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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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Bayesian nonparametric clustering in phylogenetics: modeling antigenic evolution in influenza.

Gabriela B Cybis¹, Janet S Sinsheimer^2,3,4, Trevor Bedford⁵

¹Department of Statistics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.

Statistics in Medicine

|January 19, 2017

Summary

This study introduces a new framework to model influenza's genetic and antigenic evolution, improving our understanding of viral changes and epidemic dynamics. The method helps visualize antigenic drift in influenza strains.

Keywords:

Bayesian nonparametric mixture models antigenic cartography phylodynamics

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Published on: June 28, 2013

Area of Science:

Virology and Epidemiology
Computational Biology
Immunology

Background:

Influenza causes significant global mortality, with antigenic variability being a major epidemiological challenge.
Antigenic cartography visualizes viral strain differences using multidimensional scaling on binding assay data.
Existing methods aim to correlate antigenic clustering with viral sequence evolution.

Purpose of the Study:

To develop a framework for jointly modeling genetic and antigenic evolution in influenza viruses.
To enhance the understanding of antigenic group dynamics throughout epidemics.
To improve the correlation between antigenic clusters and viral sequence evolution.

Main Methods:

Combined multidimensional scaling of binding assay data with Bayesian phylogenetic methods.
Introduced a phylogenetic Chinese restaurant process for nonparametric clustering of antigenic data.
Incorporated phylogenetic dependency structures into the modeling of antigenic clusters.

Main Results:

The proposed framework successfully models the joint evolution of genetic and antigenic properties of influenza strains.
Demonstrated improved understanding of antigenic evolution dynamics during epidemics.
Applications to H1N1 influenza showed the model's effectiveness in correlating genetic information with antigenic clusters.

Conclusions:

The novel framework provides a robust method for analyzing influenza antigenic variability.
Integrating genetic and antigenic data offers deeper insights into viral evolution and epidemic patterns.
This approach aids in tracking and understanding the spread of influenza strains.