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

Abstract

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.

Genome-wide association study suggests an independent genetic basis of zinc and cadmium concentrations in fresh sweet corn kernels

“…Given that we observed significant G×E interaction for boron, copper, iron, magnesium, and rubidium, accounting for G×E in WGP models could result in slightly improved predictive abilities for these five elements. In support of this supposition, a multi-environment model incorporating G×E resulted in higher average prediction abilities for the concentration of zinc in kernels from a tropical maize inbred panel and a double haploid population compared to those from single-environment models (Mageto et al . 2020).…”

“…Whole- genome prediction (WGP) models have been found to be moderately predictive of elemental concentrations in mature grain of tropical maize populations (zinc) (Guo et al . 2020; Mageto et al . 2020) and the Ames panel (boron, calcium, copper, iron, potassium, magnesium, manganese, molybdenum, nickel, phosphorus, and zinc) (Wu et al .…”

“…Additionally, the authors detected novel candidate gene loci for boron and nickel grain concentrations. Whole-genome prediction (WGP) models have been found to be moderately predictive of elemental concentrations in mature grain of tropical maize populations (zinc) (Guo et al 2020;Mageto et al 2020) and the Ames panel (boron, calcium, copper, iron, potassium, magnesium, manganese, molybdenum, nickel, phosphorus, and zinc) (Wu et al 2021). Notwithstanding this progress with mature grain, the genotype-phenotype map of elemental concentrations in fresh sweet corn kernels is completely nonexistent, thus there exists tremendous opportunities for studying the quantitative genetics of these nutritionally relevant phenotypes.…”

Plant Breeding Integrated with Genomic-Enabled Prediction

“…Hassen et al [105] reported the first study that explored the feasibility of breeding rice, adapted to alternate wetting and drying, using genomic prediction methods that accounted for genotype by environment interactions. Mageto et al [106] evaluated genomic prediction with G × E analysis for the level of Zn in the kernel of tropical maize germplasm and demonstrated the capability of genomic prediction to boost the breeding of plants for enhanced Zn levels in the kernel of the selected superior genotypes. Multi-environment analysis can influence G × E by applying genetic and remaining covariance roles, markers and environmental covariates, or marker with G × E [91,97,107].…”