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Article

Genome-Wide Association Studies for Grain Micronutrient Concentration in Spring Wheat (Triticum aestivum L.)

by
Chandranandani Negi
1,
Krishan Kumar
1,
Raman Dhariwal
2,*,
Pritesh Vyas
3 and
Neeraj Kumar Vasistha
4,*
1
Department of Science and Technology, Jayoti Vidyapeeth Women’s University, Jaipur 303122, India
2
Agriculture and Agri-Food Canada, Lethbridge Research and Development Centre, Lethbridge, AB T1J 4B1, Canada
3
School of Applied and Life Sciences, Uttaranchal University, Dehradun 248007, India
4
Department of Genetics & Plant Breeding, Rajiv Gandhi University, Rono Hills, Itanagar 791112, India
*
Authors to whom correspondence should be addressed.
Plants 2025, 14(22), 3472; https://doi.org/10.3390/plants14223472
Submission received: 28 August 2025 / Revised: 28 October 2025 / Accepted: 29 October 2025 / Published: 14 November 2025
(This article belongs to the Special Issue Cereals Genetics and Breeding)
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Abstract

The micronutrient content is a major aspect of food quality and has been under threat after a gain in production post-green revolution. Calcium (Ca) and magnesium (Mg) are the micronutrients that are cofactors for many enzymes and play a critical role in human physiology. Deciphering the accumulation of these micronutrients in wheat and the identification of QTLs associated with these elements is very significant for cutting the risk of malnutrition in humans. Here, a genome-wide association study (GWAS) of 105 lines from an elite panel of the Wheat Association Mapping Initiative (WAMI) was performed for the two cropping seasons of 2021–2022 and 2022–2023 for the grain calcium and magnesium content (GCaC and GMgC). Notably, two marker trait associations (MTAs), wsnp_Ex_c2718_5038582, Kukri_c11327_977, and RAC875_c9984_1003, were found for the GCaC, and similarly three MTAs (Tdurum_contig28802_213, wsnp_Ex_c34597_42879693, and RFL_Contig6053_3082) were identified for the GMgC in both the cropping seasons, proving their utility and non-redundancy. An MTA associated with a SNP marker (wsnp_Ex_c34597_42879718) was also identified in the two seasons and was significant for both the GCaC and GMgC. Candidate gene analysis showed the association of these MTAs with some of the very vital genes associated with activities where Ca and Mg play significant roles. Our study widens the insights on the genetic control of Ca and Mg accumulation in wheat and the utilization of this information for future breeding programs, wherein wheat improvement with enhanced Ca and Mg may be designed and conducted.

1. Introduction

Wheat (Triticum aestivum L.) is one of the world’s leading food crops and is the cradle for the human civilization, with an annual production of 797.86 million tons over about 223.01 million hectares [1]. It plays a fundamental role in providing global food security, with an average of 19% calories and 21% of the protein required for the daily human diet [2]. However, reports have shown that, worldwide, one out of three people suffer from some form of malnutrition. Almost 45% of deaths below the age of 5 years result from malnutrition, which predominantly occurs in poor or middle-income nations [3]. Combating malnutrition is a global health challenge, for which the United Nations declared 2016 to 2025 as the United Nations Decade of Action on Nutrition. Since it is a decade to address malnutrition in every form and achieve the Sustainable Development Goals, studies and sustainable approaches are required to remove this micronutrient malnutrition.
Calcium (Ca) in the human body forms the structure of bones and is responsible for normal body movements, including tissue rigidity, strength, and flexibility, and it undergoes continuous remodeling through the constant deposition and resorption of Ca into the bone [4]. Ca deficiency leads to osteomalacia and bone softening, which occur in children and adults. Since Ca binds to fatty acids in the body, it reduces lipid absorption and is therefore involved in lowering the risk of cardiovascular disease [5].
Another important micronutrient in human body, magnesium (Mg), is a cofactor for more than 300 enzyme systems, regulating various biochemical reactions, including blood glucose regulation, nerve and muscle functions, blood pressure regulation, and protein synthesis [6]. It is involved in the active transportation of calcium and potassium ions across the cell membrane, which is required for nerve impulse conduction, muscle contractions, and a normal heart rhythm. Essential for energy generation, glycolysis, and oxidative phosphorylation, it also plays an important role in DNA and RNA synthesis [7,8]. According to the WHO, recommended Ca allowances range from 200–400 mg/day for infants to 1300 mg/day for children of 9–18 years and 1000–1200 mg/day for older adults. Similarly, for Mg, it ranges from 30–75 mg for infants to around 400 mg for children and adults [3].
Molecular breeding is an efficient method for facilitating crop improvements targeting micronutrient-related traits. Marker-trait associations (MTAs) associated with micronutrients and various other traits in wheat have been reported using genome-wide association studies (GWASs) [9,10,11,12,13]. With the arrival of next-generation sequencing (NGS) technologies, opportunities for the evaluation of genetic variations and discovery of novel markers have occurred, via the implementation of a genotyping-by-sequencing (GBS) strategy. Deciphering complex agronomical traits by utilizing SNPs through genome-wide association studies (GWAS) has been achieved for various crops and has resulted in the detection of genomic regions, which may be either markers, QTLs, or genes for desired traits [14,15,16].
In GWASs, differentiation between true associations and false-positive MTAs resulting from the population structure and kinship has been a big hurdle [17]. To overcome such associations, various statistical models have been developed, namely the single-locus mixed linear model and multi-locus models. In the single-locus model, the incorporation of two confounding factors is considered for analysis as covariates [17]. However, this may lead to false-negative MTAs due to overfits, which results in missing chances to uncover the loci associated with targeted traits [18]. To overcome such incidences of false-negative MTAs, a variety of multi-locus models has been developed, such as fixed and random model circulating probability unification (FarmCPU) and Bayesian-information and linkage disequilibrium iteratively nested keyway (BLINK). The FarmCPU model includes multiple markers, simultaneously, as the covariates, which eliminates the confounding effect of markers and kinship, and therefore, this removes the false-positive MTAs without compromising true associations. It utilizes a fixed-effect and random effect model for the complete removal of cofounding factors. However, FarmCPU is time-consuming, wherein the numbers of markers and individuals are high. In comparison, BLINK is statistically better and time-efficient, using the Bayesian information criterion. Unlike FarmCPU, BLINK utilizes linkage disequilibrium (LD) to eliminate the assumption that causal genes are evenly distributed across the genome [19,20]. The proper execution of GWASs reveals novel genomic regions associated with targeted traits and hence facilitates an identification of the genes that control such traits. In the present investigation, a GWAS was performed for 105 diverse wheat genotypes from the Wheat Association Mapping Initiative (WAMI) panel for the characterization of the grain calcium and magnesium concentration (GCaC and, GMgC) utilizing 21,132 single nucleotide polymorphisms (SNPs), using FarmCPU and BLINK models. The identification of MTAs and the candidate genes associated with significant and non-redundant MTAs showed that the regions play a significant role in vital activities that are related to the GCaC and GMgC. These genomic regions are useful as novel markers and could be designed to study their introgression in future breeding programs.

