Complex Models of Sequence Evolution Improve Fit, But Not Gene Tree Discordance, for Tetrapod Mitogenomes.

Area of Science:

• Phylogenetics
• Molecular Evolution
• Bioinformatics

Background:

• Gene tree discordance is common in phylogenomic data, often attributed to biological factors.
• A previous study on tetrapod mitochondrial genomes found high gene tree discordance, even when controlling for biological variation.
• This prior work suggested inadequate sequence evolution models contributed to the observed discordance.

Purpose of the Study:

• To investigate if more complex sequence evolution models reduce gene tree discordance in mitochondrial data.
• To test the impact of incorporating heterotachy (site-specific rate variation) and codon-specific evolutionary patterns on gene tree inference.
• To further explore the causes of persistent gene tree discordance, termed the "Mito-Phylo Paradox".

Main Methods:

• Analysis of tetrapod mitochondrial genome data sets.
• Application of two advanced sequence evolution models: a covarion model (for heterotachy) and a partitioned model (for codon position variation).
• Comparison of gene tree discordance levels inferred by these advanced models against simpler models used previously.

Main Results:

• Both the covarion and partitioned models provided a better fit to the sequence data than the models used in the prior study.
• The covarion model was consistently preferred as the size of the data set increased.
• Despite improved model fit, both advanced models still inferred highly discordant mitochondrial gene trees.

Conclusions:

• More complex and biologically realistic models of sequence evolution improve data fit but do not fully resolve gene tree discordance in mitochondrial data.
• The persistent high levels of gene tree discordance, even with better models, suggest that biological factors might indeed be a significant contributor, reopening the investigation into the "Mito-Phylo Paradox".
• Further research is needed to reconcile the observed discordance with evolutionary processes and model adequacy.