Skip to main content
NCBI home page
Food Science & Nutrition logoLink to Food Science & Nutrition
. 2025 Apr 11;13(4):e70170. doi: 10.1002/fsn3.70170

Genome‐Wide Association Study of Grain Manganese Content in Bread Wheat (Triticum aestivum L.) Under Four Environments

Wanting Li 1, Qi Zhang 1, Jiahao Wang 1, Yanyan Gao 1, Hui Zhang 1, Lina Jiang 1, Li Wang 1,✉, Dejun Han 2,✉, Jianhui Ma 1,✉
PMCID: PMC11986263  PMID: 40223815

ABSTRACT

Microelements play important roles to maintain the normal metabolism of the human body, and deficiency of microelements always leads to a serious diseases. Manganese (Mn) is an essential microelement for the human body. However, the mechanism for regulating grain Manganese (Mn) content (GMnC) in wheat is rarely studied. In this study, we determined the GMnC of 477 wheat accessions in four environments. The changes in GMnC in different released years and backgrounds were analyzed, which revealed that the trait of GMnC in wheat was not indirectly selected in the selective breeding process. The 660 K single nucleotide polymorphism microarray about these 477 wheat accessions was further used for a genome‐wide association study using the GMnC phenotype. Fifteen quantitative trait loci (QTLs) were identified on chromosomes 2A, 2B, 3B, 5A, 5B, 5D, 6A, 6D, 7A, and 7B for wheat GMnC, which were detected in more than two environments. Four important QTLs and some candidate genes were screened. This study will contribute to further understanding the mechanism in regulating GMnC in wheat.

Keywords: genome‐wide association study, microelement, wheat

The GMnC of 477 wheat accessions under four environments was determined using ICP‐7000. The 477 wheat accessions were sequenced using a 660 K SNP microarray. GWAS was performed using the sequenced data and phenotypic data to identify the QTLs and candidate genes for GMnC in wheat.

graphic file with name FSN3-13-e70170-g003.jpg

1. Introduction

Nutrient elements mainly refer to minerals that play substantive roles in the human body to maintain normal metabolism (Pujar et al. 2020). The essential elements for the human organism are categorized into macroelements and microelements. Among these, microelements participate in various biochemical reaction procedures in the human body. Deficiency of microelements always leads to a serious diseases, such as Anemia, slow growth, and impaired immune function (Yang et al. 2023). Nearly all the microelements are ingested into the human body through food. Therefore, it is of great significance to analyze and understand the molecular mechanism for regulating grain microelements content in important crops.

Bread wheat ( Triticum aestivum L.), one of the major food crops cultivated worldwide, provides essential nutrients such as proteins, minerals, and vitamins to more than 35% of the global population (Xu et al. 2023). Many researchers have focused on the content of microelements in wheat grain to improve wheat quality. Manganese (Mn) is an essential microelement for the human body. Mn deficiency can lead to symptoms such as fatigue, joint pain, decreased bone density, skin issues, and impaired immune system function (Rondanelli et al. 2021), whereas excessive Mn intake can induce neurotoxicity in matrix tissues (Gorell et al. 1997). The maximum daily intake of Mn should not exceed 11 mg per day (Mitchell et al. 2021). Therefore, the purpose of regulating grain Mn content (GMnC) is different from the improvement of iron or zinc content in wheat grain (Ma et al. 2023; Ma et al. 2024). The GMnC in wheat should be controlled within a certain range. Therefore, investigating the variation and genetic basis of GMnC in wheat is critically important.

Previous studies had been carried out to study the mechanism for transporting Mn into grains in different plants. However, only a gene regulating GMnC, TaCNR5, was functionally identified in wheat (Qiao et al. 2019). However, GMnC in wheat is a typical quantitative trait, and more genes deserve to be investigated. Genome‐wide association analysis (GWAS) is an important approach to screen key loci for quantitative traits, which would lay a foundation for further studies. A series of studies have been carried out to analyze key loci for microelements in wheat grains, such as Fe, Zn, Cu, Ca, and others, and many candidate genes for these traits were screened. For example, Velu et al. obtained 39 SNP markers for grain Zn content in wheat using GWAS, and some candidate genes, including zinc finger motifs of transcription factors and metal ion binding genes, were screened (Velu et al. 2018). In order to analyze the genetic basis of Ca accumulation in wheat grains, Shi et al. conducted GWAS on grain Cd content in wheat and found that the hot spot of genetic locus existed at the end of chromosome 4A. The candidate gene TraesCS4A02G428900 was highly expressed in wheat grains, which may be involved in Ca accumulation (Shi et al. 2022). Liu et al. identified 23 loci for grain Cu content on 15 chromosomes and 16 loci affecting grain Zn content on 11 chromosomes using 246 wheat accessions (Liu, Huang, et al. 2021; Liu, Chen, et al. 2021). Cu et al. used GWAS to analyze the QTLs for the contents of Zn, Fe, Cu, and P in wheat grains (Cu et al. 2020). From these researches, we found that many studies had been performed for the important microelements, such as Zn, Fe, Cu, and Ca. However, only six studies have been carried out for GMnC using GWAS in wheat (Morgounov et al. 2022; Wang et al. 2021; Cu et al. 2020; Hao et al. 2024; El‐Soda and Aljabri 2022; Bhatta et al. 2018). Compared with other microelements, the related research is obviously insufficient, and more work needs to be performed.

