In addition to the fact that micronutrients are essential in human nutrition, and guarantee human health, they significantly affect the plant itself. Fe and Zn are involved in enzymatic functions, photosynthesis, and the metabolic and physiological processes that the plant undergoes (Peleg et al. 2009). For efficient enhancement of grain Fe and Zn content in a breeding program, therefore, it is vital to perceive microelement variation and find linked molecular markers. The variation in the grain Fe and Zn content of seventy prevalent wheat genotypes in Iran was assessed for three years. The results indicated considerable variation among the genotypes for grain Fe and Zn in each separate year. Several previous studies have reported significant variation in grain mineral content, including Fe and Zn, in cultivated and wild wheat relative genotypes (Cakmak et al. 2004; Velu et al. 2011, 2017; Srinivasa et al. 2014; Amiri et al. 2015; Gorafi et al. 2016; Pandey et al. 2016). The grain Fe and Zn contents varied in our study between 2.09 and 1.89 times, respectively, in the first year, 1.69 and 2.08 times, respectively, in the second year, and 3.2 and 2.10 times in the third. Similar ranges with two- or three-fold variation for grain Fe and Zn content have been reported in several previous studies on wheat populations (Velu et al. 2011; Amiri et al. 2015; Guttieri et al. 2015; Kenzhebayeva et al. 2019). To enhance the health influence and bioavailability of Fe and Zn via wheat grains, the contents have to be increased by up to 60.0 and 40.0 mg Kg− 1 (Cakmak 2008; Pandey et al. 2016). A number of our genotypes met these conditions. These genotypes could be used directly or as donors in breeding programs.
The different ranges and means and standard deviations of grain Fe and Zn contents each year are due to the environmental variation, indicating the impact of environmental factors such as agricultural practices and weather conditions. Moreover, genetic and environmental variation account for the different reported ranges of grain Fe and Zn content in various studies (Cakmak et al. 2004; Amiri et al. 2015; Pandey et al. 2016). A significant interaction between genotype and year revealed the important role of the genes controlling grains Fe and Zn content, as previously reported in other research (Peterson et al. 1986; Velu et al. 2011; Srinivasa et al. 2014; Guttieri et al. 2015; Gorafi et al. 2016). Constant ranking across the three years was exhibited by seven genotypes for grain Fe content (Hirmand, Gaspard, Arya, Shahpasand, CrossShahi, Marvdasht, and Kavir) and eleven genotypes for grain Zn content (Gaspard, Sabalan, Shahpasand, Karaj1, Karkheh, Hirmand, Arvand, Rasool, Shoeleh, Azar2, and Yavaros). These genotypes deserve consideration, and could be used as donor sources for increasing grain Fe and Zn content. The σ2g/σ2gy < 1 ratio indicated that the content is affected by the selection of environment conditions; therefore, successful breeding for enhancement of wheat grain mineral contents relies on the selection environment (Guttieri et al. 2015). In Peterson et al. (1986), the ratio was less than one for Ca, Zn, and Mn and more than one for Fe. In Guttieri et al. (2015), however, it was estimated to be more than one for Fe and Zn content in wheat grains. The lower proportion of genetic variation (σ2g) than that of genetic-environment interaction could be attributed to the complex nature of the grain Fe and Zn content controlled by polygenes (Khokhar et al. 2018). The genetic diversity of grain Fe and Zn content provides valuable sources for enhancement of the microelement concentration of wheat grains via traditional or molecular breeding approaches (Amiri et al. 2015). In our study, a high broad-sense heritability was estimated for grain Fe and Zn content, which had been reported in previous studies on bread wheat, wheat relative, and other cereal genotypes (Badakhshan et al. 2013; Khodadadi et al. 2014; Srinivasa et al. 2014; Goudia and Hash 2015; Gorafi et al. 2016). The significant broad-sense heritability could be accounted for by the impact of genetics on grain Fe and Zn content in wheat genotypes and the possibility of selection of genotypes with high Fe and Zn content in wheat genotypes.
