Next Article in Journal
Satellitome Analysis in the Southern Lapwing (Vanellus chilensis) Genome: Implications for SatDNA Evolution in Charadriiform Birds
Next Article in Special Issue
Heat-Resistant Inbred Lines Coordinate the Heat Response Gene Expression Remarkably in Maize (Zea mays L.)
Previous Article in Journal
Further Delineation of Clinical Phenotype of ZMYND11 Variants in Patients with Neurodevelopmental Dysmorphic Syndrome
Previous Article in Special Issue
Phenotypic and Proteomic Insights into Differential Cadmium Accumulation in Maize Kernels
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 15
Issue 2
10.3390/genes15020257
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Identification of Novel QTL for Mercury Accumulation in Maize Using an Enlarged SNP Panel

by
Jionghao Gao
1,†,
Jianxin Li
1,†,
Jihong Zhang
1,
Yan Sun
1,
Xiaolong Ju
1,
Wenlong Li
1,
Haiyang Duan
1,
Zhengjie Xue
1,
Li Sun
1,
Javed Hussain Sahito
1,
Zhiyuan Fu
1,
Xuehai Zhang
1,* and
Jihua Tang
1,2,*
1
Key Laboratory of Wheat and Maize Crops Science, College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China
2
The Shennong Laboratory, Zhengzhou 450002, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Genes 2024, 15(2), 257; https://doi.org/10.3390/genes15020257
Submission received: 10 January 2024 / Revised: 14 February 2024 / Accepted: 17 February 2024 / Published: 19 February 2024
(This article belongs to the Special Issue Molecular Biology of Crop Abiotic Stress Resistance)
Browse Figures
Versions Notes

Abstract

:
Mercury (Hg) pollution not only poses a threat to the environment but also adversely affects the growth and development of plants, with potential repercussions for animals and humans through bioaccumulation in the food chain. Maize, a crucial source of food, industrial materials, and livestock feed, requires special attention in understanding the genetic factors influencing mercury accumulation. Developing maize varieties with low mercury accumulation is vital for both maize production and human health. In this study, a comprehensive genome-wide association study (GWAS) was conducted using an enlarged SNP panel comprising 1.25 million single nucleotide polymorphisms (SNPs) in 230 maize inbred lines across three environments. The analysis identified 111 significant SNPs within 78 quantitative trait loci (QTL), involving 169 candidate genes under the Q model. Compared to the previous study, the increased marker density and optimized statistical model led to the discovery of 74 additional QTL, demonstrating improved statistical power. Gene ontology (GO) enrichment analysis revealed that most genes participate in arsenate reduction and stress responses. Notably, GRMZM2G440968, which has been reported in previous studies, is associated with the significant SNP chr6.S_155668107 in axis tissue. It encodes a cysteine proteinase inhibitor, implying its potential role in mitigating mercury toxicity by inhibiting cysteine. Haplotype analyses provided further insights, indicating that lines carrying hap3 exhibited the lowest mercury content compared to other haplotypes. In summary, our study significantly enhances the statistical power of GWAS, identifying additional genes related to mercury accumulation and metabolism. These findings offer valuable insights into unraveling the genetic basis of mercury content in maize and contribute to the development of maize varieties with low mercury accumulation.

1. Introduction

Mercury (Hg) pollution poses a significant threat to both the environment and plant growth. Hg is highly toxic and can accumulate in the food chain through methylation, bioaccumulation and biomagnification, potentially affecting animal and human health [1,2]. As a major source of food, industrial materials and livestock feed, maize plays a crucial role in global food production and human well-being. However, increasing industrial activities, automobile usage and pesticide applications have led to rising emissions of Hg gas and particles into the atmosphere. This atmospheric Hg eventually contaminates soil through deposition [3,4,5]. Maize cultivation on contaminated soils introduces risks, as Hg can accumulate in plant tissues and enter the food chain [6]. Inside plants, Hg disrupts critical physiological processes like water and nutrient uptake, transpiration, and photosynthesis. It also impairs enzyme activity, slowing growth and decreasing biomass production, and can even cause mortality [7]. The adverse effects of Hg toxicity extend to humans. Accumulation of Hg in vital organs such as the kidneys and central nervous system has been linked to harmful impacts [1,8]. Understanding the genetic basis of Hg accumulation in maize and developing varieties with reduced Hg uptake are essential strategies for mitigating these threats to both plant and human health.
Recent years have seen a growth in genetic studies investigating Hg accumulation mechanisms. For instance, quantitative trait locus mapping has identified regions associated with Hg tolerance in rice and maize [9,10]. Overexpression analyses have revealed genes that enhance Hg accumulation and tolerance in plants like rice, Arabidopsis and poplar [11,12,13]. These findings provide valuable insights but also highlight the complexities of Hg accumulation in plants. Further genetic investigations are needed to better understand this process. GWAS, utilizing linkage disequilibrium (LD), have emerged as a powerful tool for dissecting genetic–phenotypic relationships. Compared to traditional linkage mapping, GWAS allows the analysis of broader, more diverse populations with higher marker densities, improving mapping resolution to the single-gene level [14]. Maize is well suited for GWAS due to its rapid LD decay and abundant diversity [15,16]. The first maize GWAS in 2007 examined 8590 loci in 553 elite maize inbred lines to identify genes related to oleic acid content in maize kernels [17]. Subsequent studies have identified loci impacting oil concentration or fatty acid composition in kernels and identified QTL related to arsenic content in various maize tissues [16,18].
The statistical power of GWAS depends on various factors, including population size, diversity, marker density, and the choice of statistical models. By optimizing these aspects, more genetic determinants of target traits can be identified [19]. Studies have demonstrated that analyzing large germplasm collections with high-density molecular markers can successfully identify genetic loci [20]. Appropriate statistical models facilitate a more comprehensive resolution of the genetic structure and relationships [21]. The GWAS method SUPER augmented power through model optimization [22]. Enhancements in sequencing depth and marker density facilitate improved genomic coverage, enabling the identification of both genes and non-genetic contributions to traits [23]. Dissecting additional maize traits using an enlarged panel revealed further loci [24]. Advancements in sequencing technology, reduced costs, and continuous GWAS algorithm improvements have made it an efficient approach for identifying genome–phenotype associations and genetic bases of complex traits [25,26].
In this study, we re-conducted a GWAS to dissect the genetic basis of Hg content in five tissues of the 230 maize inbred lines [27]. Our aim was to leverage an expanded SNP panel and optimized statistical models to identify novel loci governing Hg levels in different tissues, enhancing understanding of this complex trait. Overall, characterizing the genetic basis of Hg accumulation in maize has important implications for breeding varieties with reduced Hg uptake and enhancing food safety and human health.

