Quantitative trait loci for biofortification traits in maize grain

A genome-wide association study identifies a transporter for zinc uploading to maize kernels

The Zn content in cereal seeds is an important trait for crop production as well as for human health. However, little is known about how Zn is loaded to plant seeds. Here, through a genome-wide association study (GWAS), we identify the Zn-NA (nicotianamine) transporter gene ZmYSL2 that is responsible for loading Zn to maize kernels. High promoter sequence variation in ZmYSL2 most likely drives the natural variation in Zn concentrations in maize kernels. ZmYSL2 is specifically localized on the plasma membrane facing the maternal tissue of the basal endosperm transfer cell layer (BETL) and functions in loading Zn-NA into the BETL. Overexpression of ZmYSL2 increases the Zn concentration in the kernels by 31.6%, which achieves the goal of Zn biofortification of maize. These findings resolve the mystery underlying the loading of Zn into plant seeds, providing an efficient strategy for breeding or engineering maize varieties with enriched Zn nutrition.

Quantitative trait loci and candidate genes for iron and zinc bio-fortification in genetically diverse germplasm of maize (Zea mays L): A systematic review

Genetically and economically, Maize plays a pivotal role in tackling Iron-Zinc mineral deficiency through the crop's biofortification approach to high-yielding cultivars. The objective of this study is to summarize quantitative trait loci (QTL) is useful for identifying novel genes of interest in diverse germplasm for understanding the exact genetic mechanism for Iron and zinc uptake, deposition, and biosynthesis in Zea mays L endosperm. various techniques like Germplasm Genetic Wide Association, QTL meta-analysis, and biparental linkage analysis are used by researchers in diverse germplasm of Maize for the gene of interest marking and are extracted as secondary information through a systematic review of scientific published sources in peer-reviewed sites. A literature review was focused on quantitative trait loci with candidate genes from different families like YS, NRAMP, ferritin, Cation efflux, etc., and cloned four phytase soluble genes which influence the concentration as well as bioavailability of Fe & Zn in the endosperm. More than 30 QTLs with 15-Fe, 17-Zn; 10 Meta QTLS are common and linked with micronutrient concentration as well 17 candidate genes from different families are responsible for the zinc-iron deposition on the endosperm. More than 46 Fe-Zn (20 + 26) SNPs and 22 SNPs (10 + 12) on nine different chromosomes play a significant role in the variation of the mineral value of inbreeds and Double haploid Bi-parental population of Zea mays L. In Rice and Maize, five different chromosomes are collinear for the uptake to deposition of these minerals in the endosperm. The success of marker-based biofortification depends upon the nature of germplasm, the gap between flanking marker and targeted genes, the selection of genotypes in each generation, and genotype-environment interaction which are the future area of study. This study can assist the breeders in fast-tracking Fe and Zn biofortification through frequency multiplication of these desired loci of Maize.

Crop breeding for a changing climate in the Pannonian region: towards integration of modern phenotyping tools

The Pannonian Plain, as the most productive region of Southeast Europe, has a long tradition of agronomic production as well as agronomic research and plant breeding. Many research institutions from the agri-food sector of this region have a significant impact on agriculture. Their well-developed and fruitful breeding programmes resulted in productive crop varieties highly adapted to the specific regional environmental conditions. Rapid climatic changes that occurred during the last decades led to even more investigations of complex interactions between plants and their environments and the creation of climate-smart and resilient crops. Plant phenotyping is an essential part of botanical, biological, agronomic, physiological, biochemical, genetic, and other omics approaches. Phenotyping tools and applied methods differ among these disciplines, but all of them are used to evaluate and measure complex traits related to growth, yield, quality, and adaptation to different environmental stresses (biotic and abiotic). During almost a century-long period of plant breeding in the Pannonian region, plant phenotyping methods have changed, from simple measurements in the field to modern plant phenotyping and high-throughput non-invasive and digital technologies. In this review, we present a short historical background and the most recent developments in the field of plant phenotyping, as well as the results accomplished so far in Croatia, Hungary, and Serbia. Current status and perspectives for further simultaneous regional development and modernization of plant phenotyping are also discussed.