In this study, the GMnC of 477 wheat accessions planted in four environments was determined, and the change rule of GMnC was analyzed. The 660 K SNP chip was further applied to conduct GWAS, so as to find the QTLs for GMnC. It would provide important information for further understanding the genetic basis of GMnC in wheat.

2. Materials and Methods

2.1. Plant Materials and Cultivation

In this study, 477 wheat accessions were used to construct a wheat panel based on the wheat core germplasm resources, including 55 landrace, 69 exotic cultivars, 32 unknown, and 321 domestic cultivars. These wheat accessions were planted in four locations: Luoyang, Suqian, Nanyang, and Yangling; the field was managed according to local management practices as our previous description (Ma et al. 2023, 2024).

2.2. Determination of GMnC

At the maturity stage, three spikes were sampled as a biological replicate, and three biological replicates were obtained for each wheat accession across four environments, resulting in a total of 5724 samples. Each sample was washed with distilled water and ultrapure water, dried in an oven at 80°C, and ground into powder using a stainless steel grinder (MM400, Retsch, Haan, Germany). About 0.2 g of dried wheat powder was digested in a nitric acid and hydrogen peroxide mixture using a microwave digestion system (CEM MARS6 CLASSIC), then the finally digested water was fixed to 50 mL using ultrapure water. The Mn standard reserve solution from the National Standard Material Research Centre was obtained, and a standard curve was prepared for each round of determinations. The GMnC in each sample was determined using an inductively coupled plasma system (ICP‐7000, Thermo Scientifc).

2.3. Phenotyping, Genotyping, Population Structure, and Linkage Disequilibrium (LD) Analysis

The GMnC of wheat in four environments was used as the phenotypic data for the calculation of heritability (H 2) as our previously described (Ma et al. 2023). The 477 wheat accessions were genotyped using the Affymetrix wheat 660 K SNP array. The polyploid Affymetrix Genotyping Console (GTC) software was used to analyze the specific information of SNPs, including genotype identification, allele clustering, and SNP marking with Hardy–Weinberg value > 0.01. The probe was mapped to the IWGSC RefSeq v1.0 wheat reference genome using BLASTn (Basic Local Alignment Search Tool) 2.2.29+.

Model‐based population structure analysis was used to get the subgroups. Using STRUCTURE v2.3.4 software, population structure was assessed using unlinked markers (r 2 = 0) (Pritchard et al. 2000). The value of K is set from 2 to 20, indicating the number of subgroups. Five independent runs are performed for each K value, with an aging length of 20,000, and the number of iterations set to 10,000. The most likely subgroup number was determined using the ΔK method, based on the rate of change between K values (Earl and VonHoldt 2012). The average attenuation of genome A, B, and D was analyzed by PLINK software.

2.4. GWAS For Wheat GMnC

Univariate linear mixed model in GEMMA software was used to conduct GWAS. The recommended threshold for the p value (p = 1/Ne) was calculated using Bonferroni correction, where Ne represents the effective number of independent SNPs calculated using genetic Type I error calculator software (Li et al. 2012). –log10(p value) ≥ 3.5 of FarmCPU model was used to determine the significance of the SNPs. The SNP markers of GWAS were visualized using a Manhattan map.

