Bayesian Inference of Clonal Expansions in a Dated Phylogeny.

Area of Science:

• Microbial population genomics
• Phylodynamics
• Genomic epidemiology

Background:

• Traditional microbial population genetics models assume uniform population size dynamics across all lineages.
• Neutral and selective events can lead to clonal expansion, creating lineage-specific growth patterns.
• This differential growth causes asymmetries in phylogenetic trees, posing analytical challenges.

Purpose of the Study:

• To develop a formal model for analyzing clonal expansion events and their impact on phylogenetic branching patterns.
• To enable the inference of clonal expansion parameters from dated phylogenies.
• To assess the evolutionary trajectory and long-term potential of expanding lineages.

Main Methods:

• Developed a mathematical model to describe clonal expansion dynamics.
• Utilized Bayesian statistics for parameter inference from dated phylogenetic trees.
• Applied the model to both simulated and real-world microbial genomic data.

Main Results:

• The model successfully infers the probability, emergence date, and phylodynamic trajectory of clonal expansion events.
• Demonstrated the model's applicability on diverse datasets, validating its performance.
• Identified key features in microbial evolution and infectious disease epidemiology through model inference.

Conclusions:

• The developed clonal expansion model provides a formal framework for analyzing phylogenetic asymmetries.
• Inference using this model can reveal crucial insights into pathogen evolution and epidemiology.
• This methodology aids in understanding lineage dynamics for effective disease control measures.