Genomic Prediction with Genotype by Environment Interaction Analysis for Kernel Zinc Concentration in Tropical Maize Germplasm

摘要: Zinc (Zn) deficiency is a major risk factor for human health, affecting about 30% of the world’s population. To study potential genomic selection (GS) maize with increased Zn concentration, an association panel and two doubled haploid (DH) populations were evaluated in three environments. Three prediction models, M (M1: Environment + Line, M2: Line Genomic, M3: Genomic x Environment) incorporating main effects (lines genomic) interaction between environment (G E) assessed to estimate ability (rMP) each model. Two distinct cross-validation (CV) schemes simulating breeding scenarios used. CV1 predicts performance newly developed lines, whereas CV2 lines tested sparse multi-location trials. Predictions ranged from -0.01 0.56 DH1, 0.04 0.50 DH2 -0.001 0.47 panel. For CV2, rMP values 0.67 0.71 0.40 0.64 0.72 The model which included G E had highest average both (0.39 0.44) (0.71 0.51) population, respectively. These results suggest that GS has accelerate enhanced kernel concentration by facilitating superior genotypes.