PoplarV1, Main, Exploration, bibRecord, 000E22

Inferring Continuous and Discrete Population Genetic Structure Across Space.

Identifieur interne : 000E22 ( Main/Exploration ); précédent : 000E21; suivant : 000E23

Inferring Continuous and Discrete Population Genetic Structure Across Space.

Auteurs : Gideon S. Bradburd [États-Unis] ; Graham M. Coop [États-Unis] ; Peter L. Ralph [États-Unis]

Source :

Genetics [ 1943-2631 ] ; 2018.

RBID : pubmed:30026187

Descripteurs français

KwdFr :
- Amérique du Nord (MeSH), Analyse de regroupements (MeSH), Animaux (MeSH), Flux des gènes (génétique), Groupes de population (génétique), Génétique des populations (méthodes), Génétique des populations (statistiques et données numériques), Humains (MeSH), Interprétation statistique de données (MeSH), Modèles génétiques (MeSH), Populus (génétique), Ursidae (génétique), Variation génétique (génétique).
MESH :
- génétique : Flux des gènes, Groupes de population, Populus, Ursidae, Variation génétique.
- méthodes : Génétique des populations.
- statistiques et données numériques : Génétique des populations.
- Amérique du Nord, Analyse de regroupements, Animaux, Humains, Interprétation statistique de données, Modèles génétiques.

English descriptors

KwdEn :
- Animals (MeSH), Cluster Analysis (MeSH), Data Interpretation, Statistical (MeSH), Gene Flow (genetics), Genetic Variation (genetics), Genetics, Population (methods), Genetics, Population (statistics & numerical data), Humans (MeSH), Models, Genetic (MeSH), North America (MeSH), Population Groups (genetics), Populus (genetics), Ursidae (genetics).
MESH :
- genetics : Gene Flow, Genetic Variation, Population Groups, Populus, Ursidae.
- methods : Genetics, Population.
- statistics & numerical data : Genetics, Population.
- Animals, Cluster Analysis, Data Interpretation, Statistical, Humans, Models, Genetic, North America.

Abstract

A classic problem in population genetics is the characterization of discrete population structure in the presence of continuous patterns of genetic differentiation. Especially when sampling is discontinuous, the use of clustering or assignment methods may incorrectly ascribe differentiation due to continuous processes (e.g., geographic isolation by distance) to discrete processes, such as geographic, ecological, or reproductive barriers between populations. This reflects a shortcoming of current methods for inferring and visualizing population structure when applied to genetic data deriving from geographically distributed populations. Here, we present a statistical framework for the simultaneous inference of continuous and discrete patterns of population structure. The method estimates ancestry proportions for each sample from a set of two-dimensional population layers, and, within each layer, estimates a rate at which relatedness decays with distance. This thereby explicitly addresses the "clines versus clusters" problem in modeling population genetic variation, and remedies some of the overfitting to which nonspatial models are prone. The method produces useful descriptions of structure in genetic relatedness in situations where separated, geographically distributed populations interact, as after a range expansion or secondary contact. We demonstrate the utility of this approach using simulations and by applying it to empirical datasets of poplars and black bears in North America.

DOI: 10.1534/genetics.118.301333
PubMed: 30026187
PubMed Central: PMC6116973

Affiliations:

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Le document en format XML

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<front><div type="abstract" xml:lang="en">A classic problem in population genetics is the characterization of discrete population structure in the presence of continuous patterns of genetic differentiation. Especially when sampling is discontinuous, the use of clustering or assignment methods may incorrectly ascribe differentiation due to continuous processes (<i>e.g.</i>
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<Abstract><AbstractText>A classic problem in population genetics is the characterization of discrete population structure in the presence of continuous patterns of genetic differentiation. Especially when sampling is discontinuous, the use of clustering or assignment methods may incorrectly ascribe differentiation due to continuous processes (<i>e.g.</i>
, geographic isolation by distance) to discrete processes, such as geographic, ecological, or reproductive barriers between populations. This reflects a shortcoming of current methods for inferring and visualizing population structure when applied to genetic data deriving from geographically distributed populations. Here, we present a statistical framework for the simultaneous inference of continuous and discrete patterns of population structure. The method estimates ancestry proportions for each sample from a set of two-dimensional population layers, and, within each layer, estimates a rate at which relatedness decays with distance. This thereby explicitly addresses the "clines versus clusters" problem in modeling population genetic variation, and remedies some of the overfitting to which nonspatial models are prone. The method produces useful descriptions of structure in genetic relatedness in situations where separated, geographically distributed populations interact, as after a range expansion or secondary contact. We demonstrate the utility of this approach using simulations and by applying it to empirical datasets of poplars and black bears in North America.</AbstractText>
<CopyrightInformation>Copyright © 2018 Bradburd et al.</CopyrightInformation>
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Inferring Continuous and Discrete Population Genetic Structure Across Space.

Inferring Continuous and Discrete Population Genetic Structure Across Space.

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