Identification of quantitative trait locus and prediction of candidate genes for grain mineral concentration in maize across multiple environments

Zhang, Huaduo; Liu, Jingxian; Jin, Tiantian; Huang, Yaqun; Chen, Jingtang; Zhu, Linghua; Zhao, Yang; Guo, Jian-Wei

doi:10.1007/s10681-017-1875-7

Cited by 23 publications

(26 citation statements)

References 40 publications

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“…Zhang et al. () also studied the QTLs for mineral (Fe, Zn, Cu and Mn) accumulation in maize kernels across individual environments and multiple environments. This study identified the 64 and 67 QTLs for single and multiple environment analyses, respectively.…”

Section: Strategies For Zn Biofortification Of Maizementioning

confidence: 99%

Zinc biofortification of maize (Zea mays L.): Status and challenges

Maqbool¹,

Beshir²

2018

Plant Breeding

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Zn deficiency is one of the leading health problems in children and women of developing countries. Different interventions could be used to overcome malnutrition, but biofortification is most impactful, convenient, sustainable and acceptable intervention. Maize is one of the major crops grown and consumed in the regions with prevalent Zn malnutrition; therefore, this is suitable target for Zn biofortification. Zn biofortification of maize could be achieved through agronomic and genetic approaches. Discussion of agronomic approaches with genetic approaches is prerequisite because soils in developing countries are deficit of Zn and availability of Zn in soils is mandatory for estimating the genetic responses of maize genotypes through genetic approaches. Seed priming, foliar and soil applications are agronomic tools for biofortification, but solo and combined applications of these treatments have different effects on Zn enrichment. Genetic approaches include the increase of Zn bioavailability or increase of kernel Zn concentration. Zn bioavailability could be increased by reducing the anti‐nutritional factors or by increasing the bioavailability enhancers. Kernel Zn concentration could be improved through hybridization and selections, whereas genetically engineered attempts for improving Zn uptake from soil, loading in xylem, remobilization in grains and sequestration in endosperm can further improve the kernel Zn concentration. Key challenges associated with dissemination of Zn biofortified maize are also under discussion in this draft. Current review emphasized all of above‐mentioned contents to provide roadmap for the development of Zn biofortified maize genotypes to curb the global Zn malnutrition.

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Section: Strategies For Zn Biofortification Of Maizementioning

confidence: 99%

Zinc biofortification of maize (Zea mays L.): Status and challenges

Maqbool¹,

Beshir²

2018

Plant Breeding

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“…Kernel Zn has been investigated in several analyses of quantitative trait loci (QTL), which have shown that Zn accumulation is under the control of several loci, from 4 to 20 per population [30][31][32][33][34][35]. Additionally, genomic regions associated with important QTLs for kernel Zn have been reported on chromosomes 2 and 6 [34,35]. Consistent with mating designs, additive gene effects predominantly controlled kernel Zn concentration in the QTL studies.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Kernel Zinc in Hybrids of Elite Quality Protein Maize (QPM) and Non-QPM Inbred Lines Adapted to the Tropics Based on a Mating Design

et al. 2020

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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.

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“…Kernel Zn has been investigated in several quantitative trait loci (QTL) analyses in maize and each study has reported that Zn concentration is under the control of several loci. The phenotypic variation explained by those loci ranges from 5.9 to 48.8% (Zhou et al 2010;Qin et al 2012;Simić et al 2012;Baxter et al 2013;Jin et al 2013;Zhang et al 2017a;Hindu et al 2018). A Meta-QTL analysis across several of those studies identified regions on chromosome 2 that might be important for kernel Zn concentration (Jin et al 2013).…”

mentioning

confidence: 99%

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

Mageto

Crossa

Pérez‐Rodríguez

et al. 2020

G3 Genes|Genomes|Genetics

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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 × Environment) incorporating main effects (lines and genomic) and the interaction between genomic and environment (G × E) were assessed to estimate the prediction ability (rMP) 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, rMP 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 × E had the highest average rMP 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.

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Identification of quantitative trait locus and prediction of candidate genes for grain mineral concentration in maize across multiple environments

Cited by 23 publications

References 40 publications

Zinc biofortification of maize (Zea mays L.): Status and challenges

Zinc biofortification of maize (Zea mays L.): Status and challenges

An Evaluation of Kernel Zinc in Hybrids of Elite Quality Protein Maize (QPM) and Non-QPM Inbred Lines Adapted to the Tropics Based on a Mating Design

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

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