Genomic prediction for grain yield and micro-environmental sensitivity in winter wheat

Area of Science:

• Agricultural Science
• Genetics
• Plant Breeding

Background:

• Individuals in shared environments face variations from micro-environmental factors.
• Genetic sensitivity to these variations (micro-environmental sensitivity) impacts resilience.
• Research on micro-environmental sensitivity is limited in plants, especially wheat.

Purpose of the Study:

• Quantify genetic variation's influence on residual dispersion and its correlation with genetic effects on wheat grain yield.
• Evaluate the predictive performance of a double hierarchical generalized linear model (DHGLM) for genetic effects and residual dispersion.

Main Methods:

• Utilized a double hierarchical generalized linear model (DHGLM).
• Analyzed data from 2,456 advanced wheat breeding lines genotyped with a 15K SNP-Illumina-BeadChip.
• Employed leave-one-line-out cross-validation to compare DHGLM with a linear mixed model (LMM-HET).

Main Results:

• Micro-environmental sensitivity for grain yield is heritable, with potential for reduction.
• An intermediate genetic correlation was observed between additive effects on phenotypes and residual dispersion.
• DHGLM and LMM-HET showed similar predictive performance for additive genetic effects on phenotypes.

Conclusions:

• Breeding for reduced micro-environmental sensitivity in wheat is feasible and can be integrated into breeding programs.
• Genetic selection for resilience by predicting genetic effects on residual dispersion is accurate.
• DHGLM is a suitable model for enhancing grain yield and reducing micro-environmental sensitivity in wheat breeding.