2. Results

2.1. Variability of Grain Ca and Mg Concentrations

An estimation of the micronutrient concentrations of Ca and Mg in all 105 grain samples from the cropping seasons 2021–2022 and 2022–2023 was performed. Variations were observed in the concentrations of the estimated micronutrients (Table 1). The frequency distribution of Ca and Mg, in two consecutive cropping seasons, are presented in Figure 1. The phenotypic data for both micronutrients used in this study have been provided in Table S1.
Table 1. Combined ANOVA table for grain micronutrients in the WAMI panel constituting 105 wheat accessions.
Table 1. Combined ANOVA table for grain micronutrients in the WAMI panel constituting 105 wheat accessions.
SourceDfCa_SSMg_SS
Year 142.2 *476 *
Entry1048.79 *9.68 *
Replication210.360.5
Year × Entry1045.35 *7.61 *
SS: sum of squares; Df: degrees of freedom; *: significant at p ≤ 0.001.
Based on the samples obtained during 2021–2022, the mean GCaC for all three replicates was 398.62 ppm, and for 2022–2023, it was 380.42 ppm, with a final mean of 389.52 ppm (Table 2). The mean GMgC for the years 2021–2022 for all three replicates was 1211.87 ppm, and for 2022–2023, it was 1118.01 ppm, with the final mean being 1164.94 ppm. The frequency distribution of variation for Ca and Mg in two consecutive cropping seasons is presented in Figure 1.
Table 2. Statistical analysis of the GCaC and GMgC in the WAMI panel constituting two consecutive years for 2021–2022 and 2022–2023.
Table 2. Statistical analysis of the GCaC and GMgC in the WAMI panel constituting two consecutive years for 2021–2022 and 2022–2023.
Statistics2021–20222022–2023
BLUP CaBLUP MgBLUP CaBLUP Mg
Heritability0.660.730.680.78
Genotypic variance2312.0010,528.42786.744323.17
Grand Mean398.621211.88380.421118.02
LSD49.8373.249.3685.21
CV8.363.888.555.34
LSD = least significant difference; CV = coefficient of variation.

2.2. Population Structure Analysis

The allele frequencies in the WAMI panel were found to be distributed evenly based on principal components analysis (Figure 2A). The number of components was determined by the “elbow” in the scree plot, which strongly suggested retaining three principal components (Figure S1). PC1 explained 17.9% of the variance, whereas PC2 explained 5.5% and PC3 explained 3.9%. These findings imply that PC1, PC2, and PC3 collectively account for a significant amount of the panel’s underlying genetic variation, making it possible to recognize and distinguish the subgroups that make up the WAMI population.

2.3. MTAs for the Target Trait Utilizing GWASs

To identify the significant associations between Ca and Mg contents and SNP markers in the lines, GWAS analyses were carried out. A scatter plot of r2 values of paired markers was developed, demonstrating genome-wide linkage disequilibrium (LD) degradation for 105 WAMI genotypes, assessed using the Hill and Weir formula [20]. LD analysis was performed using Trait Analysis by aSSociation, Evolution and Linkage (TASSEL) data with a sliding window of 100 SNPs. The average genome-wide r2 was 0.11, with the LD decline starting at r2 = 0.46 and ending at r2 = 0.23 (Figure 2B). The LD decay curve intersected the half-decay and standard critical (r2 = 0.3) lines at 23,754,184 and 11,877,092 base pairs (bp), respectively. This establishes 11,877,092 bp as the genome-wide threshold distance for detecting linkage, as performed in previous works [9,19].
The MTAs were investigated utilizing two multi-locus models, FarmCPU and BLINK, which identified 86 SNP loci with prominent associations with the trait studied and utilization of the Manhattan plots and Quantile-Quantile plot (Q-Q) plots (Figure 3). Genotypic data was available for 26,814 SNPs, but only 21,132 SNPs were utilized for the GWAS. We found 49 SNPs significantly associated with Ca and located on the 12 chromosomes (1A, 1B, 1D, 2A, 2B, 2D, 3A, 3B, 3D, 5A, 6B, and 7A), which was confirmed in the data provided for the consecutive years of cropping seasons using both models (Tables S2–S5). About 37 significant SNPs were found for the GMgC, located significantly on chromosomes 1B, 2A, 2B, 3A, 4A, 4B, 4D, 5B, 6A, 6B, 7A, 7B, and 7D (Tables S6–S9).

