Monte Carlo.Mol Biol Evol. 2004 Jun;21(6):1123-33. doi: 10.1093/molbev/msh123. Epub 2004 Mar 19. Mol Biol Evol. 2004. PMID: 15034130
-
Molecular-clock methods for estimating evolutionary rates and timescales.Mol Ecol. 2014 Dec;23(24):5947-65. doi: 10.1111/mec.12953. Epub 2014 Oct 30. Mol Ecol. 2014. PMID: 25290107 Review.
-
Phylogenetic model evaluation.Methods Mol Biol. 2008;452:331-64. doi: 10.1007/978-1-60327-159-2_16. Methods Mol Biol. 2008. PMID: 18566772 Review.
Cited by
-
Data integration in Bayesian phylogenetics.Annu Rev Stat Appl. 2023;10:353-377. doi: 10.1146/annurev-statistics-033021-112532. Epub 2022 Sep 28. Annu Rev Stat Appl. 2023. PMID: 38774036 Free PMC article.
-
Spatiotemporal transmission of SARS-CoV-2 lineages during 2020-2021 in Pernambuco-Brazil.Microbiol Spectr. 2024 Jun 4;12(6):e0421823. doi: 10.1128/spectrum.04218-23. Epub 2024 Apr 23. Microbiol Spectr. 2024. PMID: 38651879 Free PMC article.
-
On the surprising effectiveness of a simple matrix exponential derivative approximation, with application to global SARS-CoV-2.Proc Natl Acad Sci U S A. 2024 Jan 16;121(3):e2318989121. doi: 10.1073/pnas.2318989121. Epub 2024 Jan 12. Proc Natl Acad Sci U S A. 2024. PMID: 38215186 Free PMC article.
-
Accelerating Bayesian inference of dependency between mixed-type biological traits.PLoS Comput Biol. 2023 Aug 28;19(8):e1011419. doi: 10.1371/journal.pcbi.1011419. eCollection 2023 Aug. PLoS Comput Biol. 2023. PMID: 37639445 Free PMC article.
-
Global spatiotemporal transmission patterns of human enterovirus 71 from 1963 to 2019.Virus Evol. 2021 Aug 13;7(2):veab071. doi: 10.1093/ve/veab071. eCollection 2021 Sep. Virus Evol. 2021. PMID: 36819972 Free PMC article.
Publication types
MeSH terms
Substances
Grants and funding
LinkOut - more resources
Full Text Sources