2. Materials and Methods

2.1. Plant Materials and Field Trials

An association mapping panel (AMP) of 230 diverse maize inbred lines, encompassing temperate, tropical, and subtropical backgrounds, was utilized. The panel consisted of 151 inbred lines from temperate backgrounds and 79 from tropical/subtropical backgrounds. In 2012, the AMP underwent cultivation at two distinct locations: Xixian (XX, 114°72′ E, 32°35′ N) and Changge (CG, 113°34′ E, 34°09′ N) are located in northern China. XX experienced an average temperature of 15.2 °C and rainfall of 873.8 mm, while CG had an average temperature of 14.3 °C and rainfall of 462.8 mm, following a randomized complete block design with three replications. These locations were chosen specifically for sampling and evaluating arsenic (As) and mercury (Hg) content. The AMP had previously been employed in a GWAS study exploring the genetic basis of As and Hg accumulation in five maize tissues [27]. Notably, the soil at XX and CG exhibited Hg contents of 457.57 ± 31.30 μg kg−1 and 345.40 ± 22.24 μg kg−1 (pH 6.5), respectively. Detailed information on maize inbred lines, trial specifics, and field management practices can be found in a previous study (Supplementary Table S1, [27]).

2.2. Measurement of Hg Content

Hg content was collected from the kernel, axis, stem, bract, and leaf of the AMP at both XX and CG locations. The phenotypic measurement, as described previously [27], utilized the same dataset for the current GWAS. To facilitate the GWAS, each combination of tissue (kernel, axis, stem, bract, and leaf) at each location (XX, CG, and BLUP) was treated as an individual phenotypic variable. For instance, “Axis_CG” represents the Hg content in the axis at the Changge location. In total, we obtained 15 phenotypic variables, including 10 for the five tissues at XX and CG locations and 5 representing the BLUP value of each inbred line across the two locations.

2.3. Genotype and GWAS

In this study, the AMP comprises 513 maize inbred lines extensively used in prior studies [15]. To augment genotyping data, 556,809 SNPs (referred to as 0.55 M) with a minor allele frequency (MAF) above 0.05 were derived from this set of 513 lines. This dataset combines 56,110 SNPs from 513 lines and 1.03 million SNPs from 368 lines, a subset of the 513 lines. The integration of these datasets used a two-step imputation method based on identity by descent (IBD) and k-nearest neighbors (KNN) algorithms, as described previously [24]. Notably, utilizing 0.55 M has demonstrated a significant enhancement in statistical power in GWAS for traits such as oil concentration and tocopherol content when compared to low-density markers like 56,110 SNPs [28]. Additionally, a second genotype dataset, comprising 1.25 million SNPs (referred to as 1.25 M) with a MAF ≥ 0.05, was obtained through an effective imputation method integrating data from four genotyping platforms [29], including Illumina Maize SNP50 BeadChip [30], deep RNA-sequencing [31], genotyping by sequencing (GBS) and Affymetrix Axiom Maize 600 K array [32]. This high-density genotype dataset has been successfully used in previous GWAS studies elucidating the genetic basis of traits such as oil concentration, amino acids, and arsenic content in maize kernel and other tissues [18,33,34]. Both genotype datasets, 0.55 M and 1.25 M, were employed in the subsequent GWAS analysis of the current study, and they are available at http://www.maizego.org/Resources.html (accessed on 18 February 2024).
GWAS of the 15 variables in the 230 inbred lines underwent analysis in several steps:
(1)
Multiple statistical models, namely 5PCs + K, Q, K, and Q + K, were employed. The 5PCs + K model controls both the top five principal components (PCs) and the kinship matrix, as used in the study by Zhao et al. [27]. The Q model controls only the population structure, the K model solely controls the kinship matrix, and the Q + K model controls both the population structure and the kinship matrix. These analyses utilized the 0.55 M genotypic datasets, and the optimal statistical model was chosen based on quantile–quantile (QQ) plots.
(2)
GWAS results under 0.55 M were compared between 5PCs + K and the optimal statistical model to assess whether changing the model could enhance the statistical power of GWAS.
(3)
Under the optimal statistical model, GWAS was conducted using an enlarged genotypic dataset (1.25 M). The results from GWAS with the 0.55 M and 1.25 M datasets were compared to determine if increasing the marker density improved the statistical power of the analysis.
(4)
Finally, the GWAS results of the optimal statistical model under the enlarged 1.25 M genotypic dataset were used for subsequent analysis. The effective marker number (En) of the two genotypic datasets was calculated using GEC V1.0 software. The result indicated that En was 490,548 for the 1.25 M dataset and 250,345 for the 0.55 M dataset. In addition, the software suggested a significance threshold of p ≤ (1/En), which was used for the association analysis.

2.4. Candidate Gene Identification

In preceding studies, we employed a dataset of 1.25 million SNPs to investigate the linkage disequilibrium decay among 500 maize inbred lines. The results revealed an LD decay of approximately 30 kilobases (kb) with an R2 value of 0.1. Consequently, a 30 kb interval around both the upstream and downstream regions of a significant SNP marker’s physical position was defined as a locus. For gene identification within these identified loci from GWAS, we utilized the B73 reference genome (RefGen_v2) and obtained the maize whole-genome gene list from the Maize Genetics and Genomics Database (MaizeGDB, http://www.maizegdb.org, accessed on 18 February 2024). This list was employed to search for candidate genes within each significant locus. The selection of the most likely candidate gene at each locus was based on functional annotations and expression profiles of the genes in different tissues of the maize inbred line B73.

2.5. Analysis of Expression Level Association of Candidate Genes

In our study, we performed an analysis to explore the association between gene expression levels and genetic variation (1.25 M) using expression data from kernels collected 15 days after pollination in the 368 maize inbred lines. This analysis aimed to identify expression quantitative trait loci (eQTL), which are specific genetic loci associated with variations in gene expression. To assess the significance of these associations, we applied a stringent criterion, considering associations as significant only if they met the condition of p < 1/En, where En represents the effective marker number. This criterion ensures that only robust and biologically relevant associations between genetic variants (SNPs) and gene expression levels are deemed significant eQTL.

2.6. Haplotype Analyses of Candidate Genes

Based on the study’s results, we performed a thorough screening of all single nucleotide polymorphisms (SNPs) within candidate genes using genotype data. Haplotypes were formed by combining best linear unbiased prediction (BLUP) values from the 230 inbred lines. Specifically, haplotypes with representation by more than eight inbred lines were systematically chosen, and their differences were rigorously examined through a t-test, with a significance threshold set at p < 0.01. Additionally, we meticulously generated a heatmap illustrating pairwise linkage disequilibrium using the “LDheatmap” package in the R 3.6.0 statistical software [35].