A moderately positive correlation was estimated between grain Fe and Zn contents, as previously reported in similar studies on wheat (Cakmak et al. 2004; Srinivasa et al. 2014; Amiri et al. 2015; Pandey et al. 2016). This positive relation could be attributed either to respective co-segregation and co-localization alleles and QTLs or to the pleiotropic effects of genes such as major QTL (Gps-B1) controlling grain Fe, Zn microelements, and protein content (Cakmak et al. 2004; Distelfeld et al. 2007). The Gps-B1 locus (on chromosome 6B) derived from the wild emmer wheat (Triticum dicoccoides) affects the remobilization of protein, Zn, and Fe from leaves to the grain, and is also involved in the senescence of earlier flag leaves (Distelfeld et al. 2007; Coco et al. 2019). The positive correlation between micronutrients serves to improve wheat cultivars with enhanced microelements and protein contents (Goudia and Hash 2015) concurrently. A common molecular mechanism for grain micronutrient uptake and metabolism or common transporters could explain the positive correlation between grain Fe and Zn contents (Phuke et al. 2017).
The development of DNA-based molecular markers has enabled efficient specification of genetic diversity, QTL mapping, efficient gene pyramiding, and precise marker-assisted selection of linked targeted QTLs (Goudia and Hash 2015). We used the three marker systems SSR, ITAP, and SCoT to find informative amplicons for grain Fe and Zn content. Each marker system was designed and amplified for specific regions of the genome, including sequences flanking hypervariable regions of microsatellites (SSR), sequences flanking the start codon (ATG) in plant genes (SCoT, Collard and Mackill 2009), and 3’ widely distributed conserved intron-exon junction sequences (ITAP, Xiong et al. 2013). The origin of polymorphism detected by ITAP markers could be intron length polymorphism and point mutations of ITAP binding sites. Therefore, variable markers with different genome target sequences enabled us to detect informative markers from different parts of the genome. Informative amplicons could be sequenced and developed as sequence-characterized amplified region (SCAR) markers for reliable, precise investigation of grain Fe and Zn content in a wheat genetic background.
Various informative SSR loci were identified for grain Fe and Zn content on chromosomes 1A, 3A, 4A, 7A, 1B, 4B, 6B, 7B, and 4D (Table 6), explaining 5.04 to 8.22 percent of the Fe content and 4.29 to 20.56 percent for Zn. It seemed that the homeologous group 4 chromosomes and the 6B chromosome played important roles in the control of grain Fe and Zn content in our wheat germplasm because these loci were detected commonly for grain Fe and Zn content. Homoeology has been reported previously by (Peleg et al. 2009) for Fe, Zn, protein, and other mineral content in the tetraploid wheat population. Although macro elements such as phosphorous (P) and sulfur (S) were not addressed in the present study, high positive correlation has been reported previously by Peleg et al. (2009) to be there between P, S, Fe, and Zn. Peleg et al. (2009) identified QTLs on the homeologous group 4 chromosomes for P and S content in tetraploid germplasm, reinforcing the co-localization of ascribed QTLs for Fe, Zn, P, and S, on similar chromosomes. They concluded that the QTL homoeology might indicate synteny between different genomes. Particular SSR loci, including barc48, wmc617, and gwm160, were commonly correlated with grain Fe and Zn content, demonstrating the co-localization or pleiotropic effect of genes controlling Fe and Zn content. Dissimilar correlated markers with grain Fe content were detected in the three years, presumably because of significant genotype-environment interaction (Table 6). Identical results were reported by Gorafi et al. (2016) for Fe and Zn concentrations of wheat grains. Several genes were identified in the cereals and Arabidopsis involved in Fe and Zn uptake, translocation, and storage, including YSL (yellow stripe-like), ZIP (iron-regulated transporter-like protein), NRAMP (natural resistance-associated macrophage protein), FER (ferritin-like), and NAS (nicotianamine synthase), belonging to the Fe and Zn super-families (Mahendrakar et al. 2020). Zhou et al. (2020) identified seven candidate genes for grain Zn accumulations in wheat. The genes encoded the NAC transcription factor on the 3D chromosome, a V-type proton ATPase on chromosome 4A, a protein containing tetratricopeptide (TPR) repeats on the 1B chromosome, serine/threonine-protein kinase on the 5B chromosome, a CTP synthase on the 3B chromosome, the basic helix-loop-helix transcription factor (BHLH) on the 7A chromosome, and heavy metal transport/detoxification superfamily protein on the 5A chromosome. The identified genes involved nutrient remobilization from leaves to wheat grains, cell ion homeostasis, osmotic stress response, metal ion binding, regulation of voltage-dependent ion channels, and Zn concentration in grains, respectively.
