DUALCOR: a phylogenetic comparative method to evaluate phylogenetic correlation between a character and a non‐evolving external variable

Cladistics ◽  
2021 ◽  
Author(s):  
Norberto P. Giannini ◽  
Pablo A. Goloboff
2019 ◽  
Author(s):  
Yuki Haba ◽  
Nobuyuki Kutsukake

AbstractOne major challenge of using the phylogenetic comparative method (PCM) is the analysis of the evolution of interrelated continuous and discrete traits in a single multivariate statistical framework. In addition, more intricate parameters such as branch-specific directional selection have rarely been integrated into such multivariate PCM frameworks. Here, originally motivated to analyze the complex evolutionary trajectories of group size (continuous variable) and social systems (discrete variable) in African subterranean rodents, we develop a flexible approach using approximate Bayesian computation (ABC). Specifically, our multivariate ABC-PCM method allows the user to flexibly model an underlying latent evolutionary function between continuous and discrete traits. The ABC-PCM also simultaneously incorporates complex evolutionary parameters such as branch-specific selection. This study highlights the flexibility of ABC-PCMs in analyzing the evolution of phenotypic traits interrelated in a complex manner.


2002 ◽  
Vol 51 (6) ◽  
pp. 873-880 ◽  
Author(s):  
Emília P. Martins ◽  
Elizabeth A. Housworth

Evolution ◽  
2002 ◽  
Vol 56 (1) ◽  
pp. 1 ◽  
Author(s):  
Emília P. Martins ◽  
José Alexandre F. Diniz-Filho ◽  
Elizabeth A. Housworth

Evolution ◽  
2002 ◽  
Vol 56 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Emilia P. Martins ◽  
Joseg13 AlexandreF. Diniz-Filho ◽  
Elizabeth A. Housworth

2012 ◽  
Vol 314 ◽  
pp. 204-215 ◽  
Author(s):  
Krzysztof Bartoszek ◽  
Jason Pienaar ◽  
Petter Mostad ◽  
Staffan Andersson ◽  
Thomas F. Hansen

2016 ◽  
Author(s):  
Randi H. Griffin ◽  
Gabriel S. Yapuncich

AbstractSmaers, Mongle & Kandler (2016) (Biological Journal of the Linnean Society, 118: 78-94) introduced a new phylogenetic comparative method, multiple variance Brownian motion (mvBM), for reconstructing ancestral states given a phylogenetic tree and continuous trait data. The authors conducted a simulation study and argued that mvBM outperforms constant variance Brownian motion (BM) when rates of evolution vary across the phylogeny. In this comment, we argue that mvBM is not a viable statistical method because it is fundamentally a circular analysis that overfits phylogenetic branch lengths to the data. We further argue that the comparison of mvBM to BM under conditions where the assumptions of BM are clearly violated is not an informative performance analysis, and that the simulation study of Smaers et al. (2016) exaggerates the performance of mvBM by focusing on a narrow range of simulation conditions and reporting aggregated accuracy metrics that obscure severe inaccuracy and bias in its ancestral state estimates. Our arguments are supported by simulation results. We conclude that mvBM is not a viable phylogenetic comparative method.


2018 ◽  
Author(s):  
Dwueng-Chwuan Jhwueng

In genetic studies, quantitative traits are found possibly associated with genetic data. Due to advanced sequencing technology, many methods have been proposed in genome wide association study (GWAS) to search the single nucleotide polymorphism (SNP) associated with the traits. Currently several methods that account for the evolutionary relatedness among individuals were developed. When comparing with conventional methods without evolutionary relatedness among individuals, tree based methods are found to have better performance when the population structure increases. In this work, we extend a couple of methods in previous studies by varying the magnitude of relatedness. The magnitude of relatedness of the evolutionary history is controlled by an Ornstein-Uhlenbeck (OU) process through its parameters. Our method combines a pertinent process and phylogenetic comparative method where the incorporated evolutionary history is built by SNP data. We perform simulation as well as analyze drosophila longevity data set.


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