3. Results

3.1. Phenotypic Variations for GMnC in Wheat

The GMnC of 477 wheat accessions in four field environments was determined using ICP‐7000 (Table S1). The results exhibited that the highest mean value of GMnC was found in Nanyang (57.82 mg/kg) ranging from 24.29 to 95.88 mg/kg, followed by Yangling (44.50 mg/kg) and Luoyang (41.43 mg/kg). The lowest mean GMnC was observed in Suqian, with an average value of 30.10 mg/kg and a range of 15.41–82.80 mg/kg (Table 1). The overall variation coefficient ranged from 24.53% to 32.50%, and the standard deviation ranged from 7.50 to 14.46 mg/kg in the four environments (Table 1). Normal fitting revealed that the GMnC of 477 wheat accessions was normally distributed in different environments, which indicated a typical quantitative trait (Figure 1). The H 2 of GMnC was calculated to be 0.55, indicating that this trait was regulated by both genetic and environmental factors.

TABLE 1.

The GMnC in four environments.

Environment Min Max Mean Variation coefficient (%) Standard deviation Skewness Kurtosis
Yangling 15.73 112.25 44.50 32.50 14.46 2.31 8.20
Nanyang 24.29 95.88 57.82 24.53 14.18 0.06 1.21
Suqian 15.41 82.80 30.10 24.91 7.50 2.96 18.54
Luoyang 25.12 72.28 41.43 29.25 12.12 0.86 1.97
Open in a new tab

FIGURE 1.

FIGURE 1

Open in a new tab

Wheat GMnC density in Luoyang (A), Suqian (B), Yangling (C), and Nanyang (D). The density cruve was represent by red curves, and the black short lines represent rug plot that represent the distribution of wheat GMnC.

3.2. The Changes in Wheat GMnC in Different Released Years

There were 477 wheat accessions in this study, and the released years of 306 wheat accessions of domestic wheat accessions were confirmed. To analyze the changes of GMnC with released years, a regression analysis was conducted in four environments (Figure 2). We found that there was no significant relationship between the GMnC and released years in Luoyang, Nanyang, and Yangling (Figure 2A–C). Only a poor relationship was found in Suqian (Figure 2D). These results indicated that the GMnC in wheat may not have been indirectly selected during the selection breeding process in the past decades.

FIGURE 2.

FIGURE 2

Open in a new tab

The relationship between wheat GMnC and released years in Luoyang (A), Nanyang (B), Yangling (C), and Suqian (D).

3.3. The Genetic Diversity of SNPs in Three Subgenomes

The 660 K SNP array was used for conducting a GWAS using the phenotypes of GMnC of 477 wheat accessions. After the low‐quality SNPs (MAF < 0.05 and missing data > 0.1) were removed, 407,323 high‐quality SNPs unevenly distributed across 21 chromosomes were screened for further analysis. The number of effective SNP markers in the B subgenome was 39,631, which is the highest among the three subgenomes, followed by the A subgenome (36213) and the D subgenome (21386). The chromosome density of SNP markers showed that the number of SNP markers was the highest in 3B (8005.78) and the lowest in 4D (1715.98). The density distribution of SNP markers was not even, ranging from 5.83 markers per Mb for 4D chromosome to 66.53 markers per Mb for 3B chromosome. The marker densities of A, B, and D subgenomes were 33.01, 32.46, and 14.87, respectively. In addition, the polymorphism information content (PIC) values of 21 wheat chromosomes varied between 0.26 and 0.31 (Table 2).

TABLE 2.

The SNP information for GWAS of 477 wheat accessions.