Particular SSR loci correlated with grain Zn content, comprising gwm160, gwm149, and barc48, were identified in two consecutive years. The gwm160 locus located on homeologous group 4 was noteworthy as an informative marker. It was detected as highly positive significantly correlated alleles with a high capability of specifying grain Fe and Zn content (8.22–20.56%, Table 6) in the different years. The impacts of additive and epistatic QTLs on grain Fe and Zn content reported in previous studies have been located mainly on the chromosomes 2A, 4A, 5A, 7A, 5B, 6B, 7B, 2D, 3D, 4D, and 5D for grain Fe content and 2A, 3A, 5A, 6A, 7A. 2B. 4B, 5B, 6B, 7B, 1D, 2D, 3D, 4D, 5D, and 7D for grain Zn content (Shi et al. 2008; Peleg et al. 2009; Roshanzamir et al. 2013; Pu et al. 2014; Gorafi et al. 2016). Cakmak et al. (2004) reported that genes on chromosomes 6B, 5B, and 6A played an important role in raising Fe and Zn concentration levels in wild wheat relative Triticum dicoccoides substitution lines. According to Xu et al. (2011), D and S genomes of wheat positively affect the accumulation of Fe and Zn in grains. Mapping the microelement QTLs provides the basis of marker-assisted selection (MAS) to enforce efficient selection in conventional plant breeding, a pyramiding of genes enhancing microelement contents, also a basis for QTL cloning and gene transformation through genetic engineering approaches (Goudia and Hash 2015; Zhou et al. 2020).
Several informative SCoT amplicons were detected for grain Fe and Zn content in the three years (Table 7). The SCoT amplicons SCoT1 (400, 1300, 1400bp) and SCoT22 (400bp) were positively correlated with grain Fe and Zn content. Combined SCoT29/1 (700bp) was negatively correlated with grain Fe, while SCoT22 (550bp) and SCoT12 (250, 150bp) were negatively correlated, and SCoT35/2 (700, 800bp) was positively correlated with grain Zn content in the three years (Table 7). The SCoT22 (400bp) amplicon was repeatedly detected in the three years for high Fe and Zn content; thus, it should be considered and sequenced in future studies. Multiple informative ITAP amplicons were identified for grain Fe and Zn content in the different years (Table 8). Most of the identified amplicons were negatively correlated with grain Fe and Zn contents. However, the ITAP1-Em9 (200bp) amplicon was positively correlated with Fe and Zn content in the second year. The ITAP4-Em10 (1100bp) amplicon was revealed as positively correlated with Zn content in two different years. Variable ITAP amplicons were demonstrated in the three consecutive years for Fe content. Common informative SCoT and ITAP amplicons for both Fe and Zn content reflected the positive correlation of these elements in wheat grains.
In this study, genetic diversity was estimated based on several indices, including effective number (Ne), expected heterozygosity (He), Shannon’s information index (I), haploid diversity (H), and PIC. For the SSR markers, the average values of Ne, He, I, H, and PIC were 2.63, 0.54, 0.98, 0.54, and 0.53, respectively, indicating adequate genetic diversity among the present wheat population. The PIC values were calculated as more than 0.5 for 9 out of the 15 SSR primers, demonstrating highly informative SSR primers. Highly informative markers could be used for reliable genotyping and estimation of the genetic diversity of populations (Eltaher et al. 2018). The estimated He and PIC values were consistent with those in previous studies on wheat (Manifesto et al. 2001; Naghavi et al. 2009; Senturk Akfirat and Uncuoglu 2013; Trkulja et al. 2019). Null alleles were present for nearly all of the utilized SSR primers, as confirmed by the positive values of Fis. The positive average value of Fis (0.61) indicated a significant deviation from the Hardy-Wienberg equilibrium and presence of more homozygous genotypes, which was not a surprising result for the self-fertilized wheat population.
