PandemieGrippaleV1, PubMed, Checkpoint, bibRecord, 000E65

Reconstructing disease outbreaks from genetic data: a graph approach.

Identifieur interne : 000E65 ( PubMed/Checkpoint ); précédent : 000E64; suivant : 000E66

Reconstructing disease outbreaks from genetic data: a graph approach.

Auteurs : T. Jombart [Royaume-Uni] ; R M Eggo ; P J Dodd ; F. Balloux

Source :

Heredity [ 1365-2540 ] ; 2011.

RBID : pubmed:20551981

Descripteurs français

KwdFr :
- Dynamique des populations, Grippe humaine (virologie), Grippe humaine (épidémiologie), Humains, Hémagglutinines (génétique), Loi de Poisson, Modèles génétiques, Pandémies, Pedigree, Phylogénie, Sialidase (génétique), Simulation numérique, Sous-type H1N1 du virus de la grippe A (génétique), Sous-type H1N1 du virus de la grippe A (isolement et purification).
MESH :
- génétique : Hémagglutinines, Sialidase, Sous-type H1N1 du virus de la grippe A.
- isolement et purification : Sous-type H1N1 du virus de la grippe A.
- virologie : Grippe humaine.
- épidémiologie : Grippe humaine.
- Dynamique des populations, Humains, Loi de Poisson, Modèles génétiques, Pandémies, Pedigree, Phylogénie, Simulation numérique.

English descriptors

KwdEn :
- Computer Simulation, Hemagglutinins (genetics), Humans, Influenza A Virus, H1N1 Subtype (genetics), Influenza A Virus, H1N1 Subtype (isolation & purification), Influenza, Human (epidemiology), Influenza, Human (virology), Models, Genetic, Neuraminidase (genetics), Pandemics, Pedigree, Phylogeny, Poisson Distribution, Population Dynamics.
MESH :
- chemical , genetics : Hemagglutinins, Neuraminidase.
- epidemiology : Influenza, Human.
- genetics : Influenza A Virus, H1N1 Subtype.
- isolation & purification : Influenza A Virus, H1N1 Subtype.
- virology : Influenza, Human.
- Computer Simulation, Humans, Models, Genetic, Pandemics, Pedigree, Phylogeny, Poisson Distribution, Population Dynamics.

Abstract

Epidemiology and public health planning will increasingly rely on the analysis of genetic sequence data. In particular, genetic data coupled with dates and locations of sampled isolates can be used to reconstruct the spatiotemporal dynamics of pathogens during outbreaks. Thus far, phylogenetic methods have been used to tackle this issue. Although these approaches have proved useful for informing on the spread of pathogens, they do not aim at directly reconstructing the underlying transmission tree. Instead, phylogenetic models infer most recent common ancestors between pairs of isolates, which can be inadequate for densely sampled recent outbreaks, where the sample includes ancestral and descendent isolates. In this paper, we introduce a novel method based on a graph approach to reconstruct transmission trees directly from genetic data. Using simulated data, we show that our approach can efficiently reconstruct genealogies of isolates in situations where classical phylogenetic approaches fail to do so. We then illustrate our method by analyzing data from the early stages of the swine-origin A/H1N1 influenza pandemic. Using 433 isolates sequenced at both the hemagglutinin and neuraminidase genes, we reconstruct the likely history of the worldwide spread of this new influenza strain. The presented methodology opens new perspectives for the analysis of genetic data in the context of disease outbreaks.

DOI: 10.1038/hdy.2010.78
PubMed: 20551981

Affiliations:

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Le document en format XML

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<front><div type="abstract" xml:lang="en">Epidemiology and public health planning will increasingly rely on the analysis of genetic sequence data. In particular, genetic data coupled with dates and locations of sampled isolates can be used to reconstruct the spatiotemporal dynamics of pathogens during outbreaks. Thus far, phylogenetic methods have been used to tackle this issue. Although these approaches have proved useful for informing on the spread of pathogens, they do not aim at directly reconstructing the underlying transmission tree. Instead, phylogenetic models infer most recent common ancestors between pairs of isolates, which can be inadequate for densely sampled recent outbreaks, where the sample includes ancestral and descendent isolates. In this paper, we introduce a novel method based on a graph approach to reconstruct transmission trees directly from genetic data. Using simulated data, we show that our approach can efficiently reconstruct genealogies of isolates in situations where classical phylogenetic approaches fail to do so. We then illustrate our method by analyzing data from the early stages of the swine-origin A/H1N1 influenza pandemic. Using 433 isolates sequenced at both the hemagglutinin and neuraminidase genes, we reconstruct the likely history of the worldwide spread of this new influenza strain. The presented methodology opens new perspectives for the analysis of genetic data in the context of disease outbreaks.</div>
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   |wiki=    Sante
   |area=    PandemieGrippaleV1
   |flux=    PubMed
   |étape=   Checkpoint
   |type=    RBID
   |clé=     pubmed:20551981
   |texte=   Reconstructing disease outbreaks from genetic data: a graph approach.
}}

Pour générer des pages wiki

HfdIndexSelect -h $EXPLOR_AREA/Data/PubMed/Checkpoint/RBID.i   -Sk "pubmed:20551981" \
       | HfdSelect -Kh $EXPLOR_AREA/Data/PubMed/Checkpoint/biblio.hfd   \
       | NlmPubMed2Wicri -a PandemieGrippaleV1

This area was generated with Dilib version V0.6.34.
Data generation: Wed Jun 10 11:04:28 2020. Site generation: Sun Mar 28 09:10:28 2021

	Serveur d'exploration sur les pandémies grippales
	Attention, ce site est en cours de développement ! Attention, site généré par des moyens informatiques à partir de corpus bruts. Les informations ne sont donc pas validées.

Serveur d'exploration sur les pandémies grippales

Reconstructing disease outbreaks from genetic data: a graph approach.

Reconstructing disease outbreaks from genetic data: a graph approach.

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Pour générer des pages wiki