Improving wheat grain composition for human health: an atlas of QTLs for essential minerals

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We therefore grew three biparental populations developed from crosses between the spring cultivar Paragon and landraces originating from about 100 years ago under multiple environments and analysed the grain for minerals including six minerals which are often deficient in diets for humans (calcium, iron, magnesium, potassium, zinc) and livestock (copper). A total of 774 QTLs for minerals in grain, straw and calculated biomass were identified which were reduced to 23 strong robust QTLs for essential nutrients in grain by selecting for QTLs that were mapped in at least two sample sets with LOD scores above 5 in at least one set. The increasing alleles for sixteen of the QTLs were present in the Watkins lines and seven in Paragon. The number of QTLs for each mineral varied between three (for K and Zn) and five (for Cu) and they were located on 14 of the 21 chromosomes with clustering on chromosomes 5A (4 QTLs), 6A (3 QTLs) and 7A (3 QTLs). Several strong QTL were selected to determine the gene content within a distance of five megabases of DNA either side of the marker for the QTL with the highest LOD score. In addition, induced mutagenesis was used to identify the gene responsible for the strongest QTL (for Ca on chromosome 5AL) as the ATPase transporter gene TraesCS5A02G543300 . The identification of these QTLs with associated SNP markers and candidate genes will facilitate the improvement of grain nutritional quality. Biological sciences/Plant sciences/Natural variation in plants Biological sciences/Plant sciences/Plant genetics Wheat grain mineral micronutrients nutritional quality genetic mapping candidate genes Figures Figure 1 Figure 2 Figure 3 Introduction Humans require a range of minerals in their diets, including major elements (calcium, magnesium, phosphorus, sodium, potassium) and trace elements (iron, zinc, fluoride, selenium, copper, chromium, iodine, manganese, molybdenum). However, some of these minerals are also toxic if consumed in excess as are other elements which are normally present in trace amounts such as aluminium, cadmium, mercury, arsenic and lead (NIH, 1989 ; Pohl et al, 2013 ). Most essential minerals are present in adequate amounts in human diets, but deficiencies may occur resulting in severe and widespread symptoms. The most widespread global deficiencies are of iron and zinc: It has been estimated that 43% of children and 29% of women of reproductive age have anaemia, about half of which results from iron deficiency (WHO, 2015 ), while zinc deficiency is associated with stunted growth in children under the age of 5 in over 150 million children globally (WHO, 2013 ). The risk of deficiency is reflected in national monitoring of dietary intakes, with the UK National Diet and Nutrition Survey (NDNS) monitoring the intakes of seven minerals: iron, zinc, calcium, magnesium, potassium, selenium and iodine. In fact, Bates et al ( 2016 ) reported that a substantial proportion of UK children aged 11–18 had intakes below the recommendations for all of these minerals, particularly iron (48% of girls 11 to 18 years). Similarly, twenty seven percent of adult women (19 to 64 years) also had iron intakes below the recommended level and substantial proportion of adults had intakes below the recommended level for magnesium, potassium and selenium. Wheat is the most widely grown and consumed crop in the world, contributing between 10% and 50% of the total calories in countries ranging from Western Europe to North Africa and Central Asia. Wheat contributes many essential dietary components as well as energy including up to 20% of essential minerals in the UK (Bates et al, 2014 a, b). Hence, deficiencies in the contents of minerals in samples of wheat grain can have significant effects on human health. The mineral content of wheat grain is determined by the mineral characteristics of the soil and by the ability of the plant to take up minerals from the soil and transport them into the grain. Strategies have therefore been adopted to increase the mineral contents of wheat grains by either applying minerals as fertiliser (agronomic biofortification) or by improving the ability of the plant to extract minerals from the soil and transport them to the grain (genetic biofortification). Agronomic biofortification can have a significant impact with some minerals and farming systems. For example, the application of fertiliser containing selenium is used in some countries (Broadley et al., 2010 ) while fertilisation with zinc may also have benefits (Cakmak and Kutman, 2018 ; Joy et al., 2015 ). However, agronomic biofortification adds to costs of crop production and may not be available to farmers in less developed countries. Hence, genetic biofortification has been the major focus of research globally (reviewed by Balk et al., 2019 ; Aslam et al., 2018 ). This has resulted in the development of high zinc wheat which is being evaluated in human diets (Govindan et al., 2022 ; Lowe et al., 2022) but there has been limited success with other minerals and no other genetically biofortified lines of wheat have been developed. Genetic biofortification depends on the availability of genetic variation in mineral accumulation, either in wheat or in related species which can be used for introgression. It is well-established that modern commercial cultivars of wheat are less genetically diverse than older types including landraces (traditional types which were grown before the application of scientific breeding methods) (Pont et al., 2019 ). We are therefore using the A. E. Watkins landrace cultivar collection, a global collection originating from about 100 years ago (Wingen et al. 2014 ), to identify novel QTLs and genes which determine the accumulation of essential minerals in the grain, focusing on iron, zinc, calcium, magnesium, potassium and copper. This has enabled us to identify a number of novel QTLs and associated molecular markers which will facilitate the improvement of wheat for human health. Results Three populations of recombinant inbred lines (RILs) were selected and grown in replicated multienvironment field trials. These were from crosses between the UK Spring wheat cultivar “Paragon” and Watkins lines W160, W239 and W292. The landraces were from Cyprus (W292) and Spain (W160 and W239), representing ancestral groups C7 (W292) and C6 (W160 and W239), and were selected to represent the range of diversity in the collection including variation in height (W292), nitrogen use efficiency (NUE) (W239) and grain mineral (P and Zn) content (W160). Each population comprise 94 F4 recombinant inbred lines which were grown in three replicated randomised plots for three years with either 50 kg N/Ha (N1), 200 kg N/Ha (N2) or at both N1 and N2. This gave 11 sets of samples ( Table 1 ) which were analysed for 9 minerals (Ca, Cu, Fe, K, Mg, Mn, Na, S and Zn) by ICP-OES. Mineral concentrations were expressed as grain concentration and amount per grain and the discussion below will focus on these primary datasets. However, a range of other traits were also measured or calculated, and the data are presented in the Supplementary Table S1-3 . The determination of the yields of the plots allowed the total amounts of minerals recovered in grain per square meter plot (take-off) to be calculated. Similarly, the relative ability of the lines to accumulate minerals in the grain was calculated as “grain mineral deviation”. This is calculated by comparing the concentrations of minerals with the yields of the lines within each set of samples. In broad terms the concentrations of minerals in grain are inversely correlated with grain yield, allowing a regression line to be calculated (as discussed for nitrogen by Mosleth et al., 2020 ). Genotypes in which mineral concentrations fall above this regression line exhibit positive grain mineral deviation. Finally, the plots were measured for plant height and the weights and concentrations of minerals in straw determined. This allowed the weight and mineral content of the above ground biomass and the mineral harvest index to be calculated. The full datasets for 774 QTLs for minerals in grain, straw and calculated biomass and for 84 other QTL are provided in Supplementary Tables S1-3 , for Par x W160, W239, and W292. Environments and trait abbreviations are shown in Supplementary Tables S4 and S5 Iron, zinc, calcium, magnesium and potassium are of particular interest because they may be deficient in human diets, including developed countries such as the UK. Deficiencies of other essential minerals are rare in humans, particularly in developed countries. However, copper deficiency may occur in livestock, particularly in cattle (Wysocka et al., 2019 ) and copper content is therefore of concern when formulating feeds for livestock. The following discussion therefore focuses on the six minerals iron, zinc, calcium, magnesium, potassium and copper. The concentrations of these minerals in the populations are given in Table 1 . QTLs for essential minerals A large number of QTLs were identified for the concentrations of minerals in grain (mg/kg dry weight) and the contents of minerals per grain (µg/grain). However, many of the QTLs had low LOD scores or were only mapped in a single sample set. It was therefore decided (with one exception discussed below) to only consider QTLs that were mapped in at least two sample sets with LOD scores above 5 in at least one set. Based on these criteria 23 increasing alleles for grain minerals were mapped, with 16 present in the Watkins lines and 7 in Paragon. These are presented in Table 2 which also gives the peak SNP markers based on the dataset with the highest LOD score, with additional details presented in Supplementary Table S6 . Whereas most of the QTL were specific for a single mineral, the 7A Cu and 7A Mg QTLs co-located with each other and with a QTL for grain sulphur concentration (which is listed in Supplementary Tables 1–3 but not discussed here). The number of QTLs for each mineral varied between three (for K and Zn) and five (for Cu) and they were located on 14 of the 21 chromosomes with clustering on chromosomes 5A (4 QTLs), 6A (3 QTLs) and 7A (3 QTLs) (Fig. 1 ). Examples of the robust QTL identified are shown in Fig. 2 . Some of the QTLs for essential minerals co-located with QTLs for the concentrations/total amounts of the minerals in straw. This is noted in Table 2 and full details of the QTLs given in Supplementary Tables S1- S3 . This indicates that the trait is associated with more efficient uptake of the mineral by the plant, whereas the absence of co-located QTLs for minerals in straw/biomass indicates that partitioning of the mineral to the developing grain is more effective. Calcium The grain concentration of Ca in the populations varied from 273 to 1532 mg/kg ( Table 1 ). Increasing alleles for grain Ca were identified in all three Watkins lines and in Paragon. Increasing allele were identified on chromosome 5A from both W239 and W292, on chromosomes 5D from W160, and on chromosome 4A from W292. Finally, a Paragon increasing allele was found on chromosome 2B. The QTL on 5A had the strongest effect of all of the QTLs mapped in the study, controlling Ca/grain and Ca concentration in a total of 8 sample sets from the two crosses with LOD scores ranging from 6.1 to 12.2 ( Table 2 ). Gene content analysis of five megabases (5 Mb) of DNA either side of the marker for the QTL with the highest LOD score revealed the presence of 127 protein-coding genes ( listed in Supplementary Table S7 ). Based on the functional annotation, two candidate genes were identified, TraesCS5A02G543300 which encodes a cation transporter/plasma membrane ATPase and TraesCS5A02G542600 which encodes a major Facilitator Superfamily transporter. Loss-of-function mutations in both of these genes can be predicted to result in higher grain calcium contents and we therefore analysed EMS-induced mutations in both genes. Mutations in TraesCS5A02G543300 ( n = 8 independent mutants) resulted in greater than 10% increases in grain calcium content in five lines, with four (WCAD1641, WCAD0289, WCAD1253, WCAD1003) showing statistically highly-significant increases (14.1–18.7% increase, P value < 0.005) and one (WCAD1617) a statistically significant increase (11.7% increase, P value < 0.03) (Fig. 3 ), whereas mutations in TraesCS5A02G542600 ( n = 2 mutants) results in no significant changes ( P = 0.15 and 0.91) compared to control plants. This suggests that TraesCS5A02G543300 is responsible for the variation in Ca content in the two crosses. Of the four lines with highly significant increases in grain concentration, three showed no significant change in grain weight, demonstrating that the increase in grain calcium is not simply a result of a reduced grain weight. Copper The grain concentration of Cu in the populations ranged from 3.61 to 7.24 mg/kg ( Table 1 ). Five QTLs were identified, with increasing alleles for grain concentration and Cu/grain from W292 on chromosome 7A and from W239 on chromosome 7B. Three QTLs had increasing alleles from Paragon, on chromosomes 4B (Cu concentration), 5D and 7B (both Cu concentration and Cu/grain), with the increasing allele on 4B having LOD scores ranging from 5.1 to 8.8 in four sample sets of the cross with W239. Two strong QTLs with increasing alleles from Paragon were selected to determine gene content: 4B for copper concentration (LOD 8.8 to 5.1 in 4 sample sets) and 5B for copper concentration and copper per grain (LOD 6.7–3.6), based on 5 Mb of DNA on either side of the marker for the QTL with the highest LOD score. The total numbers of genes identified in the two QTLs were 52 (4B) and 102 (5B), as listed in Supplementary Tables S8 and S9 . Three genes ( TraesCS4B02G131400 , TraesCS4B02G131500 , TraesCS4B02G131700 ) encoding ZINC-INDUCED FACILITATOR-LIKE 1, and one gene ( TraesCS4B02G128600 ) encoding a MULTI-DRUG AND TOXIC COMPOUND EXTRUSION (MATE) protein, were found in the 5 Mb region downstream of the 4B QTL. Notably, genomic comparison between Paragon and W239 revealed the presence of rare SNPs in the 3’UTR of a gene encoding an ABC transporter C subfamily member ( TraesCS5B02G479900 ) present in W239. ABC C subfamily members play a key role in detoxification and metal ion transport. The identification of QTL on chromosome 7 of all three sub-genomes (7A, 7B, 7D) raises the question of whether the loci are homoeologues. However, a comparison of the gene contents in the region spanning the peak markers showed that this was not the case. In total, 101 and 80 protein-encoding genes were found in the 5 Mb region on either side of the 7B and 7D QTL, respectively ( Supplementary Table S10 and S11 ). Further analysis revealed that the 7B QTLs are located in a high polymorphic region, while some gene deletions were also apparent. Furthermore, copy number variation was found in the region around the 7D QTL. Iron The grain concentration of Fe in the populations varied by over 2-fold, from about 30 to 64 mg/kg (Table 1) . Four QTLs were detected, with increasing alleles from all three Watkins lines. The strongest increasing allele was for Fe concentration and Fe/grain on chromosome 2D of W239 (LOD 3.4 to 6.3), with other increasing alleles from W239 and W292 on chromosome 3A, from W160 and W292 on chromosome 5D (both for Fe concentration) and from W239 (Fe concentration) and W160 (Fe/grain) on chromosome 6A. However, the 3A, 5D and 6A alleles had LOD scores below 6. Analysis of 5 Mb of DNA on either side of the peak marker for the strongest 2D QTL (LOD 6.3) showed the presence of 120 high-confidence protein-coding genes, including four transcription factors ( Supplementary Table S12 ). Notably, there is high allelic diversity in this region, with multiple SNPs between Paragon and W239, while large deletions were identified in Paragon. This suggests a divergent genetic background in this locus, possibly a result of introgression events. A missense variant and a splice region variant were identified in TraesCS2D02G473900 , encoding a bHLH transcription factor. Interestingly, the orthologue of TraesCS2D02G473900 in rice has been linked to the iron starvation response in roots. (Wairich et al., 2019 ). Magnesium The grain concentration