Linking Genomic Offset Statistics to the Shape of Selection Gradients

Area of Science:

• Evolutionary biology
• Ecology
• Genetics

Background:

• Genomic offset metrics predict population maladaptation under changing climates.
• Existing metrics assume Gaussian selection gradients, potentially limiting broader application.
• A negative relationship between genomic offset and local relative fitness is a key assumption.

Purpose of the Study:

• To generalize genomic offset theory for diverse selection gradients.
• To introduce new genomic offset measures preserving the fitness-offset relationship.
• To validate these measures in predicting relative fitness across species.

Main Methods:

• Theoretical extension of genomic offset to non-Gaussian selection gradients.
• Development of generalized genomic offset measures.
• Simulations to validate theoretical predictions.
• Common garden experiments with pearl millet, balsam poplar, and red spruce.

Main Results:

• The generalized genomic offset measures successfully predict relative fitness.
• Assuming Gaussian selection gradients is a robust approximation for the studied plant species.
• The fitness-offset relationship holds across different selection gradient shapes.

Conclusions:

• Generalized genomic offset metrics enhance predictions of population maladaptation.
• These findings support the use of genomic offset for predicting fitness loss in changing environments.
• The study validates the robustness of Gaussian selection gradient approximations in certain contexts.