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Bayesian inference of infectious disease transmission from whole-genome sequence data

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Didelot, Xavier, Gardy, Jennifer and Colijn, Caroline (2014) Bayesian inference of infectious disease transmission from whole-genome sequence data. Molecular Biology and Evolution, 31 (7). pp. 1869-1879. doi:10.1093/molbev/msu121

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Official URL: http://dx.doi.org/10.1093/molbev/msu121

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Abstract

Genomics is increasingly being used to investigate disease outbreaks, but an important question remains unanswered—how well do genomic data capture known transmission events, particularly for pathogens with long carriage periods or large within-host population sizes? Here we present a novel Bayesian approach to reconstruct densely sampled outbreaks from genomic data while considering within-host diversity. We infer a time-labeled phylogeny using Bayesian evolutionary analysis by sampling trees (BEAST), and then infer a transmission network via a Monte Carlo Markov chain. We find that under a realistic model of within-host evolution, reconstructions of simulated outbreaks contain substantial uncertainty even when genomic data reflect a high substitution rate. Reconstruction of a real-world tuberculosis outbreak displayed similar uncertainty, although the correct source case and several clusters of epidemiologically linked cases were identified. We conclude that genomics cannot wholly replace traditional epidemiology but that Bayesian reconstructions derived from sequence data may form a useful starting point for a genomic epidemiology investigation.

Item Type: Journal Article
Divisions: Faculty of Science > Life Sciences (2010- )
Journal or Publication Title: Molecular Biology and Evolution
Publisher: Oxford University Press
ISSN: 0737-4038
Official Date: 1 July 2014
Dates:
DateEvent
1 July 2014Published
8 April 2014Available
Volume: 31
Number: 7
Page Range: pp. 1869-1879
DOI: 10.1093/molbev/msu121
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access

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