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Cited by 791Genomics and Phylogenetic StudiesMarkov Chains and Monte Carlo MethodsEvolution and Genetic Dynamics

Bayesian Selection of Continuous-Time Markov Chain Evolutionary Models

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Suchard, Marc A., Weiss, Robert E., Sinsheimer, Janet S.·2001·Molecular Biology and Evolution
DOI 10.1093/oxfordjournals.molbev.a003872
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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.

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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 [38]

    The Data and Evolutionary Relationships section describes using aligned DNA or RNA sequences to infer evolutionary relatedness, but does not distinguish observed sequence similarity from a hypothesis of homology. Searches for the claim’s wording and for “homology sequence similarity hypothesis empirical observation” found no passage addressing that distinction. (LLM, conf=0.72)

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Research works printed but not linked: 4

  1. [19]19. HOLMES, E. C., O. G. PYBUS, and P. H. HARVEY. 1999. The molecular population dynamics of HIV-1. Pp. 177-207 in K. A. CRANDALL, ed. The evolution of HIV. Johns Hopkins University Press, Baltimore, Md.
  2. [22]22. JEFFREYS, H. 1998. Theory of probability. Oxford classic texts in the physical sciences. 3rd edition. Oxford University Press, New York.
  3. [23]23. JUKES, T., and C. CANTOR. 1969. Evolution of protein mole- cules. Pp. 21-132 in H. N. MUNRO, ed. Mammalian protein metabolism. Academic Press, New York.
  4. [50]50. SWOFFORD, D. L., G. J. OLSEN, P. J. WADDELL, and D. M. HILLIS. 1996. Phylogenetic inferences. Pp. 407-514 in D. M. HILLIS, C. MORITZ, and B. K. MABLE, eds. Molecular systematics. 2nd edition. Sinauer, Sunderland, Mass.

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Authors
Suchard, M. A., Weiss, R. E., & Sinsheimer, J. S.
Affiliations
University of California, Los Angeles (US)
ORCID
Venue
Molecular Biology and Evolution
Publisher
Oxford University Press (OUP)
Published
2001-06-01
Volume
18
Issue
6
Pages
1001-1013
ISSN
  • 0737-4038 (ISSN-L)
  • 1537-1719
DOI
10.1093/oxfordjournals.molbev.a003872
OpenAlex
W2094206246
Cited by
791
References
59
Citations by year
2026: 2 · 2025: 8 · 2024: 5 · 2023: 8 · 2022: 12 · 2021: 25 · 2020: 20 · 2019: 34 · 2018: 19 · 2017: 29 · 2016: 32 · 2015: 57 · 2014: 84 · 2013: 58 · 2012: 76
FWCI
3.03
Type
journal article
Language
en
Open access
Locations
  • Molecular Biology and Evolution (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • Howard Hughes Medical Institute
  • U.S. Public Health Service
  • National Cancer Institute (CA-16 042, P30CA016042)
  • National Institute of Allergy and Infectious Diseases (AI-28 697, P30AI028697)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
Keywords
phylogenetic model selection, reversible jump Markov chain Monte Carlo, continuous-time Markov chain model, nucleotide substitution models, rate heterogeneity, molecular clock, Bayes factor, 16S rRNA, HIV genome, transversions
MeSH terms
Animals, Bayes Theorem, HIV, Humans, Markov Chains, Models, Genetic, Phylogeny, Primates, RNA, Ribosomal, 16S, Selection, Genetic, Evolution, Molecular

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