Genomic Selection Improves Response to Selection in Resilience by Exploiting Genotype by Environment Interactions

Area of Science:

• Animal Genetics
• Quantitative Genetics
• Livestock Breeding

Background:

• Genotype by environment interactions (GxE) are prevalent in livestock, complicating genetic improvement and animal adaptation.
• Traditional breeding often overlooks GxE due to model complexity and reduced accuracy in extreme environments.
• GxE presents opportunities to enhance animal resilience to environmental stressors such as heat stress and disease.

Purpose of the Study:

• To assess the exploitability of GxE using traditional and genomic selection methods.
• To compare the efficacy of reaction norm (RN) models against conventional methods that ignore GxE.
• To evaluate the impact of GxE on genetic improvement strategies in livestock.

Main Methods:

• Utilized selection index theory to address research questions.
• Modeled GxE using a linear reaction norm (RN) model with contemporary group mean as the environmental gradient.
• Incorporated linear and non-linear profit equations for economic value calculations.

Main Results:

• Reaction norm (RN) models demonstrated higher accuracies for genomic estimated breeding values (GEBV) in extreme environments compared to conventional models.
• Genomic selection consistently yielded greater selection response across all environments than sib or progeny testing.
• Genomic selection with large reference populations significantly improved response in the RN model slope, effectively reducing environmental sensitivity.

Conclusions:

• Genomic selection, particularly with extensive reference populations, offers substantial potential for leveraging GxE to boost animal resilience.
• Reaction norm models improve the accuracy of breeding values in extreme environments, aiding in the exploitation of GxE.
• Exploiting GxE through advanced genomic tools is crucial for developing livestock better adapted to diverse and challenging environmental conditions.