2.3.1. MTAs for GCaC

For the initial crop year 2021–2022, the MTAs were found to be distributed on multiple chromosomes, and they were found notably on chromosome 2A (4 MTAs), 3D (13 MTAs), 5A (2 MTAs), 6A (3 MTAs), 7B (6 MTAs), and 7A (5 MTAs). Interestingly, a dense concentration of 13 MTAs were observed between the physical position of 604.6–746.6 Mb on chromosome 3D, exhibiting a significant and rich association. This striking presence potentially underscores a genomic hotspot associated with the Ca content. The presence of four MTAs on 7B was observed within the range of 35.4–37 Mb. Another profound presence of MTAs was on 7A, with three MTAs co-located on 52.9 Mb and one on 49.8 Mb. For the year 2022–2023, these seven MTAs were identified on 2B and 5A. Notably, these four MTAs were consistent in both cropping seasons of 2021–2022 and 2022–2023, with each located on chromosome 2A and 6B and two MTAs on chromosome 5A (Table 3). There were four loci for the GCaC, which showed the presence in both cropping years, Kukri_c11327_977 on chromosome 2A, wsnp_Ex_c2718_5038582 and RAC875_c9984_1003 both on 5A, and wsnp_Ex_c34597_42879718 on 6B.

2.3.2. MTAs for GMgC

In the study year 2021–2022, MTAs for the GMgC extended on various chromosomes with a notable presence on 4A (six MTAs), 6A (three MTAs), and 7A (four MTAs). At a specific position of 99.2 Mb on chromosome 4A, two MTAs were found to be co-located. Moreover, two pairs of co-located MTAs on 7A with positions at 159.5 Mb and 696.9 Mb were identified. In the cropping year 2022–2023, a prominent number of MTAs was observed on 2B (six MTAs), 4B (two MTAs), 5B (three MTAs), and 6A (four MTAs). The most prominent observation for MTAs associated with Mg, which were consistent in both the cropping seasons, were on 6A and 6B. There were four loci for the GMgC, which showed the presence in both cropping years. Interestingly out of these, three of them were located on 6A, at 597.8 Mb, i.e., Tdurum_contig28802_213, wsnp_Ex_c34597_42879693, and RFL_Contig6053_3082, while the fourth one, wsnp_Ex_c34597_42879718, was found on 6B at 597.8 Mb.

2.3.3. Multi-Effect MTA Locus for Ca and Mg

A multi-effect MTA locus, represented by wsnp_Ex_c34597_42879718 on chromosome 6B at 597.8 Mb, was found to be responsible for controlling both the traits simultaneously and was consistent for both the consecutive crop seasons.