of Mg in the populations varied from 834 to 1532 mg/kg (Table 1) . Three QTLs for Mg concentration and Mg/grain were identified with increasing alleles from both W239 and W160 on chromosomes 5A and 6A. In addition, a QTL for Ca concentration and Ca/grain was identified on chromosome 7A with increasing alleles in all three Watkins lines. This was identified in several sample sets with LOD scores for Mg/grain ranging up to 7.8. Finally, a QTL for Mg concentration only was identified in the Paragon x W239 cross with the increasing allele (LOD up to 8.3) from Paragon. Due to the consistent presence of the 7A Mg QTL across various environments and populations, it was selected for further analysis. The analysis of 5 Mb of DNA surrounding the peak marker for the most robust 7A QTL (with a LOD score of 7.8 in W239) revealed the presence of 128 protein-encoding genes ( Supplementary Table S13 ). There was high genetic heterogeneity across the examined genomic region between Paragon and W239 with many functional SNPs identified in multiple genes. Three stop-gained SNPs were found in the examined region, affecting three genes. The first gene, TraesCS7A02G126100 , encodes a serine/threonine receptor kinase. The stop-gained SNP in this gene introduces a premature stop codon, potentially leading to a truncated protein with altered functionality. Serine/threonine receptor kinases are crucial components in signal transduction pathways, and any alteration in their structure can impact cellular responses. Stop-gained SNPs were also found in TraesCS7A02G135200 and TraesCS7A02G135400 , both encoding MYB-related transcription factors. A stop-lost SNP was found in TraesCS7A02G130400 , encoding a putative leucine-rich repeat receptor-like protein kinase. Splice region SNPs were identified in seven genes, including those encoding an ATP-dependent zinc metalloprotease ( TraesCS7A02G128400 ) and a MATE transporter protein ( TraesCS7A02G131500 ). Potassium The grain concentration of K in the populations varied from 3541 to 5935 mg/kg (Table 1) . Three QTLs with LOD above 5 were mapped, all from the cross with Watkins 239. Two of the increasing alleles (for grain concentration on 3D and 5A) were from Paragon but the increasing allele for the strongest QTL (with LODs from 8.2 to 5.4 in four sample sets), for K/grain on chromosome 4B, was from W239. Analysis of the genomic region of the strongest 4B QTL (LOD 8.2) showed that the QTL is in a gene-sparse region as only 29 protein-coding genes were found in the 10 Mb region surrounding the peak marker ( Supplementary Table S14 ). Allelic diversity analysis showed the presence of a low number of SNPs, mainly upstream or downstream of the coding region of the genes. However, copy number variation was detected in some genes, indicating structural genomic differences between Paragon and W239 in this locus. Zinc The grain concentration of Zn in the populations varied from 23.6 to 49.1 mg/kg (Table 1) . Three QTLs were identified with increasing alleles from the Watkins lines. A QTL for Zn concentration and Zn/grain was identified on chromosome 7A with increasing alleles from all three Watkins lines, although the LOD scores were low (the highest being 5). Similarly, a QTL for Zn/grain on chromosome 5A had increasing alleles in W292 (LOD 5) and 239 (LOD 3.6). Finally, a QTL for Zn concentration and Zn/grain was included although the increasing alleles in W239 had LOD scores of slightly below 5 (4.9, 4.8, 4.7 and 4.1). The increasing allele for Zn/grain was also mapped in W160 with LOD scores of 3.7 and 3.7. Analysis of 5 Mb of DNA surrounding the peak marker of the 6A QTL (LOD 4.9) showed that the QTL is located in a gene-sparse region containing only 40 protein-coding genes ( Supplementary Table S15 ). Low allelic diversity was found between Paragon and W239, with SNPs mainly located upstream or downstream of the protein-coding regions. Examination of the DNA region extending 5 Mb around the peak marker of the 7A QTL (with a LOD score of 5 in W239) revealed the presence of 91 protein-encoding genes ( Supplementary Table S16 ). Comparative analysis of the genomic region between Paragon and W239 revealed substantial structural variations, characterized by a notable number of SNPs and gene deletions, which might contribute to functional differences between the two lines. Functional SNPs were identified in TraesCS7A02G435500, encoding a form of calmodulin, which is known to be involved in mineral homeostasis. In addition, a gene encoding a bHLH transcription factor (TraesCS7A02G435800) was absent from Paragon. Correlations between minerals and between minerals and other traits. Full correlation matrices for all of the traits that were measured or calculated are presented in the Supplementary Material S17 while Figure S1 shows correlations between the combined datasets for each population and nitrogen level for mineral concentrations in grain, plant height, straw biomass, above ground biomass, grain yield, harvest index and thousand grain weight (TGW). Negative correlations between concentrations of some minerals, grain yield and TGW were observed, but these were generally weak (but stronger for Zn in PxW160 and PxW292). This is consistent with the established concept of “yield dilution”, as higher yields and larger grain are associated with higher contents of starch which dilutes other grain components. Similarly, the contents of grain minerals are often positively correlated with nitrogen (protein) content and this was also observed, with Zn showing stronger correlations than the other essential minerals discussed here (notably in PxW239 at N1). Only weak correlations between Zn and Fe concentrations were observed in PxW239 (irrespective of N level) but stronger correlations in PxW160 and PxW292. Similarly, only weak positive correlations between Ca and Mg were observed. Discussion Wheat is an important source of mineral micronutrients, including minerals that are frequently deficient in human diets. We have therefore exploited genetic variation in wheat landraces and the availability of extensive genomic databases to map QTLs for five minerals which are frequently deficient in UK diets: potassium, iron, zinc, calcium and magnesium, and for copper, which may be deficient in livestock diets. Three recombinant inbred populations were each grown in replicated field trials for three years with one or two levels of nitrogen fertilisation, giving a total of 11 datasets. Furthermore, in order to identify QTL that could be deployed in high yielding genotypes the grain mineral contents are not only expressed as concentration (as in most published studies) but also as µg/grain. This is important because high concentrations of minerals identified in old types of wheat or wild relatives may be diluted by higher starch accumulation when the trait is introgressed into modern high-yielding germplasm. Hence, the QTLs identified should be robust and amenable to exploitation by wheat breeders. QTL analysis of the individual sample sets (11 in total) identified a large number of QTLs and it was therefore decided to only consider QTLs mapped in at least two sample sets and (with one exception) with a LOD score above 5 in at least one set. In fact, alignment of the QTLs onto the IWGSC RefSeq v 1.0 genome assembly (The International Wheat Genome Sequencing Consortium 2018) showed good agreement between the QTLs mapped in the sample sets of each population, and between populations, and LOD scores were often high in several sample sets. It is of interest that QTLs with increasing alleles from Paragon were identified for Cu, Ca, Mg and K. These four minerals have not been subjected to selection by breeders and our results indicate that there may be sufficient variation in modern elite genotypes for breeders to exploit, rather than requiring introgression of variation from landraces or wild relatives. By contrast, of eight QTLs for Fe and Zn, only one increasing allele was present in Paragon (for Fe) and seven in the Watkins lines. Potassium is an essential mineral for humans, particularly as an intracellular electrolyte in the regulation of blood pressure, muscle contraction and nerve transmission. Although dietary K supplies appear to be sufficient at a national level for most countries (Kumssa et al, 2021 ), potassium deficiency (hypokalemia) does occur, including in the UK where it is most prevalent in women (Derbyshire, 2018 ). Roots and tubers are the major source of potassium globally, accounting for up to 80% in some regions, with cereals being the second most important source (Kumssa et al., 2021 ) contributing 15–20% of total potassium intake in the UK (Bates et al., 2014a , b ). Three QTLs for K were mapped, with the most robust increasing allele (LOD 5.6–8.2 in four sample sets) being from W239. Deficiencies of iron and zinc have global impacts on human health (as discussed above). Wheat is an important source of both minerals and mineral enhancement of wheat has therefore been widely studied. Despite this global interest and massive investment, including the HarvestPlus programme in CGIAR institutes ( https://www.harvestplus.org ), progress has been limited. The concentrations of Fe and Zn in wheat grain vary depending on the availability of the minerals in soil, with Zn tending to vary more than Fe. The ranges of these minerals in our samples (30 to 64 mg/kg Fe and 23.6 to 49.1 mg/kg Zn) were consistent with studies of multiple genotypes grown on several sites, for example, 28.6–42.5 mg/kg Fe and 20.7–35.2 mg/kg Zn (Zhao et al., 2009 ), 26.3–49.9 mg/kg Fe and 21.3–64.1 mg/kg Zn (Krishnappa et al., 2022 ). Velu et al ( 2018 ) carried out GWAS of 330 wheat lines, identifying 39 marker-trait associations for grain Zn. Two major QTL regions were identified on chromosomes 2 and 7 and candidate genes identified. We did not identify QTLs for high Zn on either of these chromosomes. However, more recently two GWAS using high density SNP marker arrays have reported QTLs for Zn concentration on chromosomes 2D, 3B, and 7D (Krishnappa et al., 2021 ) and on chromosomes 2B, 5A, 5B, 6A and 7B (Krishnappa et al., 2022 ). The genetic improvement of iron content has not been achieved in commercial cultivars of wheat although a large number of QTLs have been mapped. For example, the GWAS analyses cited above reported QTLs on chromosomes 6D and 7D (Krishnappa et al., 2021 ) and 1A, 3B, 5A, 6A and 7B (Krishnappa et al., 2022 ). Magnesium deficiency in humans has been associated with a number of adverse health outcomes including cardiovascular disease, hypertension and stroke, metabolic syndrome, type 2 diabetes, Alzheimer’s disease and other types of dementia, muscular diseases (muscle pain, chronic fatigue, and fibromyalgia), and types of cancer (reviewed by Barbagallo et al., 2021 ). Major QTLs for Mg in wheat have not, to our knowledge, been reported previously but Oury et al ( 2006 ) reported wide variation in concentration and high effects of genotype, indicating that it should be amenable to genetic improvement. The four QTLs identified here were all mapped in multiple sample sets and included increasing alleles with high LOD scores from Paragon and Watkins lines. They therefore provide a good basis for genetic biofortification. Calcium deficiency is widespread globally, with up to half of total population being at risk (Shlisky et al., 2022 ). Calcium deficiency has a range of adverse health outcomes, including hypertension, high serum cholesterol and increased risk of colorectal cancer, in addition to rickets (paediatric bone disease). Milk and dairy products are the major source of Ca in UK diets (61% of the intake by babies and 35–45% for other age groups), followed by cereals (37% for children aged 11–18) (Bates et al., 2014a .b). All flours and breads produced in the UK, except wholemeal, are required to be fortified with ≈ 235–390 mg Ca/100g flour to restore the level in to that in wholemeal (The Bread and Flour Regulations 1998 (legislation.gov.uk)). The increasing adoption of vegan diets is a cause of further concern and intakes from other foods need to be increased. The major QTL for Ca identified on 5A corresponds to a previously identified QTL (Alomari et al., 2017 ) and analysis of TILLING mutants confirmed the candidate gene as TraesCS5A02G543300 . This gene encodes a cation transporter/plasma membrane ATPase which is one of seven genes at the QTL which were listed by Alomari et al. ( 2017 ). The 5A Ca QTL was the strongest of all of the QTLs that were mapped, with LOD scores above 10 in some sample sets, and controlled both Ca concentration and Ca/grain. In addition, QTLs for Ca were mapped on chromosomes 2B, 4A and 5D. Alomari et al ( 2017 ) also reported a strong marker/trait association for Ca on chromosome 6A. Although copper is an essential micronutrient for humans it is rarely deficient in human diets. However, deficiency does occur in sheep and cattle, either due to grazing on pastures on low copper soils (without fertilisation) or due to ingestion of foods high in sulphur and molybdenum. Increasing the content of copper in feed grain could therefore be advantageous. We identified five QTLs for grain copper, including one on chromosome 4B with a strong increasing allele from Paragon. The contribution of wheat-based foods to the human mineral nutrition is determined by two factors: the concentrations of the minerals in the food and their bioavailability, which are in turn determined by their locations in the grain and chemical forms. Fe and Zn are concentrated in the embryo and aleurone layer of the grain, but their relative distributions between these two tissues differ with Fe being more concentrated in the aleurone layer and Zn in the embryo (particularly in the embryonic axis) (Neal et al., 2013 ; Wan et al., 2022 ). This results in depleted concentrations of both minerals when grain is milled to produce white flour (the aleurone layer and germ forming part of the bran fraction). For example, Eagling et al ( 2014 ) reported Fe contents of 11.9 mg/kg and 6.7 mg/kg in white flours of two wheat cultivars grown in the UK and 46.7 mg/kg and 30.3 mg/kg in the corresponding wholemeals. Similarly, Khokhar et al ( 2020 ) reported a range of 24–49 mg/kg of Zn in whole grains of the progeny of crosses between a modern cultivar and land races of bread wheat and 8–15 mg/kg of Zn in white flours of 24 selected genotypes. Furthermore, most of the Fe and Zn in the aleurone cells and in the scutellum of the embryo is present as phytates in discrete bodies known as phytin globoids. Phytates are complexes with phytic acid (inositol hexakisphosphate) which has a cyclic structure with six phosphate groups which can bind metal ions. Phytates have low solubility and hence the bioavailability of Fe and Zn in whole grain wheat is low, although probably higher for Zn (about 25%) than for Fe (about 10%) (Bouis and Welch 2010 ). The higher bioavailability of Zn could result from the presence of zinc which is not bound to phytin in the embryonic axis. The location of Zn in genetically biofortified high Zn lines discussed does not differ from that in conventional lines (Wan et al., 2022 ) and consequently the bioavailability may also be limited. The concentrations of Ca and Mg are much higher than those of Fe and Zn (Table 1). Precise values for Ca and Mg vary between reports but the contents of Mg are generally higher than those of Ca, but with greater proportional losses on milling. For example, Vignola et al ( 2016 ) reported mean contents of about 340 mg/kg Ca in 945 mg/kg Mg in whole grains of 11 wheats grown over two years in Argentina, the values for white flour being 145 mg/kg Ca and 276 mg/kg Mg. The fortification of white flour with calcium is mandatory in the UK and UK Flour Millers quote values of 320 mg/kg Ca and 830 mg/kg Mg in wholemeal and 260 mg/Kg Mg and 1340 mg/Kg Ca in white breadmaking flours (Nutritional contribution of flour (ukflourmillers.org) ). Both Ca and Mg may be bound to phytate in the aleurone layer and scutellum (Heard et al., 2001) but phytate is not present in the starchy endosperm (the origin of white flour) and hence the minerals should be more bioavailable. It has also been shown that increasing the fibre content of wheat flour, which is another strategic target for improving health outcomes in western countries, can