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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
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Bayesian codon substitution modelling to identify sources of pathogen evolutionary rate variation.
Guy Baele1, Marc A Suchard2, Filip Bielejec1
11Department of Microbiology and Immunology, Rega Institute, KU Leuven - University of Leuven, Leuven, Belgium.
Microbial Genomics
|March 29, 2017
Summary
This study introduces a new model to estimate pathogen evolution rates. Faster disease progression in HIV-1 patients correlates with higher synonymous substitution rates, and climate impacts rabies virus evolution.
Area of Science:
- Evolutionary biology
- Virology
- Genomics
Background:
- Phylodynamic analyses require understanding how epidemic processes leave molecular traces in pathogen genomes.
- Identifying factors influencing the rate and manner of these genomic footprints is key to understanding infectious disease evolution.
- Distinguishing between synonymous and non-synonymous substitution rates is vital for exploring evolutionary rate variations.
Purpose of the Study:
- To develop and apply a codon substitution model within a Bayesian framework for estimating absolute synonymous and non-synonymous substitution rates.
- To investigate how host factors and ecological variables influence pathogen evolutionary tempos.
Main Methods:
- Implementation of a codon substitution model in a Bayesian statistical framework.
- Application of hierarchical phylogenetic modeling with fixed effects.
- Analysis of within-host HIV-1 data and interspecies rabies virus data.
Main Results:
- Faster absolute synonymous substitution rates observed in HIV-1 viral populations from patients with more rapid disease progression, likely due to increased replication.
- Climate was identified as a predictor of absolute synonymous substitution rates in rabies viruses, linked to bat activity and transmission.
Conclusions:
- The developed model provides a robust method for estimating absolute substitution rates, offering insights into pathogen evolutionary dynamics.
- Host ecology and environmental factors, such as climate, significantly shape the tempo of pathogen evolution.
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