Marker-assisted pyramiding of γ-tocopherol methyltransferase and glutamate formiminotransferase genes for development of biofortified sweet corn hybrids

Micronutrients, including vitamins, minerals, and other bioactive compounds, have tremendous impacts on human health. Much progress has been made in improving the micronutrient content of inbred lines in various crops through biofortified breeding. However, biofortified breeding still falls short for the rapid generation of high-yielding hybrids rich in multiple micronutrients. Here, we bred multi-biofortified sweet corn hybrids efficiently through marker-assisted selection. Screening by molecular markers for vitamin E and folic acid, we obtained 15 inbred lines carrying favorable alleles (six for vitamin E, nine for folic acid, and three for both). Multiple biofortified corn hybrids were developed through crossing and genetic diversity analysis.

Genome-Wide Association Analysis Reveals the Genetic Basis of Iron-Deficiency Stress Tolerance in Maize

Iron (Fe) is an essential trace element for almost all organisms and is often the major limiting nutrient for normal growth. Fe deficiency is a worldwide agricultural problem, which affects crop productivity and product quality. Understanding the Fe-deficiency response in plants is necessary for improving both plant health and the human diet. In this study, Fe-efficient (Ye478) and Fe-inefficient maize inbred lines (Wu312) were used to identify the genotypic difference in response to low Fe stress during different developmental stages and to further determine the optimal Fe-deficient Fe(II) supply level which leads to the largest phenotypic difference between Ye478 and Wu312. Then, genome-wide association analysis was performed to further identify candidate genes associated with the molecular mechanisms under different Fe nutritional statuses. Three candidate genes involved in Fe homeostasis of strategy II plants (strategy II genes) were identified, including ZmDMAS1, ZmNAAT1, and ZmYSL11. Furthermore, candidate genes ZmNAAT1, ZmDMAS1, and ZmYSL11 were induced in Fe-deficient roots and shoots, and the expression of ZmNAAT1 and ZmDMAS1 responded to Fe deficiency more in shoots than in roots. Beyond that, several genes that may participate in Fe homeostasis of strategy I plants (strategy I genes) were identified, which were either encoding Fe transporters (ZmIRT1 and ZmZIP4), or acting as essential ethylene signal transducers (ZmEBF1). Interestingly, ZmIRT1, ZmZIP4, and ZmEBF1 were significantly upregulated under low Fe stress, suggesting that these genes may be involved in Fe-deficiency tolerance in maize which is considered as strategy II plant. This study demonstrates the use of natural variation in the association population to identify important genes associated with Fe-deficiency tolerance and may further provide insights for understanding the molecular mechanism underlying the tolerance to Fe-deficiency stress in maize.

Mapping of the Quantitative Trait Loci and Candidate Genes Associated With Iron Efficiency in Maize

Iron (Fe) is a mineral micronutrient for plants, and Fe deficiency is a major abiotic stress in crop production because of its low solubility under aerobic and alkaline conditions. In this study, 18 maize inbred lines were used to preliminarily illustrate the physiological mechanism underlying Fe deficiency tolerance. Then biparental linkage analysis was performed to identify the quantitative trait loci (QTLs) and candidate genes associated with Fe deficiency tolerance using the recombinant inbred line (RIL) population derived from the most Fe-efficient (Ye478) and Fe-inefficient (Wu312) inbred lines. A total of 24 QTLs was identified under different Fe nutritional status in the Ye478 × Wu312 RIL population, explaining 6.1-26.6% of phenotypic variation, and ten candidate genes were identified. Plants have evolved two distinct mechanisms to solubilize and transport Fe to acclimate to Fe deficiency, including reduction-based strategy (strategy I) and chelation-based strategy (strategy II), and maize uses strategy II. However, not only genes involved in Fe homeostasis verified in strategy II plants (strategy II genes), which included ZmYS1, ZmYS3, and ZmTOM2, but also several genes associated with Fe homeostasis in strategy I plants (strategy I genes) were identified, including ZmFIT, ZmPYE, ZmILR3, ZmBTS, and ZmEIN2. Furthermore, strategy II gene ZmYS1 and strategy I gene ZmBTS were significantly upregulated in the Fe-deficient roots and shoots of maize inbred lines, and responded to Fe deficiency more in shoots than in roots. Under Fe deficiency, greater upregulations of ZmYS1 and ZmBTS were observed in Fe-efficient parent Ye478, not in Fe-inefficient parent Wu312. Beyond that, ZmEIN2 and ZmILR3, were found to be Fe deficiency-inducible in the shoots. These findings indicate that these candidate genes may be associated with Fe deficiency tolerance in maize. This study demonstrates the use of natural variation to identify important Fe deficiency-regulated genes and provides further insights for understanding the response to Fe deficiency stress in maize.

