A likelihood method for detecting trait-dependent shifts in the rate of molecular evolution
Open accessAbstract
Rate heterogeneity within groups of organisms is known to exist even when closely related taxa are examined. A wide variety of phylogenetic and dating methods have been developed that aim either to test for the existence of rate variation or to correct for its bias. However, none of the existing methods track the evolution of features that account for observed rate heterogeneity. Here, we present a likelihood model that assumes that rate variation is caused, in part, by species' intrinsic characteristics, such as a particular life-history trait, morphological feature, or habitat association. The model combines models of sequence and character state evolution such that rates of sequence change depend on the character state of a lineage at each point in time. We test, using simulations, the power and accuracy of the model to determine whether rates of molecular evolution depend on a particular character state and demonstrate its utility using an empirical example with halophilic and freshwater daphniids.
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TraitRate only estimates the likelihood ratio indicating the association between sequence evolution and a given trait; it does not model the pattern of shifts in substitution rate explicitly.
“Additionally, TraitRate only estimates the likelihood ratio indicating the association between sequence evolution and a given trait; it does not model the pattern of shifts in substitution rate explicitly.”
From Bayesian Detection of Convergent Rate Changes of Conserved Noncoding Elements on Phylogenetic Trees (2019) · cites this paper as Mayrose and Otto 2011
The Introduction (page not provided) describes an explicit model in which evolutionary rates depend on a lineage’s trait state over time. The Combined Character and Sequence Model and Likelihood Computations sections (pages not provided) specify a relative substitution-rate parameter and model rate shifts along branches. The paper also uses a likelihood-ratio test, but does not only estimate that ratio. (LLM, conf=0.90)
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Experimental — accuracy not yet measuredTo our knowledge, the only model-based method for associating molecular rate changes with discrete phenotypes and which considers multiple patterns of rate/character transitions is TraitRate.
“To our knowledge, the only model-based method for associating molecular rate changes with discrete phenotypes and which considers multiple patterns of rate/character transitions is TraitRate (Mayrose and Otto 2011; Levy Karin et al. 2017).”
From Bayesian Detection of Convergent Rate Changes of Conserved Noncoding Elements on Phylogenetic Trees (2019) · cites this paper as Mayrose and Otto 2011
The Combined Character and Sequence Model and Likelihood Computations sections describe a likelihood method linking molecular rates to discrete character states while sampling multiple plausible character-transition histories. Implementation and Availability identifies its software as traitRate. The paper does not establish that TraitRate was the only such method when the citing sentence was written. (LLM, conf=0.84)
Experimental — accuracy not yet measuredTraitRate models the probability of rate shifts along a fixed ultrametric tree, an approach that was shown to deteriorate the performance in practice.
“This method models the probability of rate shifts along a fixed ultrametric tree, an approach that was shown to deteriorate the performance in practice (Mayrose and Otto 2011).”
From Bayesian Detection of Convergent Rate Changes of Conserved Noncoding Elements on Phylogenetic Trees (2019) · cites this paper as Mayrose and Otto 2011
Likelihood Computations describes mapping character histories onto an input clocklike tree, while also allowing calculations across multiple plausible ultrametric trees; the tree need not be fixed. Phylogenetic Uncertainty reports reduced power in simulations when inferred gene trees were inaccurately ultrametricized. It does not show that using a fixed ultrametric tree generally deteriorates performance in practice. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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Cites
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Cited by
Sorted by citations
Bayesian Detection of Convergent Rate Changes of Conserved Noncoding Elements on Phylogenetic Trees
Hu, Z., Sackton, T. B., Edwards, S. V., & Liu, J. S. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz049 · Cited by 69 · see citing works
- Authors
- Mayrose, I., & Otto, S. P.
- Affiliations
- University of British Columbia (CA)
- Corresponding author
- Mayrose, Itay
- Venue
- Molecular Biology and Evolution
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-10-20
- Volume
- 28
- Issue
- 1
- Pages
- 759-770
- ISSN
- 0737-4038 (ISSN-L)
- 1537-1719
- OpenAlex
- W2147334438
- Cited by
- 43
- References
- 7
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 1 · 2023: 2 · 2021: 1 · 2020: 5 · 2019: 7 · 2018: 2 · 2017: 3 · 2016: 1 · 2015: 3 · 2014: 2 · 2013: 5 · 2012: 3
- FWCI
- 1.21
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Molecular Biology and Evolution (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Killam Trusts
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Earth and Planetary Sciences › Paleontology › Evolution and Paleontology Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Keywords
- rate heterogeneity, molecular evolution rate, likelihood model, character state evolution, sequence evolution, life-history traits, phylogenetic dating
- MeSH terms
- Animals, Base Sequence, Models, Genetic, Phenotype, Phylogeny, Probability, Stochastic Processes, Likelihood Functions, Evolution, Molecular
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