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

Authors

Sivakumar Sukumaran, International maize and Wheat Improvement Center
Diego Jarquin, University of Nebraska-LincolnFollow
José Crossa, International Maize and Wheat improvement Center (CIMMYT)Follow
Matthew Reynolds, International Maize and Wheat Improvement Center

Date of this Version

2017

Citation

Plant Genome 11:170112

Comments

© Crop Science Society of America

Open access

doi: 10.3835/plantgenome2017.12.0112

Abstract

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.