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Coalescent models derived from birth-death processes.
Crespo, Fausto F; Posada, David; Wiuf, Carsten.
Afiliação
  • Crespo FF; CINBIO, Universidade de Vigo, 36310 Vigo, Spain; Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310 Vigo, Spain. Electronic address: fcrespo@uvigo.es.
  • Posada D; CINBIO, Universidade de Vigo, 36310 Vigo, Spain; Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310 Vigo, Spain; Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36310 Vigo, Spain. Electronic address: dposada@uvigo.es.
  • Wiuf C; Department of Mathematical Sciences, Universitetsparken 5, University of Copenhagen, Denmark. Electronic address: wiuf@math.ku.dk.
Theor Popul Biol ; 142: 1-11, 2021 12.
Article em En | MEDLINE | ID: mdl-34563554
A coalescent model of a sample of size n is derived from a birth-death process that originates at a random time in the past from a single founder individual. Over time, the descendants of the founder evolve into a population of large (infinite) size from which a sample of size n is taken. The parameters and time of the birth-death process are scaled in N0, the size of the present-day population, while letting N0→∞, similarly to how the standard Kingman coalescent process arises from the Wright-Fisher model. The model is named the Limit Birth-Death (LBD) coalescent model. Simulations from the LBD coalescent model with sample size n are computationally slow compared to standard coalescent models. Therefore, we suggest different approximations to the LBD coalescent model assuming the population size is a deterministic function of time rather than a stochastic process. Furthermore, we introduce a hybrid LBD coalescent model, that combines the exactness of the LBD coalescent model model with the speed of the approximations.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Genética Populacional / Modelos Genéticos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Genética Populacional / Modelos Genéticos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article