Identification and Analysis of Zinc Efficiency-Associated Loci in Maize

Zinc (Zn) deficiency, a globally predominant micronutrient disorder in crops and humans, reduces crop yields and adversely impacts human health. Despite numerous studies on the physiological mechanisms underlying Zn deficiency tolerance, its genetic basis of molecular mechanism is still poorly understood. Thus, the Zn efficiency of 20 maize inbred lines was evaluated, and a quantitative trait locus (QTL) analysis was performed in the recombination inbred line population derived from the most Zn-efficient (Ye478) and Zn-inefficient inbred line (Wu312) to identify the candidate genes associated with Zn deficiency tolerance. On this basis, we analyzed the expression of ZmZIP1-ZmZIP8. Thirteen QTLs for the traits associated with Zn deficiency tolerance were detected, explaining 7.6-63.5% of the phenotypic variation. The genes responsible for Zn uptake and transport across membranes (ZmZIP3, ZmHMA3, ZmHMA4) were identified, which probably form a sophisticated network to regulate the uptake, translocation, and redistribution of Zn. Additionally, we identified the genes involved in the indole-3-acetic acid (IAA) biosynthesis (ZmIGPS) and auxin-dependent gene regulation (ZmIAA). Notably, a high upregulation of ZmZIP3 was found in the Zn-deficient root of Ye478, but not in that of Wu312. Additionally, ZmZIP4, ZmZIP5, and ZmZIP7 were up-regulated in the Zn-deficient roots of Ye478 and Wu312. Our findings provide a new insight into the genetic basis of Zn deficiency tolerance.

High-resolution genome-wide association study pinpoints metal transporter and chelator genes involved in the genetic control of element levels in maize grain

Despite its importance to plant function and human health, the genetics underpinning element levels in maize grain remain largely unknown. Through a genome-wide association study in the maize Ames panel of nearly 2,000 inbred lines that was imputed with ∼7.7 million SNP markers, we investigated the genetic basis of natural variation for the concentration of 11 elements in grain. Novel associations were detected for the metal transporter genes rte2 (rotten ear2) and irt1 (iron-regulated transporter1) with boron and nickel, respectively. We also further resolved loci that were previously found to be associated with one or more of five elements (copper, iron, manganese, molybdenum, and/or zinc), with two metal chelator and five metal transporter candidate causal genes identified. The nas5 (nicotianamine synthase5) gene involved in the synthesis of nicotianamine, a metal chelator, was found associated with both zinc and iron and suggests a common genetic basis controlling the accumulation of these two metals in the grain. Furthermore, moderate predictive abilities were obtained for the 11 elemental grain phenotypes with two whole-genome prediction models: Bayesian Ridge Regression (0.33–0.51) and BayesB (0.33–0.53). Of the two models, BayesB, with its greater emphasis on large-effect loci, showed ∼4–10% higher predictive abilities for nickel, molybdenum, and copper. Altogether, our findings contribute to an improved genotype-phenotype map for grain element accumulation in maize.

Zinc (Zn): The Last Nutrient in the Alphabet and Shedding Light on Zn Efficiency for the Future of Crop Production under Suboptimal Zn

At a global scale, about three billion people have inadequate zinc (Zn) and iron (Fe) nutrition and 500,000 children lose their lives due to this. In recent years, the interest in adopting healthy diets drew increased attention to mineral nutrients, including Zn. Zn is an essential micronutrient for plant growth and development that is involved in several processes, like acting as a cofactor for hundreds of enzymes, chlorophyll biosynthesis, gene expression, signal transduction, and plant defense systems. Many agricultural soils are unable to supply the Zn needs of crop plants, making Zn deficiency a widespread nutritional disorder, particularly in calcareous (pH > 7) soils worldwide. Plant Zn efficiency involves Zn uptake, transport, and utilization; plants with high Zn efficiency display high yield and significant growth under low Zn supply and offer a promising and sustainable solution for the production of many crops, such as rice, beans, wheat, soybeans, and maize. The goal of this review is to report the current knowledge on key Zn efficiency traits including root system uptake, Zn transporters, and shoot Zn utilization. These mechanisms will be valuable for increasing the Zn efficiency of crops and food Zn contents to meet global needs for food production and nutrition in the 21st century. Furthermore, future research will address the target genes underlying Zn efficiency and the optimization of Zn efficiency phenotyping for the development of Zn-efficient crop varieties for more sustainable crop production under suboptimal Zn regimes, as well food security of the future.