Chr No. of markers Effective number Effective ratio Suggestive p value Markers (%) Length (Mb) Marker density He PIC
1A 28,930 5707.35 0.20 1.75E‐4 7.10 594.02 48.70 0.66 0.27
1B 20,683 4832.67 0.23 2.07E‐4 5.08 780.76 26.49 0.78 0.31
1D 10,636 3128.43 0.29 3.20E‐04 2.61 750.73 14.17 0.66 0.27
2A 28,906 6217.23 0.22 1.61E‐04 7.10 744.54 38.82 0.76 0.30
2B 29,000 6841.12 0.24 1.46E‐04 7.12 709.76 40.86 0.67 0.27
2D 10,557 3819.01 0.36 2.62E‐04 2.59 617.97 17.08 0.73 0.29
3A 19,478 4679.47 0.24 2.14E‐04 4.78 736.69 26.44 0.68 0.28
3B 45,860 8005.78 0.17 1.25E‐04 11.26 689.38 66.52 0.67 0.27
3D 7285 2705.20 0.37 3.70E‐04 1.79 801.25 9.09 0.70 0.28
4A 17,996 4332.16 0.24 2.31E‐04 4.42 830.70 21.66 0.71 0.28
4B 13,166 3072.23 0.23 3.25E‐04 3.23 673.47 19.55 0.72 0.29
4D 4157 1715.98 0.41 5.83E‐04 1.02 713.02 5.83 0.65 0.26
5A 22,698 5032.42 0.22 1.99E‐04 5.57 720.95 31.48 0.73 0.29
5B 34,064 6654.36 0.20 1.50E‐04 8.36 750.61 45.38 0.66 0.27
5D 8478 3306.79 0.39 3.02E‐04 2.08 495.44 17.11 0.68 0.27
6A 16,561 3880.80 0.23 2.58E‐04 4.07 651.81 25.41 0.69 0.28
6B 25,747 5647.72 0.22 1.77E‐04 6.32 615.48 41.83 0.78 0.31
6D 7507 2970.50 0.40 3.37E‐04 1.84 509.85 14.72 0.72 0.29
7A 28,342 6363.60 0.22 1.57E‐04 6.96 566.04 50.07 0.69 0.28
7B 17,140 4577.28 0.27 2.18E‐04 4.21 473.56 36.19 0.79 0.31
7D 10,132 3740.95 0.37 2.67E‐04 2.49 638.65 15.86 0.65 0.26
A genome 162,911 36213.03 40.00 4934.47 33.01 0.70 0.28
B genome 185,660 39631.16 45.58 5719.38 32.46 0.72 0.29
D genome 58,752 21386.86 14.42 3950.83 14.87 0.68 0.27
Total 407,323 97231.05 14064.68 28.96 0.70 0.28
Open in a new tab

3.4. The GMnC Analysis From Different Backgrounds

We analyzed the changes of wheat GMnC in different backgrounds. Based on the resources, the wheat GMnC in exotic cultivars, domestic landrace, and domestic cultivars was 43.71 ± 7.09 mg/kg, 42.96 ± 4.94 mg/kg, and 43.21 ± 4.88 mg/kg, respectively (Figure 3A), which showed no significant difference. The population structure analysis based on the Bayesian clustering method ΔK was further used, which resulted in eight subgroups, Subgroup 1–Subgroup 8 (Table S1). The wheat GMnC in eight subgroups was analyzed. The highest wheat GMnC was found in Subgroup 5, which mainly contained the wheat accessions released after 2000 in Hebei, Henan, Shanxi, and Shandong provinces, and the lowest GMnC was found in Subgroup 6, which was mainly composed of the wheat accessions in Sichuan, Jiangsu, and Hubei provinces. However, the wheat GMnC among the eight subgroups showed no significant difference (Figure 3B). These results proved the speculation by analyzing the wheat GMnC with released years, indicating that the wheat GMnC was not indirectly selected during the selection breeding process.

FIGURE 3.

FIGURE 3

Open in a new tab

The average GMnC of four environments in different resource (A) and backgrounds (B). The pellets represent the specific GMnC value of the wheat accession belonging to the groups.

3.5. GWAS For GMnC in Wheat

In this study, the GMnC of 477 wheat accessions under four environments was used as the phenotypic data for GWAS using the FarmCPU model, and scatter plots were made using log (p value), with ≥ 3.5 as the significant SNPs (Figure 4A,C,E,G), and the Q–Q plots were also provided in Figure 4B,D,F,H. The QTLs with more than two environments were screened, and 15 QTLs were identified on chromosomes 2A, 2B, 3B, 5A, 5B, 5D, 6A, 6D, 7A, and 7B for wheat GMnC, which were named GMnC01 to GMnC15 (Table 3). Among them, 11 QTLs were identified in two environments, two QTLs were identified in three environments distributing on the chromosomes 2B and 3B, and two QTLs were identified in four environments distributing on the chromosomes 2B and 6A, which are highly likely to be the key loci for subsequent attention. To fully analyze the QTLs for wheat GMnC, we collected the previous research and got 126 SNP markers (MATs, Table S2). We integrated the 15 QTLs with 126 MATs and found that two QTLs, GMnC04 and GMnC05, overlapped with previous studies (Cu et al. 2020; Wang et al. 2021). Therefore, we finally got four important QTLs for GMnC, including two QTLs, GMnC02 and GMnC10, that were found in four environments and two QTLs, GMnC04 and GMnC05, overlapping with previous studies.

FIGURE 4.

FIGURE 4

Open in a new tab

The Manhattan plots (A, C, E, G) and Q–Q plots (B, D, F, H) in Luoyang, Suqian, Yangling, and Nanyang. The significant SNPs with −log10 (p value) of > 3.5 are shown above the red lines. The four important QTLs were labeled in Manhattan plots.

