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A Quantitative Theory for Genomic Offset Statistics.
Gain, Clément; Rhoné, Bénédicte; Cubry, Philippe; Salazar, Israfel; Forbes, Florence; Vigouroux, Yves; Jay, Flora; François, Olivier.
Afiliación
  • Gain C; Centre National de la Recherche Scientifique, Université Grenoble-Alpes, Grenoble INP, TIMC UMR 5525, 38000 Grenoble, France.
  • Rhoné B; DIADE, Université de Montpellier, Institut de Recherche pour le Développement, French Agricultural Research Centre for International Development (CIRAD), Montpellier, France.
  • Cubry P; UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France.
  • Salazar I; DIADE, Université de Montpellier, Institut de Recherche pour le Développement, French Agricultural Research Centre for International Development (CIRAD), Montpellier, France.
  • Forbes F; Université Grenoble-Alpes, Centre National de la Recherche Scientifique, Grenoble INP, Inria Grenoble - Rhône-Alpes, LJK UMR 5224, 655 Avenue de l'Europe, 38335 Montbonnot, France.
  • Vigouroux Y; Université Grenoble-Alpes, Centre National de la Recherche Scientifique, Grenoble INP, Inria Grenoble - Rhône-Alpes, LJK UMR 5224, 655 Avenue de l'Europe, 38335 Montbonnot, France.
  • Jay F; DIADE, Université de Montpellier, Institut de Recherche pour le Développement, French Agricultural Research Centre for International Development (CIRAD), Montpellier, France.
  • François O; Université Paris-Saclay, Centre National de la Recherche Scientifique, Inria, Laboratoire Interdisciplinaire des Sciences du Numérique, UMR 9015, Orsay, France.
Mol Biol Evol ; 40(6)2023 06 01.
Article en En | MEDLINE | ID: mdl-37307566
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Pennisetum Idioma: En Revista: Mol Biol Evol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2023 Tipo del documento: Article País de afiliación: Francia

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Pennisetum Idioma: En Revista: Mol Biol Evol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2023 Tipo del documento: Article País de afiliación: Francia