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Identifying currents in the gene pool for bacterial populations using an integrative approach.

Tang J, Hanage WP, Fraser C, Corander J - PLoS Comput. Biol. (2009)

Bottom Line: However, the traditional statistical methods for evolutionary inference, such as phylogenetic analysis, are associated with several difficulties under such an extensive sampling scenario, in particular when a considerable amount of recombination is anticipated to have taken place.Also, we introduce a model-based description of the shape of a population in sequence space, in terms of its molecular variability and affinity towards other populations.Extensive real data from the genus Neisseria are utilized to demonstrate the potential of an approach where these population genetic tools are combined with an phylogenetic analysis.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland. jing.tang@helsinki.fi

ABSTRACT
The evolution of bacterial populations has recently become considerably better understood due to large-scale sequencing of population samples. It has become clear that DNA sequences from a multitude of genes, as well as a broad sample coverage of a target population, are needed to obtain a relatively unbiased view of its genetic structure and the patterns of ancestry connected to the strains. However, the traditional statistical methods for evolutionary inference, such as phylogenetic analysis, are associated with several difficulties under such an extensive sampling scenario, in particular when a considerable amount of recombination is anticipated to have taken place. To meet the needs of large-scale analyses of population structure for bacteria, we introduce here several statistical tools for the detection and representation of recombination between populations. Also, we introduce a model-based description of the shape of a population in sequence space, in terms of its molecular variability and affinity towards other populations. Extensive real data from the genus Neisseria are utilized to demonstrate the potential of an approach where these population genetic tools are combined with an phylogenetic analysis. The statistical tools introduced here are freely available in BAPS 5.2 software, which can be downloaded from http://web.abo.fi/fak/mnf/mate/jc/software/baps.html.

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Genetic shapes of five populations relative to population 2.The data set was generated with , ,  and Figure 2 as the underlying population structure. Each curve is a density estimation of (8) using (9) for one target population.
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pcbi-1000455-g005: Genetic shapes of five populations relative to population 2.The data set was generated with , , and Figure 2 as the underlying population structure. Each curve is a density estimation of (8) using (9) for one target population.

Mentions: We used a simulated data set for illustration of genetic shapes represented as the density estimation in (8). The data set was generated with and . Figure 5 shows the estimated genetic shapes using population 2 as the reference, as compared to the other five populations. It can be seen from Figure 5 that the influence of admixture between the populations is reflected also on the genetic shapes. For example, the density curves for population 1 (red) and for population 3 (blue) are more shifted towards zero than the other populations, and hence imply a closer relationship to population 2. This is not surprising since population 2 is a common donor of DNA to populations 1 and 3 (Figure 2). On the other hand, the density curve for population 3 appears to have two modes, which is a feature exhibited in neither population 1 nor any other populations. Note that population 3 is the only population which donates DNA to population 2. We might use the bi-modality of a density curve as a potential indicator of gene flow to the reference population.


Identifying currents in the gene pool for bacterial populations using an integrative approach.

Tang J, Hanage WP, Fraser C, Corander J - PLoS Comput. Biol. (2009)

Genetic shapes of five populations relative to population 2.The data set was generated with , ,  and Figure 2 as the underlying population structure. Each curve is a density estimation of (8) using (9) for one target population.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2713424&req=5

pcbi-1000455-g005: Genetic shapes of five populations relative to population 2.The data set was generated with , , and Figure 2 as the underlying population structure. Each curve is a density estimation of (8) using (9) for one target population.
Mentions: We used a simulated data set for illustration of genetic shapes represented as the density estimation in (8). The data set was generated with and . Figure 5 shows the estimated genetic shapes using population 2 as the reference, as compared to the other five populations. It can be seen from Figure 5 that the influence of admixture between the populations is reflected also on the genetic shapes. For example, the density curves for population 1 (red) and for population 3 (blue) are more shifted towards zero than the other populations, and hence imply a closer relationship to population 2. This is not surprising since population 2 is a common donor of DNA to populations 1 and 3 (Figure 2). On the other hand, the density curve for population 3 appears to have two modes, which is a feature exhibited in neither population 1 nor any other populations. Note that population 3 is the only population which donates DNA to population 2. We might use the bi-modality of a density curve as a potential indicator of gene flow to the reference population.

Bottom Line: However, the traditional statistical methods for evolutionary inference, such as phylogenetic analysis, are associated with several difficulties under such an extensive sampling scenario, in particular when a considerable amount of recombination is anticipated to have taken place.Also, we introduce a model-based description of the shape of a population in sequence space, in terms of its molecular variability and affinity towards other populations.Extensive real data from the genus Neisseria are utilized to demonstrate the potential of an approach where these population genetic tools are combined with an phylogenetic analysis.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland. jing.tang@helsinki.fi

ABSTRACT
The evolution of bacterial populations has recently become considerably better understood due to large-scale sequencing of population samples. It has become clear that DNA sequences from a multitude of genes, as well as a broad sample coverage of a target population, are needed to obtain a relatively unbiased view of its genetic structure and the patterns of ancestry connected to the strains. However, the traditional statistical methods for evolutionary inference, such as phylogenetic analysis, are associated with several difficulties under such an extensive sampling scenario, in particular when a considerable amount of recombination is anticipated to have taken place. To meet the needs of large-scale analyses of population structure for bacteria, we introduce here several statistical tools for the detection and representation of recombination between populations. Also, we introduce a model-based description of the shape of a population in sequence space, in terms of its molecular variability and affinity towards other populations. Extensive real data from the genus Neisseria are utilized to demonstrate the potential of an approach where these population genetic tools are combined with an phylogenetic analysis. The statistical tools introduced here are freely available in BAPS 5.2 software, which can be downloaded from http://web.abo.fi/fak/mnf/mate/jc/software/baps.html.

Show MeSH