TABLE 3.

The QTLs for GMnC in Luoyang (LY), Suqian (SQ), Yangling (YL), and Nanyang (NY).

ID Chromosome Marker Favorable allele and its ratio −Log10(p) Environment
GMnC01 2A AX‐110407685 T/C(0.89) 3.78 SQ/YL
GMnC02 2B AX‐109465969 C/A(0.91) 3.88 LY/SQ/YL/NY
GMnC03 2B AX‐109904766 A/T(0.92) 5.33 LY/YL/NY
GMnC04 3B AX‐94599391 G/A(0.75) 4.10 SQ/YL/NY
GMnC05 5A AX‐108906017 A/G(0.82) 3.96 SQ/NY
GMnC06 5A AX‐108806718 C/A(0.85) 4.56 SQ/YL
GMnC07 5B AX‐109537496 C/A(0.91) 3.97 YL/NY
GMnC08 5B AX‐108730255 G/A(0.81) 4.87 SQ/YL
GMnC09 5D AX‐109586097 C/T(0.93) 3.73 SQ/NY
GMnC10 6A AX‐109815890 G/C(0.92) 4.24 LY/SQ/YL/NY
GMnC11 6A AX‐110703144 G/T(0.93) 4.37 SQ/YL
GMnC12 6D AX‐109579147 C/T(0.92) 3.87 SQ/YL
GMnC13 7A AX‐111054494 T/C(0.76) 4.28 YL/NY
GMnC14 7A AX‐109351037 A/G(0.80) 3.92 SQ/NY
GMnC15 7B AX‐111542108 C/A(0.55) 3.66 YL/NY
Open in a new tab

Many genes had been functionally analyzed to participate in transporting metal iron in plants, such as HMA, PCS, and bHLH (Heiss et al. 2003; Meng et al. 2024; Wong and Cobbett 2009). In this study, there are 2032 genes in the 15 QTLs, and the annotation of these genes was provided in Table S3. Here, we found 2 PCSs, 1 HMA, and 12 bHLH among these 2032 genes. These may be the candidate genes for wheat GMnC for further research. The expression pattern of these candidate genes in different tissues was obtained from Table S4 using WheatOmics 1.0 (Ma et al. 2021). We found that the HMA showed high expression in the grain tissue, which may be an important candidate gene.

4. Discussion

Over the past few decades, global wheat production has risen from approximately 500 million tons to around 700 million tons, largely satisfying the demand for wheat consumption. Currently, the demand to improve wheat quality is quickly increasing with the improvement of living standards. Grain microelement content is an important indicator for wheat quality, and many researches were performed to improve this trait (Manickavelu et al. 2017). However, previous studies have predominantly concentrated on the improvement of Zn and Fe contents in wheat grains through genetic modification and cultivation measures (Ma et al. 2023, 2024), ignored other essential micronutrients. Mn is also a vital micronutrient for human health. More importantly, insufficient and excessive intake of Mn could cause several diseases (Avila et al. 2013). Therefore, understanding the GMnC in wheat is equally important.

In this study, we determined the GMnC of 477 wheat accessions under four environmental conditions. The mean wheat GMnC in four environments ranged from 30.11 to 67.89 mg/kg. Previous studies had analyzed the wheat GMnC in different environments. Cu et al. determined the wheat GMnC of 330 wheat lines cultured in Mexico during two successive crop seasons, and the wheat GMnC ranged from 38.7 to 74.1 mg/kg (Cu et al. 2020). Hao et al. collected 768 wheat accessions, which were cultured in two environments in China, to analyze the GMnC, and found that the GMnC ranged from 10.07 to 32.37 mg/kg (Hao et al. 2024). Liu et al. determined the GMnC of 161 lines from a cross between CN16 and D1 during two growing seasons, which were cultured in Chongzhou and Wenjiang of China, and found that the GMnC ranged from 16.28 to 53.85 mg/kg (Liu, Huang, et al. 2021). Bhatta et al. determined 123 synthetic hexaploid wheat cultured in Turkey during two growing seasons and found that the GMnC ranged from 20.3 to 69.8 mg/kg (Bhatta et al. 2018). Our result is in accordance with these researches. It was reported that the maximum recommended daily intake of Mn is 11 mg (Mitchell et al. 2021). With the increasing consumption of meat, eggs, and milk, the proportion of wheat flour in diets has decreased. According to the 8:1 ratio of the Mn content in bran to wheat flour, the maximum wheat flour consumption of the daily diet should not exceed 1 kg. Therefore, we recommend that the highest GMnC in wheat grain may be 99 mg/kg. In our results, we found that the GMnC for most wheat accessions in four environments was below 100 mg/kg, particularly those wheat accessions released after 2000. These results indicated that these wheat accessions for wheat production in China are reliable for the Mn nutrient element.

