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A new method for modeling coalescent processes with recombination

BACKGROUND: Recombination plays an important role in the maintenance of genetic diversity in many types of organisms, especially diploid eukaryotes. Recombination can be studied and used to map diseases. However, recombination adds a great deal of complexity to the genetic information. This renders...

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Autores principales: Wang, Ying, Zhou, Ying, Li, Linfeng, Chen, Xian, Liu, Yuting, Ma, Zhi-Ming, Xu, Shuhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4137079/
https://www.ncbi.nlm.nih.gov/pubmed/25113665
http://dx.doi.org/10.1186/1471-2105-15-273
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author Wang, Ying
Zhou, Ying
Li, Linfeng
Chen, Xian
Liu, Yuting
Ma, Zhi-Ming
Xu, Shuhua
author_facet Wang, Ying
Zhou, Ying
Li, Linfeng
Chen, Xian
Liu, Yuting
Ma, Zhi-Ming
Xu, Shuhua
author_sort Wang, Ying
collection PubMed
description BACKGROUND: Recombination plays an important role in the maintenance of genetic diversity in many types of organisms, especially diploid eukaryotes. Recombination can be studied and used to map diseases. However, recombination adds a great deal of complexity to the genetic information. This renders estimation of evolutionary parameters more difficult. After the coalescent process was formulated, models capable of describing recombination using graphs, such as ancestral recombination graphs (ARG) were also developed. There are two typical models based on which to simulate ARG: back-in-time model such as ms and spatial model including Wiuf&Hein’s, SMC, SMC’, and MaCS. RESULTS: In this study, a new method of modeling coalescence with recombination, Spatial Coalescent simulator (SC), was developed, which considerably improved the algorithm described by Wiuf and Hein. The present algorithm constructs ARG spatially along the sequence, but it does not produce any redundant branches which are inevitable in Wiuf and Hein’s algorithm. Interestingly, the distribution of ARG generated by the present new algorithm is identical to that generated by a typical back-in-time model adopted by ms, an algorithm commonly used to model coalescence. It is here demonstrated that the existing approximate methods such as the sequentially Markov coalescent (SMC), a related method called SMC′, and Markovian coalescent simulator (MaCS) can be viewed as special cases of the present method. Using simulation analysis, the time to the most common ancestor (TMRCA) in the local trees of ARGs generated by the present algorithm was found to be closer to that produced by ms than time produced by MaCS. Sample-consistent ARGs can be generated using the present method. This may significantly reduce the computational burden. CONCLUSION: In summary, the present method and algorithm may facilitate the estimation and description of recombination in population genomics and evolutionary biology. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2105-15-273) contains supplementary material, which is available to authorized users.
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spelling pubmed-41370792014-08-19 A new method for modeling coalescent processes with recombination Wang, Ying Zhou, Ying Li, Linfeng Chen, Xian Liu, Yuting Ma, Zhi-Ming Xu, Shuhua BMC Bioinformatics Methodology Article BACKGROUND: Recombination plays an important role in the maintenance of genetic diversity in many types of organisms, especially diploid eukaryotes. Recombination can be studied and used to map diseases. However, recombination adds a great deal of complexity to the genetic information. This renders estimation of evolutionary parameters more difficult. After the coalescent process was formulated, models capable of describing recombination using graphs, such as ancestral recombination graphs (ARG) were also developed. There are two typical models based on which to simulate ARG: back-in-time model such as ms and spatial model including Wiuf&Hein’s, SMC, SMC’, and MaCS. RESULTS: In this study, a new method of modeling coalescence with recombination, Spatial Coalescent simulator (SC), was developed, which considerably improved the algorithm described by Wiuf and Hein. The present algorithm constructs ARG spatially along the sequence, but it does not produce any redundant branches which are inevitable in Wiuf and Hein’s algorithm. Interestingly, the distribution of ARG generated by the present new algorithm is identical to that generated by a typical back-in-time model adopted by ms, an algorithm commonly used to model coalescence. It is here demonstrated that the existing approximate methods such as the sequentially Markov coalescent (SMC), a related method called SMC′, and Markovian coalescent simulator (MaCS) can be viewed as special cases of the present method. Using simulation analysis, the time to the most common ancestor (TMRCA) in the local trees of ARGs generated by the present algorithm was found to be closer to that produced by ms than time produced by MaCS. Sample-consistent ARGs can be generated using the present method. This may significantly reduce the computational burden. CONCLUSION: In summary, the present method and algorithm may facilitate the estimation and description of recombination in population genomics and evolutionary biology. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2105-15-273) contains supplementary material, which is available to authorized users. BioMed Central 2014-08-11 /pmc/articles/PMC4137079/ /pubmed/25113665 http://dx.doi.org/10.1186/1471-2105-15-273 Text en © Wang et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. 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 work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Wang, Ying
Zhou, Ying
Li, Linfeng
Chen, Xian
Liu, Yuting
Ma, Zhi-Ming
Xu, Shuhua
A new method for modeling coalescent processes with recombination
title A new method for modeling coalescent processes with recombination
title_full A new method for modeling coalescent processes with recombination
title_fullStr A new method for modeling coalescent processes with recombination
title_full_unstemmed A new method for modeling coalescent processes with recombination
title_short A new method for modeling coalescent processes with recombination
title_sort new method for modeling coalescent processes with recombination
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4137079/
https://www.ncbi.nlm.nih.gov/pubmed/25113665
http://dx.doi.org/10.1186/1471-2105-15-273
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