A Quantitative Theory for Genomic Offset Statistics

Clément Gain (1) , Bénédicte Rhoné (2, 3) , Philippe Cubry (4) , Israfel Salazar (5) , Florence Forbes (6) , Yves Vigouroux (4) , Flora Jay (7, 8) , Olivier François (1)

1 TIMC-MAGe - Modèles et Algorithmes pour la Génomique
2 UMR AGAP - Amélioration génétique et adaptation des plantes méditerranéennes et tropicales
3 Cirad-BIOS - Département Systèmes Biologiques
4 UMR DIADE - Diversité, adaptation, développement des plantes
5 SFNR - Société Française de Neuroradiologie [Paris]
6 STATIFY - Modèles statistiques bayésiens et des valeurs extrêmes pour données structurées et de grande dimension
7 BioInfo - BioInformatique - LISN
8 TAU - TAckling the Underspecified

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.

Mots clés: predictive ecological genomics; genomic offset; climate change; local adaptation; pearl millet

Domaines: Bio-Informatique, Biologie Systémique [q-bio.QM]; Bioclimatologie; Génétique des plantes; Applications [stat.AP]

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⟩