GWAS was widely used to identify the QTLs for grain microelement content in wheat, and many candidate genes were also identified (Cu et al. 2020; Hao et al. 2024; Liu, Huang, et al. 2021). In this study, we identified 15 QTLs for GMnC on 10 chromosomes (Table 3). Chromosome 6A was first identified to contain the QTLs for GMnC. Meanwhile, there are two QTLs that were identified in four environments, and two QTLs, GMnC04 and GMnC05, were found to overlap with previous research in chromosomes 3B and 5A by integrated analysis (Cu et al. 2020; Wang et al. 2021). Therefore, these QTLs should receive more attention in further studies. Importantly, we also identified 15 candidate genes in these QTLs according to the function annotation, which should receive more attention in further study.

In conclusion, we analyzed the changes in wheat GMnC with released years and different backgrounds and inferred that the trait of GMnC was not indirectly selected in the wheat breeding process. The GMnC in wheat was safe for people's consumption, relying on wheat as their staple food. Further GWAS and integrated analysis revealed four key QTLs for wheat GMnC, and some candidate genes were selected, which is of great significance for further analysis to understand the mechanism for transporting Mn to wheat grain.

Author Contributions

Wanting Li: data curation (equal), investigation (equal), writing – original draft (equal). Qi Zhang: data curation (equal), investigation (equal), visualization (equal), writing – original draft (equal). Jiahao Wang: data curation (equal), investigation (equal), writing – original draft (equal). Yanyan Gao: data curation (equal), investigation (equal), writing – original draft (equal). Hui Zhang: data curation (equal), investigation (equal). Lina Jiang: data curation (equal), investigation (equal). Li Wang: conceptualization (equal), data curation (equal), investigation (equal), writing – original draft (equal). Dejun Han: conceptualization (equal), investigation (equal), writing – original draft (equal), writing – review and editing (equal). Jianhui Ma: conceptualization (equal), funding acquisition (equal), investigation (equal), writing – original draft (equal), writing – review and editing (equal).

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Table S1. Detailed information and GMnC of 477 wheat accessions.

FSN3-13-e70170-s001.xlsx (272.6KB, xlsx)

Table S2. The previous identified SNP makers for wheat GMnC.

FSN3-13-e70170-s004.xlsx (15KB, xlsx)

Table S3. The annotation of 2032 genes in 15 QTLs.

FSN3-13-e70170-s003.xlsx (180KB, xlsx)

Table S4. The expression pattern of 15 candidate genes in different tissues using WheatOmics 1.0.

FSN3-13-e70170-s002.xls (27.8KB, xls)

Acknowledgments

This research was funded by the National Key Research and Development Program of China (2023YFD2301501).

Funding: This work was supported by the National Key Research and Development Program of China (Grant 2023YFD2301501).

Contributor Information

Li Wang, Email: wangli1015@htu.cn.

Dejun Han, Email: handj@nwsuaf.edu.cn.

Jianhui Ma, Email: cricaas@163.com.

Data Availability Statement

The authors confirm that data generated or analyzed supporting the findings of this study are available within the article, and any further supporting material will be provided on request from the corresponding author.