The maize shoot ionome: Its interaction partners, predictive power, and genetic determinants

An improved understanding of how to manipulate the accumulation and enrichment of mineral elements in aboveground plant tissues holds promise for future resource efficient and sustainable crop production. The objectives of this study were to (a) evaluate the influence of Fe regimes on mineral element concentrations and contents in the maize shoot as well as their correlations, (b) examine the predictive ability of physiological and morphological traits of individual genotypes of the IBM population from the concentration of mineral elements, and (c) identify genetic factors influencing the mineral element composition within and across Fe regimes. We evaluated the concentration and content of 12 mineral elements in shoots of the IBM population grown in sufficient and deficient Fe regimes and found for almost all mineral elements a significant (α = 0.05) genotypic variance. Across all mineral elements, the variance of genotype*Fe regime interactions was on average even more pronounced. High prediction abilities indicated that mineral elements are powerful predictors of morphological and physiological traits. Furthermore, our results suggest that ZmHMA2/3 and ZmMOT1 are major players in the natural genetic variation of Cd and Mo concentrations and contents of maize shoots, respectively.

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

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 (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 x 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.

Transcriptional dynamics of Zn-accumulation in developing kernels of maize reveals important Zn-uptake mechanisms

In the present study, transcriptomic analysis of 10-days old baby kernels of two contrasting maize genotypes, namely VQL-2 (high kernel Zn accumulator) and CM-145 (low kernel Zn accumulator), under low- and optimum- soil Zn conditions generated 1948 differentially expressed transcripts. Among these, 666 and 437 transcripts were up-regulated and down-regulated respectively in VQL-2; whereas, 437 and 408 transcripts were up-regulated and down-regulated respectively in CM-145. Remarkably, 135 transcription factors and 77 known Zn transporters expressed differentially. By comparing the transcripts differentially expressed between the optimum-Zn and low-Zn libraries of the contrasting genotypes, we identified 21,986 and 26,871 SNPs, respectively. Similarly, 6810 and 8192 InDels were found between optimum- and low-Zn growing conditions, respectively. Further, 21 differentially expressed genes were co-localized with already known QTLs associated with Zn uptake, such as qZn10, CQZnK9-1 and YNZnK6. These findings will be useful to develop high Zn-accumulator maize through marker-assisted breeding in future.

Molecular Breeding for Nutritionally Enriched Maize: Status and Prospects

Maize is a major source of food security and economic development in sub-Saharan Africa (SSA), Latin America, and the Caribbean, and is among the top three cereal crops in Asia. Yet, maize is deficient in certain essential amino acids, vitamins, and minerals. Biofortified maize cultivars enriched with essential minerals and vitamins could be particularly impactful in rural areas with limited access to diversified diet, dietary supplements, and fortified foods. Significant progress has been made in developing, testing, and deploying maize cultivars biofortified with quality protein maize (QPM), provitamin A, and kernel zinc. In this review, we outline the status and prospects of developing nutritionally enriched maize by successfully harnessing conventional and molecular marker-assisted breeding, highlighting the need for intensification of efforts to create greater impacts on malnutrition in maize-consuming populations, especially in the low- and middle-income countries. Molecular marker-assisted selection methods are particularly useful for improving nutritional traits since conventional breeding methods are relatively constrained by the cost and throughput of nutritional trait phenotyping.

