Bayesian modelling of compositional heterogeneity in molecular phylogenetics
Open accessAbstract
In molecular phylogenetics, standard models of sequence evolution generally assume that sequence composition remains constant over evolutionary time. However, this assumption is violated in many datasets which show substantial heterogeneity in sequence composition across taxa. We propose a model which allows compositional heterogeneity across branches, and formulate the model in a Bayesian framework. Specifically, the root and each branch of the tree is associated with its own composition vector whilst a global matrix of exchangeability parameters applies everywhere on the tree. We encourage borrowing of strength between branches by developing two possible priors for the composition vectors: one in which information can be exchanged equally amongst all branches of the tree and another in which more information is exchanged between neighbouring branches than between distant branches. We also propose a Markov chain Monte Carlo (MCMC) algorithm for posterior inference which uses data augmentation of substitutional histories to yield a simple complete data likelihood function that factorises over branches and allows Gibbs updates for most parameters. Standard phylogenetic models are not informative about the root position. Therefore a significant advantage of the proposed model is that it allows inference about rooted trees. The position of the root is fundamental to the biological interpretation of trees, both for polarising trait evolution and for establishing the order of divergence among lineages. Furthermore, unlike some other related models from the literature, inference in the model we propose can be carried out through a simple MCMC scheme which does not require problematic dimension-changing moves. We investigate the performance of the model and priors in analyses of two alignments for which there is strong biological opinion about the tree topology and root position.
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Misalignments as well as varying evolutionary rates and compositional biases between distinct lineages can adversely impact the recovered phylogenetic tree and result in incorrect topologies.
“Second, although this is true for any phylogenetic analysis, misalignments as well as varying evolutionary rates and compositional biases between distinct lineages can adversely impact the recovered phylogenetic tree and result in incorrect topologies (Felsenstein 1981;Gribaldo and Philippe 2002;Heaps et al. 2014).”
From The Emergence of Life (2019) · cites this paper as Heaps et al. 2014
Cites
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Self-citationModeling compositional heterogeneity
Foster, P. G. (2004) · Systematic Biology · doi:10.1080/10635150490445779 · Cited by 474 · see citing works
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“2005, 2011); they model changes in composition at discrete breakpoints that can occur anywhere on the tree (Blanquart and Lartillot 2008); or they model composition changes at speciation events, such that different branches can have different compositions (Foster 2004; Heaps et al. 2014; Williams et al. 2015).”
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“For example, while models of protein evolution taking into account the compositional heterogeneities of amino acid sequences across branches and sites have already been developed (Blanquart and Lartillot, 2008; Heaps et al., 2014), current implementations of these models are computationally intensive and can only be a…”
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Incorporating compositional heterogeneity into Lie Markov models for phylogenetic inference
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“Heaps et al. (2014) investigated a similar model in a Bayesian framework, with a prior that assumed positive correlation among the set of composition vectors, thereby allowing information to be shared between branches.”
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Heaps, S., Nye, T., Boys, R., Williams, T., Cherlin, S., & Embley, T. (2017) · Statistical Modelling · doi:10.1177/1471082x19829937 · Cited by 1 · see citing works
“Blanquart and Lartillot, 2006), this assumption is often made (e.g. Yang and Roberts, 1995; Foster, 2004; Heaps et al., 2014; Cherlin, 2016) and we retain it here for simplicity of notation.”
BackgroundMethodInfluentialSelf-citation
- Authors
- Heaps, S. E., Nye, T. M. W., Boys, R. J., Williams, T. A., & Embley, T. M.
- Affiliations
- Newcastle University (GB)
- Corresponding author
- Nye, Tom M.W.
- Venue
- Statistical Applications in Genetics and Molecular Biology
- Publisher
- Walter de Gruyter GmbH
- Published
- 2014-08-23
- Volume
- 13
- Issue
- 5
- Pages
- 589-609
- ISSN
- 1544-6115 (ISSN-L)
- 2194-6302
- OpenAlex
- W2071867171
- Cited by
- 20
- References
- 51
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2022: 1 · 2021: 2 · 2020: 4 · 2019: 5 · 2018: 2 · 2017: 2 · 2015: 1
- FWCI
- 0.41
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Statistical Applications in Genetics and Molecular Biology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Bristol Research (University of Bristol) (publishedVersion)
- Durham Research Online (Durham University) (submittedVersion, cc-by)
- Explore Bristol Research (submittedVersion)
- Funders
- FP7 Ideas: European Research Council (268701)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
- Keywords
- compositional heterogeneity, sequence evolution models, Bayesian phylogenetics, rooted trees, data augmentation, Markov chain Monte Carlo
- MeSH terms
- Bayes Theorem, Markov Chains, Monte Carlo Method, Phylogeny
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