Genetic improvement of maize with elevated levels of zinc (Zn) can reduce Zn deficiency among populations who rely on maize as a staple. Inbred lines of quality protein maize (QPM) and non-QPM with elevated Zn levels in the kernel have been identified. However, information about the optimal strategy to utilize the germplasm in breeding for high-Zn concentration is lacking. As a preliminary step, this study was conducted to ascertain the potential of QPM, non-QPM, or a combination of QPM and non-QPM hybrids for attaining desirable Zn concentration. Twenty elite inbreds, 10 QPM and 10 non-QPM, were crossed according to a modified mating design to generate hybrids, which were evaluated in four environments in Mexico during 2015 and 2016 in order to evaluate their merits as parents of hybrids. The highest mean values of Zn were observed when high-Zn QPM lines were crossed with high-Zn non-QPM lines. Hybrids with high Zn and grain yield were identified. General combining ability (GCA) effects for Zn concentration were more preponderant than specific combining ability (SCA) effects, suggesting the importance of additive gene action for the inheritance of Zn.

Zinc (Zn) deficiency is a major risk factor for human health, affecting about 30% of the world’s population. To study the potential of genomic selection (GS) for maize with increased Zn concentration, an association panel and two doubled haploid (DH) populations were evaluated in three environments. Three genomic prediction models, M (M1: Environment + Line, M2: Environment + Line + Genomic, and M3: Environment + Line + Genomic + Genomic x Environment) incorporating main effects (lines and genomic) and the interaction between genomic and environment (G x E) were assessed to estimate the prediction ability ( r _MP ) for each model. Two distinct cross-validation (CV) schemes simulating two genomic prediction breeding scenarios were used. CV1 predicts the performance of newly developed lines, whereas CV2 predicts the performance of lines tested in sparse multi-location trials. Predictions for Zn in CV1 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 panel. For CV2, r _MP values ranged from 0.67 to 0.71 for DH1, 0.40 to 0.56 for DH2 and 0.64 to 0.72 for the association panel. The genomic prediction model which included G x E had the highest average r _MP for both CV1 (0.39 and 0.44) and CV2 (0.71 and 0.51) for the association panel and DH2 population, respectively. These results suggest that GS has potential to accelerate breeding for enhanced kernel Zn concentration by facilitating selection of superior genotypes.