Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Gain, Clément, Rhoné, Bénédicte, Cubry, Philippe, Salazar, Israfel, Forbes, Florence, Vigouroux, Yves, Jay, Flora, François, Olivier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
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
_version_ 1785065927587397632
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
work_keys_str_mv AT gainclement aquantitativetheoryforgenomicoffsetstatistics
AT rhonebenedicte aquantitativetheoryforgenomicoffsetstatistics
AT cubryphilippe aquantitativetheoryforgenomicoffsetstatistics
AT salazarisrafel aquantitativetheoryforgenomicoffsetstatistics
AT forbesflorence aquantitativetheoryforgenomicoffsetstatistics
AT vigourouxyves aquantitativetheoryforgenomicoffsetstatistics
AT jayflora aquantitativetheoryforgenomicoffsetstatistics
AT francoisolivier aquantitativetheoryforgenomicoffsetstatistics
AT gainclement quantitativetheoryforgenomicoffsetstatistics
AT rhonebenedicte quantitativetheoryforgenomicoffsetstatistics
AT cubryphilippe quantitativetheoryforgenomicoffsetstatistics
AT salazarisrafel quantitativetheoryforgenomicoffsetstatistics
AT forbesflorence quantitativetheoryforgenomicoffsetstatistics
AT vigourouxyves quantitativetheoryforgenomicoffsetstatistics
AT jayflora quantitativetheoryforgenomicoffsetstatistics
AT francoisolivier quantitativetheoryforgenomicoffsetstatistics