Linking Genomic Offset Statistics to the Shape of Selection Gradients.

Abstract

AbstractGenomic offset metrics are increasingly used to predict population maladaptation under changing climates, based on the assumption of a negative statistical relationship between offset measures and local relative fitness. Recent theoretical advances have confirmed this relationship by relating genomic offset to phenotypic trait distances along selection gradients. However, these metrics typically rely on the assumption that stabilizing selection, which maintains local adaptive optima, operates on fitness-related traits through Gaussian-shaped selection gradients. In this study, we extend the theory to accommodate more diverse forms of selection gradients and introduce more general genomic offset measures that preserve the fitness-offset relationship. We validate this generalization through simulations and demonstrate the utility of these new measures in predicting relative fitness in common garden experiments involving three plant species: pearl millet, a vital staple cereal grown in arid soils, and two emblematic North American tree species, balsam poplar and red spruce. Our findings indicate that assuming a local Gaussian-shaped selection gradient for climate adaptation is a robust approximation for these species. These results have important implications for validating genomic offset predictions using fitness proxies and for studies that aim to predict fitness loss based on genomic offset metrics.