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Likelihood-based inferences under isolation by distance: two-dimensional habitats and confidence intervals.
Rousset, François; Leblois, Raphaël.
Afiliação
  • Rousset F; Institut des Sciences de l'Evolution (UM2-CNRS), Université Montpellier 2, Montpellier, France. francois.rousset@univ-montp2.fr
Mol Biol Evol ; 29(3): 957-73, 2012 Mar.
Article em En | MEDLINE | ID: mdl-22016575
ABSTRACT
Likelihood-based methods of inference of population parameters from genetic data in structured populations have been implemented but still little tested in large networks of populations. In this work, a previous software implementation of inference in linear habitats is extended to two-dimensional habitats, and the coverage properties of confidence intervals are analyzed in both cases. Both standard likelihood and an efficient approximation are considered. The effects of misspecification of mutation model and dispersal distribution, and of spatial binning of samples, are considered. In the absence of model misspecification, the estimators have low bias, low mean square error, and the coverage properties of confidence intervals are consistent with theoretical expectations. Inferences of dispersal parameters and of the mutation rate are sensitive to misspecification or to approximations inherent to the coalescent algorithms used. In particular, coalescent approximations are not appropriate to infer the shape of the dispersal distribution. However, inferences of the neighborhood parameter (or of the product of population density and mean square dispersal rate) are generally robust with respect to complicating factors, such as misspecification of the mutation process and of the shape of the dispersal distribution, and with respect to spatial binning of samples. Likelihood inferences appear feasible in moderately sized networks of populations (up to 400 populations in this work), and they are more efficient than previous moment-based spatial regression method in realistic conditions.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Demografia / Ecossistema / Genética Populacional / Modelos Genéticos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2012 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Demografia / Ecossistema / Genética Populacional / Modelos Genéticos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2012 Tipo de documento: Article