Genomic Prediction with Pedigree and Genotype x Environment Interaction in Spring Wheat Grown in South and West Asia, North Africa, and Mexico

Developing genomic selection (GS) models is an important step in applying GS to accelerate the rate of genetic gain in grain yield in plant breeding. In this study, seven genomic prediction models under two cross-validation scenarios were tested on 287 advanced elite spring wheat lines phenotyped for grain yield (GY), thousand-grain weight (GW), grain number (GN), and thermal time for flowering (TTF) in 18 international environments (year-location combinations) in major wheat producing countries in 2010 and 2011. Prediction models with genomic and pedigree information included main effects and interaction with environments. Two random cross-validation (CV) schemes were applied to predict a subset of lines that were not observed in any of the 18 environments (CV1) and a subset of lines that were not observed in a set of the environments but were observed in other environments (CV2). Genomic prediction models including genotype x environment (GxE) interaction had the highest average prediction ability under the CV1 scenario for GY (0.31), GN (0.32), GW (0.45) and TTF (0.27). For CV2, the average prediction ability of the model including the interaction terms was generally high for GY (0.38), GN (0.43), GW (0.63), and TTF (0.53). Wheat lines in site-year combinations in Mexico and India had relatively high prediction ability for grain yield and grain weight. Results indicated that prediction accuracy of lines not observed in certain environments could be relatively high for genomic selection when predicting GxE interaction in multi-environment trials.

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