3. Discussion

One of the biggest global health concerns, which needs immediate attention, is micronutrient malnutrition. High-throughput genotyping technologies, coupled with statistics, provide us with vast information and a better understanding of genomes. Genome-wide association mapping is an effective strategy to facilitate the identification of genes that regulate traits of interest, and its efficiency is dependent on the genetic diversity within the germplasm that is utilized as association panels. Since genome sequencing and genotyping technologies have advanced so quickly, GWASs have been widely used for wheat and many other crops [23,24,25,26,27]. Preliminary work on the WAMI population was undertaken to investigate these advanced wheat lines for SNPs associated with complex traits, without the confounding effects of phenology and plant height [28]. Gene discovery and cloning have been made easier by the wheat reference genome and a wealth of transcriptomic resources [29,30]. Additionally, this has made it much easier to investigate the QTLs for traits related to wheat yields and quality. Since wheat is one of the most important crops in the world, breeding it to accumulate more nutrients will help reduce nutrient deficiencies. For fulfilling such objectives, GWASs are used to identify genomic regions associated with micronutrient traits. The pyramiding of different micronutrients related markers into high-yield genotypes can remove micronutrient deficiency in populations. The availability of multiple micronutrient-related QTLs/MTAs in wheat germplasms utilized from different countries has also been studied previously [31,32]. Various panels for investigating QTLs associated with grain Ca have been used earlier, one of which utilized a European wheat diversity panel of 353 varieties with 90k and 35k SNP markers [27]. Moreover, for the mitigation of false positive associations, it is important to consider population structure in GWASs [32].
In the present investigation, for deciphering the novel GCaC- and GMgC-associated MTAs, the crops grown in the years 2021–2022 and 2022–2023 were used, utilizing the diverse wheat panel of the WAMI. The study decoded the involvement of multiple QTLs that contribute to the desired traits and highlighted a total of 86 MTAs (49 for the GCaC and 37 for the GMgC). Many MTAs identified in our studies have not been reported earlier, and therefore, they are potentially novel MTAs controlling the GCaC and GMgC. This may be due to the different origins of the panels/populations and different methods used for their detection. Here, chromosomes 1A, 1B, 1D, 2A,2B, 2D, 3A, 3B, 3D, 5A, 6B, 7A, and 7B were found to carry QTLs for the GCaC in wheat, as reported earlier [33,34,35,36,37,38,39,40], indicating the potential role of these chromosomes in different populations in the GCaC. The QTLs wsnp_Ex_c2718_5038582, Kukri_c11327_977, and RAC875_c9984_1003 were found to be associated with the GCaC earlier [26] and are consistent with the QTLs detected in the present investigation for both cropping seasons, making them the most significant loci for the GCaC and its accumulation. The QTLs Excalibur_c23906_303 and wsnp_Ra_c193_406396 were found to be in proximity of another QTL reported [41] on 1D for the GCaC under hydroponic conditions, indicating their utility as potential markers under different conditions.
For the GMgC, chromosomes 1B, 2A, 2B, 3A, 4A, 4B, 4D, 5B, 6A, 6B, 7A, 7B, and 7D were found to carry QTLs for the indicated traits that were found to be involved in previous studies [34,39,36,42,43,44,45]. The QTLs Tdurum_contig28802_213, wsnp_Ex_c34597_42879693, and RFL_Contig6053_3082 were associated with the GMgC and reported in the present investigation for both cropping seasons, and they have been identified [24] in another study, but under different environmental conditions, and thus, they promise to be genuine for GMgC accumulation. The QTL wsnp_Ex_c34597_42879718, located on 6B at 597.8 Mb, had been associated with the GCaC [24] and was also found to be present in both cropping seasons and thus promises to be a genuine locus for the GCaC.
In this study, we identified credible candidate genes for major MTAs, including Kukri_c11327_977 (TraesCS2A03G0585200), which has a major role in the STI1/HOP, DP domain. Sti1/Hop (Stress-inducible phosphoprotein 1 or Hsp-organizing protein) is a cochaperone exclusive to eukaryotes and is highly involved in regulating the heat shock proteins Hsp70 and Hsp90 through ATP binding or hydrolysis, the delivery of client proteins, or the modulation of intermediate conformations, for effectiveness against the stress response in plants [46]. This association of Hsp with STIP1 occurs in a calcium-dependent manner [47]. Moreover, STI1 is involved in protein folding and cellular homeostasis, eventually protecting cells from stress, resulting in cellular resistance and normal growth [48].
An effective marker for GCaC, wsnp_Ex_c2718_5038582, encodes stomatal closure-related actin-binding protein (TraesCS5A03G0124000). A recent report validated the role of stomatal closure-related actin-binding protein (SCAB), which is considered a molecular switch for F-actin, resulting in stomatal closure [49]. Additionally, phosphorylation through calcium sensors, such as calcium-dependent protein kinase (CPK), acts as a sensor for the increase and decrease in Ca concentrations. This leads to the regulation and activation of various transcription factors, enzymes, ion channels, and genes. CPKs have important functions in plant adaptation under salinity, drought, heat, and cold stress environments [50]. Another candidate gene, TraesCS5A03G0126700, is associated with the bHLH transcription factor MYC, which is responsible for plant hormone signal transduction.. Calcium signaling is perceived by calcium-binding proteins, activating downstream pathways involving protein kinases and transcription factors (bHLH transcription factor MYC2) [51]. It is transcription-regulatory activity that modulates the transcription of gene sets through selective and non-covalent binding to a specific double-stranded genomic DNA sequence (sometimes referred to as a motif). The function of this gene has also been studied in wheat for plant hormone signal transduction during biotic stress [52]. The role of Ca2+ has been studied in plant stress adaptation, which fluctuates in response to the stress signals. The Ca2+ level modulates various physiological processes responsible for stress adaptation, which is accomplished via Ca2+/calmodulin (CaM)-binding transcription factors involved in the stress signaling pathway [53].
We noted that another major MTA, i.e., RAC875_c9984_1003 encoding TraesCS5A03G0926200, is involved in the biological process of photosystem II, where Ca2+ acts as functional and structural cofactor [54,55]. The MTAs identified in this region were involved in gene-coding regions related to the extracellular region, cellular component, calmodulin binding, and signaling, as reported in previous studies [56,57,58,59]. Likewise, significant work by Taneja et al. [60,61] suggested the diversified role of Ca2+ ATPases, antiporters in wheat. The MTA for GMgC, RFL_Contig6053_3082, encodes nucleobase-containing compound metabolic processes (TraesCS6A03G0934500). It is involved in hydrolase activity, nuclease activity, and any cellular metabolic process involving nucleobases, nucleotides, nucleosides, and nucleic acids [62]. Similarly, we detected TraesCS6A03G0937800, which participates in phytohormone signaling, transcriptional regulatory factors, and post-translation modifications, where Mg plays a crucial role in influencing the grain weight and processes leading to wheat seed formation [63,64]. It is accomplished via carbohydrate metabolism and ATP-dependent processes, which are significant for energy-consuming grain development and dry matter accumulation [65].
Considering the GMgC, one of the major MTAs, Tdurum_contig28802_213, encodes the Ribonuclease H-like superfamily (TraesCS6A03G0934500), which has been found to be involved in the catalytic activity of the Ribonuclease H-like superfamily [66]. The same region has a gene encoding the GroES-like superfamily (TraesCS6A03G0939400). GroES harnesses ATP hydrolysis for power generation and catalyzes protein folding, where Mg2+ is essentially involved y in this process of protein folding and refolding [67]. For the region wsnp_Ex_c34597_42879693, a gene encoding oxidoreductase activity (TraesCS6A03G0939400) in plants was found, where Mg2+ acts as an essential cofactor for enzymes including RuBisCO [68,69]. This enzyme has a key role in carbon fixation involving electron-transfer reactions. A major MTA, wsnp_Ex_c34597_42879718, associated with the GCaC and GMgC, harbors gene TraesCS6A03G0936800 (EF-hand domain). The EF-hand domain consists of proteins with motifs that actively bind Ca2+ and occasionally Mg2+, which brings about conformational changes for protein activation and downstream signaling [70,71]. EF-hand-containing calcium-binding proteins include calcium-dependent protein kinases (CDPK/CPKs), calmodulins (CaMs), CPK-related protein kinases (CRKs), calmodulin-like (CML), calcium calmodulin-dependent protein kinases (CCaMKs), and calcineurin B-like (CBL) [72,73,74]. In an interesting study on wheat, EF-hand domain-containing proteins were identified, along with Ca2+-mediated signals regulated by EF-hand proteins [75].

