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Bayesian Estimation of Population Size Changes by Sampling Tajima’s Trees

The large state space of gene genealogies is a major hurdle for inference methods based on Kingman’s coalescent. Here, we present a new Bayesian approach for inferring past population sizes, which relies on a lower-resolution coalescent process that we refer to as “Tajima’s coalescent.” Tajima’s coa...

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Autores principales: Palacios, Julia A., Véber, Amandine, Cappello, Lorenzo, Wang, Zhangyuan, Wakeley, John, Ramachandran, Sohini
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Genetics Society of America 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6827370/
https://www.ncbi.nlm.nih.gov/pubmed/31511299
http://dx.doi.org/10.1534/genetics.119.302373
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author Palacios, Julia A.
Véber, Amandine
Cappello, Lorenzo
Wang, Zhangyuan
Wakeley, John
Ramachandran, Sohini
author_facet Palacios, Julia A.
Véber, Amandine
Cappello, Lorenzo
Wang, Zhangyuan
Wakeley, John
Ramachandran, Sohini
author_sort Palacios, Julia A.
collection PubMed
description The large state space of gene genealogies is a major hurdle for inference methods based on Kingman’s coalescent. Here, we present a new Bayesian approach for inferring past population sizes, which relies on a lower-resolution coalescent process that we refer to as “Tajima’s coalescent.” Tajima’s coalescent has a drastically smaller state space, and hence it is a computationally more efficient model, than the standard Kingman coalescent. We provide a new algorithm for efficient and exact likelihood calculations for data without recombination, which exploits a directed acyclic graph and a correspondingly tailored Markov Chain Monte Carlo method. We compare the performance of our Bayesian Estimation of population size changes by Sampling Tajima’s Trees (BESTT) with a popular implementation of coalescent-based inference in BEAST using simulated and human data. We empirically demonstrate that BESTT can accurately infer effective population sizes, and it further provides an efficient alternative to the Kingman’s coalescent. The algorithms described here are implemented in the R package phylodyn, which is available for download at https://github.com/JuliaPalacios/phylodyn.
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spelling pubmed-68273702019-11-05 Bayesian Estimation of Population Size Changes by Sampling Tajima’s Trees Palacios, Julia A. Véber, Amandine Cappello, Lorenzo Wang, Zhangyuan Wakeley, John Ramachandran, Sohini Genetics Investigations The large state space of gene genealogies is a major hurdle for inference methods based on Kingman’s coalescent. Here, we present a new Bayesian approach for inferring past population sizes, which relies on a lower-resolution coalescent process that we refer to as “Tajima’s coalescent.” Tajima’s coalescent has a drastically smaller state space, and hence it is a computationally more efficient model, than the standard Kingman coalescent. We provide a new algorithm for efficient and exact likelihood calculations for data without recombination, which exploits a directed acyclic graph and a correspondingly tailored Markov Chain Monte Carlo method. We compare the performance of our Bayesian Estimation of population size changes by Sampling Tajima’s Trees (BESTT) with a popular implementation of coalescent-based inference in BEAST using simulated and human data. We empirically demonstrate that BESTT can accurately infer effective population sizes, and it further provides an efficient alternative to the Kingman’s coalescent. The algorithms described here are implemented in the R package phylodyn, which is available for download at https://github.com/JuliaPalacios/phylodyn. Genetics Society of America 2019-11 2019-09-11 /pmc/articles/PMC6827370/ /pubmed/31511299 http://dx.doi.org/10.1534/genetics.119.302373 Text en Copyright © 2019 by the Genetics Society of America Available freely online through the author-supported open access option.
spellingShingle Investigations
Palacios, Julia A.
Véber, Amandine
Cappello, Lorenzo
Wang, Zhangyuan
Wakeley, John
Ramachandran, Sohini
Bayesian Estimation of Population Size Changes by Sampling Tajima’s Trees
title Bayesian Estimation of Population Size Changes by Sampling Tajima’s Trees
title_full Bayesian Estimation of Population Size Changes by Sampling Tajima’s Trees
title_fullStr Bayesian Estimation of Population Size Changes by Sampling Tajima’s Trees
title_full_unstemmed Bayesian Estimation of Population Size Changes by Sampling Tajima’s Trees
title_short Bayesian Estimation of Population Size Changes by Sampling Tajima’s Trees
title_sort bayesian estimation of population size changes by sampling tajima’s trees
topic Investigations
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6827370/
https://www.ncbi.nlm.nih.gov/pubmed/31511299
http://dx.doi.org/10.1534/genetics.119.302373
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