3. Results

3.1. Model Comparison and Selection

Previous research investigated the genetic factors influencing Hg content in various maize tissues. However, limitations were identified in the statistical model used (5PCs + K), which exhibited excessive conservatism, leading to reduced detection of true associations (type II errors). Additionally, the significance threshold for declaring SNP-trait associations was relatively lenient [36]. To address these limitations, we reanalyzed the Hg content using different GWAS models and an enlarged SNP panel. We first analyzed the Hg content using four statistical models (Q, K, Q + K, 5PCs + K) with 0.55 million SNPs. Examination of quantile–quantile (QQ) plots revealed that the Q model consistently had the best fit to the data (Figure 1 and Figure S2). Comparing results from the Q and 5PCs + K models affirmed the improved power of the Q model (Supplementary Figure S1). Using a Bonferroni-corrected significance threshold (p ≤ 1/EN1, EN1 = 250,345), the Q model identified 46 significant SNPs associated with 32 QTL, whereas 5PCs + K identified only three QTL (all also detected by Q). Similarly, the Q model is also found to be superior to other models at 1.25 M (Supplementary Figure S3).

3.2. Boosting GWAS Power through Increased Marker Density

We then investigated whether increasing the marker density could further improve power. Re-running GWAS with 1.25 million SNPs under the Q model revealed more significant SNPs than with 0.55 million SNPs (Supplementary Figures S4 and S5). Specifically, 1.25 million SNPs identified 111 significant SNPs associated with 78 QTL at (p ≤ 1/EN2, EN2 = 490,548, Figure 2), versus 26 SNPs and 22 QTL with 0.55 million SNPs. These findings demonstrate that increasing the marker density augments GWAS power to discover novel trait-associated loci.

3.3. Significant Loci and Tissue-Specific Variability

To assess the impact of increased marker density on GWAS detection efficiency, we conducted an analysis at a family-wise error rate of 0.05. In Dataset 1, we identified 24 non-redundant QTL (p ≤ 4.0 × 10−6, EN1 = 250,345) and in Dataset 2, we identified 61 non-redundant QTL (p ≤ 2.0 × 10−6, EN2 = 490,548). Only two loci were common between the two datasets. When the p-value was set to p ≤ 4.0 × 10−6, consistent with Dataset 1, a total of 777 QTL were identified, including 24 non-redundant QTL from Dataset 1 (Figure 3).
Expanding our analysis to different tissues revealed 111 SNP-trait associations across 78 loci categorized into 61 non-redundant QTL at a significance level of p ≤ 2.0 × 10−6 (Table S2). Within the CG environment, 50 SNPs involving 34 loci were identified, each explaining phenotypic variation ranging from 8.38% to 24.30%. Notably, three significant SNPs associated with axis tissue were discovered across three QTL, explaining phenotypic variance ranging from 10.3% to 24.3%, with a mean of 16.8%. Similar findings were observed for bract, kernel, leaf, and stem tissues. In the Xixian environment, 37 SNPs involving 25 loci were identified, each explaining 8.47% to 24.71% of the phenotypic variation. For each tissue, specific SNPs and QTL were discovered, contributing to our understanding of the genetic basis of these traits. In the BLUP environment, 24 SNPs and 19 loci were identified, each explaining 9.70% to 23.27% of the variance. Comprehensive analysis across distinct tissues and locations enriched our understanding of the diverse landscape of significant loci, highlighting the genetic contributions to these traits.

3.4. Colocalized Loci Identified across Various Tissues and Environments

Notably, we identified 15 non-redundant QTL encompassing 36 SNP-trait associations consistently detected across tissues and environments (Figure 4). Particularly noteworthy were non-redundant QTL associated with stem tissue housing three QTL within a 127.32~127.37 Mb interval on chromosome 10. These QTL were consistently identified in all environments, explaining sizable phenotypic variations. Another non-redundant QTL linked to axis tissue appeared in three environments within a 155.63~155.69 Mb region on chromosome 6. The observed co-localization patterns indicate stability of these loci against environmental influences.

3.5. Functional Analysis of Identified Genes

To pinpoint the most likely candidate gene and understand their molecular functions. Subsequently, we conducted gene ontology (GO) analysis to reveal the functions served by these genes (Figure 5). Our analysis revealed that these genes predominantly cluster around essential molecular activities, including DNA binding, arsenate reductase (glutaredoxin) activity, jasmonate-amino synthetase activity and other functions. This GO analysis provides valuable insights into the molecular functions of these genes, shedding light on their involvement in the mechanisms of resistance to metal stress and significantly enhancing our understanding of their contributions to plant biology.

3.6. Candidate Gene Analysis

Our exploration into candidate genes within the co-located loci unveiled several noteworthy findings (Table 1). For example, on chromosome 6, GRMZM2G440968 encodes a cysteine proteinase inhibitor, which may alleviate mercury toxicity by inhibiting cysteine, as indicated by its known role in detecting kidney damage [37,38]. Moving to chromosome 10, GRMZM2G005633 encodes Endochitinase B, a pivotal player in bacterial adaptation to stresses, promoting plant growth and development [39]. GRMZM2G125991 encodes endoglucanase 7-like, a cellulose-related enzyme potentially involved in responding to copper pollution in willow roots, impacting cellulase content [40]. GRMZM2G125943 encodes a histidine kinase, recognized for its pivotal role in plant stress and hormone regulation. Previous studies have illuminated its involvement in responding to challenges such as cold stress and Cordyceps sinensis mimicry [41,42]. An intriguing observation comes from a locus (the peak SNP is chr5.S_2356674) on chromosome 5, exclusively detected in the XX environment, housing seven candidate genes. Notably, GRMZM2G002805, GRMZM2G144188 and GRMZM2G144172 within this locus encode zinc finger proteins, known to play a role in alternative splicing under conditions of arsenic poisoning. Specifically, As3+ can displace Zn2+ in ZRANB2, leading to structural changes affecting protein function. Studies have also highlighted the impact of Hg (II) leading to structural changes affecting protein function [43,44,45,46]. Subsequently, 19 candidate genes were identified in 9 QTL, involved in protein synthesis and lipid metabolism. Further functional analysis, combining expression GWAS with the expression levels of 16 genes and 1.25 M SNPs, revealed significant correlations for all 16 genes (Table 1). Haplotype analysis of GRMZM2G440968 demonstrated three haplotypes, with Hap3 exhibiting the lowest mercury content compared to other haplotypes. LD analysis within one LD decay distance (±30 kb) upstream and downstream of the lead SNP unveiled linkage relationships between them, emphasizing co-inheritance and linkage on the genome (Figure 6). These findings provide valuable insights into the genetic mechanisms underlying various physiological and metabolic processes in maize.