increase calcium absorption in the human colon, and consequently bone density, probably due to the fermentation to short chain fatty acids that reduce the pH in the colon (Wallace et al., 2017 ). Consequently, combining biofortification of wheat flour with Ca and fibre (Lovegrove et al., 2020 ) could result in synergistic improvements in health. Materials and Methods Populations Three biparental segregating populations were developed as described in Wingen et al. ( 2017 ) from crosses between the spring bread wheat cultivar Paragon as the common variety and a single-seed descendent (SSD) from a landrace accession from the A. E. Watkins collection (Wingen et al. 2014 ); Details of the landrace cultivars are given in Supplementary Table S18 . Each population comprised 94 F4 recombinant inbred lines. The 35K Axiom Wheat Breeder array was used for population ParW292 and the 44k Axiom TaNG array for the other two populations and was performed at the Bristol Genomics Facility using established protocols (Winfield et al. 2018 ). Field trials Field Trials were carried out at Rothamsted Research, Harpenden, UK (latitude 51.80N, longitude 0.40W) between 2012 and 2020. Each population was grown for three years, and at two levels of nitrogen fertilization, N1 and N2 ( Table 1 ). Abbreviated environment names are given in Supplementary Table S4 . The experiments followed a split plot randomised block design with blocks split for the nitrogen treatment, and three replicate blocks. The plot size was 1 x 1m and plots were sown and harvested with small plot drills and combine harvesters. Grain and straw weights per plot were measured on the combine, and sub-samples taken for analysis. Soil mineral nitrogen in the 0-90cm layer was measured each spring. Grain and straw analyses Ontologies for measured traits are given in Supplementary Table S5 . Post harvest, samples were analysed for dry matter, minerals, and the thousand grain weight recorded. Straw dry matter was determined by weighing before and after drying overnight at 80 0 C. Grain dry matter was similarly measured, but with drying at 105 0 C. Mineral concentrations were determined by ICP-AES following nitric acid digestion. Thousand grain weight was determined by counting a known number of grains, drying at 105 0 C overnight and recording the weight. Quantitative Genetics and Bioinformatics The R software suite (v4.3.1) was used for quantitative genetic analysis. Genetic maps were constructed using package ASMap (v1.0-4) following the same strategy as described in Min et al ( 2020 ). QTL mapping was conducted using package “qtl” (v1.50). Interesting QTL were selected using a custom written script, which identified QTL with a LOD over 5, where at least one further QTL on the same chromosome for the same trait and the same effect direction was present. QTL were aligned along the IWGSC RefSeq vs1.0, represented by the peak marker, the CI border markers and all CI internal markers. Gene Content Analysis and Genomic Comparison. The gene ID and genomic location information of the genes within the 5 Mb region either side of the QTL with the highest LOD score for selected traits, as detailed in Supplementary Table S6 , were obtained from Ensembl BioMarts (Kinsella et al., 2011 ). Functional annotation was retrieved from WheatOmics ( http://wheatomics.sdau.edu.cn/ ) (Ma et al., 2021 ). Knetminer was used to explore any association between genes and the traits of interest (Hassani-Pak et al. 2021 ). Subsequently, whole genome sequence data were used to identify functional SNPs and copy number variations between Paragon and the Watkins lines (Cheng et al., under review). Variation data can be accessed from https://opendata.earlham.ac.uk/wheat/ . Calcium candidate gene proof of function. Wheat lines carrying induced mutations in either of two candidate genes for grain calcium content (GrnCaCnc) TraesCS5A02G542600 and TraesCS5A02G543300 , were identified in the Cadenza TILLING population (Krasileva et al., 2017 ) following the method described in Adamski et al. ( 2020 ). Only mutations that were predicted to lead to a gained stop codon, to a missense variant or to a splice donor variant were selected. Two independent mutations were selected for TraesCS5A02G542600 and eight for TraesCS5A02G543300 . For each mutation, 24 seeds of the TILLING lines were grown under standard glasshouse conditions. Ten wildtype Cadenza plants were grown as control. Plants were genotyped with KASP markers specific for the presence/absence of the mutations and only homozygous mutant plants were taken forward (in total 53 plants, between 4 to 7 individual plants for each tilling mutation, mean 5.3 plants per mutation). From each of these plants, all grains were harvested and grain number per plant (GNplant), grain yield per plant (GYplant) and the seed characteristics GW, GLng and GWid were measured using a Marvin seed analyser. Grain moisture content was measured using DA 7250 Near-infrared spectrometer. GrnCaCnc was measured using X-Supreme8000, a benchtop X-ray fluorescence spectrometer, equipped with XSP-Minerals’ Package and calibrated with data collected using an ICP-OES using 187 data points with GrnCaCnc levels ranging from 242.4 ppm to 726.7 ppm. No outliers for GrnCaCnc were detected and the average over the three technical reps was calculated. This data set (GrnCaCnc range 380.0-564.2 ppm, mean 461.3 ppm) was used to statistical compare the GrnCaCnc between the Cadenza wildtype and the independent mutants in a linear model (ANOVA). Declarations Acknowledgements Rothamsted Research and the John Innes Centre receive strategic funding from the Biotechnology and Biological Sciences Research Council (BBSRC) and we acknowledge support from the Delivering Sustainable Wheat (BB/X011003/1) Institute Strategic Programme. References Adamski NM, Borrill P, Brinton J, Harrington S, Marchal C, Bentley AR, Bovill WD, Cattivelli L, Cockram J, Contreras-Moreira B, Ford B, Ghosh S, Harwood W, Hassani-Pak K, Hayta S, Hickey LT, Kanyuka K, King J, Maccaferrri M, Naamati G, Pozniak CJ, Ramirez-Gonzalez RH, Sansaloni C, Trevaskis B, Wingen LU, Wulff BBH, Uauy C. (2020). A roadmap for gene functional characterisation in crops with large genomes: Lessons from polyploid wheat. eLife 9, e55646. https://doi.org/10.7554/eLife.55646 . Alomari DZ, Eggert K, von Wiren N, Pillen K, Order MS. (2017). Genome-wide association study of calcium accumulation in grains of European wheat cultivars. Frontiers in Plant Science, 8, 1797. https://doi.org/10.3389/fpls.2017.01797 . Aslam MF, Ellis PR, Berry SE, Latunde-Dada GO, Sharp PA. (2018). Enhancing mineral bioavailability from cereals: Current strategies and future perspectives. Nutrition Bulletin, 43, 184–188. https://doi.org/10.1111/nbu.12324 . Balk J, Connorton JM, Wan Y, Lovegrove A, Moore KL, Uauy C, Sharp PA, Shewry PR. (2019). Improving wheat as a source of iron and zinc for global nutrition. Nutrition Bulletin, 44, 53–59. https://doi.org/10.1111/nbu.12361 . Barbagallo M, Veronese N, Dominguez LJ. (2021). Magnesium in Aging, Health and Diseases. Nutrients, 13, 463. https://doi.org/10.3390/nu13020 . Bates B, Lennox A, Prentice A, Page P, Nicholson S, Swan G. (2014a). National Diet and Nutrition Survey: Results from Years 1–4 (combined) of the Rolling Programme (2008/2009–2011/2012). Executive Summary . Public Health England. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/594360/NDNS_Y1_to_4_UK_report_executive_summary_revised_February_2017.pdf Bates B, Lennox A, Prentice A, Page P, Nicholson S, Swan G. (2014b). National Diet and Nutrition Survey: Results from Years 1–4 (combined) of the Rolling Programme (2008/2009–2011/2012) . Public Health England. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/216484/dh_128550.pdf Bates B, Cox L, Nicholson S. et al (2016) National Diet and Nutrition Survey Results from Years 5 and 6 (combined) of the Rolling Programme (2012/2013–2013/2014) . Public Health England and the Food Standards Agency. Bouis HE, Welch RM. (2010). Biofortification-a sustainable agricultural strategy for reducing micronutrient malnutrition in the global south. Crop Science, 50, S20–S32. https://doi.org/10.2135/cropsci2009.09.0531 . Broadley MR, Alcock J, Alford J, Cartwright P, Foot I, Fairweather-Tait SJ, Hart DJ, Hurst R, Knott P, McGrath SP, Meacham MC, Norman K, Mowat H, Scott P, Stroud JL, Tovey M, Tucker M, White PJ, Young SD, Zhao F-J. (2010). Selenium biofortification of high-yielding winter wheat ( Triticum aestivum L.) by liquid or granular Se fertilisation. Plant and Soil 332, 5–18 https://doi.org/10.1007/s11104-009-0234-4 Cakmak I, Kutman UB. (2018). Agronomic biofortification of cereals with zinc: a review. European Journal of Soil Science, 69, 172–180. https://doi.org/10.1111/ejss.12437 . Cheng S, Feng C, Wingen LU, Cheng H, Riche AB, Jiang M. et al . (2023). Harnessing Landrace Diversity Empowers Climate-resilient Wheat Breeding. Submitted to Nature . Derbyshire E. (2018). Micronutrient intakes of British adults across mid-life: a secondary analysis of the UK National Diet and Nutrition Survey. Frontiers in Nutrition, 5, 55. https://doi.org/10.3389/fnut.2018.00055 Eagling T, Neal AL, McGrath SP, Fairweather-Tait S, Shewry PR, Zhao F-J. (2014) Distribution and speciation of iron and zinc in grain of two wheat genotypes. Journal of Agricultural and Food Chemistry, 62, 708–716. https://doi.org/10.1021/jf403331p https://doi.org/10.1007/s13197-014-1503-7 Govindan V, Singh RP, Juliana P, Mondal S, Bentley AR. (2022). Mainstreaming grain zinc and iron concentrations in CIMMYT wheat germplasm. Journal of Cereal Science, 105, 103473. https://doi.org/10.1016/j.jcs.2022.103473 Hassani-Pak K, Singh A, Brandizi M, Hearnshaw J, Parsons JD, Amberkar S, Phillips AL, Doonan JH, Rawlings C. (2021) KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species. Plant Biotechnology Journal. https://doi.org/10.1111/pbi.13583 Heard PJ, Feeney KA, Allen GC, Shewry PR. (2002). Determination of the elemental composition of mature wheat grain using a modified Secondary Ion Mass Spectrometer (SIMS). The Plant Journal 30, 237–245. https://doi.org/10.1046/j.1365-313X.2001.01276.x Joy EJM, Stein AJ, Young SD, Ander EL, Watts MJ, Broadley MR. (2015). Zinc-enriched fertilisers as a potential public health intervention in Africa. Plant and Soil 389, 1–24. https://doi.org/10.1007/s11104-015-2430-8 Kinsella RJ, Kähäri A, Haider S, Zamora J, Proctor G, Spudich G, Almeida-King J, Staines D, Derwent P, Kerhornou A, Kersey P, Flicek P. (2011). bEnsembl BioMarts: a hub for data retrieval across taxonomic space Database: the journal of biological databases and curation PMID: 21785142 https://doi.org/10.1093/database/bar030 Khokhar JS, King J, King IP, Young SD, Foulkes MJ, De Silva J, et al. (2020) Novel sources of variation in grain Zinc (Zn) concentration in bread wheat germplasm derived from Watkins landraces. PLoS ONE 15(2): e0229107. https://doi.org/10.1371/journal.pone.0229107 Krasileva KV, Vasquez-Gross HA, Howell T, Bailey P, Paraiso F, Clissold L, Simmonds J, Ramirez-Gonzalez RH, Wang X, Borrill P, Fosker C, Ayling S, Phillips AL, Uauy C, Dubcovsky J. (2017). Uncovering hidden variation in polyploid wheat. Proceedings of the National Academy of Sciences , 114, E913-E921 https://doi.org:doi/10.1073/pnas.1619268114 . Krishnappa G, Rathan ND, Sehgal D, Ahlawat AK, Singh SK, Singh SK, Shukla RB, Jaiswal JP, Solanki IS, Singh GP, Singh AM (2021) Identification of novel genomic regions for biofortification traits Using an SNP marker-enriched linkage map in wheat (Triticum aestivum L.). Frontiers in Nutrition, 8, 669444. https://doi.org/10.3389/fnut.2021.669444 Krishnappa G, Khan H, Krishna H, Kumar S, Mishra CN, Parkash O, Devate NB, Nepolean T, Rathan ND, Mamrutha HM, Srivastava P, Biradar S, Uday G, Kumar M, Singh G Singh GP. (2022). Genetic dissection of grain iron and zinc, and thousand kernel weight in wheat ( Triticum aestivum L.) using genome-wide association study. Scientific Reports , 12, 12444. https://doi.org/10.1038/s41598-022-15992-z . Kumssa DB, Joy EJM, Broadley MR. Global Trends (1961–2017) in Human Dietary Potassium Supplies. Nutrients. 2021; 13(4):1369. https://doi.org/10.3390/nu13041369 Lovegrove A, Wingen LU, Plummer A, Wood A, Passmore D, Kosik O, Freeman J, Mitchell RAC, Hassall KL, Ulker M, Tremmel-Bede K, Rakszegi M, Bedo Z, Perretant M-R, Charmet G, Pont C, Salse J, Leverington-Waite M, Orford S, Burridge A, Pellny TK, Shewry PR, Griffiths S. (2020). Identification of a major QTL and associated molecular marker for high arabinoxylan fibre in white wheat flour. PLoS ONE 15(2), e0227826. https://doi.org/10.1371/journal.pone.0227826 Ma S, Wang M, Wu J, Guo W, Chen Y, Li G, Wang Y, Shi W, Xia G, Fu D, Kang Z, Ni F. WheatOmics: A platform combining multiple omics data to accelerate functional genomics studies in wheat. Mol Plant. 2021;14(12):1965–1968. doi: 10.1016/j.molp.2021.10.006. Epub 2021 Oct 27. PMID: 34715393. Min B, Salt L, Wilde P, Kosik O, Hassall K, Przewieslik-Allen A, Burridge AJ, Poole M, Snape J, Wingen L, Haslam R, Griffiths S, Shewry PR. (2020). Genetic variation in wheat grain quality is associated with differences in the galactolipid content of flour and the gas bubble properties of dough liquor. Food Chemistry, 6, 100093. https://doi.org/10.1016/j.fochx.2020.100093 . Mosleth EF, Lillehammer M, Pellny TK, Wood A, Riche AB, Hussain A, Griffiths S, Hawkesford MJ, Shewry PR. (2020). Genetic variation and heritability of grain protein deviation in European wheat genotypes. Field Crops Research, 255, 107896. https://doi.org/10.1016/j.fcr.2020.107896 . Neal AL, Geraki K, Borg S, Quinn P, Mosselmans JF, Brinch-Pedersen H, Shewry PR. (2013). Iron and zinc complexation in wild-type and ferritin-expressing wheat grain: implications for mineral transport into developing grain. Journal of Bioinorganic Chemistry, 18, 557–570. https://doi:10.1007/s00775-013-1000-x . NIH (1989). Diet and Health: Implications for Reducing Chronic Disease Risk. National Research Council (US) Committee on Diet and Health. Washington (DC): National Academies Press (US) Oury F-X, Leenhardt F, Remesy C, Chanliaud E, Duperrier B, Balfourier F, Charmet G. (2006). Genetic variability and stability of grain magnesium, zinc and iron concentrations in bread wheat. European Journal of Agronomy, 25, 177–185. https://doi.org/10.1016/j.eja.2006.04.011 . Pont C, Leroy T, Seidel M, Tondelli A, Duchemin W, Armisen D, Lang D, Bustos-Korts D, Goué N, Balfourier F, Molnár-Láng M, Lage J, Kilian B, Özkan H, Waite D, Dyer S, Letellier T, Alaux M., Wheat and Barley Legacy for Breeding Improvement (WHEALBI) consortium, Russell J, Keller B, van Eeuwijk F, Spannagl M, Mayer KFX, Waugh R, Stein N, Cattivelli L, Haberer G, Charmet G, Salse J. (2019) Tracing the ancestry of modern bread wheats. Nature Genetics , 51, 905–911. https://doi.org/10.1038/s41588-019-0393-z . Pohl HA, Wheeler JS, Murray HE. (2013). Sodium and potassium in health and disease. Metal Ions in Life Sciences, 13, 29–47. https://doi.org/10.1007/978-94-007-7500-8_2 . Shlisky J, Mandlik R, Askari S, Abrams S, Belizan JM, Bourassa MW, Cormick G, Driller-Colangelo A, Gomes F, Khadilkar A, Owino V, Pettifor JM, Rana ZH, Roth DE, Weaver C. (2022). Calcium deficiency worldwide: prevalence of inadequate intakes and associated health outcomes. Annals of the New York Academy of Sciences. 1512, 10–28. https://doi.org/10.1111/nyas.14758 . The International Wheat Genome Sequencing Consortium (IWGSC) et al. (2018). Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science, 361, eaar7191. https://doi.org/10.1126/science.aar7191 Velu G, Singh RP, Crespo-Herrera L, Juliana P, Dreisigacker S, Valluru R, Stangoulis J, Sohu VS, Mavi GS, Mishra VK, Balasubramaniam A, Chatrath R, Gupta V, Singh GP, Joshi AK. (2018). Genetic dissection of grain zinc concentration in spring wheat for mainstreaming biofortification in CIMMYT wheat breeding. Science. Reports, 8, 13526. https://www.nature.com/articles/s41598-018-31951-z . Vignola MB, Moiraghi M, Salvucci E, Baroni V, Perez GT. (2016). Whole meal and white flour from Argentine wheat genotypes: mineral and arabinoxylan differences. Journal of Cereal Science, 71, 217–223. https://doi.org/10.1016/j.jcs.2016.09.002 . Wairich A, de Oliveira BHN, Arend EB, Duarte GH, Ponte LR, Sperotto RA, Ricachenevshy FK, Fett JP. (2019). The Combined Strategy for iron uptake is not exclusive to domesticated rice ( Oryza sativa ). Sci Rep 9, 16144. https://doi.org/10.1038/s41598-019-52502-0 . Wallace TC, Marzorati M, Spence L, Weaver CM, Williamson PS. (2017) New frontiers in fibers: innovative and emerging research on the gut microbiome and bone health. Journal of the American College of Nutrition, 36, 218–222. https://doi.org/10.1080/07315724.2016.1257961 . Wan Y, Stewart T, Amrahli M, Evans J, Sharp P, Govindan V, Hawkesford MJ, Shewry PR. (2022) Localisation of Iron and Zinc in grain of biofortified wheat. Journal of Cereal Science, 105, 103470. https://doi.org/10.1016/j.jcs.2022.103470 . WHO (2013) World health report: research for universal health coverage. World Health Organization, Geneva, Switzerland. WHO (2015) The global prevalence of anaemia in 2011. World Health Organization, Geneva, Switzerland. Winfield MO, Allen AM, Wilkinson PA, Burridge AJ, Barker GLA, Coghill J, Waterfall C, Wingen LU, Griffiths S, Edwards KJ (2018) High-density genotyping of the A.E. Watkins Collection of hexaploid landraces identifies a large molecular diversity compared to elite bread wheat. Plant Biotechnology Journal, 16(1), 165–175. https://doiorg/10.1111/pbi.12757 . Wingen LU, Orford S, Goram R, Leverington-Waite M, Bilham L, Patsiou TS, Ambrose M, Dicks J, Griffiths S. (2014). Establishing the AE Watkins landrace cultivar collection as a resource for systematic gene discovery in bread wheat. Theoretical and Applied Genetics, 127, 1831–1842. https://doi.org/10.1007/s00122-014-2344-5 Wingen LU, West C, Leverington-Waite M, Collier S, Orford S, Goram R, Yang C-Y, King J, Allen AM, Burridge A, Edwards K, Griffiths S. (2017) Wheat Landrace Genome Diversity, Genetics, 205(4), 1657–1676. https://doi.org/10.1534/genetics.116.194688 . Wysocka D, Snarska A, Sobiech P. (2019). Copper - an essential micronutrient for calves and adult cattle. Journal of Elementology, 24, 101–110. https://doi.org/10.5601/jelem.2018.23.2.1645 . Zhao FJ, Su YH, Dunham SJ, Rakszegi M, Bedo Z, McGrath SP, Shewry PR. (2009). Variation in mineral micronutrient concentrations in grain of wheat lines of diverse origin. Journal of Cereal Science, 49, 290–295. https://doi.org/10.1016/j.jcs.2008.11.007 . Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryTableS1.xlsx Table S1. Full list of QTLs mapped in Paragon x Watkins 160. SupplementaryTableS2.xlsx Table S2. Full list of QTLs mapped in Paragon x Watkins 239. SupplementaryTableS3.xlsx Table S3. Full list of QTLs mapped in Paragon x Watkins 292. SupplementaryTableS4.xlsx Table S4. Abbreviations of environments. SupplementaryTableS5.xlsx Table S5. Crop ontology terms. SupplementaryTableS6.xlsx Table S6. Major QTLs mapped for six minerals. SupplementaryTableS7.xlsx Table S7.Full list of genes in the 5 Mb of DNA surrounding the peak marker of the Ca 5A QTL. SupplementaryTableS8.xlsx Table S8.Full list of genes in the 5 Mb of DNA surrounding the peak marker of the Cu 4B QTL. SupplementaryTableS9.xlsx Table S9.Full list of genes in the 5 Mb of DNA surrounding the peak marker of the Cu 5B QTL. SupplementaryTableS10.xlsx Table S10.Full list of genes in the 5 Mb of DNA surrounding the peak marker of the Cu 7B QTL. SupplementaryTableS11.xlsx Table S11.Full list of genes in the 5 Mb of DNA surrounding the peak marker of the Cu 7D QTL. SupplementaryTableS12.xlsx Table S12.Full list of genes in the 5 Mb of DNA surrounding the peak marker of the Fe 2D QTL. SupplementaryTableS13.xlsx Table S13.Full list of genes in the 5 Mb of DNA surrounding the peak marker of the Mg 7A QTL. SupplementaryTableS14.xlsx Table S14.Full list of genes in the 5 Mb of DNA surrounding the peak marker of the K 4B QTL. SupplementaryTableS15.xlsx Table S15.Full list of genes in the 5 Mb of DNA surrounding the peak marker of the Zn 6A QTL. SupplementaryTableS16.xlsx Table S16.Full list of genes in the 5 Mb of DNA surrounding the peak marker of Zn 7A QTL. SupplementaryTableS17.xlsx Table S17. Correlation between concentrations of minerals in grain and plant height, straw biomass, above ground biomass, grain yield, harvest index and thousand grain weight for combined datasets for each population and nitrogen level. SupplementaryTableS18.xlsx Table S18.Landrace parents of the three populations, selected from the Watkins bread wheat landrace collection. 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1","display":"","copyAsset":false,"role":"figure","size":851695,"visible":true,"origin":"","legend":"\u003cp\u003eApproximate locations of QTLs for essential minerals on the chromosomes of wheat based on IWGSC RefSeq v1.0.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/a43adf04a5a8d442de0be3e2.png"},{"id":51513118,"identity":"10cf9658-36e2-4926-afca-c0fca3fa5621","added_by":"auto","created_at":"2024-02-22 21:25:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1660724,"visible":true,"origin":"","legend":"\u003cp\u003eExamples of robust QTL for calcium (5A), copper (7D), iron (2D), potassium (4B), magnesium (7A) and zinc (7A). Vertical axes show LOD score. Horizontal axes are Axiom35K genetic linkage maps of the respective chromosomes. Environments and trait abbreviations are shown in \u003cstrong\u003eSupplementary Tables S4\u003c/strong\u003e and \u003cstrong\u003eS5\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/bdb64c03875a775df72c4009.png"},{"id":51513120,"identity":"59e6f265-6c25-4de5-b0e1-fd855aaea7df","added_by":"auto","created_at":"2024-02-22 21:25:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":154820,"visible":true,"origin":"","legend":"\u003cp\u003eGrain calcium concentration (ppm) of TILLING mutants (blue) of \u003cem\u003eTraesCS5A02G543300\u003c/em\u003e together with wild-type Cadenza (pink).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/cbf990785f161d380719ac15.png"},{"id":62585558,"identity":"ec4a20ba-754a-4cc7-9e25-b4fcfc1ae41e","added_by":"auto","created_at":"2024-08-16 07:09:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2998079,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/16c688ae-30e9-4ead-a63a-8911fb4a2139.pdf"},{"id":51512541,"identity":"316dab90-2a00-4acf-907f-f83e0f01dbbc","added_by":"auto","created_at":"2024-02-22 21:17:53","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":56800,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1. \u003c/strong\u003eFull list of QTLs mapped in Paragon x Watkins 160.\u003c/p\u003e","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/bf79490b86d723994d951d77.xlsx"},{"id":51513116,"identity":"b57f7cda-e5ef-46f5-a31b-bb143b6bfe96","added_by":"auto","created_at":"2024-02-22 21:25:53","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":96418,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S2. \u003c/strong\u003eFull list of QTLs mapped in Paragon x Watkins 239.\u003c/p\u003e","description":"","filename":"SupplementaryTableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/e263cad35e3405ba5347b2ef.xlsx"},{"id":51513121,"identity":"7dfbdf7d-fa12-46c1-ac58-4382ab846b0d","added_by":"auto","created_at":"2024-02-22 21:25:54","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":43470,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S3. \u0026nbsp;\u003c/strong\u003eFull list of QTLs mapped in Paragon x Watkins 292.\u003c/p\u003e","description":"","filename":"SupplementaryTableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/4ddb0bbd0c36adf8dfca1928.xlsx"},{"id":51512552,"identity":"fd81d16d-a290-4990-a687-403e276dfd27","added_by":"auto","created_at":"2024-02-22 21:17:53","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":12538,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S4. \u0026nbsp;\u003c/strong\u003eAbbreviations of environments\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplementaryTableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/34b633e80009c505992ff543.xlsx"},{"id":51512550,"identity":"ad10fd26-0b17-404e-9596-11da317e2bd0","added_by":"auto","created_at":"2024-02-22 21:17:53","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":46028,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S5. \u0026nbsp;\u003c/strong\u003eCrop ontology terms.\u003c/p\u003e","description":"","filename":"SupplementaryTableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/7f089766c46232bcfd84b49d.xlsx"},{"id":51512545,"identity":"37dd6d74-68c1-4fff-bd5d-29e0661c926a","added_by":"auto","created_at":"2024-02-22 21:17:53","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":21063,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S6. \u0026nbsp;\u003c/strong\u003eMajor QTLs mapped for six minerals.\u003c/p\u003e","description":"","filename":"SupplementaryTableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/c6bac097a2d8d4fe4d7da06d.xlsx"},{"id":51512556,"identity":"8e08ac12-97ce-400f-aa26-918cdd29700d","added_by":"auto","created_at":"2024-02-22 21:17:54","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":20326,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S7.\u003c/strong\u003eFull list of genes in the 5 Mb of DNA surrounding the peak marker of the Ca 5A QTL.\u003c/p\u003e","description":"","filename":"SupplementaryTableS7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/273fd6c2696822e19a4ad74b.xlsx"},{"id":51512561,"identity":"c46afc05-abee-4d88-8d24-88d244fc306f","added_by":"auto","created_at":"2024-02-22 21:17:54","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":14960,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S8.\u003c/strong\u003eFull list of genes in the 5 Mb of DNA surrounding the peak marker of the Cu 4B QTL.\u003c/p\u003e","description":"","filename":"SupplementaryTableS8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/9e36bf0fdf10e2281ce9aa79.xlsx"},{"id":51512557,"identity":"b9a17c90-33d1-4beb-b553-9f2262e1ba64","added_by":"auto","created_at":"2024-02-22 21:17:54","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":18111,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S9.\u003c/strong\u003eFull list of genes in the 5 Mb of DNA surrounding the peak marker of the Cu 5B QTL.\u003c/p\u003e","description":"","filename":"SupplementaryTableS9.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/1ff3e09af98437c999794442.xlsx"},{"id":51512555,"identity":"d214a5df-8062-4d3e-b87b-2fc5f60302fc","added_by":"auto","created_at":"2024-02-22 21:17:54","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":18035,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S10.\u003c/strong\u003eFull list of genes in the 5 Mb of DNA surrounding the peak marker of the Cu 7B QTL.\u003c/p\u003e","description":"","filename":"SupplementaryTableS10.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/68b0cc2fd02e8f2ad297f3b1.xlsx"},{"id":51513126,"identity":"87d8c2db-dfad-46b7-b4de-8e91854d6f85","added_by":"auto","created_at":"2024-02-22 21:25:54","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":17398,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S11.\u003c/strong\u003eFull list of genes in the 5 Mb of DNA surrounding the peak marker of the Cu 7D QTL.\u003c/p\u003e","description":"","filename":"SupplementaryTableS11.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/bc4af60e515b51888d5363f4.xlsx"},{"id":51513125,"identity":"bb9626d2-2ed4-4164-99fa-c7fcba3fc970","added_by":"auto","created_at":"2024-02-22 21:25:54","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":19455,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S12.\u003c/strong\u003eFull list of genes in the 5 Mb of DNA surrounding the peak marker of the Fe 2D QTL.\u003c/p\u003e","description":"","filename":"SupplementaryTableS12.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/443dff459e652184b045d90f.xlsx"},{"id":51512554,"identity":"6d40be7c-5829-40c5-9137-d740c610bde2","added_by":"auto","created_at":"2024-02-22 21:17:53","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":20350,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S13.\u003c/strong\u003eFull list of genes in the 5 Mb of DNA surrounding the peak marker of the Mg 7A QTL.\u003c/p\u003e","description":"","filename":"SupplementaryTableS13.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/4233c9ff1c0f90229f59a7a7.xlsx"},{"id":51513122,"identity":"4f3f360c-4c8e-4645-a5cf-88aec3f5ffe1","added_by":"auto","created_at":"2024-02-22 21:25:54","extension":"xlsx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":13281,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S14.\u003c/strong\u003eFull list of genes in the 5 Mb of DNA surrounding the peak marker of the K 4B QTL.\u003c/p\u003e","description":"","filename":"SupplementaryTableS14.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/39ec8a3ddaa043f3f46d9182.xlsx"},{"id":51512548,"identity":"92ae4ffd-46ef-4c77-bc8d-48e34a9b3a35","added_by":"auto","created_at":"2024-02-22 21:17:53","extension":"xlsx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":14255,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S15.\u003c/strong\u003eFull list of genes in the 5 Mb of DNA surrounding the peak marker of the Zn 6A QTL.\u003c/p\u003e","description":"","filename":"SupplementaryTableS15.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/6e22ae9a4c77cf76f7f60f46.xlsx"},{"id":51512547,"identity":"6f3e2f4e-982d-4806-a5c6-ec63e987d3c0","added_by":"auto","created_at":"2024-02-22 21:17:53","extension":"xlsx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":17245,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S16.\u003c/strong\u003eFull list of genes in the 5 Mb of DNA surrounding the peak marker of Zn 7A QTL.\u003c/p\u003e","description":"","filename":"SupplementaryTableS16.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/b4578a93a2bdf19b6896dab3.xlsx"},{"id":51512553,"identity":"243abea6-794d-4bb5-9c51-828728d79ba0","added_by":"auto","created_at":"2024-02-22 21:17:53","extension":"xlsx","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":23774,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S17. \u003c/strong\u003eCorrelation between concentrations of minerals in grain and plant height, straw biomass, above ground biomass, grain yield, harvest index and thousand grain weight for combined datasets for each population and nitrogen level.\u003c/p\u003e","description":"","filename":"SupplementaryTableS17.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/6105e55fd8b3f4baa3b4f8f4.xlsx"},{"id":51512558,"identity":"c4a36b62-3649-47b0-acf4-215e2f3c90b2","added_by":"auto","created_at":"2024-02-22 21:17:54","extension":"xlsx","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":10818,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S18.\u003c/strong\u003eLandrace parents of the three populations, selected from the Watkins bread wheat landrace collection.\u003c/p\u003e","description":"","filename":"SupplementaryTableS18.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3714819/v1/4e036ded87dbfc494b09805d.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Improving wheat grain composition for human health: an atlas of QTLs for essential minerals","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHumans require a range of minerals in their diets, including major elements (calcium, magnesium, phosphorus, sodium, potassium) and trace elements (iron, zinc, fluoride, selenium, copper, chromium, iodine, manganese, molybdenum). However, some of these minerals are also toxic if consumed in excess as are other elements which are normally present in trace amounts such as aluminium, cadmium, mercury, arsenic and lead (NIH, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Pohl et al, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMost essential minerals are present in adequate amounts in human diets, but deficiencies may occur resulting in severe and widespread symptoms. The most widespread global deficiencies are of iron and zinc: It has been estimated that 43% of children and 29% of women of reproductive age have anaemia, about half of which results from iron deficiency (WHO, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), while zinc deficiency is associated with stunted growth in children under the age of 5 in over 150\u0026nbsp;million children globally (WHO, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe risk of deficiency is reflected in national monitoring of dietary intakes, with the UK National Diet and Nutrition Survey (NDNS) monitoring the intakes of seven minerals: iron, zinc, calcium, magnesium, potassium, selenium and iodine. In fact, Bates et al (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported that a substantial proportion of UK children aged 11\u0026ndash;18 had intakes below the recommendations for all of these minerals, particularly iron (48% of girls 11 to 18 years). Similarly, twenty seven percent of adult women (19 to 64 years) also had iron intakes below the recommended level and substantial proportion of adults had intakes below the recommended level for magnesium, potassium and selenium.