Genomic Prediction of Kernel Zinc Concentration in Multiple Maize Populations Using Genotyping-by-Sequencing and Repeat Amplification Sequencing Markers

Enriching of kernel zinc (Zn) concentration in maize is one of the most effective ways to solve the problem of Zn deficiency in low and middle income countries where maize is the major staple food, and 17% of the global population is affected with Zn deficiency. Genomic selection (GS) has shown to be an effective approach to accelerate genetic gains in plant breeding. In the present study, an association-mapping panel and two maize double-haploid (DH) populations, both genotyped with genotyping-by-sequencing (GBS) and repeat amplification sequencing (rAmpSeq) markers, were used to estimate the genomic prediction accuracy of kernel Zn concentration in maize. Results showed that the prediction accuracy of two DH populations was higher than that of the association mapping population using the same set of markers. The prediction accuracy estimated with the GBS markers was significantly higher than that estimated with the rAmpSeq markers in the same population. The maximum prediction accuracy with minimum standard error was observed when half of the genotypes were included in the training set and 3,000 and 500 markers were used for prediction in the association mapping panel and the DH populations, respectively. Appropriate levels of minor allele frequency and missing rate should be considered and selected to achieve good prediction accuracy and reduce the computation burden by balancing the number of markers and marker quality. Training set development with broad phenotypic variation is possible to improve prediction accuracy. The transferability of the GS models across populations was assessed, the prediction accuracies in a few pairwise populations were above or close to 0.20, which indicates the prediction accuracies across years and populations have to be assessed in a larger breeding dataset with closer relationship between the training and prediction sets in further studies. GS outperformed MAS (marker-assisted-selection) on predicting the kernel Zn concentration in maize, the decision of a breeding strategy to implement GS individually or to implement MAS and GS stepwise for improving kernel Zn concentration in maize requires further research. Results of this study provide valuable information for understanding how to implement GS for improving kernel Zn concentration in maize.

GWAS and Coexpression Network Reveal Ionomic Variation in Cultivated Peanut

Peanut is an important legume providing products with nutrient dense including mineral nutrition. However, the genetic basis underlying the variations in the mineral composition in peanut is still unknown. Genome-wide association studies (GWAS) of the concentrations of 13 mineral elements combined with coexpression network were performed using a diverse panel of 120 genotypes mainly selected from the U.S. peanut mini core collection. A total of 36 significant quantitative trait loci (QTLs) associated with five elemental concentrations were identified with phenotypic variation explained (PVE) from 18.35% to 27.56%, in which 24 QTLs were for boron (B), 2 QTLs for copper (Cu), 6 QTLs for sodium (Na), 3 QTLs for sulfur (S), and 1 QTL for zinc (Zn). A total of 110 nonredundant candidate causal genes identified were significantly associated with elemental accumulation, which included one high-priority overlap (HPO) candidate gene arahy.KQD4NT, an important elemental/metal transporter gene located on LGB04 with position 5,413,913-5,417,353.

Identification and validation of genomic regions influencing kernel zinc and iron in maize

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. 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.

Genetic Basis and Breeding Perspectives of Grain Iron and Zinc Enrichment in Cereals

Micronutrient deficiency, also known as "hidden hunger," is an increasingly serious global challenge to humankind. Among the mineral elements, Fe (Iron) and Zn (Zinc) have earned recognition as micronutrients of outstanding and diverse biological relevance, as well as of clinical importance to global public health. The inherently low Fe and Zn content and poor bioavailability in cereal grains seems to be at the root of these mineral nutrient deficiencies, especially in the developing world where cereal-based diets are the most important sources of calories. The emerging physiological and molecular understanding of the uptake of Fe and Zn and their translocation in cereal grains regrettably also indicates accumulation of other toxic metals, with chemically similar properties, together with these mineral elements. This review article emphasizes breeding to develop bioavailable Fe- and Zn-efficient cereal cultivars to overcome malnutrition while minimizing the risks of toxic metals. We attempt to critically examine the genetic diversity regarding these nutritionally important traits as well as the progress in terms of quantitative genetics. We sought to integrate findings from the rhizosphere with Fe and Zn accumulation in grain, and to discuss the promoters as well as the anti-nutritional factors affecting Fe and Zn bioavailability in humans while restricting the content of toxic metals.