4. Discussion

In plant GWAS studies, maximizing the statistical power is paramount. Different maize traits have varying sensitivities to different statistical models, a phenomenon intricately linked to population structure. Yang’s seminal work underscored the pivotal role of selecting the most appropriate statistical model to augment GWAS statistical power [24]. Extending this line of inquiry, Zhao et al. conducted a GWAS study on arsenic content across five maize tissues, meticulously assessing different statistical models to balance minimizing false positives and optimizing analysis sensitivity. Their findings illuminated that the K model and Q + K model exhibited excessive conservatism, potentially increasing type II errors. In contrast, the Q model emerged as the optimal choice, effectively controlling false positives while preserving power [18].
In the present study, we encountered a similar scenario where both the K model and Q + K model demonstrated unwarranted stringency, while the Q model provided an ideal balance. Therefore, we selected the Q model to investigate the genetic basis of Hg content in five maize tissues. Using this model with 0.55 M SNPs, we identified more significant SNPs and loci compared to the previous study. However, increasing marker density and population size can further enhance GWAS power, as demonstrated by others [24,47]. To explore this, we reanalyzed the data using 1.25 M SNPs under the Q model. Directly comparing the results revealed substantial gains in power, with over double the number of significant SNPs and loci detected. This increase derives from a richer representation of the genome’s genetic variation, consistent with prior reports. Innovative statistical models have also proven effective by optimizing genetic and pedigree relationships [48]. Thus, both expanding genotype data and refining statistical models contribute synergistically to the increased statistical power of GWAS.
Analysis of candidate genes revealed several with notable functions. For example, GRMZM2G162413 plays a key role in resisting ear rot and temperature stress [49]. Another significant candidate, GRMZM2G011520 encodes arsenate reductase critical for arsenic detoxification [50]. GRMZM5G877941 links to growth inhibition and senescence under methylglyoxal stress [51]. GRMZM2G150496 exhibits hypersensitivity to arsenate stress and reduced uptake compared to wild-type [52]. The emerging candidate GRMZM2G440968 encodes a cysteine proteinase inhibitor that may alleviate mercury toxicity by inhibiting cysteine, as indicated by its role in detecting kidney damage and ameliorating nephrotoxicity. We speculate that plants may undergo oxidative stress under mercury stress, leading to the production of reactive oxygen species and causing damage to cellular components. Cysteine proteinase inhibitors, by regulating the activity of cysteine proteinases, may help modulate the plant’s response to oxidative stress and maintain cellular homeostasis under mercury exposure (Supplementary Figure S6).
Mercury poisoning poses a substantial threat, with potential exposure pathways including ingestion through the food chain or contact with mercury-containing air, water, or specific occupational settings, leading to adverse effects on the nervous system [8]. Intriguingly, research indicates that even mercury levels below 50 µg/g in hair can pose neurological risks, manifesting as sensory disturbances [53]. Furthermore, studies underscore the neurobehavioral and neurochemical toxicity associated with prolonged low-dose cinnabar (HgS) exposure, shedding light on the sedative and neurotoxic effects of this mineral medicine [54]. The primary approach to mitigating mercury poisoning involves chelation therapy, designed to enhance methylmercury excretion by forming complexes with mercury ions. However, existing chelating agents have limitations, including reduced therapeutic efficacy and the potential loss of essential elements like iron and calcium. Some chelating agents may even exhibit toxicity [55,56]. Consequently, effective mercury poisoning treatment remains a formidable challenge. Amid these challenges, the cultivation of mercury-tolerant maize emerges as a promising solution to address mercury poisoning. Additionally, exploration of organic selenium compounds and thiourea resin has shown promise in reducing methylmercury toxicity. However, these agents primarily focus on excretion rather than tissue repair. Research has also spotlighted the beneficial effects of natural plant extracts in mitigating the toxic impact of methylmercury and reducing metal neurotoxicity. Notably, garlic’s mercaptans bind with methylmercury, reducing its toxicity. Fisetin, derived from various fruits, effectively reduces brain methylmercury accumulation and related toxicity. These findings open avenues for exploring natural substances in maize with therapeutic potential against mercury poisoning [57,58].

5. Conclusions

In this study, we leveraged an enlarged SNP panel comprising 1.25 million SNPs and improved statistical models to re-conduct a genome-wide association study of mercury content in five tissues of 230 maize inbred lines. Compared to prior research utilizing lower-density markers, our approach identified considerably more significant quantitative trait loci associated with mercury accumulation. Specifically, we uncovered 74 additional QTL, for a total of 169 candidate genes implicated in mercury content, of which 142 have annotated functions. Notably, GRMZM2G440968, encoding a cysteine proteinase inhibitor, emerges as a potential key regulator in alleviating mercury toxicity in plants. By inhibiting cysteine proteinases, this gene may help mitigate the strong binding of mercury to cysteine thiolate anions in vital enzymes. Haplotype analysis showed that lines containing haplotype 3 exhibited significantly lower mercury levels. Through enhanced marker density and improved modeling, our research provides deeper insights into the genetic mechanisms underlying mercury accumulation in maize. Ultimately, these findings are critical for developing maize varieties with reduced mercury content, thereby contributing to broader efforts at ensuring food safety and human health. In conclusion, we demonstrate that augmenting GWAS approaches can uncover additional genetic factors influencing metal stress tolerance in plants.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/genes15020257/s1, Table S1. Pedigree information of the 230 inbred lines used in this study. Table S2. List of candidate genes and their annotations. Figure S1. Comparison of Manhattan plots resulting from GWAS, based on 0.55 M SNPs, using Q model and 5PCs + K model for mercury content in maize axis, stem, bract, leaf, and kernel at BLUP environments. Figure S2. Comparison of quantile–quantile (QQ) plots resulting from GWAS, based on 0.55 M SNPs, using three models (Q, K, and Q + K) for mercury content in maize axis, stem, bract, leaf, and kernel in three environments. Figure S3. Comparison of quantile–quantile (QQ) plots resulting from GWAS, based on 1.25 M SNPs, using three models (Q, K, and Q + K) for mercury content in maize axis, stem, bract, leaf, and kernel in three environments. Figure S4. Comparison of quantile–quantile (QQ) plots resulting from GWAS, based on 0.55 M SNPs and 1.25 M SNPs, using Q model for mercury content in maize axis, stem, bract, leaf, and kernel in three environments. Figure S5. Comparison of Manhattan plots resulting from GWAS, based on 0.55 M SNPs and 1.25 M SNPs, using Q model for mercury content in maize axis, stem, bract, leaf, and kernel in three environments. Figure S6. Schematic representation of GRMZM2G440968 modulating cysteine protease in response to oxidative stress.

