Genomic-enabled Prediction Accuracies Increased by Modeling Genotype × Environment Interaction in Durum Wheat

Genomic prediction studies incorporating genotype × environment (G×E) interaction effects are limited in durum wheat. We tested the genomic-enabled prediction accuracy (PA) of Genomic Best Linear Unbiased Predictor (GBLUP) models-six non-G × E and three G × E models-on three basic cross-validation (CV) schemes- in predicting incomplete field trials (CV2), new lines (CV1), and lines in untested environments (CV0)- in a durum wheat panel grown under yield potential, drought stress, and heat stress conditions. For CV0, three scenarios were considered: (i) leave-one environment out (CV0-Env); (ii) leave one site out (CV0-Site); and (iii) leave 1 yr out (CV0-Year). The reaction norm models with G × E effects showed higher PA than the non-G × E models. Among the CV schemes, CV2 and CV0-Env had higher PA (0.58 each) than the CV1 scheme (0.35). When the average of all the models and CV schemes were considered, among the eight traits- grain yield, thousand grain weight, grain number, days to anthesis, days to maturity, plant height, and normalized difference vegetation index at vegetative (NDVIvg) and grain filling (NDVIllg)-, plant height had the highest PA (0.68) and moderate values were observed for grain yield (0.34). The results indicated that genomic selection models incorporating G × E interaction show great promise for forward prediction and application in durum wheat breeding to increase genetic gains.