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Related Experiment Video

Updated: May 29, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Statistics and truth in phylogenomics.

Sudhir Kumar¹, Alan J Filipski, Fabia U Battistuzzi

¹Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University, Arizona, USA. s.kumar@asu.edu

Molecular Biology and Evolution

|August 30, 2011

Summary

Phylogenomics uses genome data to study species relationships. Focusing on effect sizes and bias, rather than just P values, is crucial for accurate evolutionary inferences from large datasets.

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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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Area of Science:

Evolutionary biology
Genomics
Bioinformatics

Background:

Phylogenomics leverages genome-scale data for evolutionary analysis.
Decreasing sequencing costs enable large, densely sampled datasets.
Phylogenetic inference aims for high statistical confidence (P value).

Purpose of the Study:

To address challenges in establishing true evolutionary relationships using phylogenomics.
To highlight the importance of assessing robustness to biological factors that can bias results.
To advocate for considering effect sizes alongside P values for reliable inferences.

Main Methods:

Theoretical overview of statistical inference in phylogenomics.
Discussion of practical aspects of effect sizes, bias, and P values.
Analysis of data limitations in specific phylogenomic applications.

Main Results:

Increasing reports of significant P values for conflicting hypotheses.
Potential for biological factors to systematically mislead statistical estimation.
Sequence data may be insufficient for certain detailed analyses.

Conclusions:

Robustness assessment against bias is key to avoiding incorrect phylogenomic inferences.
Emphasis on effect sizes and biological relevance is warranted, especially when P values are misleading or data is limited.
A balanced approach considering P values, effect sizes, and bias is essential for inferring evolutionary truth.