\u003c/p\u003e \u003cp\u003eWheat is the most widely grown and consumed crop in the world, contributing between 10% and 50% of the total calories in countries ranging from Western Europe to North Africa and Central Asia. Wheat contributes many essential dietary components as well as energy including up to 20% of essential minerals in the UK (Bates et al, 2014 a, b). Hence, deficiencies in the contents of minerals in samples of wheat grain can have significant effects on human health.\u003c/p\u003e \u003cp\u003eThe mineral content of wheat grain is determined by the mineral characteristics of the soil and by the ability of the plant to take up minerals from the soil and transport them into the grain. Strategies have therefore been adopted to increase the mineral contents of wheat grains by either applying minerals as fertiliser (agronomic biofortification) or by improving the ability of the plant to extract minerals from the soil and transport them to the grain (genetic biofortification). Agronomic biofortification can have a significant impact with some minerals and farming systems. For example, the application of fertiliser containing selenium is used in some countries (Broadley et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) while fertilisation with zinc may also have benefits (Cakmak and Kutman, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Joy et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, agronomic biofortification adds to costs of crop production and may not be available to farmers in less developed countries. Hence, genetic biofortification has been the major focus of research globally (reviewed by Balk et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Aslam et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This has resulted in the development of high zinc wheat which is being evaluated in human diets (Govindan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lowe et al., 2022) but there has been limited success with other minerals and no other genetically biofortified lines of wheat have been developed.\u003c/p\u003e \u003cp\u003eGenetic biofortification depends on the availability of genetic variation in mineral accumulation, either in wheat or in related species which can be used for introgression. It is well-established that modern commercial cultivars of wheat are less genetically diverse than older types including landraces (traditional types which were grown before the application of scientific breeding methods) (Pont et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We are therefore using the A. E. Watkins landrace cultivar collection, a global collection originating from about 100 years ago (Wingen et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), to identify novel QTLs and genes which determine the accumulation of essential minerals in the grain, focusing on iron, zinc, calcium, magnesium, potassium and copper. This has enabled us to identify a number of novel QTLs and associated molecular markers which will facilitate the improvement of wheat for human health.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThree populations of recombinant inbred lines (RILs) were selected and grown in replicated multienvironment field trials. These were from crosses between the UK Spring wheat cultivar \u0026ldquo;Paragon\u0026rdquo; and Watkins lines W160, W239 and W292. The landraces were from Cyprus (W292) and Spain (W160 and W239), representing ancestral groups C7 (W292) and C6 (W160 and W239), and were selected to represent the range of diversity in the collection including variation in height (W292), nitrogen use efficiency (NUE) (W239) and grain mineral (P and Zn) content (W160). Each population comprise 94 F4 recombinant inbred lines which were grown in three replicated randomised plots for three years with either 50 kg N/Ha (N1), 200 kg N/Ha (N2) or at both N1 and N2. This gave 11 sets of samples (\u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e) which were analysed for 9 minerals (Ca, Cu, Fe, K, Mg, Mn, Na, S and Zn) by ICP-OES.\u003c/p\u003e \u003cp\u003eMineral concentrations were expressed as grain concentration and amount per grain and the discussion below will focus on these primary datasets.\u003c/p\u003e \u003cp\u003eHowever, a range of other traits were also measured or calculated, and the data are presented in the \u003cb\u003eSupplementary Table S1-3\u003c/b\u003e. The determination of the yields of the plots allowed the total amounts of minerals recovered in grain per square meter plot (take-off) to be calculated. Similarly, the relative ability of the lines to accumulate minerals in the grain was calculated as \u0026ldquo;grain mineral deviation\u0026rdquo;. This is calculated by comparing the concentrations of minerals with the yields of the lines within each set of samples. In broad terms the concentrations of minerals in grain are inversely correlated with grain yield, allowing a regression line to be calculated (as discussed for nitrogen by Mosleth et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Genotypes in which mineral concentrations fall above this regression line exhibit positive grain mineral deviation. Finally, the plots were measured for plant height and the weights and concentrations of minerals in straw determined. This allowed the weight and mineral content of the above ground biomass and the mineral harvest index to be calculated. The full datasets for 774 QTLs for minerals in grain, straw and calculated biomass and for 84 other QTL are provided in \u003cb\u003eSupplementary Tables S1-3\u003c/b\u003e, for Par x W160, W239, and W292. Environments and trait abbreviations are shown in \u003cb\u003eSupplementary Tables S4\u003c/b\u003e and \u003cb\u003eS5\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIron, zinc, calcium, magnesium and potassium are of particular interest because they may be deficient in human diets, including developed countries such as the UK. Deficiencies of other essential minerals are rare in humans, particularly in developed countries. However, copper deficiency may occur in livestock, particularly in cattle (Wysocka et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and copper content is therefore of concern when formulating feeds for livestock. The following discussion therefore focuses on the six minerals iron, zinc, calcium, magnesium, potassium and copper. The concentrations of these minerals in the populations are given in \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eQTLs for essential minerals\u003c/h2\u003e \u003cp\u003eA large number of QTLs were identified for the concentrations of minerals in grain (mg/kg dry weight) and the contents of minerals per grain (\u0026micro;g/grain). However, many of the QTLs had low LOD scores or were only mapped in a single sample set. It was therefore decided (with one exception discussed below) to only consider QTLs that were mapped in at least two sample sets with LOD scores above 5 in at least one set.\u003c/p\u003e \u003cp\u003eBased on these criteria 23 increasing alleles for grain minerals were mapped, with 16 present in the Watkins lines and 7 in Paragon. These are presented in \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e which also gives the peak SNP markers based on the dataset with the highest LOD score, with additional details presented in \u003cb\u003eSupplementary Table S6\u003c/b\u003e. Whereas most of the QTL were specific for a single mineral, the 7A Cu and 7A Mg QTLs co-located with each other and with a QTL for grain sulphur concentration (which is listed in \u003cb\u003eSupplementary Tables\u0026nbsp;1\u0026ndash;3\u003c/b\u003e but not discussed here). The number of QTLs for each mineral varied between three (for K and Zn) and five (for Cu) and they were located on 14 of the 21 chromosomes with clustering on chromosomes 5A (4 QTLs), 6A (3 QTLs) and 7A (3 QTLs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Examples of the robust QTL identified are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSome of the QTLs for essential minerals co-located with QTLs for the concentrations/total amounts of the minerals in straw. This is noted in \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e and full details of the QTLs given in \u003cb\u003eSupplementary Tables S1- S3\u003c/b\u003e. This indicates that the trait is associated with more efficient uptake of the mineral by the plant, whereas the absence of co-located QTLs for minerals in straw/biomass indicates that partitioning of the mineral to the developing grain is more effective.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCalcium\u003c/h3\u003e\n\u003cp\u003eThe grain concentration of Ca in the populations varied from 273 to 1532 mg/kg (\u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eIncreasing alleles for grain Ca were identified in all three Watkins lines and in Paragon. Increasing allele were identified on chromosome 5A from both W239 and W292, on chromosomes 5D from W160, and on chromosome 4A from W292. Finally, a Paragon increasing allele was found on chromosome 2B.\u003c/p\u003e \u003cp\u003eThe QTL on 5A had the strongest effect of all of the QTLs mapped in the study, controlling Ca/grain and Ca concentration in a total of 8 sample sets from the two crosses with LOD scores ranging from 6.1 to 12.2 (\u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e). Gene content analysis of five megabases (5 Mb) of DNA either side of the marker for the QTL with the highest LOD score revealed the presence of 127 protein-coding genes (\u003cb\u003elisted in Supplementary Table S7\u003c/b\u003e). Based on the functional annotation, two candidate genes were identified, \u003cem\u003eTraesCS5A02G543300\u003c/em\u003e which encodes a cation transporter/plasma membrane ATPase and \u003cem\u003eTraesCS5A02G542600\u003c/em\u003e which encodes a major Facilitator Superfamily transporter. Loss-of-function mutations in both of these genes can be predicted to result in higher grain calcium contents and we therefore analysed EMS-induced mutations in both genes. Mutations in \u003cem\u003eTraesCS5A02G543300\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8 independent mutants) resulted in greater than 10% increases in grain calcium content in five lines, with four (WCAD1641, WCAD0289, WCAD1253, WCAD1003) showing statistically highly-significant increases (14.1\u0026ndash;18.7% increase, \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.005) and one (WCAD1617) a statistically significant increase (11.7% increase, \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.03) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), whereas mutations in \u003cem\u003eTraesCS5A02G542600\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2 mutants) results in no significant changes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.15 and 0.91) compared to control plants. This suggests that \u003cem\u003eTraesCS5A02G543300\u003c/em\u003e is responsible for the variation in Ca content in the two crosses. Of the four lines with highly significant increases in grain concentration, three showed no significant change in grain weight, demonstrating that the increase in grain calcium is not simply a result of a reduced grain weight.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCopper\u003c/h2\u003e \u003cp\u003eThe grain concentration of Cu in the populations ranged from 3.61 to 7.24 mg/kg (\u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e). Five QTLs were identified, with increasing alleles for grain concentration and Cu/grain from W292 on chromosome 7A and from W239 on chromosome 7B. Three QTLs had increasing alleles from Paragon, on chromosomes 4B (Cu concentration), 5D and 7B (both Cu concentration and Cu/grain), with the increasing allele on 4B having LOD scores ranging from 5.1 to 8.8 in four sample sets of the cross with W239.\u003c/p\u003e \u003cp\u003eTwo strong QTLs with increasing alleles from Paragon were selected to determine gene content: 4B for copper concentration (LOD 8.8 to 5.1 in 4 sample sets) and 5B for copper concentration and copper per grain (LOD 6.7\u0026ndash;3.6), based on 5 Mb of DNA on either side of the marker for the QTL with the highest LOD score. The total numbers of genes identified in the two QTLs were 52 (4B) and 102 (5B), as listed in \u003cb\u003eSupplementary Tables S8 and S9\u003c/b\u003e. Three genes (\u003cem\u003eTraesCS4B02G131400\u003c/em\u003e, \u003cem\u003eTraesCS4B02G131500\u003c/em\u003e, \u003cem\u003eTraesCS4B02G131700\u003c/em\u003e) encoding ZINC-INDUCED FACILITATOR-LIKE 1, and one gene (\u003cem\u003eTraesCS4B02G128600\u003c/em\u003e) encoding a MULTI-DRUG AND TOXIC COMPOUND EXTRUSION (MATE) protein, were found in the 5 Mb region downstream of the 4B QTL. Notably, genomic comparison between Paragon and W239 revealed the presence of rare SNPs in the 3\u0026rsquo;UTR of a gene encoding an ABC transporter C subfamily member (\u003cem\u003eTraesCS5B02G479900\u003c/em\u003e) present in W239. ABC C subfamily members play a key role in detoxification and metal ion transport.\u003c/p\u003e \u003cp\u003eThe identification of QTL on chromosome 7 of all three sub-genomes (7A, 7B, 7D) raises the question of whether the loci are homoeologues. However, a comparison of the gene contents in the region spanning the peak markers showed that this was not the case. In total, 101 and 80 protein-encoding genes were found in the 5 Mb region on either side of the 7B and 7D QTL, respectively (\u003cb\u003eSupplementary Table S10 and S11\u003c/b\u003e). Further analysis revealed that the 7B QTLs are located in a high polymorphic region, while some gene deletions were also apparent. Furthermore, copy number variation was found in the region around the 7D QTL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eIron\u003c/h2\u003e \u003cp\u003eThe grain concentration of Fe in the populations varied by over 2-fold, from about 30 to 64 mg/kg \u003cb\u003e(Table\u0026nbsp;1)\u003c/b\u003e. Four QTLs were detected, with increasing alleles from all three Watkins lines. The strongest increasing allele was for Fe concentration and Fe/grain on chromosome 2D of W239 (LOD 3.4 to 6.3), with other increasing alleles from W239 and W292 on chromosome 3A, from W160 and W292 on chromosome 5D (both for Fe concentration) and from W239 (Fe concentration) and W160 (Fe/grain) on chromosome 6A. However, the 3A, 5D and 6A alleles had LOD scores below 6.\u003c/p\u003e \u003cp\u003eAnalysis of 5 Mb of DNA on either side of the peak marker for the strongest 2D QTL (LOD 6.3) showed the presence of 120 high-confidence protein-coding genes, including four transcription factors (\u003cb\u003eSupplementary Table S12\u003c/b\u003e). Notably, there is high allelic diversity in this region, with multiple SNPs between Paragon and W239, while large deletions were identified in Paragon. This suggests a divergent genetic background in this locus, possibly a result of introgression events. A missense variant and a splice region variant were identified in \u003cem\u003eTraesCS2D02G473900\u003c/em\u003e, encoding a bHLH transcription factor. Interestingly, the orthologue of \u003cem\u003eTraesCS2D02G473900\u003c/em\u003e in rice has been linked to the iron starvation response in roots. (Wairich et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMagnesium\u003c/h2\u003e \u003cp\u003eThe grain concentration of Mg in the populations varied from 834 to 1532 mg/kg \u003cb\u003e(Table\u0026nbsp;1)\u003c/b\u003e. Three QTLs for Mg concentration and Mg/grain were identified with increasing alleles from both W239 and W160 on chromosomes 5A and 6A. In addition, a QTL for Ca concentration and Ca/grain was identified on chromosome 7A with increasing alleles in all three Watkins lines. This was identified in several sample sets with LOD scores for Mg/grain ranging up to 7.8. Finally, a QTL for Mg concentration only was identified in the Paragon x W239 cross with the increasing allele (LOD up to 8.3) from Paragon.