Search

Genomic selection (GS) can improve genetic gain of complex traits in plant breeding. Phenotyping agronomic traits of winter wheat (Triticum aestivum L.) for dual‐purpose use is expensive and time‐consuming. In this study, we compared the prediction accuracies of four GS models (RR‐BLUP, GBLUP, GAUSS, and BL) for forage yield (FY), plant height (PH) and heading date (HD) of the hard winter wheat diversity panel (n = 298) using random and stratified sampling methods. In addition, we determined the appropriate training population (TP) size and marker density for GS of the traits. Moderate to high prediction accuracies ranging from 0.66 to 0.69 for FY, 0.46 to 0.49 for PH, and 0.71 to 0.74 for HD were observed for the GS models. However, the sampling method had little or no impact on prediction accuracy. The RR‐BLUP, GBLUP, and GAUSS models produced slightly greater prediction accuracies than BL for all traits studied. Prediction accuracies increased with increasing TP size and marker density in all the GS models tested. However, increase of prediction accuracy started to plateau at nTP = 180 lines and 1,000; 1,500; or 3,000 SNPs suggesting that the minimum TP size and marker density were about 180 lines and 1,000 or more SNPs, depending on the model and trait. The impact of TP size on prediction accuracy was greater for RR‐BLUP, GAUSS, and GBLUP than for BL model. This study suggests that RR‐BLUP, GBLUP, and GAUSS are viable models for selecting the forage agronomic traits during dual‐purpose wheat breeding. [ABSTRACT FROM AUTHOR]

Phenotyping forage quality traits is time‐consuming in forage wheat breeding. In this study, prediction accuracies of three genomic selection (GS) models (ridge regression best linear unbiased prediction [RRBLUP], Gaussian kernel [GAUSS], and Bayesian LASSO [BL, where LASSO stands for least absolute shrinkage and selection operator]) for forage quality traits of winter wheat (Triticum aestivum L.) were compared using two genotype sampling methods. In addition, the impact of training population (TP) size and marker density on prediction accuracy was explored. The study was done using a diversity panel (n = 298) that was genotyped using 90K single nucleotide polymorphisms (SNPs) and phenotyped for forage quality traits including crude protein, acid detergent fiber, neutral detergent fiber, sugars, lignin content, and in vitro true dry matter digestibility. Generally, the three models produced similar prediction accuracies, which ranged from 0.34 to 0.61, for all traits. The sampling method had little effect on accuracy. Crude protein was one of the traits with the highest prediction accuracy, and it required only 1000 markers to attain its highest prediction accuracy value. Increasing TP size and marker density increased accuracies of all traits, and increasing the TP size was more effective than increasing marker density. For this panel, the optimal TP size (nTP) was 150, at which point prediction accuracies of all traits, except for sugars, reached over 90% of the highest value at nTP = 250. However, the sampling method for marker density had no effect on accuracy. The results suggest that GS can be an alternative approach to facilitate selection of forage quality traits during forage wheat breeding. [ABSTRACT FROM AUTHOR]