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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Genetics Society of America
2019
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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. |
format | Online Article Text |
id | pubmed-6827370 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Genetics Society of America |
record_format | MEDLINE/PubMed |
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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