A Quantitative Theory for Genomic Offset Statistics - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Molecular Biology and Evolution Year : 2023

A Quantitative Theory for Genomic Offset Statistics

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.
Fichier principal
Vignette du fichier
Gain - A Quantitative Theory for Genomic Offset Statistic.pdf (5.13 Mo) Télécharger le fichier
Origin Publisher files allowed on an open archive
Licence

Dates and versions

hal-04243951 , version 1 (28-03-2023)
hal-04243951 , version 2 (16-10-2023)

Licence

Identifiers

Cite

Clément Gain, Bénédicte Rhoné, Philippe Cubry, Israfel Salazar, Florence Forbes, et al.. A Quantitative Theory for Genomic Offset Statistics. Molecular Biology and Evolution, 2023, 40 (6), pp.msad140. ⟨10.1093/molbev/msad140⟩. ⟨hal-04243951v2⟩
578 View
38 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More