Author Contributions

Z.F.; Methodology, Y.S. and J.H.S.; Software, J.Z. and X.J.; Investigation, W.L., H.D., Z.X. and L.S.; Data curation, X.Z.; Writing—original draft, J.G., J.L. and X.Z.; Writing—review & editing, X.Z.; Project administration, X.Z. and J.T.; Funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Natural Science Foundation of China (32171980 to X.Z.); the project was funded by the China Postdoctoral Science Foundation (2020M682295 to X.Z.), the Henan Province Science and Technology Attack Project (232102110181 to X.Z.), the Henan Provincial Higher Education Key Research Project (24B210003 to X.Z.), the First-class postdoctoral research grant in Henan Province (202001032 to X.Z.), and the Research start-up fund to youth talents of Henan Agricultural University (30500563 to X.Z.).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets supporting the conclusions of this article are included within the article (and its supplementary files). Genotypic data could download from http://www.maizego.org/Resources.html (accessed on 18 February 2024), and the phenotypic data could obtain from reference [27] or be shared on reasonable request of the corresponding authors.

Acknowledgments

We thank the research group of Jianbing Yan at Huazhong Agricultural University for providing genotypic data.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Rahman, Z.; Singh, V.P. The relative impact of toxic heavy metals (THMs) (arsenic (As); cadmium (Cd); chromium (Cr)(VI); mercury (Hg); and lead (Pb)) on the total environment: An overview. Environ. Monit. Assess. 2019, 191, 419. [Google Scholar] [CrossRef]
  2. Shao, R.; Zhang, J.; Shi, W.; Wang, Y.; Tang, Y.; Liu, Z.; Sun, W.; Wang, H.; Guo, J.; Meng, Y.; et al. Mercury stress tolerance in wheat and maize is achieved by lignin accumulation controlled by nitric oxide. Environ. Pollut. 2022, 307, 119488. [Google Scholar] [CrossRef]
  3. Boerleider, R.Z.; Roeleveld, N.; Scheepers, P. Human biological monitoring of mercury for exposure assessment. AIMS Environ. Sci. 2017, 4, 251–276. [Google Scholar] [CrossRef]
  4. Wang, C.; Wang, Z.; Gao, Y.; Zhang, X. Planular-vertical distribution and pollution characteristics of cropland soil Hg and the estimated soil-air exchange fluxes of gaseous Hg over croplands in northern China. Environ. Res. 2021, 195, 110810. [Google Scholar] [CrossRef]
  5. Zhang, L.; Wong, M.H. Environmental mercury contamination in China: Sources and impacts. Environ. Int. 2017, 33, 108–121. [Google Scholar] [CrossRef]
  6. Sun, T.; Wang, Z.; Zhang, X.; Niu, Z.; Chen, J. Influences of high-level atmospheric gaseous elemental mercury on methylmercury accumulation in maize (Zea mays L.). Environ. Pollut. 2020, 265 Pt B, 114890. [Google Scholar] [CrossRef]
  7. Liu, S.; Wang, X.; Guo, G.; Yan, Z. Status and environmental management of soil mercury pollution in China: A review. J. Environ. Manag. 2021, 277, 111442. [Google Scholar] [CrossRef]
  8. Rocha, J.; Aschner, M.; Dorea, J.G.; Ceccatelli, S.; Farina, M.; Silveira, L. Mercury toxicity. J. Biomed. Biotechnol. 2012, 2012, 831890. [Google Scholar] [CrossRef]
  9. Wang, C.; Wang, T.; Mu, P.; Li, Z.; Yang, L. Quantitative Trait Loci for Mercury Tolerance in Rice Seedlings. Rice Sci. 2013, 20, 238–242. [Google Scholar] [CrossRef]
  10. Fu, Z.; Li, W.; Zhang, Q.; Wang, L.; Zhang, X.; Song, G.; Fu, Z.; Ding, D.; Liu, Z.; Tang, J. Quantitative trait loci for mercury accumulation in maize (Zea mays L.) identified using a RIL population. PLoS ONE 2014, 9, e107243. [Google Scholar] [CrossRef]
  11. Hu, T.; Liu, Y.; Zhu, S.; Qin, J.; Li, W.; Zhou, N. Overexpression of OsLea14-A improves the tolerance of rice and increases Hg accumulation under diverse stresses. Environ. Sci. Pollut. Res. Int. 2019, 26, 10537–10551. [Google Scholar] [CrossRef]
  12. Sun, L.; Ma, Y.; Wang, H.; Huang, W.; Wang, X.; Han, L.; Sun, W.; Han, E.; Wang, B. Overexpression of PtABCC1 contributes to mercury tolerance and accumulation in Arabidopsis and poplar. Biochem. Biophys. Res. Commun. 2018, 497, 997–1002. [Google Scholar] [CrossRef]
  13. Xu, S.; Sun, B.; Wang, R.; He, J.; Xia, B.; Xue, Y.; Wang, R. Overexpression of a bacterial mercury transporter MerT in Arabidopsis enhances mercury tolerance. Biochem. Biophys. Res. Commun. 2017, 490, 528–534. [Google Scholar] [CrossRef]
  14. Yu, J.; Buckler, E.S. Genetic association mapping and genome organization of maize. Curr. Opin. Biotechnol. 2006, 17, 155–160. [Google Scholar] [CrossRef] [PubMed]
  15. Xiao, Y.; Liu, H.; Wu, L.; Warburton, M.; Yan, J. Genome-wide Association Studies in Maize: Praise and Stargaze. Mol. Plant 2017, 10, 359–374. [Google Scholar] [CrossRef] [PubMed]
  16. Li, H.; Peng, Z.; Yang, X.; Wang, W.; Fu, J.; Wang, J.; Han, Y.; Chai, Y.; Guo, T.; Yang, N.; et al. Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels. Nat. Genet. 2013, 45, 43–50. [Google Scholar] [CrossRef] [PubMed]
  17. Beló, A.; Zheng, P.; Luck, S.; Shen, B.; Meyer, D.J.; Li, B.; Tingey, S.; Rafalski, A. Whole genome scan detects an allelic variant of fad2 associated with increased oleic acid levels in maize. Mol. Genet. Genom. 2008, 279, 1–10. [Google Scholar] [CrossRef]
  18. Zhao, Z.; Zhang, H.; Fu, Z.; Chen, H.; Lin, Y.; Yan, P.; Li, W.; Xie, H.; Guo, Z.; Zhang, X.; et al. Genetic-based dissection of arsenic accumulation in maize using a genome-wide association analysis method. Plant Biotechnol. J. 2018, 16, 1085–1093. [Google Scholar] [CrossRef]
