52Zinc (Zn) deficiency is a major risk factor for human health, affecting about 30% of the 53 world's population. To study the potential of genomic selection (GS) for maize with increased 54 Zn concentration, an association panel and two doubled haploid (DH) populations were 55 evaluated in three environments. Three genomic prediction models, M (M1: Environment + 56 Line, M2: Environment + Line + Genomic, and M3: Environment + Line + Genomic + Genomic 57 x Environment) incorporating main effects (lines and genomic) and the interaction between 58 genomic and environment (G x E) were assessed to estimate the prediction ability (rMP) for each 59 model. Two distinct cross-validation (CV) schemes simulating two genomic prediction breeding 60 scenarios were used. CV1 predicts the performance of newly developed lines, whereas CV2 61 predicts the performance of lines tested in sparse multi-location trials. Predictions for Zn in CV1 62 ranged from -0.01 to 0.56 for DH1, 0.04 to 0.50 for DH2 and -0.001 to 0.47 for the association 63 panel. For CV2, rMP values ranged from 0.67 to 0.71 for DH1, 0.40 to 0.56 for DH2 and 0.64 to 64 0.72 for the association panel. The genomic prediction model which included G x E had the 65 highest average rMP for both CV1 (0.39 and 0.44) and CV2 (0.71 and 0.51) for the association 66 panel and DH2 population, respectively. These results suggest that GS has potential to accelerate 67 breeding for enhanced kernel Zn concentration by facilitating selection of superior genotypes. 68 69 93 2015; Manickavelu et al. 2017; Arojju et al. 2019).
94The utility and effectiveness of GS has been examined for many different crop species, 95 marker densities, traits and statistical models and varying levels of prediction accuracy have been 96 achieved (de los Campos et al.