An Optimal Model to Improve Genomic Prediction for Protein Content and Test Weight in a Diverse Spring Wheat Panel

Abstract

In recent years, genomic selection has been widely used in plant breeding to increase genetic gain. Selections are based on breeding values of each genotype estimated using genome-wide markers. The present study developed genomic prediction models for grain protein content (GPC) and test weight (TW) in a diverse panel of 170 spring wheat lines phenotyped in five environments. Five prediction models (GBLUP, RRBLUP, EGBLUP, RF, RKHS) were investigated. The population was genotyped for genome-wide markers with the Infinium iSelect 90 K SNP assay. Environmental variation was adjusted by calculating BLUPs across environments using the complete random effect GxE model. Both GPC and TW showed high heritability of 0.867 and 0.854, respectively. When using the five-fold cross-validation scheme in the five statistical models, we found that the EGBLUP model had the highest mean prediction accuracy (0.743) for GPC, while the RRBLUP model showed the highest mean prediction accuracy (0.650) for TW. Testing various proportions of the training population indicated that a minimum of 100 genotypes were required to train the model for optimum accuracy. Testing the prediction across environments showed that BLUPs outperformed 80% of the tested environments, even though at least one of the environments had higher prediction accuracies for each trait. Thus, the optimized GS model for GPC and TW has the potential to predict trait values accurately. Implementing GS would aid breeding through accurate early generation selection of superior lines, leading to higher genetic gain per breeding cycle.