A Bayesian model of acquisition and clearance of bacterial colonization incorporating within-host variation

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A Bayesian model of acquisition and clearance of bacterial colonization incorporating within-host variation. / Järvenpää, Marko; Sater, Mohamad R.Abdul; Lagoudas, Georgia K.; Blainey, Paul C.; Miller, Loren G.; McKinnell, James A.; Huang, Susan S.; Grad, Yonatan H.; Marttinen, Pekka.

In: PLoS computational biology, Vol. 15, No. 4, e1006534, 01.04.2019, p. 1-25.

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Järvenpää, Marko ; Sater, Mohamad R.Abdul ; Lagoudas, Georgia K. ; Blainey, Paul C. ; Miller, Loren G. ; McKinnell, James A. ; Huang, Susan S. ; Grad, Yonatan H. ; Marttinen, Pekka. / A Bayesian model of acquisition and clearance of bacterial colonization incorporating within-host variation. In: PLoS computational biology. 2019 ; Vol. 15, No. 4. pp. 1-25.

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@article{b9847cd72b494723809babc0acbaa43d,
title = "A Bayesian model of acquisition and clearance of bacterial colonization incorporating within-host variation",
abstract = "Bacterial populations that colonize a host can play important roles in host health, including serving as a reservoir that transmits to other hosts and from which invasive strains emerge, thus emphasizing the importance of understanding rates of acquisition and clearance of colonizing populations. Studies of colonization dynamics have been based on assessment of whether serial samples represent a single population or distinct colonization events. With the use of whole genome sequencing to determine genetic distance between isolates, a common solution to estimate acquisition and clearance rates has been to assume a fixed genetic distance threshold below which isolates are considered to represent the same strain. However, this approach is often inadequate to account for the diversity of the underlying within-host evolving population, the time intervals between consecutive measurements, and the uncertainty in the estimated acquisition and clearance rates. Here, we present a fully Bayesian model that provides probabilities of whether two strains should be considered the same, allowing us to determine bacterial clearance and acquisition from genomes sampled over time. Our method explicitly models the within-host variation using population genetic simulation, and the inference is done using a combination of Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC). We validate the method with multiple carefully conducted simulations and demonstrate its use in practice by analyzing a collection of methicillin resistant Staphylococcus aureus (MRSA) isolates from a large recently completed longitudinal clinical study. An R-code implementation of the method is freely available at: https://github.com/mjarvenpaa/bacterial-colonization-model.",
author = "Marko J{\"a}rvenp{\"a}{\"a} and Sater, {Mohamad R.Abdul} and Lagoudas, {Georgia K.} and Blainey, {Paul C.} and Miller, {Loren G.} and McKinnell, {James A.} and Huang, {Susan S.} and Grad, {Yonatan H.} and Pekka Marttinen",
year = "2019",
month = "4",
day = "1",
doi = "10.1371/journal.pcbi.1006534",
language = "English",
volume = "15",
pages = "1--25",
journal = "PLoS computational biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "4",

}

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TY - JOUR

T1 - A Bayesian model of acquisition and clearance of bacterial colonization incorporating within-host variation

AU - Järvenpää, Marko

AU - Sater, Mohamad R.Abdul

AU - Lagoudas, Georgia K.

AU - Blainey, Paul C.

AU - Miller, Loren G.

AU - McKinnell, James A.

AU - Huang, Susan S.

AU - Grad, Yonatan H.

AU - Marttinen, Pekka

PY - 2019/4/1

Y1 - 2019/4/1

N2 - Bacterial populations that colonize a host can play important roles in host health, including serving as a reservoir that transmits to other hosts and from which invasive strains emerge, thus emphasizing the importance of understanding rates of acquisition and clearance of colonizing populations. Studies of colonization dynamics have been based on assessment of whether serial samples represent a single population or distinct colonization events. With the use of whole genome sequencing to determine genetic distance between isolates, a common solution to estimate acquisition and clearance rates has been to assume a fixed genetic distance threshold below which isolates are considered to represent the same strain. However, this approach is often inadequate to account for the diversity of the underlying within-host evolving population, the time intervals between consecutive measurements, and the uncertainty in the estimated acquisition and clearance rates. Here, we present a fully Bayesian model that provides probabilities of whether two strains should be considered the same, allowing us to determine bacterial clearance and acquisition from genomes sampled over time. Our method explicitly models the within-host variation using population genetic simulation, and the inference is done using a combination of Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC). We validate the method with multiple carefully conducted simulations and demonstrate its use in practice by analyzing a collection of methicillin resistant Staphylococcus aureus (MRSA) isolates from a large recently completed longitudinal clinical study. An R-code implementation of the method is freely available at: https://github.com/mjarvenpaa/bacterial-colonization-model.

AB - Bacterial populations that colonize a host can play important roles in host health, including serving as a reservoir that transmits to other hosts and from which invasive strains emerge, thus emphasizing the importance of understanding rates of acquisition and clearance of colonizing populations. Studies of colonization dynamics have been based on assessment of whether serial samples represent a single population or distinct colonization events. With the use of whole genome sequencing to determine genetic distance between isolates, a common solution to estimate acquisition and clearance rates has been to assume a fixed genetic distance threshold below which isolates are considered to represent the same strain. However, this approach is often inadequate to account for the diversity of the underlying within-host evolving population, the time intervals between consecutive measurements, and the uncertainty in the estimated acquisition and clearance rates. Here, we present a fully Bayesian model that provides probabilities of whether two strains should be considered the same, allowing us to determine bacterial clearance and acquisition from genomes sampled over time. Our method explicitly models the within-host variation using population genetic simulation, and the inference is done using a combination of Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC). We validate the method with multiple carefully conducted simulations and demonstrate its use in practice by analyzing a collection of methicillin resistant Staphylococcus aureus (MRSA) isolates from a large recently completed longitudinal clinical study. An R-code implementation of the method is freely available at: https://github.com/mjarvenpaa/bacterial-colonization-model.

UR - http://www.scopus.com/inward/record.url?scp=85065555964&partnerID=8YFLogxK

U2 - 10.1371/journal.pcbi.1006534

DO - 10.1371/journal.pcbi.1006534

M3 - Article

VL - 15

SP - 1

EP - 25

JO - PLoS computational biology

JF - PLoS computational biology

SN - 1553-734X

IS - 4

M1 - e1006534

ER -

ID: 34094722