Quantitative Trait Loci and Inter-Organ Partitioning for Essential Metal and Toxic Analogue Accumulation in Barley

The concentrations of both essential nutrients and chemically similar toxic analogues accumulated in cereal grains have a major impact on the nutritional quality and safety of crops. Naturally occurring genetic diversity can be exploited for the breeding of improved varieties through introgression lines (ILs). In this study, multi-element analysis was conducted on vegetative leaves, senesced flag leaves and mature grains of a set of 54 ILs of the wild ancestral Hordeum vulgare ssp. spontaneum in the cultivated variety Hordeum vulgare ssp. vulgare cv. Scarlett. Plants were cultivated on an anthropogenically heavy metal-contaminated soil collected in an agricultural field, thus allowing simultaneous localization of quantitative trait loci (QTL) for the accumulation of both essential nutrients and toxic trace elements in barley as a model cereal crop. For accumulation of the micronutrients Fe and Zn and the interfering toxin Cd, we identified 25, 16 and 5 QTL, respectively. By examining the gene content of the introgressions, we associated QTL with candidate genes based on homology to known metal homeostasis genes of Arabidopsis and rice. Global comparative analyses suggested the preferential remobilization of Cu and Fe, over Cd, from the flag leaf to developing grains. Our data identifies grain micronutrient filling as a regulated and nutrient-specific process, which operates differently from vegetative micronutrient homoeostasis. In summary, this study provides novel QTL for micronutrient accumulation in the presence of toxic analogues and supports a higher degree of metal specificity of trace element partitioning during grain filling in barley than previously reported for other cereals.

Comprehensive phenotypic analysis and quantitative trait locus identification for grain mineral concentration, content, and yield in maize (Zea mays L.)

Understanding the correlations of seven minerals for concentration, content and yield in maize grain, and exploring their genetic basis will help breeders to develop high grain quality maize. Biofortification by enhanced mineral accumulation in grain through genetic improvement is an efficient way to solve global nutrient malnutrition, in which one key step is to detect the underlying quantitative trait loci (QTL). Herein, a maize recombinant inbred population (RIL) was field grown to maturity across four environments (two locations × two years). Phenotypic data for grain mineral concentration, content and yield were determined for copper (Cu), iron (Fe), manganese (Mn), zinc (Zn), magnesium (Mg), potassium (K) and phosphorus (P). Significant effects of genotype, location and year were observed for all investigated traits. The strongest location effects were found for Zn accumulation traits probably due to distinct soil Zn availabilities across locations. Heritability (H (2)) of different traits varied with higher H (2) (72-85 %) for mineral concentration and content, and lower (48-63 %) for mineral yield. Significant positive correlations for grain concentration were revealed between several minerals. QTL analysis revealed 28, 25, and 12 QTL for mineral concentration, content and yield, respectively; and identified 8 stable QTL across at least two environments. All these QTL were assigned into 12 distinct QTL clusters. A cluster at chromosome Bin 6.07/6.08 contained 6 QTL for kernel weight, mineral concentration (Mg) and content (Zn, K, Mg, P). Another cluster at Bin 4.05/4.06 contained a stable QTL for Mn concentration, which were previously identified in other maize and rice RIL populations. These results highlighted the phenotypic and genetic performance of grain mineral accumulation, and revealed two promising chromosomal regions for genetic improvement of grain biofortification in maize.

Comparative mapping combined with homology-based cloning of the rice genome reveals candidate genes for grain zinc and iron concentration in maize

BACKGROUND

Grain zinc and iron concentration is a complex trait that is controlled by quantitative trait loci (QTL) and is important for maintaining body health. Despite the substantial effort that has been put into identifying QTL for grain zinc and iron concentration, the integration of independent QTL is useful for understanding the genetic foundation of traits. The number of QTL for grain zinc and iron concentration is relatively low in a single species. Therefore, combined analysis of different genomes may help overcome this challenge.

RESULTS

As a continuation of our work on maize, meta-analysis of QTL for grain zinc and iron concentration in rice was performed to identify meta-QTL (MQTL). Based on MQTL in rice and maize, comparative mapping combined with homology-based cloning was performed to identify candidate genes for grain zinc and iron concentration in maize. In total, 22 MQTL in rice, 4 syntenic MQTL-related regions, and 3 MQTL-containing candidate genes in maize (ortho-mMQTL) were detected. Two maize orthologs of rice, GRMZM2G366919 and GRMZM2G178190, were characterized as natural resistance-associated macrophage protein (NRAMP) genes and considered to be candidate genes. Phylogenetic analysis of NRAMP genes among maize, rice, and Arabidopsis thaliana further demonstrated that they are likely responsible for the natural variation of maize grain zinc and iron concentration.