4. Materials and Methods

4.1. Genetic Material and Experimental Conditions

The grain micronutrient concentrations (Ca and Mg) were investigated using spring wheat lines from the Wheat Association Mapping Initiative (WAMI) population from the International Maize and Wheat Improvement Center (CIMMYT), Mexico [29]. This consisted of 105 elite and genetically diverse wheat lines, the distribution of which was assessed via the International Wheat Improvement Network (IWIN) by CIMMYT. Due to the presence of a narrow range of variation in the days to heading and plant height, this is suitable for gene discovery with no confounding effects of plant height and phenology. The crops were grown for two consecutive cropping seasons in the years 2021–2022 and 2022–2023 at the research farm of Eternal University, Baru Sahib, Himachal Pradesh, India, using three replications. Each block of a 2 m row with a 0.10 m plant spacing was designated to represent a genotype. The fertilizers used were 120 kg N, 40 kg K2O, and 60 kg P2O5, with the thorough use of all fertilizers at the time of sowing, except nitrogen. The dose of N was used in three parts: half at sowing, one-fourth on initial irrigation i.e., 21 days after sowing, and the remaining one-fourth during the second irrigation i.e., 45 days after sowing. The distance in the plantation was 20 cm, with 5 cm between the plants.

4.2. DNA Extraction and Genotyping

The extraction of DNA, genotyping of the samples, and data processing were performed as previously described [76]. Further, genotyping was carried out by the USDA-ARS Small Grain Genotyping Center, Fargo, ND, USA using an Illumina 90K Infinium iSelect assay (Illumina Inc., San Diego, CA, USA) [45]. The process of SNP calling used the default clustering algorithm integrated into Genome Studio v2011.1 (Illumina Inc., San Diego, CA, USA), which resulted in the identification of a total of 26,814 bi-allelic SNPs [44,76]. For upholding standards in data quality, SNPs characterized with a minor allele frequency (MAF) lower than 0.05 were eliminated from the analysis, including monomorphic and low-quality SNPs. Such thorough filtration resulted in the retention of around 21,132 polymorphic SNPs, which were utilized for the GWAS in our study [44,77].

4.3. Elemental Analysis of GCaC and GMgC

Approximately 0.5 g of homogenized seed material was measured into a digestion tube and subjected to digestion with suprapure nitric acid with a microwave digester. The digestate was filtered using Whatman® filter paper no. 42 and diluted to a volume of 10 mL using ultrapure water in a calibrated volumetric flask.
The concentrations of analytes were quantified utilizing inductively coupled plasma–optical emission spectroscopy (ICP-OES; Manufacturer: Perkin Elmer, Shelton, CT, USA, Model: 7300DV). Calibration was conducted using blanks and five matrix-matched standards. The calibration curve was generated based on linear regression, requiring a minimum correlation coefficient of 0.995. The GCaC and GMgC were examined in radial mode, using the standard approach (approach 984.27).

4.4. Statistical Analyses

The analysis of Pearson’s correlation coefficients was conducted utilizing the Agricolae (version 1.2–4) package of R (version 4.0.3) software [78]. Components of variance were studied using the restricted maximum likelihood (REML) method implemented in META-R software v6.0.4 [79]. Data of BLUP was extracted utilizing the ‘lme40 package in R [80], using the following formula:
y = Xb + Zu + e
Of these, y is the observed phenotype, Xb is the fixed effects (environment), Zu is the random effect (genotype), and e is the residual effect.
Statistical analysis, including the mean, coefficient of variation (CV), and standard deviation, was performed utilizing SPSS v. 17.0 (SPSS Inc., Chicago, IL, USA, 2008). Variance components were used to calculate the broad sense heritability (hb2) of micronutrient-related traits, as follows:
hb2 = σg2/(σg2 + σge2/r + σε2/re)
where σg2, σge2, and σε2 represent the genotype, genotype × environment interaction, and residual error variances, respectively, and e and r are the numbers of environments and replicates per environment, respectively.

4.5. Population Structure, Kinship Matrix, and Principal Components Analyses (PCAs)

The population structure matrix or Q matrix was modeled utilizing a PCA for the genotypic data of a total of 21,132 high-quality SNPs, as described earlier [15]. The kinship matrix (relatedness or K matrix) was evaluated using R (version 4.0.3) software, utilizing the parameters given by VanRaden and Yin et al. [81,82]. Utilizing the Bayesian information criterion (BIC), the optimum numbers of PCAs were identified [83]. A scatter plot was formed using the first two principal components, which demonstrated the genotype distribution.

4.6. Genome-Wide Association Analyses

The GWAS was performed utilizing 21,132 high-quality SNPs available from the CIMMYT, Mexico website (https://data.cimmyt.org/dataset.xhtml?persistentId=hdl:11529/10714; accessed on 22 December 2023). Among the SNP markers, pairwise squared allele–frequency correlations (r2) were determined using TASSEL software (Trait Analysis by aSSociation, Evolution, and Linkage) ver. 5.2.9, with a sliding window size of 100. A plot between the r2 values and genetic distance (cM) was made for assessing the LD between the loci. Forming a smoothing spline regression line at the genome level, the LD decay curve utilizing the Hill and Weir method [20] in the R environment was achieved with a script utilized by Marroni et al. [84]. Following earlier studies for the identification of significant MTAs, a significant threshold of p < 0.001 (−log10(p) > 3.0) was considered [9,85,86]. However, as no MTAs passed the false discovery rate (FDR) test, those with p ≤ 0.001 in both FarmCPU and BLINK models were considered significantly associated with traits in the present study. In the present study, this QTL was also identified for the GMgC for both cropping seasons, proving its non-redundancy and ability to be used for detecting the contents of multiple micronutrients in wheat. When breeding for higher GCaC and GMgC contents, attention may be paid to these MTAs. These identified MTAs provide valuable insights into the molecular mechanisms corresponding to grain micronutrients in wheat. However, it is a prerequisite to consider the environmental factors while making breeding decisions, with proper validation using independent populations, which plays a crucial role in the performance of the traits.

