CEGA: a method for inferring natural selection by comparative population genomic analysis across species

We developed maximum likelihood method for detecting positive selection or balancing selection using multilocus or genomic polymorphism and divergence data from two species. The method is especially useful for investigating natural selection in noncoding regions. Simulations demonstrate that the met...

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Detalles Bibliográficos
Autores principales: Zhao, Shilei, Chi, Lianjiang, Chen, Hua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10548728/
https://www.ncbi.nlm.nih.gov/pubmed/37789379
http://dx.doi.org/10.1186/s13059-023-03068-8
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author Zhao, Shilei
Chi, Lianjiang
Chen, Hua
author_facet Zhao, Shilei
Chi, Lianjiang
Chen, Hua
author_sort Zhao, Shilei
collection PubMed
description We developed maximum likelihood method for detecting positive selection or balancing selection using multilocus or genomic polymorphism and divergence data from two species. The method is especially useful for investigating natural selection in noncoding regions. Simulations demonstrate that the method outperforms existing methods in detecting both positive and balancing selection. We apply the method to population genomic data from human and chimpanzee. The list of genes identified under selection in the noncoding regions is prominently enriched in pathways related to the brain and nervous system. Therefore, our method will serve as a useful tool for comparative population genomic analysis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03068-8.
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spelling pubmed-105487282023-10-05 CEGA: a method for inferring natural selection by comparative population genomic analysis across species Zhao, Shilei Chi, Lianjiang Chen, Hua Genome Biol Method We developed maximum likelihood method for detecting positive selection or balancing selection using multilocus or genomic polymorphism and divergence data from two species. The method is especially useful for investigating natural selection in noncoding regions. Simulations demonstrate that the method outperforms existing methods in detecting both positive and balancing selection. We apply the method to population genomic data from human and chimpanzee. The list of genes identified under selection in the noncoding regions is prominently enriched in pathways related to the brain and nervous system. Therefore, our method will serve as a useful tool for comparative population genomic analysis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03068-8. BioMed Central 2023-10-03 /pmc/articles/PMC10548728/ /pubmed/37789379 http://dx.doi.org/10.1186/s13059-023-03068-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Zhao, Shilei
Chi, Lianjiang
Chen, Hua
CEGA: a method for inferring natural selection by comparative population genomic analysis across species
title CEGA: a method for inferring natural selection by comparative population genomic analysis across species
title_full CEGA: a method for inferring natural selection by comparative population genomic analysis across species
title_fullStr CEGA: a method for inferring natural selection by comparative population genomic analysis across species
title_full_unstemmed CEGA: a method for inferring natural selection by comparative population genomic analysis across species
title_short CEGA: a method for inferring natural selection by comparative population genomic analysis across species
title_sort cega: a method for inferring natural selection by comparative population genomic analysis across species
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10548728/
https://www.ncbi.nlm.nih.gov/pubmed/37789379
http://dx.doi.org/10.1186/s13059-023-03068-8
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AT chilianjiang cegaamethodforinferringnaturalselectionbycomparativepopulationgenomicanalysisacrossspecies
AT chenhua cegaamethodforinferringnaturalselectionbycomparativepopulationgenomicanalysisacrossspecies

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