CONCLUSIONS

Syntenic MQTL-related regions and ortho-mMQTL are prime areas for future investigation as well as for marker-assisted selection breeding programs. Furthermore, the combined method using the rice genome that was used in this study can shed light on other species and help direct future quantitative trait research. In conclusion, these results help elucidate the molecular mechanism that underlies grain zinc and iron concentration in maize.

Barley (Hordeum vulgare L.) low phytic acid 1-1: an endosperm-specific, filial determinant of seed total phosphorus

Inositol hexaphosphate (Ins P6 or "phytic acid") typically accounts for 75 (± 10%) of seed total phosphorus (P). In some cases, genetic blocks in seed Ins P6 accumulation can also alter the distribution or total amount of seed P. In nonmutant barley (Hordeum vulgare L.) caryopses, ~80% of Ins P6 and total P accumulate in the aleurone layer, the outer layer of the endosperm, with the remainder in the germ. In barley low phytic acid 1-1 (Hvlpa1-1) seed, both endosperm Ins P6 and total P are reduced (~45% and ~25%, respectively), but germs are phenotypically wild type. This translates into a net reduction in whole-seed total P of ~15%. Nutrient culture studies demonstrate that the reduction in endosperm total P is not due to a reduction in the uptake of P into the maternal plant. Genetic tests (analyses of testcross and F2 seed) reveal that the Hvlpa1-1 genotype of the filial seed conditions the seed total P reduction; sibling seed in the same head of barley that differ in their Hvlpa1-1 genotype (heterozygous vs. homozygous recessive) differ in their total P (normal vs. reduced, respectively). Therefore, Hvlpa1 functions as a seed-specific or filial determinant of barley endosperm total P.

Quantitative genetic analysis of chlorophyll a fluorescence parameters in maize in the field environments

Chlorophyll fluorescence transient from initial to maximum fluorescence ("P" step) throughout two intermediate steps ("J" and "I") (JIP-test) is considered a reliable early quantitative indicator of stress in plants. The JIP-test is particularly useful for crop plants when applied in variable field environments. The aim of the present study was to conduct a quantitative trait loci (QTL) analysis for nine JIP-test parameters in maize during flowering in four field environments differing in weather conditions. QTL analysis and identification of putative candidate genes might help to explain the genetic relationship between photosynthesis and different field scenarios in maize plants. The JIP-test parameters were analyzed in the intermated B73 × Mo17 (IBM) maize population of 205 recombinant inbred lines. A set of 2,178 molecular markers across the whole maize genome was used for QTL analysis revealing 10 significant QTLs for seven JIP-test parameters, of which five were co-localized when combined over the four environments indicating polygenic inheritance and pleiotropy. Our results demonstrate that QTL analysis of chlorophyll fluorescence parameters was capable of detecting one pleiotropic locus on chromosome 7, coinciding with the gene gst23 that may be associated with efficient photosynthesis under different field scenarios.

Combating Mineral Malnutrition through Iron and Zinc Biofortification of Cereals

 Iron and zinc are 2 important nutrients in the human diet. Their deficiencies in humans lead to a variety of health-related problems. Iron and zinc biofortification of cereals is considered a cost-effective solution to overcome the malnutrition of these minerals. Biofortification aims at either increasing accumulation of these minerals in edible parts, endosperm, or to increase their bioavailability. Iron and zinc fertilization management positively influence their accumulation in cereal grains. Regarding genetic strategies, quantitative genetic studies show the existence of ample variation for iron and zinc accumulation as well as inhibitors or promoters of their bioavailability in cereal grains. However, the genes underlying this variation have rarely been identified and never used in breeding programs. Genetically modified cereals developed by modulation of genes involved in iron and zinc homeostasis, or genes influencing bioavailability, have shown promising results. However, iron and zinc concentration were quantified in the whole grains during most of the studies, whereas a significant proportion of them is lost during milling. This makes it difficult to realistically assess the effectiveness of the different strategies. Moreover, modifications in the accumulation of toxic elements, like cadmium and arsenic, that are of concern for food safety are rarely determined. Trials in living organisms with iron- and zinc-biofortified cereals also remain to be undertaken. This review focuses on the common challenges and their possible solutions related to agronomic as well as genetic iron and zinc biofortification of cereals.

