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Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology

The shapes of phylogenetic trees relating virus populations are determined by the adaptation of viruses within each host, and by the transmission of viruses among hosts. Phylodynamic inference attempts to reverse this flow of information, estimating parameters of these processes from the shape of a...

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Autor principal: Poon, Art F.Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4540972/
https://www.ncbi.nlm.nih.gov/pubmed/26006189
http://dx.doi.org/10.1093/molbev/msv123
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author Poon, Art F.Y.
author_facet Poon, Art F.Y.
author_sort Poon, Art F.Y.
collection PubMed
description The shapes of phylogenetic trees relating virus populations are determined by the adaptation of viruses within each host, and by the transmission of viruses among hosts. Phylodynamic inference attempts to reverse this flow of information, estimating parameters of these processes from the shape of a virus phylogeny reconstructed from a sample of genetic sequences from the epidemic. A key challenge to phylodynamic inference is quantifying the similarity between two trees in an efficient and comprehensive way. In this study, I demonstrate that a new distance measure, based on a subset tree kernel function from computational linguistics, confers a significant improvement over previous measures of tree shape for classifying trees generated under different epidemiological scenarios. Next, I incorporate this kernel-based distance measure into an approximate Bayesian computation (ABC) framework for phylodynamic inference. ABC bypasses the need for an analytical solution of model likelihood, as it only requires the ability to simulate data from the model. I validate this “kernel-ABC” method for phylodynamic inference by estimating parameters from data simulated under a simple epidemiological model. Results indicate that kernel-ABC attained greater accuracy for parameters associated with virus transmission than leading software on the same data sets. Finally, I apply the kernel-ABC framework to study a recent outbreak of a recombinant HIV subtype in China. Kernel-ABC provides a versatile framework for phylodynamic inference because it can fit a broader range of models than methods that rely on the computation of exact likelihoods.
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spelling pubmed-45409722015-08-20 Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology Poon, Art F.Y. Mol Biol Evol Methods The shapes of phylogenetic trees relating virus populations are determined by the adaptation of viruses within each host, and by the transmission of viruses among hosts. Phylodynamic inference attempts to reverse this flow of information, estimating parameters of these processes from the shape of a virus phylogeny reconstructed from a sample of genetic sequences from the epidemic. A key challenge to phylodynamic inference is quantifying the similarity between two trees in an efficient and comprehensive way. In this study, I demonstrate that a new distance measure, based on a subset tree kernel function from computational linguistics, confers a significant improvement over previous measures of tree shape for classifying trees generated under different epidemiological scenarios. Next, I incorporate this kernel-based distance measure into an approximate Bayesian computation (ABC) framework for phylodynamic inference. ABC bypasses the need for an analytical solution of model likelihood, as it only requires the ability to simulate data from the model. I validate this “kernel-ABC” method for phylodynamic inference by estimating parameters from data simulated under a simple epidemiological model. Results indicate that kernel-ABC attained greater accuracy for parameters associated with virus transmission than leading software on the same data sets. Finally, I apply the kernel-ABC framework to study a recent outbreak of a recombinant HIV subtype in China. Kernel-ABC provides a versatile framework for phylodynamic inference because it can fit a broader range of models than methods that rely on the computation of exact likelihoods. Oxford University Press 2015-09 2015-05-29 /pmc/articles/PMC4540972/ /pubmed/26006189 http://dx.doi.org/10.1093/molbev/msv123 Text en © The Author 2015. 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
Poon, Art F.Y.
Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology
title Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology
title_full Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology
title_fullStr Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology
title_full_unstemmed Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology
title_short Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology
title_sort phylodynamic inference with kernel abc and its application to hiv epidemiology
topic Methods
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4540972/
https://www.ncbi.nlm.nih.gov/pubmed/26006189
http://dx.doi.org/10.1093/molbev/msv123
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