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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

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Published on: December 7, 2021

Reconstructing disease outbreaks from genetic data: a graph approach.

T Jombart1, R M Eggo, P J Dodd

  • 1Department of Infectious Disease Epidemiology, MRC Centre for Outbreak Analysis and Modelling, Imperial College Faculty of Medicine, London, UK. t.jombart@imperial.ac.uk

Heredity
|June 17, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel graph-based method for directly reconstructing pathogen transmission trees from genetic data. The approach outperforms traditional phylogenetic methods, especially in reconstructing outbreak genealogies.

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Area of Science:

  • Epidemiology
  • Genomic epidemiology
  • Computational biology

Background:

  • Genetic sequence data analysis is crucial for public health planning and understanding pathogen spatiotemporal dynamics during outbreaks.
  • Phylogenetic methods, while informative, do not directly reconstruct transmission trees and can be inadequate for densely sampled recent outbreaks.
  • Inferring most recent common ancestors between isolates may not accurately represent complex transmission events in recent outbreaks.

Purpose of the Study:

  • To introduce a novel graph-based method for direct transmission tree reconstruction from genetic data.
  • To demonstrate the method's efficacy in situations where classical phylogenetic approaches falter.
  • To analyze the early global spread of the swine-origin A/H1N1 influenza pandemic using the new methodology.

Main Methods:

  • Development of a novel graph-based approach for reconstructing transmission trees directly from genetic sequence data.
  • Utilizing simulated data to assess the method's performance in reconstructing isolate genealogies.
  • Application of the method to analyze genetic data from the early stages of the A/H1N1 influenza pandemic.

Main Results:

  • The novel graph-based method efficiently reconstructs isolate genealogies, particularly in scenarios where traditional phylogenetic methods are less effective.
  • Analysis of A/H1N1 influenza data revealed the likely history of the pandemic's worldwide spread.
  • The method successfully reconstructed transmission patterns from hemagglutinin and neuraminidase gene sequences of 433 isolates.

Conclusions:

  • The introduced graph-based methodology offers a powerful new tool for reconstructing transmission trees directly from genetic data.
  • This approach enhances the analysis of pathogen spatiotemporal dynamics and disease outbreak investigations.
  • The method provides new perspectives for utilizing genetic data in epidemiology and public health planning.