An update on magnesium homeostasis mechanisms in plants

Worldwide, nearly two-thirds of the population do not consume the recommended amount of magnesium (Mg) in their diet. Furthermore, low Mg status (hypomagnesaemia) is known to contribute to a number of human chronic disease conditions. Because the principal dietary Mg source is of plant origin, agronomic and genetic biofortification strategies are aimed at improving nutritional Mg content in food crops to overcome this mineral deficiency in humans. This update incorporates the contributions of annotated permeases involved in Mg uptake, storage and recycling with a schematic model of Mg movement at the organ and cellular levels in the model species Arabidopsis thaliana. Furthermore, approaches using mutagenesis or natural ionomic variation to identify loci involved in Mg homeostasis in roots, leaves and seeds will be summarised. A brief overview will be presented on how Arabidopsis research can help to develop strategies for biofortification of crops.

Abiotic stress growth conditions induce different responses in kernel iron concentration across genotypically distinct maize inbred varieties

The improvement of grain nutrient profiles for essential minerals and vitamins through breeding strategies is a target important for agricultural regions where nutrient poor crops like maize contribute a large proportion of the daily caloric intake. Kernel iron concentration in maize exhibits a broad range. However, the magnitude of genotype by environment (GxE) effects on this trait reduces the efficacy and predictability of selection programs, particularly when challenged with abiotic stress such as water and nitrogen limitations. Selection has also been limited by an inverse correlation between kernel iron concentration and the yield component of kernel size in target environments. Using 25 maize inbred lines for which extensive genome sequence data is publicly available, we evaluated the response of kernel iron density and kernel mass to water and nitrogen limitation in a managed field stress experiment using a factorial design. To further understand GxE interactions we used partition analysis to characterize response of kernel iron and weight to abiotic stressors among all genotypes, and observed two patterns: one characterized by higher kernel iron concentrations in control over stress conditions, and another with higher kernel iron concentration under drought and combined stress conditions. Breeding efforts for this nutritional trait could exploit these complementary responses through combinations of favorable allelic variation from these already well-characterized genetic stocks.

Comparison of whole-genome prediction models for traits with contrasting genetic architecture in a diversity panel of maize inbred lines

Background

There is increasing empirical evidence that whole-genome prediction (WGP) is a powerful tool for predicting line and hybrid performance in maize. However, there is a lack of knowledge about the sensitivity of WGP models towards the genetic architecture of the trait. Whereas previous studies exclusively focused on highly polygenic traits, important agronomic traits such as disease resistances, nutrifunctional or climate adaptational traits have a genetic architecture which is either much less complex or unknown. For such cases, information about model robustness and guidelines for model selection are lacking. Here, we compared five WGP models with different assumptions about the distribution of the underlying genetic effects. As contrasting model traits, we chose three highly polygenic agronomic traits and three metabolites each with a major QTL explaining 22 to 30% of the genetic variance in a panel of 289 diverse maize inbred lines genotyped with 56,110 SNPs.

Results

We found the five WGP models to be remarkable robust towards trait architecture with the largest differences in prediction accuracies ranging between 0.05 and 0.14 for the same trait, most likely as the result of the high level of linkage disequilibrium prevailing in elite maize germplasm. Whereas RR-BLUP performed best for the agronomic traits, it was inferior to LASSO or elastic net for the three metabolites. We found the approach of genome partitioning of genetic variance, first applied in human genetics, as useful in guiding the breeder which model to choose, if prior knowledge of the trait architecture is lacking.

Conclusions

Our results suggest that in diverse germplasm of elite maize inbred lines with a high level of LD, WGP models differ only slightly in their accuracies, irrespective of the number and effects of QTL found in previous linkage or association mapping studies. However, small gains in prediction accuracies can be achieved if the WGP model is selected according to the genetic architecture of the trait. If the trait architecture is unknown e.g. for novel traits which only recently received attention in breeding, we suggest to inspect the distribution of the genetic variance explained by each chromosome for guiding model selection in WGP.