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adaPop: Bayesian inference of dependent population dynamics in coalescent models
The coalescent is a powerful statistical framework that allows us to infer past population dynamics leveraging the ancestral relationships reconstructed from sampled molecular sequence data. In many biomedical applications, such as in the study of infectious diseases, cell development, and tumorgene...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10063170/ https://www.ncbi.nlm.nih.gov/pubmed/36940209 http://dx.doi.org/10.1371/journal.pcbi.1010897 |
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author | Cappello, Lorenzo Kim, Jaehee Palacios, Julia A. |
author_facet | Cappello, Lorenzo Kim, Jaehee Palacios, Julia A. |
author_sort | Cappello, Lorenzo |
collection | PubMed |
description | The coalescent is a powerful statistical framework that allows us to infer past population dynamics leveraging the ancestral relationships reconstructed from sampled molecular sequence data. In many biomedical applications, such as in the study of infectious diseases, cell development, and tumorgenesis, several distinct populations share evolutionary history and therefore become dependent. The inference of such dependence is a highly important, yet a challenging problem. With advances in sequencing technologies, we are well positioned to exploit the wealth of high-resolution biological data for tackling this problem. Here, we present adaPop, a probabilistic model to estimate past population dynamics of dependent populations and to quantify their degree of dependence. An essential feature of our approach is the ability to track the time-varying association between the populations while making minimal assumptions on their functional shapes via Markov random field priors. We provide nonparametric estimators, extensions of our base model that integrate multiple data sources, and fast scalable inference algorithms. We test our method using simulated data under various dependent population histories and demonstrate the utility of our model in shedding light on evolutionary histories of different variants of SARS-CoV-2. |
format | Online Article Text |
id | pubmed-10063170 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-100631702023-03-31 adaPop: Bayesian inference of dependent population dynamics in coalescent models Cappello, Lorenzo Kim, Jaehee Palacios, Julia A. PLoS Comput Biol Research Article The coalescent is a powerful statistical framework that allows us to infer past population dynamics leveraging the ancestral relationships reconstructed from sampled molecular sequence data. In many biomedical applications, such as in the study of infectious diseases, cell development, and tumorgenesis, several distinct populations share evolutionary history and therefore become dependent. The inference of such dependence is a highly important, yet a challenging problem. With advances in sequencing technologies, we are well positioned to exploit the wealth of high-resolution biological data for tackling this problem. Here, we present adaPop, a probabilistic model to estimate past population dynamics of dependent populations and to quantify their degree of dependence. An essential feature of our approach is the ability to track the time-varying association between the populations while making minimal assumptions on their functional shapes via Markov random field priors. We provide nonparametric estimators, extensions of our base model that integrate multiple data sources, and fast scalable inference algorithms. We test our method using simulated data under various dependent population histories and demonstrate the utility of our model in shedding light on evolutionary histories of different variants of SARS-CoV-2. Public Library of Science 2023-03-20 /pmc/articles/PMC10063170/ /pubmed/36940209 http://dx.doi.org/10.1371/journal.pcbi.1010897 Text en © 2023 Cappello et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Cappello, Lorenzo Kim, Jaehee Palacios, Julia A. adaPop: Bayesian inference of dependent population dynamics in coalescent models |
title | adaPop: Bayesian inference of dependent population dynamics in coalescent models |
title_full | adaPop: Bayesian inference of dependent population dynamics in coalescent models |
title_fullStr | adaPop: Bayesian inference of dependent population dynamics in coalescent models |
title_full_unstemmed | adaPop: Bayesian inference of dependent population dynamics in coalescent models |
title_short | adaPop: Bayesian inference of dependent population dynamics in coalescent models |
title_sort | adapop: bayesian inference of dependent population dynamics in coalescent models |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10063170/ https://www.ncbi.nlm.nih.gov/pubmed/36940209 http://dx.doi.org/10.1371/journal.pcbi.1010897 |
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