References

  1. Avila, D. S. , Puntel R. L., Aschner M., et al. 2013. “Manganese in Health and Disease.” Metal Ions in Life Sciences 13: 199–227. 10.1007/978-94-007-7500-87. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bhatta, M. , Baenziger P. S., Waters B. M., et al. 2018. “Genome‐Wide Association Study Reveals Novel Genomic Regions Associated With 10 Grain Minerals in Synthetic Hexaploid Wheat.” International Journal of Molecular Sciences 19, no. 10: 3237. 10.3390/ijms19103237. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Cu, S. T. , Guild G., Nicolson A., Velu G., Singh R., and Stangoulis J.. 2020. “Genetic Dissection of Zinc, Iron, Copper, Manganese and Phosphorus in Wheat ( Triticum aestivum L.) Grain and Rachis at Two Developmental Stages.” Plant Science 291: 110338. 10.1016/j.plantsci.2019.110338. [DOI] [PubMed] [Google Scholar]
  4. Earl, D. A. , and VonHoldt B. M.. 2012. “STRUCTURE HARVESTER: A Website and Program for Visualizing STRUCTURE Output and Implementing the Evanno Method.” Conservation Genetics Resources 4: 359–361. 10.1007/s12686-011-9548-7. [DOI] [Google Scholar]
  5. El‐Soda, M. , and Aljabri M.. 2022. “Genome‐Wide Association Mapping of Grain Metal Accumulation in Wheat.” Genes 13, no. 6: 1052. 10.3390/genes13061052. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Gorell, J. M. , Johnson C. C., Rybicki B. A., et al. 1997. “Occupational Exposures to Metals as Risk Factors for Parkinson's Disease.” Neurology 48, no. 3: 650–658. 10.1212/WNL.48.3.650. [DOI] [PubMed] [Google Scholar]
  7. Hao, Y. , Kong F., Wang L., et al. 2024. “Genome‐Wide Association Study of Grain Micronutrient Concentrations in Bread Wheat.” Journal of Integrative Agriculture 23, no. 5: 1468–1480. 10.1016/j.jia.2023.06.030. [DOI] [Google Scholar]
  8. Heiss, S. , Wachter A., Bogs J., Cobbett C., and Rausch T.. 2003. “Phytochelatin Synthase (PCS) Protein Is Induced in Brassica juncea Leaves After Prolonged cd Exposure.” Journal of Experimental Botany 54, no. 389: 1833–1839. 10.1093/jxb/erg205. [DOI] [PubMed] [Google Scholar]
  9. Li, M. X. , Yeung J. M., Cherny S. S., Yeung J. M. Y., and Sham P. C.. 2012. “Evaluating the Effective Numbers of Independent Tests and Significant p‐Value Thresholds in Commercial Genotyping Arrays and Public Imputation Reference Datasets.” Human Genetics 131, no. 5: 747–756. 10.1007/s00439-011-1118-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Liu, J. , Huang L., Li T., et al. 2021. “Genome‐Wide Association Study for Grain Micronutrient Concentrations in Wheat Advanced Lines Derived From Wild Emmer.” Frontiers in Plant Science 12: 651283. 10.3389/fpls.2021.651283. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Liu, Y. , Chen Y., Yang Y., et al. 2021. “A Thorough Screening Based on QTLs Controlling Zinc and Copper Accumulation in the Grain of Different Wheat Genotypes.” Environmental Science and Pollution Research 28: 15043–15054. 10.1007/s11356-020-11690-3. [DOI] [PubMed] [Google Scholar]
  12. Ma, J. , Wang Y., Li S., et al. 2024. “Genome‐Wide Association Study Revealed the Reason for the Decrease in Grain Iron Concentration During Wheat Breeding Process in China.” Field Crops Research 309: 109326. 10.1016/j.fcr.2024.109326. [DOI] [Google Scholar]
  13. Ma, J. , Ye M., Liu Q., et al. 2023. “Genome‐Wide Association Study for Grain Zinc Concentration in Bread Wheat (Triticum aestivum L.).” Frontiers in Plant Science 14: 1169858. 10.3389/fpls.2023.1169858. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Ma, S. , Wang M., Wu J., et al. 2021. “WheatOmics: A Platform Combining Multiple Omics Data to Accelerate Functional Genomics Studies in Wheat.” Molecular Plant 14: 1965–1968. 10.1016/j.molp.2021.10.006. [DOI] [PubMed] [Google Scholar]
  15. Manickavelu, A. , Hattori T., Yamaoka S., et al. 2017. “Genetic Nature of Elemental Contents in Wheat Grains and Its Genomic Prediction: Toward the Effective Use of Wheat Landraces From Afghanistan.” PLoS One 12, no. 1: e0169416. 10.1371/journal.pone.0169416. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Meng, Y. , Li M., Guo Z., Chen J., Wu J., and Xia Z.. 2024. “The Transcription Factor ZmbHLH105 Confers Cadmium Tolerance by Promoting Abscisic Acid Biosynthesis in Maize.” Journal of Hazardous Materials 480: 135826. 10.1016/j.jhazmat.2024.135826. [DOI] [PubMed] [Google Scholar]
