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Variational inference using approximate likelihood under the coalescent with recombination

Coalescent methods are proven and powerful tools for population genetics, phylogenetics, epidemiology, and other fields. A promising avenue for the analysis of large genomic alignments, which are increasingly common, is coalescent hidden Markov model (coalHMM) methods, but these methods have lacked...

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Detalles Bibliográficos
Autores principales: Liu, Xinhao, Ogilvie, Huw A., Nakhleh, Luay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8559707/
https://www.ncbi.nlm.nih.gov/pubmed/34426513
http://dx.doi.org/10.1101/gr.273631.120
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author Liu, Xinhao
Ogilvie, Huw A.
Nakhleh, Luay
author_facet Liu, Xinhao
Ogilvie, Huw A.
Nakhleh, Luay
author_sort Liu, Xinhao
collection PubMed
description Coalescent methods are proven and powerful tools for population genetics, phylogenetics, epidemiology, and other fields. A promising avenue for the analysis of large genomic alignments, which are increasingly common, is coalescent hidden Markov model (coalHMM) methods, but these methods have lacked general usability and flexibility. We introduce a novel method for automatically learning a coalHMM and inferring the posterior distributions of evolutionary parameters using black-box variational inference, with the transition rates between local genealogies derived empirically by simulation. This derivation enables our method to work directly with three or four taxa and through a divide-and-conquer approach with more taxa. Using a simulated data set resembling a human–chimp–gorilla scenario, we show that our method has comparable or better accuracy to previous coalHMM methods. Both species divergence times and population sizes were accurately inferred. The method also infers local genealogies, and we report on their accuracy. Furthermore, we discuss a potential direction for scaling the method to larger data sets through a divide-and-conquer approach. This accuracy means our method is useful now, and by deriving transition rates by simulation, it is flexible enough to enable future implementations of various population models.
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spelling pubmed-85597072022-05-01 Variational inference using approximate likelihood under the coalescent with recombination Liu, Xinhao Ogilvie, Huw A. Nakhleh, Luay Genome Res Method Coalescent methods are proven and powerful tools for population genetics, phylogenetics, epidemiology, and other fields. A promising avenue for the analysis of large genomic alignments, which are increasingly common, is coalescent hidden Markov model (coalHMM) methods, but these methods have lacked general usability and flexibility. We introduce a novel method for automatically learning a coalHMM and inferring the posterior distributions of evolutionary parameters using black-box variational inference, with the transition rates between local genealogies derived empirically by simulation. This derivation enables our method to work directly with three or four taxa and through a divide-and-conquer approach with more taxa. Using a simulated data set resembling a human–chimp–gorilla scenario, we show that our method has comparable or better accuracy to previous coalHMM methods. Both species divergence times and population sizes were accurately inferred. The method also infers local genealogies, and we report on their accuracy. Furthermore, we discuss a potential direction for scaling the method to larger data sets through a divide-and-conquer approach. This accuracy means our method is useful now, and by deriving transition rates by simulation, it is flexible enough to enable future implementations of various population models. Cold Spring Harbor Laboratory Press 2021-11 /pmc/articles/PMC8559707/ /pubmed/34426513 http://dx.doi.org/10.1101/gr.273631.120 Text en © 2021 Liu et al.; Published by Cold Spring Harbor Laboratory Press https://creativecommons.org/licenses/by-nc/4.0/This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see https://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Method
Liu, Xinhao
Ogilvie, Huw A.
Nakhleh, Luay
Variational inference using approximate likelihood under the coalescent with recombination
title Variational inference using approximate likelihood under the coalescent with recombination
title_full Variational inference using approximate likelihood under the coalescent with recombination
title_fullStr Variational inference using approximate likelihood under the coalescent with recombination
title_full_unstemmed Variational inference using approximate likelihood under the coalescent with recombination
title_short Variational inference using approximate likelihood under the coalescent with recombination
title_sort variational inference using approximate likelihood under the coalescent with recombination
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8559707/
https://www.ncbi.nlm.nih.gov/pubmed/34426513
http://dx.doi.org/10.1101/gr.273631.120
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