  19. Korte, A.; Farlow, A. The advantages and limitations of trait analysis with GWAS: A review. Plant Methods 2013, 9, 29. [Google Scholar] [CrossRef]
  20. Huang, X.; Zhao, Y.; Wei, X.; Li, C.; Wang, A.; Zhao, Q.; Li, W.; Guo, Y.; Deng, L.; Zhu, C.; et al. Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat. Genet. 2011, 44, 32–39. [Google Scholar] [CrossRef]
  21. Wang, S.B.; Feng, J.Y.; Ren, W.L.; Huang, B.; Zhou, L.; Wen, Y.J.; Zhang, J.; Dunwell, J.M.; Xu, S.; Zhang, Y.M. Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology. Sci. Rep. 2016, 6, 19444. [Google Scholar] [CrossRef]
  22. Wang, Q.; Tian, F.; Pan, Y.; Buckler, E.S.; Zhang, Z. A SUPER powerful method for genome wide association study. PLoS ONE 2014, 9, e107684. [Google Scholar] [CrossRef]
  23. Li, X.; Zhu, C.; Yeh, C.T.; Wu, W.; Takacs, E.M.; Petsch, K.A.; Tian, F.; Bai, G.; Buckler, E.S.; Muehlbauer, G.J.; et al. Genic and nongenic contributions to natural variation of quantitative traits in maize. Genome Res. 2012, 22, 2436–2444. [Google Scholar] [CrossRef]
  24. Yang, N.; Lu, Y.; Yang, X.; Huang, J.; Zhou, Y.; Ali, F.; Wen, W.; Liu, J.; Li, J.; Yan, J. Genome wide association studies using a new nonparametric model reveal the genetic architecture of 17 agronomic traits in an enlarged maize association panel. PLoS Genet. 2014, 10, e1004573. [Google Scholar] [CrossRef]
  25. Tian, F.; Bradbury, P.; Brown, P.; Hung, H.; Sun, Q.; Flint-Garcia, S.; Rocheford, T.R.; McMullen, M.D.; Holland, J.B.; Buckler, E.S. Genome-wide association study of leaf architecture in the maize nested association mapping population. Nat. Genet. 2011, 43, 159–162. [Google Scholar] [CrossRef] [PubMed]
  26. Zhao, K.; Tung, C.W.; Eizenga, G.C.; Wright, M.H.; Ali, M.L.; Price, A.H.; Norton, G.J.; Islam, M.R.; Reynolds, A.; Mezey, J.; et al. Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat. Commun. 2011, 2, 467. [Google Scholar] [CrossRef] [PubMed]
  27. Zhao, Z.; Fu, Z.; Lin, Y.; Chen, H.; Liu, K.; Xing, X.; Liu, Z.; Li, W.; Tang, J. Genome-wide association analysis identifies loci governing mercury accumulation in maize. Sci. Rep. 2017, 7, 247. [Google Scholar] [CrossRef] [PubMed]
  28. Wang, H.; Xu, S.; Fan, Y.; Liu, N.; Zhan, W.; Liu, H.; Xiao, Y.; Li, K.; Pan, Q.; Li, W.; et al. Beyond pathways: Genetic dissection of tocopherol content in maize kernels by combining linkage and association analyses. Plant Biotechnol. J. 2018, 16, 1464–1475. [Google Scholar] [CrossRef]
  29. Liu, H.; Luo, X.; Niu, L.; Xiao, Y.; Chen, L.; Liu, J.; Wang, X.; Jin, M.; Li, W.; Zhang, Q.; et al. Distant eQTLs and Non-coding Sequences Play Critical Roles in Regulating Gene Expression and Quantitative Trait Variation in Maize. Mol. Plant 2017, 10, 414–426. [Google Scholar] [CrossRef]
  30. Li, Q.; Yang, X.; Xu, S.; Cai, Y.; Zhang, D.; Han, Y.; Li, L.; Zhang, Z.; Gao, S.; Li, J.; et al. Genome-wide association studies identified three independent polymorphisms associated with alpha-tocopherol content in maize kernels. PLoS ONE 2012, 7, e36807. [Google Scholar]
  31. Fu, J.; Cheng, Y.; Linghu, J.; Yang, X.; Kang, L.; Zhang, Z.; Zhang, J.; He, C.; Du, X.; Peng, Z.; et al. RNA sequencing reveals the complex regulatory network in the maize kernel. Nat. Commun. 2013, 4, 2832. [Google Scholar] [CrossRef] [PubMed]
  32. Elshire, R.; Glaubitz, J.; Sun, Q.; Poland, J.; Kawamoto, K.; Buckler, E.; Mitchell, S. A robust; simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 2011, 6, e19379. [Google Scholar] [CrossRef]
  33. Deng, M.; Li, D.; Luo, J.; Xiao, Y.; Liu, H.; Pan, Q.; Zhang, X.; Jin, M.; Zhao, M.; Yan, J. The genetic architecture of amino acids dissection by association and linkage analysis in maize. Plant Biotechnol. J. 2017, 15, 1250–1263. [Google Scholar] [CrossRef]
  34. Liu, H.; Wang, X.; Warburton, M.L.; Wen, W.; Jin, M.; Deng, M.; Liu, J.; Tong, H.; Pan, Q.; Yang, X.; et al. Genomic; Transcriptomic; and Phenomic Variation Reveals the Complex Adaptation of Modern Maize Breeding. Mol. Plant 2015, 8, 871–884. [Google Scholar] [CrossRef] [PubMed]
  35. Liu, B.; Zhang, B.; Yang, Z.; Liu, Y.; Yang, S.; Shi, Y.; Jiang, C.; Qin, F. Manipulating ZmEXPA4 expression ameliorates the drought-induced prolonged anthesis and silking interval in maize. Plant Cell 2021, 33, 2058–2071. [Google Scholar] [CrossRef]
  36. Huang, X.; Wei, X.; Sang, T.; Zhao, Q.; Feng, Q.; Zhao, Y.; Li, C.; Zhu, C.; Lu, T.; Zhang, Z.; et al. Genome-wide association studies of 14 agronomic traits in rice landraces. Nat. Genet. 2010, 42, 961–967. [Google Scholar] [CrossRef]
  37. Khorchid, A.; Ikura, M. Bacterial histidine kinase as signal sensor and transducer. Int. J. Biochem. Cell Biol. 2006, 38, 307–312. [Google Scholar] [CrossRef]
  38. Yu, X.; Meng, X.; Xu, M.; Zhang, X.; Zhang, Y.; Ding, G.; Huang, S.; Zhang, A.; Jia, Z. Celastrol ameliorates cisplatin nephrotoxicity by inhibiting NF-kappaB and improving mitochondrial function. EBioMedicine 2018, 36, 266–280. [Google Scholar] [CrossRef]
  39. Stratton, A.; Ericksen, M.; Harris, T.V.; Symmonds, N.; Silverstein, T.P. Mercury(II) binds to both of chymotrypsin’s histidines, causing inhibition followed by irreversible denaturation/aggregation. Protein Sci. 2017, 26, 292–305. [Google Scholar] [CrossRef]
  40. Cao, Y.; Ma, C.; Chen, H.; Chen, G.; White, J.C.; Xing, B. Copper stress in flooded soil: Impact on enzyme activities; microbial community composition and diversity in the rhizosphere of Salix integra. Sci. Total Environ. 2020, 704, 135350. [Google Scholar] [CrossRef] [PubMed]