  17. Mitchell, E. J. , Frisbie S. H., Roudeau S., Carmona A., and Ortega R.. 2021. “How Much Manganese Is Safe for Infants? A Review of the Scientific Basis of Intake Guidelines and Regulations Relevant to the Manganese Content of Infant Formulas.” Journal of Trace Elements in Medicine and Biology 65: 126710. 10.1016/j.jtemb.2020.126710. [DOI] [PubMed] [Google Scholar]
  18. Morgounov, A. , Li H., Shepelev S., et al. 2022. “Genetic Characterization of Spring Wheat Germplasm for Macro‐, Microelements and Trace Metals.” Plants 11, no. 16: 2173. 10.3390/plants11162173. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Pritchard, J. K. , Stephens M., and Donnelly P.. 2000. “Inference of Population Structure Using Multilocus Genotype Data.” Genetics 155, no. 2: 945–959. 10.1093/genetics/155.2.945. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Pujar, M. , Gangaprasad S., Govindaraj M., Gangurde S. S., Kanatti A., and Kudapa H.. 2020. “Genome‐Wide Association Study Uncovers Genomic Regions Associated With Grain Iron, Zinc and Protein Content in Pearl Millet.” Scientific Reports 10, no. 1: 19473. 10.1038/s41598-020-76230-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Qiao, K. , Wang F., Liang S., Wang H., Hu Z., and Chai T.. 2019. “New Biofortification Tool: Wheat TaCNR5 Enhances Zinc and Manganese Tolerance and Increases Zinc and Manganese Accumulation in Rice Grains.” Food Chemistry 67: 9877–9884. 10.1021/acs.jafc.9b04210. [DOI] [PubMed] [Google Scholar]
  22. Rondanelli, M. , Faliva M. A., Peroni G., et al. 2021. “Essentiality of Manganese for Bone Health: An Overview and Update.” Natural Product Communications 16, no. 5: 1934578X211016649. 10.1177/1934578X211016649. [DOI] [Google Scholar]
  23. Shi, X. , Zhou Z., Li W., et al. 2022. “Genome‐Wide Association Study Reveals the Genetic Architecture for Calcium Accumulation in Grains of Hexaploid Wheat (Triticum aestivum L.).” BMC Plant Biology 22, no. 1: 229. 10.1186/s12870-022-03602-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Velu, G. , Singh R. P., Crespo‐Herrera L., et al. 2018. “Genetic Dissection of Grain Zinc Concentration in Spring Wheat for Mainstreaming Biofortification in CIMMYT Wheat Breeding.” Scientific Reports 8, no. 1: 13526. 10.1038/s41598-018-31951-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Wang, W. , Guo H., Wu C., et al. 2021. “Identification of Novel Genomic Regions Associated With Nine Mineral Elements in Chinese Winter Wheat Grain.” BMC Plant Biology 21, no. 1: 311. 10.1186/s12870-021-03105-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Wong, C. K. E. , and Cobbett C. S.. 2009. “HMA P‐Type ATPases Are the Major Mechanism for Root‐To‐Shoot cd Translocation in Arabidopsis thaliana .” New Phytologist 181, no. 1: 71–78. 10.1111/j.1469-8137.2008.02638.x. [DOI] [PubMed] [Google Scholar]
  27. Xu, W. , Chen Y., Liu B., et al. 2023. “TaANR1‐TaMADS25 Module Regulates Lignin Biosynthesis and Root Development in Wheat ( Triticum aestivum L.).” Journal of Genetics and Genomics 50: 917–920. 10.1016/j.jgg.2023.08.011. [DOI] [PubMed] [Google Scholar]
  28. Yang, J. , Li Q., Feng Y., and Zeng Y.. 2023. “Iron Deficiency and Iron Deficiency Anemia: Potential Risk Factors in Bone Loss.” International Journal of Molecular Sciences 24, no. 8: 6891. 10.3390/ijms24086891. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1. Detailed information and GMnC of 477 wheat accessions.

FSN3-13-e70170-s001.xlsx (272.6KB, xlsx)

Table S2. The previous identified SNP makers for wheat GMnC.

FSN3-13-e70170-s004.xlsx (15KB, xlsx)

Table S3. The annotation of 2032 genes in 15 QTLs.

FSN3-13-e70170-s003.xlsx (180KB, xlsx)

Table S4. The expression pattern of 15 candidate genes in different tissues using WheatOmics 1.0.

FSN3-13-e70170-s002.xls (27.8KB, xls)

Data Availability Statement

The authors confirm that data generated or analyzed supporting the findings of this study are available within the article, and any further supporting material will be provided on request from the corresponding author.

Articles from Food Science & Nutrition are provided here courtesy of Wiley

RESOURCES