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A Quantitative Theory for Genomic Offset Statistics
Genomic offset statistics predict the maladaptation of populations to rapid habitat alteration based on association of genotypes with environmental variation. Despite substantial evidence for empirical validity, genomic offset statistics have well-identified limitations, and lack a theory that would...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10306404/ https://www.ncbi.nlm.nih.gov/pubmed/37307566 http://dx.doi.org/10.1093/molbev/msad140 |
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author | Gain, Clément Rhoné, Bénédicte Cubry, Philippe Salazar, Israfel Forbes, Florence Vigouroux, Yves Jay, Flora François, Olivier |
author_facet | Gain, Clément Rhoné, Bénédicte Cubry, Philippe Salazar, Israfel Forbes, Florence Vigouroux, Yves Jay, Flora François, Olivier |
author_sort | Gain, Clément |
collection | PubMed |
description | Genomic offset statistics predict the maladaptation of populations to rapid habitat alteration based on association of genotypes with environmental variation. Despite substantial evidence for empirical validity, genomic offset statistics have well-identified limitations, and lack a theory that would facilitate interpretations of predicted values. Here, we clarified the theoretical relationships between genomic offset statistics and unobserved fitness traits controlled by environmentally selected loci and proposed a geometric measure to predict fitness after rapid change in local environment. The predictions of our theory were verified in computer simulations and in empirical data on African pearl millet (Cenchrus americanus) obtained from a common garden experiment. Our results proposed a unified perspective on genomic offset statistics and provided a theoretical foundation necessary when considering their potential application in conservation management in the face of environmental change. |
format | Online Article Text |
id | pubmed-10306404 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-103064042023-06-29 A Quantitative Theory for Genomic Offset Statistics Gain, Clément Rhoné, Bénédicte Cubry, Philippe Salazar, Israfel Forbes, Florence Vigouroux, Yves Jay, Flora François, Olivier Mol Biol Evol Methods Genomic offset statistics predict the maladaptation of populations to rapid habitat alteration based on association of genotypes with environmental variation. Despite substantial evidence for empirical validity, genomic offset statistics have well-identified limitations, and lack a theory that would facilitate interpretations of predicted values. Here, we clarified the theoretical relationships between genomic offset statistics and unobserved fitness traits controlled by environmentally selected loci and proposed a geometric measure to predict fitness after rapid change in local environment. The predictions of our theory were verified in computer simulations and in empirical data on African pearl millet (Cenchrus americanus) obtained from a common garden experiment. Our results proposed a unified perspective on genomic offset statistics and provided a theoretical foundation necessary when considering their potential application in conservation management in the face of environmental change. Oxford University Press 2023-06-12 /pmc/articles/PMC10306404/ /pubmed/37307566 http://dx.doi.org/10.1093/molbev/msad140 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Methods Gain, Clément Rhoné, Bénédicte Cubry, Philippe Salazar, Israfel Forbes, Florence Vigouroux, Yves Jay, Flora François, Olivier A Quantitative Theory for Genomic Offset Statistics |
title | A Quantitative Theory for Genomic Offset Statistics |
title_full | A Quantitative Theory for Genomic Offset Statistics |
title_fullStr | A Quantitative Theory for Genomic Offset Statistics |
title_full_unstemmed | A Quantitative Theory for Genomic Offset Statistics |
title_short | A Quantitative Theory for Genomic Offset Statistics |
title_sort | quantitative theory for genomic offset statistics |
topic | Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10306404/ https://www.ncbi.nlm.nih.gov/pubmed/37307566 http://dx.doi.org/10.1093/molbev/msad140 |
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