  41. Liu, Y.N.; Liu, B.Y.; Ma, Y.C.; Yang, H.L.; Liu, G.Q. Analysis of reference genes stability and histidine kinase expression under cold stress in Cordyceps militaris. PLoS ONE 2020, 15, e0236898. [Google Scholar] [CrossRef] [PubMed]
  42. Kumar, M.; Verslues, P. Stress physiology functions of the Arabidopsis histidine kinase cytokinin receptors. Physiol. Plant 2015, 154, 369–380. [Google Scholar] [CrossRef]
  43. Abbehausen, C. Zinc finger domains as therapeutic targets for metal-based compounds—An update. Metallomics 2019, 11, 15–28. [Google Scholar] [CrossRef]
  44. Banerjee, M.; Ferragut Cardoso, A.P.; Lykoudi, A.; Wilkey, D.W.; Pan, J.; Watson, W.H.; Garbett, N.C.; Rai, S.N.; Merchant, M.L.; States, J.C. Arsenite Exposure Displaces Zinc from ZRANB2 Leading to Altered Splicing. Chem. Res. Toxicol. 2020, 33, 1403–1417. [Google Scholar] [CrossRef]
  45. Razmiafshari, M.; Kao, J.; d’Avignon, A.; Zawia, N.H. NMR identification of heavy metal-binding sites in a synthetic zinc finger peptide: Toxicological implications for the interactions of xenobiotic metals with zinc finger proteins. Toxicol. Appl. Pharmacol. 2001, 172, 1–10. [Google Scholar] [CrossRef]
  46. Sivo, V.; D’Abrosca, G.; Baglivo, I.; Iacovino, R.; Pedone, P.V.; Fattorusso, R.; Russo, L.; Malgieri, G.; Isernia, C. Ni(II); Hg(II); and Pb(II) Coordination in the Prokaryotic Zinc-Finger Ros87. Inorg. Chem. 2019, 58, 1067–1080. [Google Scholar] [CrossRef]
  47. Zhang, X.; Warburton, M.L.; Setter, T.; Liu, H.; Xue, Y.; Yang, N.; Yan, J.; Xiao, Y. Genome-wide association studies of drought-related metabolic changes in maize using an enlarged SNP panel. Theor. Appl. Genet. 2016, 129, 1449–1463. [Google Scholar] [CrossRef]
  48. Tibbs Cortes, L.; Zhang, Z.; Yu, J. Status and prospects of genome-wide association studies in plants. Plant Genome 2021, 14, e20077. [Google Scholar] [CrossRef]
  49. Liao, X.; Sun, J.; Li, Q.; Ding, W.; Zhao, B.; Wang, B.; Zhou, S.; Wang, H. ZmSIZ1a and ZmSIZ1b play an indispensable role in resistance against Fusarium ear rot in maize. Mol. Plant Pathol. 2023, 24, 711–724. [Google Scholar] [CrossRef]
  50. Kumari, N.; Jagadevan, S. Genetic identification of arsenate reductase and arsenite oxidase in redox transformations carried out by arsenic metabolising prokaryotes—A comprehensive review. Chemosphere 2016, 163, 400–412. [Google Scholar] [CrossRef]
  51. An, B.; Lan, J.; Deng, X.; Chen, S.; Ouyang, C.; Shi, H.; Yang, J.; Li, Y. Silencing of D-Lactate Dehydrogenase Impedes Glyoxalase System and Leads to Methylglyoxal Accumulation and Growth Inhibition in Rice. Front. Plant Sci. 2017, 8, 2071. [Google Scholar] [CrossRef]
  52. Sun, Y.; Xu, W.; Wu, L.; Wang, R.; He, Z.; Ma, M. An Arabidopsis mutant of inositol pentakisphosphate 2-kinase AtIPK1 displays reduced arsenate tolerance. Plant Cell Environ. 2016, 39, 416–426. [Google Scholar] [CrossRef]
  53. Maruyama, K.; Yorifuji, T.; Tsuda, T.; Sekikawa, T.; Nakadaira, H.; Saito, H. Methyl mercury exposure at Niigata; Japan: Results of neurological examinations of 103 adults. J. Biomed. Biotechnol. 2012, 2012, 635075. [Google Scholar] [CrossRef]
  54. Huang, C.; Liu, S.; Lin-Shiau, S. Neurotoxicological effects of cinnabar (a Chinese mineral medicine; HgS) in mice. Toxicol. Appl. Pharmacol. 2007, 224, 192–201. [Google Scholar] [CrossRef]
  55. Canabady-Rochelle, L.L.; Harscoat-Schiavo, C.; Kessler, V.; Aymes, A.; Fournier, F.; Girardet, J.M. Determination of reducing power and metal chelating ability of antioxidant peptides: Revisited methods. Food Chem. 2015, 183, 129–135. [Google Scholar] [CrossRef]
  56. Torres-Fuentes, C.; Alaiz, M.; Vioque, J. Iron-chelating activity of chickpea protein hydrolysate peptides. Food Chem. 2012, 134, 1585–1588. [Google Scholar] [CrossRef]
  57. Chang, J.; Zhou, Y.; Wang, Q.; Aschner, M.; Lu, R. Plant components can reduce methylmercury toxication: A mini-review. Biochim. Biophys. Acta Gen. Subj. 2019, 1863, 129290. [Google Scholar] [CrossRef]
  58. Li, Y.; Takada, M.; Mizuno, N. Premotor neurons projecting simultaneously to two orofacial motor nuclei by sending their branched axons. A study with a fluorescent retrograde double-labeling technique in the rat. Neurosci. Lett. 1993, 152, 29–32. [Google Scholar] [CrossRef] [PubMed]
Genes 15 00257 g001
Figure 1. Comparison of Quantile–Quantile (QQ) plots resulting from GWAS. QQ plots were generated based on 0.55 M SNPs using four models (Q, K, Q + K and 5PCs + K) for BLUP values of mercury content in maize axis, stem, bract, leaf, and kernel.
Figure 1. Comparison of Quantile–Quantile (QQ) plots resulting from GWAS. QQ plots were generated based on 0.55 M SNPs using four models (Q, K, Q + K and 5PCs + K) for BLUP values of mercury content in maize axis, stem, bract, leaf, and kernel.
Genes 15 00257 g001
Genes 15 00257 g002
Figure 2. Manhattan plots for mercury content based on 1.25 M SNPs in five different tissues across different locations. Significant loci are highlighted in red and black. The red locus indicates significance at p ≤ 2.0 × 10−6 (EN2 = 490,548), while the black locus corresponds to significance reported in a previous study at p ≤ 1.0 × 10−4. The threshold lines are colored in red and green, respectively. The ring represents CG, XX and BLUP from the inside out.
