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Protocol for Projecting Allele Frequency Change under Future Climate Change at Adaptive-Associated Loci

We describe how to predict population-level allele frequency change at loci associated with locally adapted traits under future climate conditions. Our method can identify populations that are at higher risk of local extinction and those that might be prime targets for conservation intervention. We...

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Detalles Bibliográficos
Autores principales: Blumstein, Meghan, Richardson, Andrew, Weston, David, Zhang, Jin, Muchero, Wellington, Hopkins, Robin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7580235/
https://www.ncbi.nlm.nih.gov/pubmed/33111101
http://dx.doi.org/10.1016/j.xpro.2020.100061
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author Blumstein, Meghan
Richardson, Andrew
Weston, David
Zhang, Jin
Muchero, Wellington
Hopkins, Robin
author_facet Blumstein, Meghan
Richardson, Andrew
Weston, David
Zhang, Jin
Muchero, Wellington
Hopkins, Robin
author_sort Blumstein, Meghan
collection PubMed
description We describe how to predict population-level allele frequency change at loci associated with locally adapted traits under future climate conditions. Our method can identify populations that are at higher risk of local extinction and those that might be prime targets for conservation intervention. We draw on previously developed community ecology statistical methods and apply them in novel ways to plant genomes. While a powerful diagnostic tool, our method requires a wealth of genomic data for use. For complete details on the use and execution of this protocol, please refer to Blumstein et al. (2020).
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spelling pubmed-75802352020-10-26 Protocol for Projecting Allele Frequency Change under Future Climate Change at Adaptive-Associated Loci Blumstein, Meghan Richardson, Andrew Weston, David Zhang, Jin Muchero, Wellington Hopkins, Robin STAR Protoc Protocol We describe how to predict population-level allele frequency change at loci associated with locally adapted traits under future climate conditions. Our method can identify populations that are at higher risk of local extinction and those that might be prime targets for conservation intervention. We draw on previously developed community ecology statistical methods and apply them in novel ways to plant genomes. While a powerful diagnostic tool, our method requires a wealth of genomic data for use. For complete details on the use and execution of this protocol, please refer to Blumstein et al. (2020). Elsevier 2020-07-15 /pmc/articles/PMC7580235/ /pubmed/33111101 http://dx.doi.org/10.1016/j.xpro.2020.100061 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Protocol
Blumstein, Meghan
Richardson, Andrew
Weston, David
Zhang, Jin
Muchero, Wellington
Hopkins, Robin
Protocol for Projecting Allele Frequency Change under Future Climate Change at Adaptive-Associated Loci
title Protocol for Projecting Allele Frequency Change under Future Climate Change at Adaptive-Associated Loci
title_full Protocol for Projecting Allele Frequency Change under Future Climate Change at Adaptive-Associated Loci
title_fullStr Protocol for Projecting Allele Frequency Change under Future Climate Change at Adaptive-Associated Loci
title_full_unstemmed Protocol for Projecting Allele Frequency Change under Future Climate Change at Adaptive-Associated Loci
title_short Protocol for Projecting Allele Frequency Change under Future Climate Change at Adaptive-Associated Loci
title_sort protocol for projecting allele frequency change under future climate change at adaptive-associated loci
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7580235/
https://www.ncbi.nlm.nih.gov/pubmed/33111101
http://dx.doi.org/10.1016/j.xpro.2020.100061
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