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A Practical Guide to Phylogenetics for Nonexperts
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Automatic generation of evolutionary hypotheses using mixed Gaussian phylogenetic models.

Venelin Mitov1,2, Krzysztof Bartoszek3, Tanja Stadler4,2

  • 1Computational Evolution Group, Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland; vmitov@gmail.com.

Proceedings of the National Academy of Sciences of the United States of America
|August 4, 2019
PubMed
Summary
This summary is machine-generated.

Phylogenetic comparative methods now use mixed Gaussian phylogenetic models (MGPMs) to better model trait evolution across large species trees. This study introduces a new method to fit these complex models, revealing 12 distinct evolutionary regimes in mammals.

Keywords:
clusteringcorrelated quantitative traitsevolutionary regimesnonultrametric treeselection

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Area of Science:

  • Evolutionary biology
  • Phylogenetics
  • Quantitative genetics

Background:

  • Phylogenetic comparative methods analyze trait evolution using phylogenetic trees and species measurements.
  • Current Gaussian phylogenetic models (e.g., Brownian motion) struggle with large, complex trees due to evolutionary heterogeneity.
  • Existing methods for incorporating model shifts are limited to intramodel parameter changes, neglecting broader evolutionary regime shifts.

Purpose of the Study:

  • To develop and apply an approximate maximum-likelihood method for fitting Mixed Gaussian Phylogenetic Models (MGPMs).
  • To analyze trait evolution in mammals using a comprehensive dataset and the proposed MGPM fitting method.
  • To investigate the evolution of brain-body-mass allometry over 160 million years.

Main Methods:

  • Proposed an approximate maximum-likelihood method for fitting MGPMs.
  • Applied the method to comparative data including possibly incomplete measurements for multiple traits.
  • Utilized the largest published phylogenetic tree of mammal species with body and brain mass data.

Main Results:

  • Demonstrated strong statistical support for an MGPM with 12 distinct evolutionary regimes in mammal trait evolution.
  • The new method successfully fitted complex evolutionary models to a large dataset.
  • Identified significant heterogeneity in evolutionary processes across different mammalian lineages.

Conclusions:

  • MGPMs provide a more realistic framework for modeling continuous trait evolution across large phylogenetic trees.
  • The identified 12 evolutionary regimes suggest distinct patterns in mammalian trait evolution.
  • The study proposes a novel hypothesis for the evolution of brain-body-mass allometry in mammals over the last 160 million years.