The trace element selenium (Se) is a crucial element for many living organisms, including soil microorganisms, plants and animals, including humans. Generally, in Nature Se is taken up in the living cells of microorganisms, plants, animals and humans in several inorganic forms such as selenate, selenite, elemental Se and selenide. These forms are converted to organic forms by biological process, mostly as the two selenoamino acids selenocysteine (SeCys) and selenomethionine (SeMet). The biological systems of plants, animals and humans can fix these amino acids into Se-containing proteins by a modest replacement of methionine with SeMet. While the form SeCys is usually present in the active site of enzymes, which is essential for catalytic activity. Within human cells, organic forms of Se are significant for the accurate functioning of the immune and reproductive systems, the thyroid and the brain, and to enzyme activity within cells. Humans ingest Se through plant and animal foods rich in the element. The concentration of Se in foodstuffs depends on the presence of available forms of Se in soils and its uptake and accumulation by plants and herbivorous animals. Therefore, improving the availability of Se to plants is, therefore, a potential pathway to overcoming human Se deficiencies. Among these prospective pathways, the Se-biofortification of plants has already been established as a pioneering approach for producing Se-enriched agricultural products. To achieve this desirable aim of Se-biofortification, molecular breeding and genetic engineering in combination with novel agronomic and edaphic management approaches should be combined. This current review summarizes the roles, responses, prospects and mechanisms of Se in human nutrition. It also elaborates how biofortification is a plausible approach to resolving Se-deficiency in humans and other animals.

Key message Genome-wide association study (GWAS) on 923 maize lines and validation in bi-parental populations identified significant genomic regions for kernel-Zinc and-Iron in maize. Abstract Bio-fortification of maize with elevated Zinc (Zn) and Iron (Fe) holds considerable promise for alleviating under-nutrition among the world’s poor. Bio-fortification through molecular breeding could be an economical strategy for developing nutritious maize, and hence in this study, we adopted GWAS to identify markers associated with high kernel-Zn and Fe in maize and subsequently validated marker-trait associations in independent bi-parental populations. For GWAS, we evaluated a diverse maize association mapping panel of 923 inbred lines across three environments and detected trait associations using high-density Single nucleotide polymorphism (SNPs) obtained through genotyping-by-sequencing. Phenotyping trials of the GWAS panel showed high heritability and moderate correlation between kernel-Zn and Fe concentrations. GWAS revealed a total of 46 SNPs (Zn-20 and Fe-26) significantly associated (P ≤ 5.03 × 10⁻⁰⁵) with kernel-Zn and Fe concentrations with some of these associated SNPs located within previously reported QTL intervals for these traits. Three double-haploid (DH) populations were developed using lines identified from the panel that were contrasting for these micronutrients. The DH populations were phenotyped at two environments and were used for validating significant SNPs (P ≤ 1 × 10⁻⁰³) based on single marker QTL analysis. Based on this analysis, 11 (Zn) and 11 (Fe) SNPs were found to have significant effect on the trait variance (P ≤ 0.01, R² ≥ 0.05) in at least one bi-parental population. These findings are being pursued in the kernel-Zn and Fe breeding program, and could hold great value in functional analysis and possible cloning of high-value genes for these traits in maize.

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.