\u003c/p\u003e \u003cp\u003eDue to the consistent presence of the 7A Mg QTL across various environments and populations, it was selected for further analysis. The analysis of 5 Mb of DNA surrounding the peak marker for the most robust 7A QTL (with a LOD score of 7.8 in W239) revealed the presence of 128 protein-encoding genes (\u003cb\u003eSupplementary Table S13\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThere was high genetic heterogeneity across the examined genomic region between Paragon and W239 with many functional SNPs identified in multiple genes. Three stop-gained SNPs were found in the examined region, affecting three genes. The first gene, \u003cem\u003eTraesCS7A02G126100\u003c/em\u003e, encodes a serine/threonine receptor kinase. The stop-gained SNP in this gene introduces a premature stop codon, potentially leading to a truncated protein with altered functionality. Serine/threonine receptor kinases are crucial components in signal transduction pathways, and any alteration in their structure can impact cellular responses. Stop-gained SNPs were also found in \u003cem\u003eTraesCS7A02G135200\u003c/em\u003e and \u003cem\u003eTraesCS7A02G135400\u003c/em\u003e, both encoding MYB-related transcription factors. A stop-lost SNP was found in \u003cem\u003eTraesCS7A02G130400\u003c/em\u003e, encoding a putative leucine-rich repeat receptor-like protein kinase. Splice region SNPs were identified in seven genes, including those encoding an ATP-dependent zinc metalloprotease (\u003cem\u003eTraesCS7A02G128400\u003c/em\u003e) and a MATE transporter protein (\u003cem\u003eTraesCS7A02G131500\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePotassium\u003c/h2\u003e \u003cp\u003eThe grain concentration of K in the populations varied from 3541 to 5935 mg/kg \u003cb\u003e(Table\u0026nbsp;1)\u003c/b\u003e. Three QTLs with LOD above 5 were mapped, all from the cross with Watkins 239. Two of the increasing alleles (for grain concentration on 3D and 5A) were from Paragon but the increasing allele for the strongest QTL (with LODs from 8.2 to 5.4 in four sample sets), for K/grain on chromosome 4B, was from W239.\u003c/p\u003e \u003cp\u003eAnalysis of the genomic region of the strongest 4B QTL (LOD 8.2) showed that the QTL is in a gene-sparse region as only 29 protein-coding genes were found in the 10 Mb region surrounding the peak marker (\u003cb\u003eSupplementary Table S14\u003c/b\u003e). Allelic diversity analysis showed the presence of a low number of SNPs, mainly upstream or downstream of the coding region of the genes. However, copy number variation was detected in some genes, indicating structural genomic differences between Paragon and W239 in this locus.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eZinc\u003c/h2\u003e \u003cp\u003eThe grain concentration of Zn in the populations varied from 23.6 to 49.1 mg/kg \u003cb\u003e(Table\u0026nbsp;1)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThree QTLs were identified with increasing alleles from the Watkins lines. A QTL for Zn concentration and Zn/grain was identified on chromosome 7A with increasing alleles from all three Watkins lines, although the LOD scores were low (the highest being 5). Similarly, a QTL for Zn/grain on chromosome 5A had increasing alleles in W292 (LOD 5) and 239 (LOD 3.6). Finally, a QTL for Zn concentration and Zn/grain was included although the increasing alleles in W239 had LOD scores of slightly below 5 (4.9, 4.8, 4.7 and 4.1). The increasing allele for Zn/grain was also mapped in W160 with LOD scores of 3.7 and 3.7.\u003c/p\u003e \u003cp\u003eAnalysis of 5 Mb of DNA surrounding the peak marker of the 6A QTL (LOD 4.9) showed that the QTL is located in a gene-sparse region containing only 40 protein-coding genes (\u003cb\u003eSupplementary Table S15\u003c/b\u003e). Low allelic diversity was found between Paragon and W239, with SNPs mainly located upstream or downstream of the protein-coding regions. Examination of the DNA region extending 5 Mb around the peak marker of the 7A QTL (with a LOD score of 5 in W239) revealed the presence of 91 protein-encoding genes (\u003cb\u003eSupplementary Table S16\u003c/b\u003e). Comparative analysis of the genomic region between Paragon and W239 revealed substantial structural variations, characterized by a notable number of SNPs and gene deletions, which might contribute to functional differences between the two lines. Functional SNPs were identified in TraesCS7A02G435500, encoding a form of calmodulin, which is known to be involved in mineral homeostasis. In addition, a gene encoding a bHLH transcription factor (TraesCS7A02G435800) was absent from Paragon.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelations between minerals and between minerals and other traits.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFull correlation matrices for all of the traits that were measured or calculated are presented in the \u003cb\u003eSupplementary Material S17\u003c/b\u003e while \u003cb\u003eFigure S1\u003c/b\u003e shows correlations between the combined datasets for each population and nitrogen level for mineral concentrations in grain, plant height, straw biomass, above ground biomass, grain yield, harvest index and thousand grain weight (TGW).\u003c/p\u003e \u003cp\u003eNegative correlations between concentrations of some minerals, grain yield and TGW were observed, but these were generally weak (but stronger for Zn in PxW160 and PxW292). This is consistent with the established concept of \u0026ldquo;yield dilution\u0026rdquo;, as higher yields and larger grain are associated with higher contents of starch which dilutes other grain components. Similarly, the contents of grain minerals are often positively correlated with nitrogen (protein) content and this was also observed, with Zn showing stronger correlations than the other essential minerals discussed here (notably in PxW239 at N1). Only weak correlations between Zn and Fe concentrations were observed in PxW239 (irrespective of N level) but stronger correlations in PxW160 and PxW292. Similarly, only weak positive correlations between Ca and Mg were observed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWheat is an important source of mineral micronutrients, including minerals that are frequently deficient in human diets. We have therefore exploited genetic variation in wheat landraces and the availability of extensive genomic databases to map QTLs for five minerals which are frequently deficient in UK diets: potassium, iron, zinc, calcium and magnesium, and for copper, which may be deficient in livestock diets.\u003c/p\u003e \u003cp\u003eThree recombinant inbred populations were each grown in replicated field trials for three years with one or two levels of nitrogen fertilisation, giving a total of 11 datasets. Furthermore, in order to identify QTL that could be deployed in high yielding genotypes the grain mineral contents are not only expressed as concentration (as in most published studies) but also as \u0026micro;g/grain. This is important because high concentrations of minerals identified in old types of wheat or wild relatives may be diluted by higher starch accumulation when the trait is introgressed into modern high-yielding germplasm. Hence, the QTLs identified should be robust and amenable to exploitation by wheat breeders.\u003c/p\u003e \u003cp\u003eQTL analysis of the individual sample sets (11 in total) identified a large number of QTLs and it was therefore decided to only consider QTLs mapped in at least two sample sets and (with one exception) with a LOD score above 5 in at least one set. In fact, alignment of the QTLs onto the IWGSC RefSeq v 1.0 genome assembly (The International Wheat Genome Sequencing Consortium 2018) showed good agreement between the QTLs mapped in the sample sets of each population, and between populations, and LOD scores were often high in several sample sets.\u003c/p\u003e \u003cp\u003eIt is of interest that QTLs with increasing alleles from Paragon were identified for Cu, Ca, Mg and K. These four minerals have not been subjected to selection by breeders and our results indicate that there may be sufficient variation in modern elite genotypes for breeders to exploit, rather than requiring introgression of variation from landraces or wild relatives. By contrast, of eight QTLs for Fe and Zn, only one increasing allele was present in Paragon (for Fe) and seven in the Watkins lines.\u003c/p\u003e \u003cp\u003ePotassium is an essential mineral for humans, particularly as an intracellular electrolyte in the regulation of blood pressure, muscle contraction and nerve transmission. Although dietary K supplies appear to be sufficient at a national level for most countries (Kumssa et al, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), potassium deficiency (hypokalemia) does occur, including in the UK where it is most prevalent in women (Derbyshire, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Roots and tubers are the major source of potassium globally, accounting for up to 80% in some regions, with cereals being the second most important source (Kumssa et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) contributing 15\u0026ndash;20% of total potassium intake in the UK (Bates et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003eb\u003c/span\u003e). Three QTLs for K were mapped, with the most robust increasing allele (LOD 5.6\u0026ndash;8.2 in four sample sets) being from W239.\u003c/p\u003e \u003cp\u003eDeficiencies of iron and zinc have global impacts on human health (as discussed above). Wheat is an important source of both minerals and mineral enhancement of wheat has therefore been widely studied. Despite this global interest and massive investment, including the HarvestPlus programme in CGIAR institutes (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.harvestplus.org\u003c/span\u003e\u003cspan address=\"https://www.harvestplus.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), progress has been limited.\u003c/p\u003e \u003cp\u003eThe concentrations of Fe and Zn in wheat grain vary depending on the availability of the minerals in soil, with Zn tending to vary more than Fe. The ranges of these minerals in our samples (30 to 64 mg/kg Fe and 23.6 to 49.1 mg/kg Zn) were consistent with studies of multiple genotypes grown on several sites, for example, 28.6\u0026ndash;42.5 mg/kg Fe and 20.7\u0026ndash;35.2 mg/kg Zn (Zhao et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), 26.3\u0026ndash;49.9 mg/kg Fe and 21.3\u0026ndash;64.1 mg/kg Zn (Krishnappa et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVelu et al (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) carried out GWAS of 330 wheat lines, identifying 39 marker-trait associations for grain Zn. Two major QTL regions were identified on chromosomes 2 and 7 and candidate genes identified. We did not identify QTLs for high Zn on either of these chromosomes. However, more recently two GWAS using high density SNP marker arrays have reported QTLs for Zn concentration on chromosomes 2D, 3B, and 7D (Krishnappa et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and on chromosomes 2B, 5A, 5B, 6A and 7B (Krishnappa et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe genetic improvement of iron content has not been achieved in commercial cultivars of wheat although a large number of QTLs have been mapped. For example, the GWAS analyses cited above reported QTLs on chromosomes 6D and 7D (Krishnappa et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and 1A, 3B, 5A, 6A and 7B (Krishnappa et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMagnesium deficiency in humans has been associated with a number of adverse health outcomes including cardiovascular disease, hypertension and stroke, metabolic syndrome, type 2 diabetes, Alzheimer\u0026rsquo;s disease and other types of dementia, muscular diseases (muscle pain, chronic fatigue, and fibromyalgia), and types of cancer (reviewed by Barbagallo et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Major QTLs for Mg in wheat have not, to our knowledge, been reported previously but Oury et al (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) reported wide variation in concentration and high effects of genotype, indicating that it should be amenable to genetic improvement. The four QTLs identified here were all mapped in multiple sample sets and included increasing alleles with high LOD scores from Paragon and Watkins lines. They therefore provide a good basis for genetic biofortification.\u003c/p\u003e \u003cp\u003eCalcium deficiency is widespread globally, with up to half of total population being at risk (Shlisky et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Calcium deficiency has a range of adverse health outcomes, including hypertension, high serum cholesterol and increased risk of colorectal cancer, in addition to rickets (paediatric bone disease). Milk and dairy products are the major source of Ca in UK diets (61% of the intake by babies and 35\u0026ndash;45% for other age groups), followed by cereals (37% for children aged 11\u0026ndash;18) (Bates et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e.b). All flours and breads produced in the UK, except wholemeal, are required to be fortified with \u0026asymp;\u0026thinsp;235\u0026ndash;390 mg Ca/100g flour to restore the level in to that in wholemeal (The Bread and Flour Regulations 1998 (legislation.gov.uk)). The increasing adoption of vegan diets is a cause of further concern and intakes from other foods need to be increased.\u003c/p\u003e \u003cp\u003eThe major QTL for Ca identified on 5A corresponds to a previously identified QTL (Alomari et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and analysis of TILLING mutants confirmed the candidate gene as \u003cem\u003eTraesCS5A02G543300\u003c/em\u003e. This gene encodes a cation transporter/plasma membrane ATPase which is one of seven genes at the QTL which were listed by Alomari et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The 5A Ca QTL was the strongest of all of the QTLs that were mapped, with LOD scores above 10 in some sample sets, and controlled both Ca concentration and Ca/grain. In addition, QTLs for Ca were mapped on chromosomes 2B, 4A and 5D. Alomari et al (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) also reported a strong marker/trait association for Ca on chromosome 6A.\u003c/p\u003e \u003cp\u003eAlthough copper is an essential micronutrient for humans it is rarely deficient in human diets. However, deficiency does occur in sheep and cattle, either due to grazing on pastures on low copper soils (without fertilisation) or due to ingestion of foods high in sulphur and molybdenum. Increasing the content of copper in feed grain could therefore be advantageous. We identified five QTLs for grain copper, including one on chromosome 4B with a strong increasing allele from Paragon.\u003c/p\u003e \u003cp\u003eThe contribution of wheat-based foods to the human mineral nutrition is determined by two factors: the concentrations of the minerals in the food and their bioavailability, which are in turn determined by their locations in the grain and chemical forms.\u003c/p\u003e \u003cp\u003eFe and Zn are concentrated in the embryo and aleurone layer of the grain, but their relative distributions between these two tissues differ with Fe being more concentrated in the aleurone layer and Zn in the embryo (particularly in the embryonic axis) (Neal et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wan et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This results in depleted concentrations of both minerals when grain is milled to produce white flour (the aleurone layer and germ forming part of the bran fraction). For example, Eagling et al (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) reported Fe contents of 11.9 mg/kg and 6.7 mg/kg in white flours of two wheat cultivars grown in the UK and 46.7 mg/kg and 30.3 mg/kg in the corresponding wholemeals. Similarly, Khokhar et al (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported a range of 24\u0026ndash;49 mg/kg of Zn in whole grains of the progeny of crosses between a modern cultivar and land races of bread wheat and 8\u0026ndash;15 mg/kg of Zn in white flours of 24 selected genotypes.