The average values of Ne, He, and I for the SCoT markers were 1.619, 0.350, and 0.515, respectively. On the other hand, the mean values for the ITAP markers were Ne = 1.535, He = 0.419, and I = 0.462. These results indicated that the SCoT markers detected more genetic variability in the wheat population than the ITAP markers. Besides, the discriminating power of the SCoT and ITAP markers was compared based on the EMR and MI values, demonstrating that the SCoT markers could potentially be more efficient in detection of genetic diversity. Previous research has reported that SCoT markers have effectively indicated genetic diversity in the wheat population (Nasrollahi et al. 2019; Khodaee et al. 2021), but there is yet no reported study on application of ITAP markers to wheat genotypes.
Population Structures and Relationships
Based on the SSR data, the wheat genotypes were divided into three subgroups, in accordance with the PCoA results. The genotypes with the largest numbers, the highest consistency with the pedigree information, were classified as subpopulation III. The lowest-number genotypes were classified as subgroup I. However, the number of common parents of the genotypes in this group was larger than that in subpopulation II. Moreover, two tetraploid durum wheat genotypes (Karkheh and Arya) and five landraces with similar origins (specifically, RashaGol, GolSepi, and AarasGolsoor from the Kurdistan region of Iraq), were classified altogether into group I. The maximum numbers of private alleles, 0.594 (He) and 1.1080 (I), were calculated for subpopulation III. Private alleles are indicators of private genetic diversity, and loci with different alleles could potentially be useful in breeding programs (Eltaher et al. 2018). The majority of wheat genotypes ranked as possessing high grain Fe and Zn contents were categorized into subgroups I and III, respectively.
Using the SCoT data, the wheat genotypes were divided into four admixture subgroups. The number and quality of genotypes in each subpopulation were in line with those in the PCoA results. Most wheat genotypes were classified into subpopulation IV, with the largest number of common parents. Unlike in the SSR subpopulations, none of the above-mentioned landraces or tetraploid genotypes was grouped. Instead, two tetraploids, including Yavaros and Karkheh, were categorized in the same group. The highest genetic diversity (He = 0.326, I = 0.481) and the largest number of private amplicons (5) were estimated for subpopulation III, indicating proprietary genetic diversity within the subpopulation.
Based on the ITAP data, the wheat genotypes were portioned into six subpopulations. Using PCoA analysis, the wheat population was divided into five subpopulations, closely consistent with the results of the structure analysis. The largest subpopulations were V and VI, comprising 15 wheat genotypes each. However, subpopulation II exhibited the maximum consistency with the pedigree information. As in the SCoT-based subpopulations, two tetraploid wheat genotypes, including Yavaros and Karkheh, were grouped together. The greatest genetic variation (He = 0.2, I = 0.30) was estimated for subpopulation VI. In contrast, subpopulation I involved the maximum number of private alleles (6), indicating unique genetic diversity, presumably due to the presence of wheat genotypes with various, uncommon parents.
Based on the three different marker data, the numbers of subpopulations were different, but the groupings of the genotypes were identical in some cases. The two landraces GolSepi and Sorkhtokhm and cultivars like Golestan and Chamran, Rasool and Shiroodi, and Falat and Karaj2 were paired together due to equal parents, similar geo-ecological conditions, and presumably similar traits as considered by breeders. The SSR markers were the most consistent with the pedigree information, followed by SCoT and ITAP, in that order. This finding was not unexpected because of the specificity of the SSR primers.
The results of AMOVA using the SSR, SCoT, and ITAP data indicated high variation within the subpopulations (88, 89, and 51 percent, respectively). Nevertheless, the divergence among the subpopulations (12, 11, and 49 percent, respectively) was also significant (p < 0.001). The low genetic variation among the subpopulations could be attributed to the gene flow due to the presence of common parents in the subpopulations. It could be assumed that different growth habits, ecological adaptation, breeding goals, and ploidy possibly determined the genetic diversity within the subpopulations. Consistent results have been reported in earlier studies on wheat populations. In accordance with Eltaher et al. (2018), the distinct structure observed in our wheat population could be accounted for broadly in terms of common parents, common breeding goals for selection of genotypes, or common geographic conditions. Most of the studied genotypes were obtained mainly from the International Maize and Wheat Improvement Center (CIMMYT) breeding programs.