Identification of kernel iron- and zinc-rich maize inbreds and analysis of genetic diversity using microsatellite markers

Chakraborti, Mridul; Prasanna, B. M.; Hossain, Firoz; Mazumdar, S.P.; Singh, Anju Mahendru; Guleria, S. K.; Gupta, H. S.

doi:10.1007/s13562-011-0050-9

Cited by 35 publications

(24 citation statements)

References 19 publications

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“…It is the most common mineral deficiency in the world (Ghandilyan et al, 2006;Chakraborti et al, 2011a;Tako et al, 2016;Finkelstein et al, 2017). It is the most common mineral deficiency in the world (Ghandilyan et al, 2006;Chakraborti et al, 2011a;Tako et al, 2016;Finkelstein et al, 2017).…”

Section: Micronutrient Deficiencymentioning

confidence: 99%

“…Breeding for mineral-rich cereal crops, such as high Fe and Zn maize, is a sustainable and cost-effective approach to lower micronutrient deficiencies (Bouis et al, 2011;Chakraborti et al, 2011a;Prasanna et al, 2011;Bouis and Saltzman, 2017;Hindu et al, 2018). Biofortification will benefit the people of low-income countries that do not have enough money to buy nutrient-rich fruits, vegetables, pulses, fish, and animal products to cope with these deficiencies (Cakmak, 2008a;Hindu et al, 2018).…”

Section: Breeding Strategiesmentioning

confidence: 99%

“…Iron is critical for human health because its deficiency can be life threatening. It is the most common mineral deficiency in the world (Ghandilyan et al, 2006;Chakraborti et al, 2011a;Tako et al, 2016;Finkelstein et al, 2017). Iron deficiency is widespread in developing countries because of lack of consumption of animal products (which can enhance nonheme Fe absorption and provide highly bioavailable heme Fe) and reliance on cereals and legumes as basic staple foods.…”

Section: Micronutrient Deficiencymentioning

confidence: 99%

“…Breeding for Fe and Zn also focuses on retaining micronutrients after processing, their bioavailability, and daily requirement (Cakmak, 2010). Numerous researchers have reported genetic variation for Fe and Zn in maize germplasm (Welch and Graham, 2004;Ghandilyan et al, 2006;Menkir, 2008;Chakraborti et al, 2011a;Prasanna et al, 2011;Kumar et al, 2015). Genetic variation in maize grain for Fe and Zn reported since 2000 is presented in Table 1.…”

Section: Genetic Variation For Iron and Zinc In Maize Grainmentioning

confidence: 99%

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Iron and Zinc in Maize in the Developing World: Deficiency, Availability, and Breeding

et al. 2018

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Maize (Zea mays L.) is the third most important cereal in the world and the most important food security crop in sub‐Saharan Africa. Maize provides energy and micronutrients. Deficiencies of the essential micronutrients Zn and Fe are fifth and sixth ranked among the top 10 most important risk factors for conditions such as anemia, low cognitive functioning, and impaired immune system (Fe deficiency) and diarrhea, skin inflammation, and recurrent infections (Zn deficiency) in humans, affecting more than two billion people worldwide. Poverty, lack of access to balanced diets and awareness, and low phytoavailability and bioavailability of these nutrients are major reasons for deficiencies. Breeding for mineral‐rich maize is a sustainable and cost‐effective approach to reduce micronutrient deficiencies. Since 2004, there has been significant progress in improving maize for Zn content. The aim of this review was to capture recent developments, trends, and progress in maize Fe and Zn biofortification and to identify challenges and ways to overcome them. HarvestPlus has set target levels for Fe (60 μg g−1) and Zn (38 μg g−1) in maize. Zinc target levels have been reached, but conventional breeding alone cannot enhance Fe to the recommended levels. Techniques such as oligo‐directed mutagenesis, reverse breeding, RNA‐directed DNA methylation, and gene editing could be used in future to speed up maize Fe biofortification. Additional research is required on Fe and Zn bioavailability in maize products, and on interactions of Fe and Zn with Ca and phytate and their influence on absorption, to better understand the underlying mechanisms.

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Section: Micronutrient Deficiencymentioning

confidence: 99%

Section: Breeding Strategiesmentioning

confidence: 99%

Section: Micronutrient Deficiencymentioning

confidence: 99%

Section: Genetic Variation For Iron and Zinc In Maize Grainmentioning

confidence: 99%

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Iron and Zinc in Maize in the Developing World: Deficiency, Availability, and Breeding

et al. 2018

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“…1993; Bänziger and Long 2000; Brkic et al . 2004; Menkir 2008; Chakraborti et al . 2011; Prasanna et al .…”

Section: Introductionmentioning

confidence: 99%

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

Lee

Mageto

Crossa

et al. 2020

Preprint

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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.

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Biofortified Maize – A Genetic Avenue for Nutritional Security

Babu¹,

Palacios²,

Prasanna³

2013

Translational Genomics for Crop Breeding

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Identification of kernel iron- and zinc-rich maize inbreds and analysis of genetic diversity using microsatellite markers

Cited by 35 publications

References 19 publications

Iron and Zinc in Maize in the Developing World: Deficiency, Availability, and Breeding

Iron and Zinc in Maize in the Developing World: Deficiency, Availability, and Breeding

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

Biofortified Maize – A Genetic Avenue for Nutritional Security

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