4.7. Putative Candidate Gene Predictions

The Ensembl Plants database was employed for extracting the molecular and biological information of MTAs by utilizing IWGSC RefSeq v.2.1 [Available at: https://plants.ensembl.org/biomart/martview/6898327221e3d05165d778da85766c12; accessed 21 July 2025]. A window of a physical distance of 2 Mb in the genomic area, both in the upstream and downstream vicinity of the selected SNPs, was utilized for the prediction of candidate genes influencing the trait.

5. Conclusions

The MTAs reported in this study were found to be significant in both the cropping seasons, and the non-redundancy was further confirmed using the two GWAS models. This makes them robust enough to be used for breeding for a higher GCaC and GMgC, and therefore, attention may be paid to these MTAs. These identified MTAs provide valuable insights into the molecular mechanisms corresponding to grain micronutrients in wheat. However, it is prerequisite to consider the environmental factors while making breeding decisions, with proper validation using independent populations, which plays a crucial role in the performance of the traits.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants14223472/s1. Figure S1. Scree plot of Principal Component Analysis (PCA) eigenvalues for genotype data. Table S1. Concentrations of calcium (Ca) and magnesium (Mg) in 105 WAMI panel lines. Table S2. Identification of Marker-Trait Associations (MTAs) for Ca concentration using Blink (2021–2022). Table S3. Identification of Marker-Trait Associations (MTAs) for Ca concentration using FarmCPU (2021–2022). Table S4. Identification of Marker-Trait Associations (MTAs) for Ca concentration using Blink (2022–2023). Table S5. Identification of Marker-Trait Associations (MTAs) for Ca concentration using Farm CPU (2022–2023). Table S6: Identification of Marker-Trait Associations (MTAs) for Mg concentration using Blink (2021-2022). Table S7. Identification of Marker-Trait Associations (MTAs) for Mg concentration using Farm CPU (2021–2022). Table S8. Identification of Marker-Trait Associations (MTAs) for Mg concentration using Blink (2022-2023). Table S9. Identification of Marker-Trait Associations (MTAs) for Mg concentration using Farm CPU (2022–2023).

Author Contributions

Conceptualization, N.K.V.; methodology, C.N. and N.K.V.; formal analysis, C.N. and N.K.V.; investigation, C.N., N.K.V., and K.K.; resources, K.K., P.V., and N.K.V.; data curation, N.K.V. and C.N.; writing—original draft preparation, C.N. and N.K.V.; writing—review and editing, R.D., P.V., and N.K.V.; visualization, C.N., R.D., and N.K.V.; supervision, N.K.V. and K.K.; project administration, N.K.V.; funding acquisition, N.K.V. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Science and Engineering Research Board, New Delhi, grant number SRG/2020/000091.

Data Availability Statement

The single-nucleotide polymorphism (SNP) genotyping data of the Wheat Association Mapping Initiative (WAMI) germplasm panel of spring wheat, which are used for the present study, have been published previously by Sukumaran et al. [44] and are available publicly to download from the link: http://hdl.handle.net/11529/10714 (accessed on 12 July 2024). All other data generated or analyzed during this study are included in this published article.

Acknowledgments

We sincerely acknowledge the International Maize and Wheat Improvement Center (CIMMYT), Mexico, for generously providing the WAMI population and granting access to the molecular data used in this study.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
BICBayesian information criterion 
BLINKBayesian-information and linkage disequilibrium iteratively nested keyway 
BLUPBest Linear Unbiased Prediction
CIMMYTInternational Maize and Wheat Improvement Center 
CVCoefficient of variation
FarmCPUFixed, and random model circulating probability unification 
GCaC Grain calcium content 
GMgCGrain magnesium content 
GWASGenome-wide association study 
ICP-OESInductively coupled plasma–optical emission spectroscopy
MgMagnesium 
MTAsMarker trait associations 
PCAPrincipal component analysis 
QTLQuantitative trait loci 
r2Pairwise squared allele–frequency correlations 
REMLRestricted maximum likelihood 
TASSEL Trait Analysis by aSSociation, Evolution and Linkage
WAMIWheat Association Mapping Initiative 
CaCalcium 
IWINInternational Wheat Improvement Network 
LDUtilizes linkage disequilibrium 
LSDLeast significant difference
MAFMinor allele frequency 
Q-QQuantile–Quantile

