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Coalescence computations for large samples drawn from populations of time-varying sizes

We present new results concerning probability distributions of times in the coalescence tree and expected allele frequencies for coalescent with large sample size. The obtained results are based on computational methodologies, which involve combining coalescence time scale changes with techniques of...

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Autores principales: Polanski, Andrzej, Szczesna, Agnieszka, Garbulowski, Mateusz, Kimmel, Marek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5295683/
https://www.ncbi.nlm.nih.gov/pubmed/28170404
http://dx.doi.org/10.1371/journal.pone.0170701
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author Polanski, Andrzej
Szczesna, Agnieszka
Garbulowski, Mateusz
Kimmel, Marek
author_facet Polanski, Andrzej
Szczesna, Agnieszka
Garbulowski, Mateusz
Kimmel, Marek
author_sort Polanski, Andrzej
collection PubMed
description We present new results concerning probability distributions of times in the coalescence tree and expected allele frequencies for coalescent with large sample size. The obtained results are based on computational methodologies, which involve combining coalescence time scale changes with techniques of integral transformations and using analytical formulae for infinite products. We show applications of the proposed methodologies for computing probability distributions of times in the coalescence tree and their limits, for evaluation of accuracy of approximate expressions for times in the coalescence tree and expected allele frequencies, and for analysis of large human mitochondrial DNA dataset.
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spelling pubmed-52956832017-02-17 Coalescence computations for large samples drawn from populations of time-varying sizes Polanski, Andrzej Szczesna, Agnieszka Garbulowski, Mateusz Kimmel, Marek PLoS One Research Article We present new results concerning probability distributions of times in the coalescence tree and expected allele frequencies for coalescent with large sample size. The obtained results are based on computational methodologies, which involve combining coalescence time scale changes with techniques of integral transformations and using analytical formulae for infinite products. We show applications of the proposed methodologies for computing probability distributions of times in the coalescence tree and their limits, for evaluation of accuracy of approximate expressions for times in the coalescence tree and expected allele frequencies, and for analysis of large human mitochondrial DNA dataset. Public Library of Science 2017-02-07 /pmc/articles/PMC5295683/ /pubmed/28170404 http://dx.doi.org/10.1371/journal.pone.0170701 Text en © 2017 Polanski et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://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
Polanski, Andrzej
Szczesna, Agnieszka
Garbulowski, Mateusz
Kimmel, Marek
Coalescence computations for large samples drawn from populations of time-varying sizes
title Coalescence computations for large samples drawn from populations of time-varying sizes
title_full Coalescence computations for large samples drawn from populations of time-varying sizes
title_fullStr Coalescence computations for large samples drawn from populations of time-varying sizes
title_full_unstemmed Coalescence computations for large samples drawn from populations of time-varying sizes
title_short Coalescence computations for large samples drawn from populations of time-varying sizes
title_sort coalescence computations for large samples drawn from populations of time-varying sizes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5295683/
https://www.ncbi.nlm.nih.gov/pubmed/28170404
http://dx.doi.org/10.1371/journal.pone.0170701
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