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Phylodynamic Inference across Epidemic Scales

Within-host genetic diversity and large transmission bottlenecks confound phylodynamic inference of epidemiological dynamics. Conventional phylodynamic approaches assume that nodes in a time-scaled pathogen phylogeny correspond closely to the time of transmission between hosts that are ancestral to...

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Autores principales: Volz, Erik M., Romero-Severson, Ethan, Leitner, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5400386/
https://www.ncbi.nlm.nih.gov/pubmed/28204593
http://dx.doi.org/10.1093/molbev/msx077
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author Volz, Erik M.
Romero-Severson, Ethan
Leitner, Thomas
author_facet Volz, Erik M.
Romero-Severson, Ethan
Leitner, Thomas
author_sort Volz, Erik M.
collection PubMed
description Within-host genetic diversity and large transmission bottlenecks confound phylodynamic inference of epidemiological dynamics. Conventional phylodynamic approaches assume that nodes in a time-scaled pathogen phylogeny correspond closely to the time of transmission between hosts that are ancestral to the sample. However, when hosts harbor diverse pathogen populations, node times can substantially pre-date infection times. Imperfect bottlenecks can cause lineages sampled in different individuals to coalesce in unexpected patterns. To address realistic violations of standard phylodynamic assumptions we developed a new inference approach based on a multi-scale coalescent model, accounting for nonlinear epidemiological dynamics, heterogeneous sampling through time, non-negligible genetic diversity of pathogens within hosts, and imperfect transmission bottlenecks. We apply this method to HIV-1 and Ebola virus (EBOV) outbreak sequence data, illustrating how and when conventional phylodynamic inference may give misleading results. Within-host diversity of HIV-1 causes substantial upwards bias in the number of infected hosts using conventional coalescent models, but estimates using the multi-scale model have greater consistency with reported number of diagnoses through time. In contrast, we find that within-host diversity of EBOV has little influence on estimated numbers of infected hosts or reproduction numbers, and estimates are highly consistent with the reported number of diagnoses through time. The multi-scale coalescent also enables estimation of within-host effective population size using single sequences from a random sample of patients. We find within-host population genetic diversity of HIV-1 p17 to be [Formula: see text] (95% CI 0.0066–0.023), which is lower than estimates based on HIV envelope serial sequencing of individual patients.
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spelling pubmed-54003862017-04-28 Phylodynamic Inference across Epidemic Scales Volz, Erik M. Romero-Severson, Ethan Leitner, Thomas Mol Biol Evol Methods Within-host genetic diversity and large transmission bottlenecks confound phylodynamic inference of epidemiological dynamics. Conventional phylodynamic approaches assume that nodes in a time-scaled pathogen phylogeny correspond closely to the time of transmission between hosts that are ancestral to the sample. However, when hosts harbor diverse pathogen populations, node times can substantially pre-date infection times. Imperfect bottlenecks can cause lineages sampled in different individuals to coalesce in unexpected patterns. To address realistic violations of standard phylodynamic assumptions we developed a new inference approach based on a multi-scale coalescent model, accounting for nonlinear epidemiological dynamics, heterogeneous sampling through time, non-negligible genetic diversity of pathogens within hosts, and imperfect transmission bottlenecks. We apply this method to HIV-1 and Ebola virus (EBOV) outbreak sequence data, illustrating how and when conventional phylodynamic inference may give misleading results. Within-host diversity of HIV-1 causes substantial upwards bias in the number of infected hosts using conventional coalescent models, but estimates using the multi-scale model have greater consistency with reported number of diagnoses through time. In contrast, we find that within-host diversity of EBOV has little influence on estimated numbers of infected hosts or reproduction numbers, and estimates are highly consistent with the reported number of diagnoses through time. The multi-scale coalescent also enables estimation of within-host effective population size using single sequences from a random sample of patients. We find within-host population genetic diversity of HIV-1 p17 to be [Formula: see text] (95% CI 0.0066–0.023), which is lower than estimates based on HIV envelope serial sequencing of individual patients. Oxford University Press 2017-05 2017-02-14 /pmc/articles/PMC5400386/ /pubmed/28204593 http://dx.doi.org/10.1093/molbev/msx077 Text en © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methods
Volz, Erik M.
Romero-Severson, Ethan
Leitner, Thomas
Phylodynamic Inference across Epidemic Scales
title Phylodynamic Inference across Epidemic Scales
title_full Phylodynamic Inference across Epidemic Scales
title_fullStr Phylodynamic Inference across Epidemic Scales
title_full_unstemmed Phylodynamic Inference across Epidemic Scales
title_short Phylodynamic Inference across Epidemic Scales
title_sort phylodynamic inference across epidemic scales
topic Methods
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5400386/
https://www.ncbi.nlm.nih.gov/pubmed/28204593
http://dx.doi.org/10.1093/molbev/msx077
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