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Figure 1. Histograms and boxplots showing the frequency distribution. (A) GCaC for 2021-2022, (B) GCaC for 2022-2023, (C) GMgC for 2021-2022 and (D) GMgC for 2022-2023 in WAMI panel.
Figure 1. Histograms and boxplots showing the frequency distribution. (A) GCaC for 2021-2022, (B) GCaC for 2022-2023, (C) GMgC for 2021-2022 and (D) GMgC for 2022-2023 in WAMI panel.
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Plants 14 03472 g002
Figure 2. Population structure, linkage disequilibrium (LD), and correlation coefficients. (A). Scatterplot depicting the results of the principal component analysis (PCA), analyzed based on the genotypic data of the WAMI panel, consisting of 105 wheat accessions. It highlights the population structure of the WAMI panel, as shown by the three components (PC1, PC2, and PC3), providing insights into the genetic relationships among the individuals [21]. (B). Scatter plot of r2 values of pairwise markers representing disequilibrium decay for 105 accessions. The curve shown in red color represents the smoothing spline regression model fitted for LD decay. The horizontal and vertical lines represent the standard critical r2 value and physical distance in base pairs (bp), respectively. The sky blue vertical line represents the physical distance (23,754,184 bp), where the LD half decay intersects the LD decay curve [21]. (C). Pearson’s correlation between two consecutive years for Ca and Mg grain micronutrients.
Figure 2. Population structure, linkage disequilibrium (LD), and correlation coefficients. (A). Scatterplot depicting the results of the principal component analysis (PCA), analyzed based on the genotypic data of the WAMI panel, consisting of 105 wheat accessions. It highlights the population structure of the WAMI panel, as shown by the three components (PC1, PC2, and PC3), providing insights into the genetic relationships among the individuals [21]. (B). Scatter plot of r2 values of pairwise markers representing disequilibrium decay for 105 accessions. The curve shown in red color represents the smoothing spline regression model fitted for LD decay. The horizontal and vertical lines represent the standard critical r2 value and physical distance in base pairs (bp), respectively. The sky blue vertical line represents the physical distance (23,754,184 bp), where the LD half decay intersects the LD decay curve [21]. (C). Pearson’s correlation between two consecutive years for Ca and Mg grain micronutrients.
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Plants 14 03472 g003
Figure 3. Manhattan and Q-Q plots of SNPs associated with the GCaC and GMgC, obtained using BLINK and FarmCPU. Q-Q (Quantile-Quantile) plots consist of grey areas, indicating the 95% confidence interval under the null hypothesis, with no association between the SNP and the investigated trait. The significance threshold was −log10(p) = 3.0, and SNPs above this threshold are significantly associated with the GCaC and GMgC in the plots.
Figure 3. Manhattan and Q-Q plots of SNPs associated with the GCaC and GMgC, obtained using BLINK and FarmCPU. Q-Q (Quantile-Quantile) plots consist of grey areas, indicating the 95% confidence interval under the null hypothesis, with no association between the SNP and the investigated trait. The significance threshold was −log10(p) = 3.0, and SNPs above this threshold are significantly associated with the GCaC and GMgC in the plots.
Plants 14 03472 g003
Table 3. Description of marker–trait associations (MTAs) detected for the GCaC and GMgC in the WAMI panel for the years 2021–2022 and 2022–2023.
Table 3. Description of marker–trait associations (MTAs) detected for the GCaC and GMgC in the WAMI panel for the years 2021–2022 and 2022–2023.
MarkerChrPos (cM) #Pos
(Mb) *		EffectTraitYearMAFRef.
wsnp_BE591290B_Ta_2_71A133.0661.8−21.85Ca2022–20230.19PNF
wsnp_BG274294B_Ta_2_31B77.0543.036.33Mg2021–20220.37PNF
IAAV5651B122.0652.418.64Ca2022–20230.26PNF
Excalibur_c23906_3031D115.0436.9−34.07Ca2022–20230.14PNF
wsnp_Ra_c193_4063961D115.0435.8−34.07Ca2022–20230.14PNF
BS00068139_512A62.030.434.28Ca2021–20220.07PNF
Kukri_c11327_9772A101.0361.319.62
10.69		Ca2021–2022,
2022–2023		0.13[21]
wsnp_Ex_c61879_617486262A62.030.421.38Ca2021–20220.13PNF
RAC875_c39634_3702A27.010.7−35.81Mg2021–20220.40PNF
wsnp_Ex_c11827_189863762A133.0733.9−17.24Ca2022–20230.29PNF
RFL_Contig3509_2292A128.0723.8−32.11Mg2022–20230.09PNF
TA005606-12822B96.0212.2−36.83Mg2021–20220.23PNF
Ra_c10607_5242B114.0692.9−26.26Ca2022–20230.11PNF
Kukri_c19751_8732B108.0594.120.46Ca2022–20230.47PNF
wsnp_Ex_rep_c67543_661653722B108.0593.620.25Ca2022–20230.48PNF
BS00022800_512B108.0595.119.23Ca2022–20230.48PNF
Kukri_c25815_2632B108.0594.818.76Ca2022–20230.49PNF