\u003c/p\u003e \u003cp\u003eFurthermore, most of the Fe and Zn in the aleurone cells and in the scutellum of the embryo is present as phytates in discrete bodies known as phytin globoids. Phytates are complexes with phytic acid (inositol hexakisphosphate) which has a cyclic structure with six phosphate groups which can bind metal ions. Phytates have low solubility and hence the bioavailability of Fe and Zn in whole grain wheat is low, although probably higher for Zn (about 25%) than for Fe (about 10%) (Bouis and Welch \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The higher bioavailability of Zn could result from the presence of zinc which is not bound to phytin in the embryonic axis. The location of Zn in genetically biofortified high Zn lines discussed does not differ from that in conventional lines (Wan et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and consequently the bioavailability may also be limited.\u003c/p\u003e \u003cp\u003eThe concentrations of Ca and Mg are much higher than those of Fe and Zn (Table\u0026nbsp;1). Precise values for Ca and Mg vary between reports but the contents of Mg are generally higher than those of Ca, but with greater proportional losses on milling. For example, Vignola et al (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported mean contents of about 340 mg/kg Ca in 945 mg/kg Mg in whole grains of 11 wheats grown over two years in Argentina, the values for white flour being 145 mg/kg Ca and 276 mg/kg Mg. The fortification of white flour with calcium is mandatory in the UK and UK Flour Millers quote values of 320 mg/kg Ca and 830 mg/kg Mg in wholemeal and 260 mg/Kg Mg and 1340 mg/Kg Ca in white breadmaking flours (Nutritional contribution of flour (ukflourmillers.org) ).\u003c/p\u003e \u003cp\u003eBoth Ca and Mg may be bound to phytate in the aleurone layer and scutellum (Heard et al., 2001) but phytate is not present in the starchy endosperm (the origin of white flour) and hence the minerals should be more bioavailable. It has also been shown that increasing the fibre content of wheat flour, which is another strategic target for improving health outcomes in western countries, can increase calcium absorption in the human colon, and consequently bone density, probably due to the fermentation to short chain fatty acids that reduce the pH in the colon (Wallace et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Consequently, combining biofortification of wheat flour with Ca and fibre (Lovegrove et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) could result in synergistic improvements in health.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePopulations\u003c/h2\u003e \u003cp\u003eThree biparental segregating populations were developed as described in Wingen et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) from crosses between the spring bread wheat cultivar Paragon as the common variety and a single-seed descendent (SSD) from a landrace accession from the A. E. Watkins collection (Wingen et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e); Details of the landrace cultivars are given in \u003cb\u003eSupplementary Table S18\u003c/b\u003e. Each population comprised 94 F4 recombinant inbred lines. The 35K Axiom Wheat Breeder array was used for population ParW292 and the 44k Axiom TaNG array for the other two populations and was performed at the Bristol Genomics Facility using established protocols (Winfield et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eField trials\u003c/h2\u003e \u003cp\u003eField Trials were carried out at Rothamsted Research, Harpenden, UK (latitude 51.80N, longitude 0.40W) between 2012 and 2020. Each population was grown for three years, and at two levels of nitrogen fertilization, N1 and N2 (\u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e). Abbreviated environment names are given in \u003cb\u003eSupplementary Table S4\u003c/b\u003e. The experiments followed a split plot randomised block design with blocks split for the nitrogen treatment, and three replicate blocks. The plot size was 1 x 1m and plots were sown and harvested with small plot drills and combine harvesters. Grain and straw weights per plot were measured on the combine, and sub-samples taken for analysis. Soil mineral nitrogen in the 0-90cm layer was measured each spring.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGrain and straw analyses\u003c/h2\u003e \u003cp\u003eOntologies for measured traits are given in \u003cb\u003eSupplementary Table S5\u003c/b\u003e. Post harvest, samples were analysed for dry matter, minerals, and the thousand grain weight recorded. Straw dry matter was determined by weighing before and after drying overnight at 80\u003csup\u003e0\u003c/sup\u003eC. Grain dry matter was similarly measured, but with drying at 105\u003csup\u003e0\u003c/sup\u003eC. Mineral concentrations were determined by ICP-AES following nitric acid digestion. Thousand grain weight was determined by counting a known number of grains, drying at 105\u003csup\u003e0\u003c/sup\u003eC overnight and recording the weight.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative Genetics and Bioinformatics\u003c/h2\u003e \u003cp\u003eThe R software suite (v4.3.1) was used for quantitative genetic analysis. Genetic maps were constructed using package ASMap (v1.0-4) following the same strategy as described in Min et al (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). QTL mapping was conducted using package \u0026ldquo;qtl\u0026rdquo; (v1.50). Interesting QTL were selected using a custom written script, which identified QTL with a LOD over 5, where at least one further QTL on the same chromosome for the same trait and the same effect direction was present. QTL were aligned along the IWGSC RefSeq vs1.0, represented by the peak marker, the CI border markers and all CI internal markers.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGene Content Analysis and Genomic Comparison.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe gene ID and genomic location information of the genes within the 5 Mb region either side of the QTL with the highest LOD score for selected traits, as detailed in \u003cb\u003eSupplementary Table S6\u003c/b\u003e, were obtained from Ensembl BioMarts (Kinsella et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Functional annotation was retrieved from WheatOmics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://wheatomics.sdau.edu.cn/\u003c/span\u003e\u003cspan address=\"http://wheatomics.sdau.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Ma et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Knetminer was used to explore any association between genes and the traits of interest (Hassani-Pak et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Subsequently, whole genome sequence data were used to identify functional SNPs and copy number variations between Paragon and the Watkins lines (Cheng et al., under review). Variation data can be accessed from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://opendata.earlham.ac.uk/wheat/\u003c/span\u003e\u003cspan address=\"https://opendata.earlham.ac.uk/wheat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCalcium candidate gene proof of function.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWheat lines carrying induced mutations in either of two candidate genes for grain calcium content (GrnCaCnc) \u003cem\u003eTraesCS5A02G542600\u003c/em\u003e and \u003cem\u003eTraesCS5A02G543300\u003c/em\u003e, were identified in the Cadenza TILLING population (Krasileva et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) following the method described in Adamski et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Only mutations that were predicted to lead to a gained stop codon, to a missense variant or to a splice donor variant were selected. Two independent mutations were selected for \u003cem\u003eTraesCS5A02G542600\u003c/em\u003e and eight for \u003cem\u003eTraesCS5A02G543300\u003c/em\u003e. For each mutation, 24 seeds of the TILLING lines were grown under standard glasshouse conditions. Ten wildtype Cadenza plants were grown as control. Plants were genotyped with KASP markers specific for the presence/absence of the mutations and only homozygous mutant plants were taken forward (in total 53 plants, between 4 to 7 individual plants for each tilling mutation, mean 5.3 plants per mutation). From each of these plants, all grains were harvested and grain number per plant (GNplant), grain yield per plant (GYplant) and the seed characteristics GW, GLng and GWid were measured using a Marvin seed analyser. Grain moisture content was measured using DA 7250 Near-infrared spectrometer. GrnCaCnc was measured using X-Supreme8000, a benchtop X-ray fluorescence spectrometer, equipped with XSP-Minerals\u0026rsquo; Package and calibrated with data collected using an ICP-OES using 187 data points with GrnCaCnc levels ranging from 242.4 ppm to 726.7 ppm. No outliers for GrnCaCnc were detected and the average over the three technical reps was calculated. This data set (GrnCaCnc range 380.0-564.2 ppm, mean 461.3 ppm) was used to statistical compare the GrnCaCnc between the Cadenza wildtype and the independent mutants in a linear model (ANOVA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRothamsted Research and the John Innes Centre receive strategic funding from the Biotechnology and Biological Sciences Research Council (BBSRC) and we acknowledge support from the Delivering Sustainable Wheat (BB/X011003/1) Institute Strategic Programme.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdamski NM, Borrill P, Brinton J, Harrington S, Marchal C, Bentley AR, Bovill WD, Cattivelli L, Cockram J, Contreras-Moreira B, Ford B, Ghosh S, Harwood W, Hassani-Pak K, Hayta S, Hickey LT, Kanyuka K, King J, Maccaferrri M, Naamati G, Pozniak CJ, Ramirez-Gonzalez RH, Sansaloni C, Trevaskis B, Wingen LU, Wulff BBH, Uauy C. (2020). A roadmap for gene functional characterisation in crops with large genomes: Lessons from polyploid wheat. eLife 9, e55646. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7554/eLife.55646\u003c/span\u003e\u003cspan address=\"10.7554/eLife.55646\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlomari DZ, Eggert K, von Wiren N, Pillen K, Order MS. (2017). Genome-wide association study of calcium accumulation in grains of European wheat cultivars. Frontiers in Plant Science, 8, 1797. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2017.01797\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2017.01797\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAslam MF, Ellis PR, Berry SE, Latunde-Dada GO, Sharp PA. (2018). Enhancing mineral bioavailability from cereals: Current strategies and future perspectives. Nutrition Bulletin, 43, 184\u0026ndash;188. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nbu.12324\u003c/span\u003e\u003cspan address=\"10.1111/nbu.12324\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalk J, Connorton JM, Wan Y, Lovegrove A, Moore KL, Uauy C, Sharp PA, Shewry PR. (2019). Improving wheat as a source of iron and zinc for global nutrition. Nutrition Bulletin, 44, 53\u0026ndash;59. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nbu.12361\u003c/span\u003e\u003cspan address=\"10.1111/nbu.12361\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarbagallo M, Veronese N, Dominguez LJ. (2021). Magnesium in Aging, Health and Diseases. Nutrients, 13, 463. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu13020\u003c/span\u003e\u003cspan address=\"10.3390/nu13020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBates B, Lennox A, Prentice A, Page P, Nicholson S, Swan G. (2014a). \u003cem\u003eNational Diet and Nutrition Survey: Results from Years 1\u0026ndash;4 (combined) of the Rolling Programme (2008/2009\u0026ndash;2011/2012). Executive Summary\u003c/em\u003e. Public Health England. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/594360/NDNS_Y1_to_4_UK_report_executive_summary_revised_February_2017.pdf\u003c/span\u003e\u003cspan address=\"https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/594360/NDNS_Y1_to_4_UK_report_executive_summary_revised_February_2017.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBates B, Lennox A, Prentice A, Page P, Nicholson S, Swan G. (2014b). \u003cem\u003eNational Diet and Nutrition Survey: Results from Years 1\u0026ndash;4 (combined) of the Rolling Programme (2008/2009\u0026ndash;2011/2012)\u003c/em\u003e. Public Health England. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/216484/dh_128550.pdf\u003c/span\u003e\u003cspan address=\"https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/216484/dh_128550.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBates B, Cox L, Nicholson S. \u003cem\u003eet al\u003c/em\u003e (2016) \u003cem\u003eNational Diet and Nutrition Survey Results from Years 5 and 6 (combined) of the Rolling Programme (2012/2013\u0026ndash;2013/2014)\u003c/em\u003e. Public Health England and the Food Standards Agency.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouis HE, Welch RM. (2010). Biofortification-a sustainable agricultural strategy for reducing micronutrient malnutrition in the global south. Crop Science, 50, S20\u0026ndash;S32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2009.09.0531\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2009.09.0531\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBroadley MR, Alcock J, Alford J, Cartwright P, Foot I, Fairweather-Tait SJ, Hart DJ, Hurst R, Knott P, McGrath SP, Meacham MC, Norman K, Mowat H, Scott P, Stroud JL, Tovey M, Tucker M, White PJ, Young SD, Zhao F-J. (2010). Selenium biofortification of high-yielding winter wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) by liquid or granular Se fertilisation. \u003cem\u003ePlant and Soil\u003c/em\u003e 332, 5\u0026ndash;18 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11104-009-0234-4\u003c/span\u003e\u003cspan address=\"10.1007/s11104-009-0234-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCakmak I, Kutman UB. (2018). Agronomic biofortification of cereals with zinc: a review. European Journal of Soil Science, 69, 172\u0026ndash;180. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ejss.12437\u003c/span\u003e\u003cspan address=\"10.1111/ejss.12437\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng S, Feng C, Wingen LU, Cheng H, Riche AB, Jiang M. \u003cem\u003eet al\u003c/em\u003e. (2023). Harnessing Landrace Diversity Empowers Climate-resilient Wheat Breeding. Submitted to \u003cem\u003eNature\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDerbyshire E. (2018). Micronutrient intakes of British adults across mid-life: a secondary analysis of the UK National Diet and Nutrition Survey. Frontiers in Nutrition, 5, 55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnut.2018.00055\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2018.00055\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEagling T, Neal AL, McGrath SP, Fairweather-Tait S, Shewry PR, Zhao F-J. (2014) Distribution and speciation of iron and zinc in grain of two wheat genotypes. Journal of Agricultural and Food Chemistry, 62, 708\u0026ndash;716. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/jf403331p\u003c/span\u003e\u003cspan address=\"10.1021/jf403331p\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13197-014-1503-7\u003c/span\u003e\u003cspan address=\"10.1007/s13197-014-1503-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGovindan V, Singh RP, Juliana P, Mondal S, Bentley AR. (2022). Mainstreaming grain zinc and iron concentrations in CIMMYT wheat germplasm. Journal of Cereal Science, 105, 103473. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jcs.2022.103473\u003c/span\u003e\u003cspan address=\"10.1016/j.jcs.2022.103473\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHassani-Pak K, Singh A, Brandizi M, Hearnshaw J, Parsons JD, Amberkar S, Phillips AL, Doonan JH, Rawlings C. (2021) KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species. Plant Biotechnology Journal. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/pbi.13583\u003c/span\u003e\u003cspan address=\"10.1111/pbi.13583\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeard PJ, Feeney KA, Allen GC, Shewry PR. (2002). Determination of the elemental composition of mature wheat grain using a modified Secondary Ion Mass Spectrometer (SIMS). The Plant Journal 30, 237\u0026ndash;245. