Building an adaptive trait simulator package to infer parametric diffusion model along phylogenetic tree

Jhwueng, Dwueng-Chwuan

doi:10.1016/j.mex.2020.100978

Cited by 4 publications

(1 citation statement)

References 20 publications

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“…Our models provide feasible options to users in the community to account for nonlinearity in the relationship between the trait optima undergoing stabilizing selection and predictor traits. The models and procedures included in this study were implemented into the R package ouxy [50].…”

Section: Discussionmentioning

confidence: 99%

Phylogenetic Curved Optimal Regression for Adaptive Trait Evolution

Jhwueng

Wang

2021

Entropy

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Regression analysis using line equations has been broadly applied in studying the evolutionary relationship between the response trait and its covariates. However, the characteristics among closely related species in nature present abundant diversities where the nonlinear relationship between traits have been frequently observed. By treating the evolution of quantitative traits along a phylogenetic tree as a set of continuous stochastic variables, statistical models for describing the dynamics of the optimum of the response trait and its covariates are built herein. Analytical representations for the response trait variables, as well as their optima among a group of related species, are derived. Due to the models’ lack of tractable likelihood, a procedure that implements the Approximate Bayesian Computation (ABC) technique is applied for statistical inference. Simulation results show that the new models perform well where the posterior means of the parameters are close to the true parameters. Empirical analysis supports the new models when analyzing the trait relationship among kangaroo species.

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Section: Discussionmentioning

confidence: 99%

Phylogenetic Curved Optimal Regression for Adaptive Trait Evolution

Jhwueng

Wang

2021

Entropy

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A novel phylogenetic comparative method for evaluating the strength of branch-specific directional selection

2022

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Phylogenetic comparative methods (PCMs) have played a central role in studying the evolution of phenotypic traits. However, when a trait experienced directional selection, previous PCMs have faced a dilemma between mathematically tractable but restrictive models (i.e., simple Gaussian process models) and flexible but intractable approaches (i.e., a simulation-based process model of phenotype evolution built on population genetics frameworks). This paper proposes a novel Gaussian process macroevolutionary model, called the “branch-specific directional selection (BSDS),” for evaluating the strength of directional selection to reconcile these two approaches. This model is based on a second-order approximation of a previous simulation-based process model but has a closed-form likelihood function. This can also be extended to incorporate intraspecies variations and to linear mixed models, which are necessary for meta-analysis. We conduct numerical experiments to validate the proposed method and apply it to the brain volume of Hominidae species. The results show that the proposed methods yield statistically more reliable inferences and computational time is about hundred thousand times faster than the previous simulation-based methods. Further extensions of the BSDS model are expected to provide a clearer picture of the connection of microevolutionary processes and macroevolutionary patterns.

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Multivariate Trait Evolution: Models for the Evolution of the Quantitative Genetic G-Matrix on Phylogenies

Blomberg,

Muniz,

Bui

et al. 2024

Preprint

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Genetic covariance matrices (G-matrices) are a key focus for research and predictions from quantitative genetic evolutionary models of multiple traits. There is a consensus among quantitative geneticists that the G-matrix can evolve through "deep" time. Yet, quantitative genetic models for the evolution of the G-matrix are conspicuously lacking. In contrast, the field of macroevolution has several stochastic models for univariate traits evolving on phylogenies. However, despite much research into multivariate phylogenetic comparative methods, analytical models of how multivariate trait matrices might evolve on phylogenies have not been considered. Here we show how three analytical models for the evolution of matrices and multivariate traits on phylogenies, based on Lie group theory, Riemannian geometry and stochastic differential (diffusion) equations, can be combined to unify quantitative genetics and macroevolutionary theory in a coherent mathematical framework. The models provide a basis for understanding how G-matrices might evolve on phylogenies, and we show how to fit models to data via simulation using Approximate Bayesian Computation. Such models can be used to generate and test hypotheses about the evolution of genetic variances and covariances, together with the evolution of the traits themselves, and how these might vary across a phylogeny. This unification of macroevolutionary theory and quantitative genetics is an important advance in the study of phenotypes, allowing for the construction of a synthetic quantitative theory of the evolution of species and multivariate traits over "deep" time.

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Building an adaptive trait simulator package to infer parametric diffusion model along phylogenetic tree

Cited by 4 publications

References 20 publications

Phylogenetic Curved Optimal Regression for Adaptive Trait Evolution

Phylogenetic Curved Optimal Regression for Adaptive Trait Evolution

A novel phylogenetic comparative method for evaluating the strength of branch-specific directional selection

Multivariate Trait Evolution: Models for the Evolution of the Quantitative Genetic G-Matrix on Phylogenies

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