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LDJump: Estimating variable recombination rates from population genetic data

As recombination plays an important role in evolution, its estimation and the identification of hotspot positions is of considerable interest. We propose a novel approach for estimating population recombination rates based on genotyping or sequence data that involves a sequential multiscale change p...

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Detalles Bibliográficos
Autores principales: Hermann, Philipp, Heissl, Angelika, Tiemann‐Boege, Irene, Futschik, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6519033/
https://www.ncbi.nlm.nih.gov/pubmed/30666785
http://dx.doi.org/10.1111/1755-0998.12994
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author Hermann, Philipp
Heissl, Angelika
Tiemann‐Boege, Irene
Futschik, Andreas
author_facet Hermann, Philipp
Heissl, Angelika
Tiemann‐Boege, Irene
Futschik, Andreas
author_sort Hermann, Philipp
collection PubMed
description As recombination plays an important role in evolution, its estimation and the identification of hotspot positions is of considerable interest. We propose a novel approach for estimating population recombination rates based on genotyping or sequence data that involves a sequential multiscale change point estimator. Our method also permits demography to be taken into account. It uses several summary statistics within a regression model fitted on suitable scenarios. Our proposed method is accurate, computationally fast, and provides a parsimonious solution by ensuring a type I error control against too many changes in the recombination rate. An application to human genome data suggests a good congruence between our estimated and experimentally identified hotspots. Our method is implemented in the R‐package LDJump, which is freely available at https://github.com/PhHermann/LDJump.
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spelling pubmed-65190332019-05-21 LDJump: Estimating variable recombination rates from population genetic data Hermann, Philipp Heissl, Angelika Tiemann‐Boege, Irene Futschik, Andreas Mol Ecol Resour RESOURCE ARTICLES As recombination plays an important role in evolution, its estimation and the identification of hotspot positions is of considerable interest. We propose a novel approach for estimating population recombination rates based on genotyping or sequence data that involves a sequential multiscale change point estimator. Our method also permits demography to be taken into account. It uses several summary statistics within a regression model fitted on suitable scenarios. Our proposed method is accurate, computationally fast, and provides a parsimonious solution by ensuring a type I error control against too many changes in the recombination rate. An application to human genome data suggests a good congruence between our estimated and experimentally identified hotspots. Our method is implemented in the R‐package LDJump, which is freely available at https://github.com/PhHermann/LDJump. John Wiley and Sons Inc. 2019-04-04 2019-05 /pmc/articles/PMC6519033/ /pubmed/30666785 http://dx.doi.org/10.1111/1755-0998.12994 Text en © 2019 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle RESOURCE ARTICLES
Hermann, Philipp
Heissl, Angelika
Tiemann‐Boege, Irene
Futschik, Andreas
LDJump: Estimating variable recombination rates from population genetic data
title LDJump: Estimating variable recombination rates from population genetic data
title_full LDJump: Estimating variable recombination rates from population genetic data
title_fullStr LDJump: Estimating variable recombination rates from population genetic data
title_full_unstemmed LDJump: Estimating variable recombination rates from population genetic data
title_short LDJump: Estimating variable recombination rates from population genetic data
title_sort ldjump: estimating variable recombination rates from population genetic data
topic RESOURCE ARTICLES
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6519033/
https://www.ncbi.nlm.nih.gov/pubmed/30666785
http://dx.doi.org/10.1111/1755-0998.12994
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