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Updated: Nov 12, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Evolutionary Sample Size and Consilience in Phylogenetic Comparative Analysis.

Jacob D Gardner¹, Chris L Organ¹

¹Department of Earth Sciences, Montana State University, Bozeman, MT 59717, USA.

Systematic Biology

|March 15, 2021

Summary

This summary is machine-generated.

Phylogenetic comparative methods (PCMs) can misinterpret single evolutionary events as correlated evolution. Researchers should maximize evolutionary sample sizes and use consilience for robust evolutionary studies.

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Last Updated: Nov 12, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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Published on: August 14, 2018

A Practical Guide to Phylogenetics for Nonexperts

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Published on: February 5, 2014

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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Published on: December 7, 2021

Area of Science:

Evolutionary biology
Comparative genomics

Background:

Phylogenetic comparative methods (PCMs) are vital for studying evolution and adaptation.
Common PCMs for discrete traits often misinterpret single evolutionary transitions as correlated evolution.

Purpose of the Study:

To identify limitations in current PCMs for discrete traits, particularly concerning single evolutionary transitions.
To propose solutions for accurately assessing correlated evolution in phylogenetic studies.

Main Methods:

Simulations were used to evaluate the performance of PCMs, specifically Pagel's Discrete model, under scenarios with small evolutionary sample sizes.
Analysis focused on rate parameter estimation and model selection biases.
Introduced the phylogenetic imbalance ratio for assessing discrete trait models.

Main Results:

Pagel's Discrete model frequently detects spurious correlated evolution when traits evolve only once.
Poor rate parameter estimation occurs due to small effective evolutionary sample sizes.
Models with continuous data distributions are less prone to bias but still affected by small sample sizes.

Conclusions:

Researchers should design studies to maximize evolutionary sample sizes and test a priori hypotheses.
Assessing model suitability using metrics like the phylogenetic imbalance ratio is crucial.
A consilience of evidence from multiple fields (e.g., biogeography, developmental biology) strengthens evolutionary hypotheses.