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Estimating Phylogenies from Shape and Similar Multidimensional Data: Why It Is Not Reliable.

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Estimating evolutionary relationships using multidimensional data like gene expression is challenging. High dimensionality aids reliable phylogeny estimation, but phenotypic integration and stabilizing selection severely reduce accuracy.

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Area of Science:

  • Evolutionary Biology
  • Computational Biology
  • Phylogenetics

Background:

  • Controversy exists regarding the use of multidimensional data (geometric morphometrics, gene expression) for phylogenetic estimation.
  • Simulations are crucial for understanding factors influencing the reliability of phylogenetic inference from complex datasets.

Purpose of the Study:

  • To investigate the reliability of phylogenetic estimation using multidimensional phenotype data.
  • To identify key evolutionary factors impacting phylogenetic accuracy.

Main Methods:

  • Simulations of evolution in multidimensional phenotype spaces with varying dimensionality and evolutionary models.
  • Comparison of phylogenetic methods including squared-change parsimony, maximum likelihood, Wagner, Euclidean parsimony, neighbor-joining, and UPGMA.
  • Analysis of phylogenies with 4 to 8 taxa under Brownian motion and stabilizing selection models.

Main Results:

  • Squared-change parsimony and maximum likelihood outperformed other methods.
  • High dimensionality under isotropic Brownian motion supports reliable phylogeny estimation.
  • Phenotypic integration and stabilizing selection significantly reduce phylogenetic estimate reliability.
  • Effects of dimensionality and integration extend to phylogenies with more than four taxa.

Conclusions:

  • Multidimensional data can be unreliable for phylogenetic estimation under plausible evolutionary models.
  • Phenotypic integration and specific evolutionary pressures (e.g., stabilizing selection) pose significant challenges.
  • Careful consideration of evolutionary models and data properties is essential for accurate phylogenetic inference.