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1365-313X.2001.01276.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1365-313X.2001.01276.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoy EJM, Stein AJ, Young SD, Ander EL, Watts MJ, Broadley MR. (2015). Zinc-enriched fertilisers as a potential public health intervention in Africa. \u003cem\u003ePlant and Soil\u003c/em\u003e 389, 1\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11104-015-2430-8\u003c/span\u003e\u003cspan address=\"10.1007/s11104-015-2430-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKinsella RJ, K\u0026auml;h\u0026auml;ri A, Haider S, Zamora J, Proctor G, Spudich G, Almeida-King J, Staines D, Derwent P, Kerhornou A, Kersey P, Flicek P. (2011). bEnsembl BioMarts: a hub for data retrieval across taxonomic space Database: the journal of biological databases and curation PMID: 21785142 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/database/bar030\u003c/span\u003e\u003cspan address=\"10.1093/database/bar030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhokhar JS, King J, King IP, Young SD, Foulkes MJ, De Silva J, et al. (2020) Novel sources of variation in grain Zinc (Zn) concentration in bread wheat germplasm derived from Watkins landraces. PLoS ONE 15(2): e0229107. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0229107\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0229107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrasileva KV, Vasquez-Gross HA, Howell T, Bailey P, Paraiso F, Clissold L, Simmonds J, Ramirez-Gonzalez RH, Wang X, Borrill P, Fosker C, Ayling S, Phillips AL, Uauy C, Dubcovsky J. (2017). Uncovering hidden variation in polyploid wheat. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, 114, E913-E921 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:doi/10.1073/pnas.1619268114\u003c/span\u003e\u003cspan address=\"https://doi.org:doi/10.1073/pnas.1619268114\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrishnappa G, Rathan ND, Sehgal D, Ahlawat AK, Singh SK, Singh SK, Shukla RB, Jaiswal JP, Solanki IS, Singh GP, Singh AM (2021) Identification of novel genomic regions for biofortification traits Using an SNP marker-enriched linkage map in wheat (Triticum aestivum L.). Frontiers in Nutrition, 8, 669444. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnut.2021.669444\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2021.669444\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrishnappa G, Khan H, Krishna H, Kumar S, Mishra CN, Parkash O, Devate NB, Nepolean T, Rathan ND, Mamrutha HM, Srivastava P, Biradar S, Uday G, Kumar M, Singh G Singh GP. (2022). Genetic dissection of grain iron and zinc, and thousand kernel weight in wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) using genome-wide association study. \u003cem\u003eScientific Reports\u003c/em\u003e, 12, 12444. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-022-15992-z\u003c/span\u003e\u003cspan address=\"10.1038/s41598-022-15992-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumssa DB, Joy EJM, Broadley MR. Global Trends (1961\u0026ndash;2017) in Human Dietary Potassium Supplies. Nutrients. 2021; 13(4):1369. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu13041369\u003c/span\u003e\u003cspan address=\"10.3390/nu13041369\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLovegrove A, Wingen LU, Plummer A, Wood A, Passmore D, Kosik O, Freeman J, Mitchell RAC, Hassall KL, Ulker M, Tremmel-Bede K, Rakszegi M, Bedo Z, Perretant M-R, Charmet G, Pont C, Salse J, Leverington-Waite M, Orford S, Burridge A, Pellny TK, Shewry PR, Griffiths S. (2020). Identification of a major QTL and associated molecular marker for high arabinoxylan fibre in white wheat flour. PLoS ONE 15(2), e0227826. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0227826\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0227826\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa S, Wang M, Wu J, Guo W, Chen Y, Li G, Wang Y, Shi W, Xia G, Fu D, Kang Z, Ni F. WheatOmics: A platform combining multiple omics data to accelerate functional genomics studies in wheat. Mol Plant. 2021;14(12):1965\u0026ndash;1968. doi: 10.1016/j.molp.2021.10.006. Epub 2021 Oct 27. PMID: 34715393.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMin B, Salt L, Wilde P, Kosik O, Hassall K, Przewieslik-Allen A, Burridge AJ, Poole M, Snape J, Wingen L, Haslam R, Griffiths S, Shewry PR. (2020). Genetic variation in wheat grain quality is associated with differences in the galactolipid content of flour and the gas bubble properties of dough liquor. Food Chemistry, 6, 100093. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fochx.2020.100093\u003c/span\u003e\u003cspan address=\"10.1016/j.fochx.2020.100093\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMosleth EF, Lillehammer M, Pellny TK, Wood A, Riche AB, Hussain A, Griffiths S, Hawkesford MJ, Shewry PR. (2020). Genetic variation and heritability of grain protein deviation in European wheat genotypes. Field Crops Research, 255, 107896. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fcr.2020.107896\u003c/span\u003e\u003cspan address=\"10.1016/j.fcr.2020.107896\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeal AL, Geraki K, Borg S, Quinn P, Mosselmans JF, Brinch-Pedersen H, Shewry PR. (2013). Iron and zinc complexation in wild-type and ferritin-expressing wheat grain: implications for mineral transport into developing grain. Journal of Bioinorganic Chemistry, 18, 557\u0026ndash;570. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1007/s00775-013-1000-x\u003c/span\u003e\u003cspan address=\"https://doi:10.1007/s00775-013-1000-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNIH (1989). Diet and Health: Implications for Reducing Chronic Disease Risk. National Research Council (US) Committee on Diet and Health. Washington (DC): National Academies Press (US)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOury F-X, Leenhardt F, Remesy C, Chanliaud E, Duperrier B, Balfourier F, Charmet G. (2006). Genetic variability and stability of grain magnesium, zinc and iron concentrations in bread wheat. European Journal of Agronomy, 25, 177\u0026ndash;185. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eja.2006.04.011\u003c/span\u003e\u003cspan address=\"10.1016/j.eja.2006.04.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePont C, Leroy T, Seidel M, Tondelli A, Duchemin W, Armisen D, Lang D, Bustos-Korts D, Gou\u0026eacute; N, Balfourier F, Moln\u0026aacute;r-L\u0026aacute;ng M, Lage J, Kilian B, \u0026Ouml;zkan H, Waite D, Dyer S, Letellier T, Alaux M., Wheat and Barley Legacy for Breeding Improvement (WHEALBI) consortium, Russell J, Keller B, van Eeuwijk F, Spannagl M, Mayer KFX, Waugh R, Stein N, Cattivelli L, Haberer G, Charmet G, Salse J. (2019) Tracing the ancestry of modern bread wheats. \u003cem\u003eNature Genetics\u003c/em\u003e, 51, 905\u0026ndash;911. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41588-019-0393-z\u003c/span\u003e\u003cspan address=\"10.1038/s41588-019-0393-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePohl HA, Wheeler JS, Murray HE. (2013). Sodium and potassium in health and disease. Metal Ions in Life Sciences, 13, 29\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-94-007-7500-8_2\u003c/span\u003e\u003cspan address=\"10.1007/978-94-007-7500-8_2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShlisky J, Mandlik R, Askari S, Abrams S, Belizan JM, Bourassa MW, Cormick G, Driller-Colangelo A, Gomes F, Khadilkar A, Owino V, Pettifor JM, Rana ZH, Roth DE, Weaver C. (2022). Calcium deficiency worldwide: prevalence of inadequate intakes and associated health outcomes. Annals of the New York Academy of Sciences. 1512, 10\u0026ndash;28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nyas.14758\u003c/span\u003e\u003cspan address=\"10.1111/nyas.14758\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe International Wheat Genome Sequencing Consortium (IWGSC) et al. (2018). Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science, 361, eaar7191. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.aar7191\u003c/span\u003e\u003cspan address=\"10.1126/science.aar7191\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVelu G, Singh RP, Crespo-Herrera L, Juliana P, Dreisigacker S, Valluru R, Stangoulis J, Sohu VS, Mavi GS, Mishra VK, Balasubramaniam A, Chatrath R, Gupta V, Singh GP, Joshi AK. (2018). Genetic dissection of grain zinc concentration in spring wheat for mainstreaming biofortification in CIMMYT wheat breeding. Science. Reports, 8, 13526. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nature.com/articles/s41598-018-31951-z\u003c/span\u003e\u003cspan address=\"https://www.nature.com/articles/s41598-018-31951-z\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVignola MB, Moiraghi M, Salvucci E, Baroni V, Perez GT. (2016). Whole meal and white flour from Argentine wheat genotypes: mineral and arabinoxylan differences. Journal of Cereal Science, 71, 217\u0026ndash;223. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jcs.2016.09.002\u003c/span\u003e\u003cspan address=\"10.1016/j.jcs.2016.09.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWairich A, de Oliveira BHN, Arend EB, Duarte GH, Ponte LR, Sperotto RA, Ricachenevshy FK, Fett JP. (2019). The Combined Strategy for iron uptake is not exclusive to domesticated rice (\u003cem\u003eOryza sativa\u003c/em\u003e). Sci Rep 9, 16144. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-019-52502-0\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-52502-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWallace TC, Marzorati M, Spence L, Weaver CM, Williamson PS. (2017) New frontiers in fibers: innovative and emerging research on the gut microbiome and bone health. Journal of the American College of Nutrition, 36, 218\u0026ndash;222. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/07315724.2016.1257961\u003c/span\u003e\u003cspan address=\"10.1080/07315724.2016.1257961\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWan Y, Stewart T, Amrahli M, Evans J, Sharp P, Govindan V, Hawkesford MJ, Shewry PR. (2022) Localisation of Iron and Zinc in grain of biofortified wheat. Journal of Cereal Science, 105, 103470. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jcs.2022.103470\u003c/span\u003e\u003cspan address=\"10.1016/j.jcs.2022.103470\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO (2013) World health report: research for universal health coverage. World Health Organization, Geneva, Switzerland.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO (2015) The global prevalence of anaemia in 2011. World Health Organization, Geneva, Switzerland.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinfield MO, Allen AM, Wilkinson PA, Burridge AJ, Barker GLA, Coghill J, Waterfall C, Wingen LU, Griffiths S, Edwards KJ (2018) High-density genotyping of the A.E. Watkins Collection of hexaploid landraces identifies a large molecular diversity compared to elite bread wheat. Plant Biotechnology Journal, 16(1), 165\u0026ndash;175. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doiorg/10.1111/pbi.12757\u003c/span\u003e\u003cspan address=\"https://doi10.1111/pbi.12757\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWingen LU, Orford S, Goram R, Leverington-Waite M, Bilham L, Patsiou TS, Ambrose M, Dicks J, Griffiths S. (2014). Establishing the AE Watkins landrace cultivar collection as a resource for systematic gene discovery in bread wheat. Theoretical and Applied Genetics, 127, 1831\u0026ndash;1842. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-014-2344-5\u003c/span\u003e\u003cspan address=\"10.1007/s00122-014-2344-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWingen LU, West C, Leverington-Waite M, Collier S, Orford S, Goram R, Yang C-Y, King J, Allen AM, Burridge A, Edwards K, Griffiths S. (2017) Wheat Landrace Genome Diversity, Genetics, 205(4), 1657\u0026ndash;1676. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1534/genetics.116.194688\u003c/span\u003e\u003cspan address=\"10.1534/genetics.116.194688\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWysocka D, Snarska A, Sobiech P. (2019). Copper - an essential micronutrient for calves and adult cattle. Journal of Elementology, 24, 101\u0026ndash;110. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5601/jelem.2018.23.2.1645\u003c/span\u003e\u003cspan address=\"10.5601/jelem.2018.23.2.1645\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao FJ, Su YH, Dunham SJ, Rakszegi M, Bedo Z, McGrath SP, Shewry PR. (2009). Variation in mineral micronutrient concentrations in grain of wheat lines of diverse origin. Journal of Cereal Science, 49, 290\u0026ndash;295. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jcs.2008.11.007\u003c/span\u003e\u003cspan address=\"10.1016/j.jcs.2008.11.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Wheat grain, mineral micronutrients, nutritional quality, genetic mapping, candidate genes","lastPublishedDoi":"10.21203/rs.3.rs-3714819/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3714819/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWheat is an important source of mineral micronutrients for humans and livestock. We therefore grew three biparental populations developed from crosses between the spring cultivar Paragon and landraces originating from about 100 years ago under multiple environments and analysed the grain for minerals including six minerals which are often deficient in diets for humans (calcium, iron, magnesium, potassium, zinc) and livestock (copper). A total of 774 QTLs for minerals in grain, straw and calculated biomass were identified which were reduced to 23 strong robust QTLs for essential nutrients in grain by selecting for QTLs that were mapped in at least two sample sets with LOD scores above 5 in at least one set. The increasing alleles for sixteen of the QTLs were present in the Watkins lines and seven in Paragon. The number of QTLs for each mineral varied between three (for K and Zn) and five (for Cu) and they were located on 14 of the 21 chromosomes with clustering on chromosomes 5A (4 QTLs), 6A (3 QTLs) and 7A (3 QTLs). \u0026nbsp;Several strong QTL were selected to determine the gene content within a distance of five megabases of DNA either side of the marker for the QTL with the highest LOD score. In addition, induced mutagenesis was used to identify the gene responsible for the strongest QTL (for Ca on chromosome 5AL) as the ATPase transporter gene \u003cem\u003eTraesCS5A02G543300\u003c/em\u003e. The identification of these QTLs with associated SNP markers and candidate genes will facilitate the improvement of grain nutritional quality.\u003c/p\u003e","manuscriptTitle":"Improving wheat grain composition for human health: an atlas of QTLs for essential minerals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-22 21:17:48","doi":"10.21203/rs.3.rs-3714819/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-biology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsbio","sideBox":"Learn more about [Communications Biology](http://www.nature.com/commsbio/)","snPcode":"","submissionUrl":"","title":"Communications Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5e564744-862b-4ed3-a2dd-322d0bf8b28a","owner":[],"postedDate":"February 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":27984106,"name":"Biological sciences/Plant sciences/Natural variation in plants"},{"id":27984107,"name":"Biological sciences/Plant sciences/Plant genetics"}],"tags":[],"updatedAt":"2024-08-16T07:09:08+00:00","versionOfRecord":{"articleIdentity":"rs-3714819","link":"https://doi.org/10.1038/s42003-024-06692-7","journal":{"identity":"communications-biology","isVorOnly":false,"title":"Communications Biology"},"publishedOn":"2024-08-15 04:00:00","publishedOnDateReadable":"August 15th, 2024"},"versionCreatedAt":"2024-02-22 21:17:48","video":"","vorDoi":"10.1038/s42003-024-06692-7","vorDoiUrl":"https://doi.org/10.1038/s42003-024-06692-7","workflowStages":[]},"version":"v1","identity":"rs-3714819","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3714819","identity":"rs-3714819","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}