Excalibur_c7963_17222B69.031.0−19.95Mg2022–20230.46PNF
GENE-1421_8022B69.046.0−19.80Mg2022–20230.45PNF
Tdurum_contig12879_12732B115.0712.6−21.51Ca2022–20230.20PNF
Ku_c51309_2122B115.0714.7−22.06Ca2022–20230.19PNF
Kukri_c29640_2122B69.047.1−20.51Mg2022–20230.47PNF
Gene_1421_7062B69.046.019.73Mg2022–20230.45PNF
Excalibur_c2050_7482B69.046.1−20.77Mg2022–20230.45PNF
GENE-1421_1242B69.047.1−21.02Mg2022–20230.44PNF
RAC875_c66820_6842D91.0622.920.76Ca2022–20230.23PNF
wsnp_Ku_c2249_43352793A188.0611.225.67Ca2021–20220.10PNF
D_contig35269_3943A33.016.1−34.31Mg2022–20230.13PNF
RAC875_rep_c111781_1793B5.013.0−22.87Ca2022–20230.20PNF
Kukri_c17082_5193B5.024.8−23.06Ca2022–20230.18PNF
RAC875_c13385_12683B5.024.8−23.06Ca2022–20230.18PNF
BS00062806_513D143.0604.626.18Ca2021–20220.10PNF
BS00070060_513D143.0614.626.18Ca2021–20220.10PNF
D_GBF1XID02HLMWB_653D143.0604.426.18Ca2021–20220.10PNF
Excalibur_c51976_1193D143.0611.226.18Ca2021–20220.10PNF
TA006354-09373D143.0611.226.18Ca2021–20220.10PNF
BobWhite_c5246_1963D143.0746.625.67Ca2021–20220.10PNF
BS00070059_513D143.0614.625.67Ca2021–20220.10PNF
BS00105800_513D143.0611.525.67Ca2021–20220.10PNF
D_GDEEGVY01CO81T_813D143.0604.325.67Ca2021–20220.10PNF
Excalibur_c17654_10903D143.0611.225.67Ca2021–20220.10PNF
Excalibur_c6906_8043D143.0612.825.67Ca2021–20220.10PNF
wsnp_Ex_c12963_205299643D143.0612.925.67Ca2021–20220.10PNF
wsnp_Ku_c7264_125451353D143.0612.925.67Ca2021–20220.10PNF
Excalibur_c12032_11014A26.010.639.71Mg2021–20220.36PNF
wsnp_Ex_c7280_124981934A144.0725.648.11Mg2021–20220.12PNF
Ra_c7973_11854A43.046.146.51Mg2021–20220.14PNF
Tdurum_contig59603_744A26.099.237.64Mg2021–20220.39PNF
Tdurum_contig59603_944A26.099.237.64Mg2021–20220.39PNF
Tdurum_contig31139_1434B35.013.9−22.53Mg2022–20230.22PNF
Tdurum_contig31139_794B35.013.9−22.53Mg2022–20230.22PNF
wsnp_Ku_c9140_153901664D79.050.1−35.87Mg2022–20230.07PNF
wsnp_Ex_c2718_50385825A43.046.716.98
18.48		Ca2021–2022,
2022–2023		0.43
0.23		[21]
RAC875_c9984_10035A89.0585.416.55
11.10		Ca2021–2022,
2022–2023		0.31
0.23		[21]
Excalibur_c52167_3555A76.0549.5−28.63Ca2022–20230.12PNF
wsnp_Ra_c17216_260447905A76.0549.5−22.78Ca2022–20230.13PNF
wsnp_Ku_c5308_94500935B21.016.4−20.67Mg2022–20230.39PNF
GENE-3277_1455B20.016.9−20.05Mg2022–20230.40PNF
wsnp_Ex_c12927_204801635B20.016.4−20.05Mg2022–20230.40PNF
Tdurum_contig28802_2136A125.0597.817.94 18.12Mg2021–2022, 2022–20230.19 0.38[22]
BS00077044_516A140.0614.6−33.63Mg2022–20230.09PNF
wsnp_Ex_c34597_428796936A125.0597.819.21 21.02Mg2021–2022, 2022–20230.29 0.37[22]
RFL_Contig6053_30826A126.0597.717.76
24.84		Mg2021–2022,
2022–2023		0.24
0.26		[22]
wsnp_Ex_c34597_428797186B93.0597.8 Ca, Mg2021–2022, 2022–20230.35 0.31
0.46 0.32		PNF
CAP11_c1473_3207A82.052.9−19.47Ca2021–20220.20PNF
BS00078460_517A82.052.9−18.50Ca2021–20220.22PNF
Ex_c9615_12027A82.052.9−18.50Ca2021–20220.22PNF
Ex_c9615_5747A82.049.8−18.50Ca2021–20220.22PNF
RAC875_c52560_1237A76.046.722.30Ca2021–20220.11PNF
BS00022751_517A126.0159.5−41.85Mg2021–20220.34PNF
wsnp_Ex_c25025_342854787A126.0159.4−41.85Mg2021–20220.34PNF
Tdurum_contig45437_16677A74.042.145.24Mg2021–20220.12PNF
Kukri_c31824_6367A183.0696.924.44Mg2022–20230.32PNF
Tdurum_contig31699_2767A183.0696.924.44Mg2022–20230.32PNF
RAC875_c10555_1787B8.035.420.69Ca2021–20220.29PNF
IAAV19027B8.036.820.44Ca2021–20220.29PNF
wsnp_JD_c1285_18482927B10.037.019.93Ca2021–20220.30PNF
BobWhite_c47269_1287B10.037.019.65Ca2021–20220.30PNF
Tdurum_contig97814_3557B95.0641.118.45Ca2022–20230.35PNF
wsnp_Ex_c10430_170640017D118.0112.4−37.60Mg2021–20220.48PNF
# = Genetic position in centiMorgan (cM); * = physical position in base pairs (bp); PNF = potentially new finding.
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Negi, C.; Kumar, K.; Dhariwal, R.; Vyas, P.; Vasistha, N.K. Genome-Wide Association Studies for Grain Micronutrient Concentration in Spring Wheat (Triticum aestivum L.). Plants 2025, 14, 3472. https://doi.org/10.3390/plants14223472

AMA Style

Negi C, Kumar K, Dhariwal R, Vyas P, Vasistha NK. Genome-Wide Association Studies for Grain Micronutrient Concentration in Spring Wheat (Triticum aestivum L.). Plants. 2025; 14(22):3472. https://doi.org/10.3390/plants14223472

Chicago/Turabian Style

Negi, Chandranandani, Krishan Kumar, Raman Dhariwal, Pritesh Vyas, and Neeraj Kumar Vasistha. 2025. "Genome-Wide Association Studies for Grain Micronutrient Concentration in Spring Wheat (Triticum aestivum L.)" Plants 14, no. 22: 3472. https://doi.org/10.3390/plants14223472

APA Style

Negi, C., Kumar, K., Dhariwal, R., Vyas, P., & Vasistha, N. K. (2025). Genome-Wide Association Studies for Grain Micronutrient Concentration in Spring Wheat (Triticum aestivum L.). Plants, 14(22), 3472. https://doi.org/10.3390/plants14223472

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