Figure 2. Manhattan plots for mercury content based on 1.25 M SNPs in five different tissues across different locations. Significant loci are highlighted in red and black. The red locus indicates significance at p ≤ 2.0 × 10−6 (EN2 = 490,548), while the black locus corresponds to significance reported in a previous study at p ≤ 1.0 × 10−4. The threshold lines are colored in red and green, respectively. The ring represents CG, XX and BLUP from the inside out.
Genes 15 00257 g002
Genes 15 00257 g003
Figure 3. GWAS discoveries for two genotypic datasets with different numbers of markers. A Venn diagram depicts significant loci identified in the two genotypic datasets along with their respective thresholds. Dataset 1 (D1) comprises 0.55 million SNPs, while Dataset 2 (D2) contains 1.25 million SNPs. P1 and P2 represent the significance thresholds for p ≤ 1/NE, where NE is 250, 345 for D1 and 490, 548 for D2.
Figure 3. GWAS discoveries for two genotypic datasets with different numbers of markers. A Venn diagram depicts significant loci identified in the two genotypic datasets along with their respective thresholds. Dataset 1 (D1) comprises 0.55 million SNPs, while Dataset 2 (D2) contains 1.25 million SNPs. P1 and P2 represent the significance thresholds for p ≤ 1/NE, where NE is 250, 345 for D1 and 490, 548 for D2.
Genes 15 00257 g003
Genes 15 00257 g004
Figure 4. Chromosomal distribution of QTL for Hg content identified by GWAS. QTL positions and significance (represented by circle size) across the maize genome are indicated by black circles. The x axis displays physical positions across the maize genome in Mb. A heat map below the x axis illustrates the density of QTL across the genome, with a window size of 10 Mb. Detailed information on all detected QTL is provided in Table S2. Different traits are marked by distinct colors as shown on the right.
Figure 4. Chromosomal distribution of QTL for Hg content identified by GWAS. QTL positions and significance (represented by circle size) across the maize genome are indicated by black circles. The x axis displays physical positions across the maize genome in Mb. A heat map below the x axis illustrates the density of QTL across the genome, with a window size of 10 Mb. Detailed information on all detected QTL is provided in Table S2. Different traits are marked by distinct colors as shown on the right.
Genes 15 00257 g004
Genes 15 00257 g005
Figure 5. Enriched GO terms among the candidate genes. Enriched GO terms in (a) biological process; (b) molecular process; (c) cellular process.
Figure 5. Enriched GO terms among the candidate genes. Enriched GO terms in (a) biological process; (b) molecular process; (c) cellular process.
Genes 15 00257 g005
Genes 15 00257 g006
Figure 6. GRMZM2G440968 affect Hg content in maize axis. (a). Haplotype analysis of GRMZM2G440968. Significance levels: ** p < 0.01, *** p < 0.001. (b). Expanded Manhattan plots showing the main SNPs, with the red dot indicating the lead SNP. R2 values of SNPs associated with GRMZM2G440968 are also depicted.
Figure 6. GRMZM2G440968 affect Hg content in maize axis. (a). Haplotype analysis of GRMZM2G440968. Significance levels: ** p < 0.01, *** p < 0.001. (b). Expanded Manhattan plots showing the main SNPs, with the red dot indicating the lead SNP. R2 values of SNPs associated with GRMZM2G440968 are also depicted.
Genes 15 00257 g006
Table 1. Candidate genes revealed by multiple locations.
Table 1. Candidate genes revealed by multiple locations.
ID aChr.Position bTraitLocationSNPp Value cR2 dCandidate Gene eAnnotationExpressed or Not fVerified by Expression GWAS g
14167403585AxisBLUPchr4.S_1674035851.54 × 10−60.18GRMZM2G125943histidine kinaseexpressTure
GRMZM2G125991endoglucanase 7-likeexpressTure
GRMZM2G029859pentatricopeptide repeat-containing protein At3g16610expressTure
24167403585AxisXXchr4.S_1674035851.33 × 10−60.18GRMZM2G125943histidine kinaseexpressTure
GRMZM2G125991endoglucanase 7-likeexpressTure
GRMZM2G029859pentatricopeptide repeat-containing protein At3g16610expressTure
352356674KernelXXchr5.S_23566741.31 × 10−60.10GRMZM2G002825actin-depolymerizing factor 3expressTure
GRMZM2G002805zinc finger protein ZAT5expressTure
GRMZM2G002815NAexpressTure
GRMZM2G144188dof zinc finger protein DOF2.4-likenot
GRMZM2G144172dof zinc finger protein DOF2.4-likeexpressTure
GRMZM2G003068NAexpressTure
GRMZM2G003108CRAL/TRIO domain containing proteinexpressTure
46155668107AxisBLUPchr6.S_1556681072.82 × 10−80.13GRMZM2G566873NAnot
GRMZM2G140805NAnot
GRMZM2G440949dr1-associated corepressorexpressTure
GRMZM2G440968cystatin 3expressTure
GRMZM2G140817putative cytochrome P450 superfamily proteinexpressTure
56155668107AxisCGchr6.S_1556681076.77 × 10−70.10GRMZM2G566873NAnot
GRMZM2G140805NAnot
GRMZM2G440949dr1-associated corepressorexpressTure
GRMZM2G440968cystatin 3expressTure
GRMZM2G140817putative cytochrome P450 superfamily proteinexpressTure
66155668107AxisXXchr6.S_1556681071.46 × 10−80.13GRMZM2G566873NAnot
GRMZM2G140805NAnot
GRMZM2G440949dr1-associated corepressorexpressTure
GRMZM2G440968cystatin 3expressTure
GRMZM2G140817putative cytochrome P450 superfamily proteinexpressTure
710127359876StemBLUPchr10.S_1273598768.01 × 10−90.23GRMZM2G005633Endochitinase BexpressTure
GRMZM2G006428NAexpressTure
GRMZM2G006216S-adenosyl-L-methionine-dependent methyltransferase superfamily proteinexpressTure
GRMZM2G005939NAexpressTure
810127359876StemCGchr10.S_1273598761.11 × 10−60.17GRMZM2G005633Endochitinase BexpressTure
GRMZM2G006428NAexpressTure
GRMZM2G006216S-adenosyl-L-methionine-dependent methyltransferase superfamily proteinexpressTure
GRMZM2G005939NAexpressTure
910127359876StemXXchr10.S_1273598765.81 × 10−90.24GRMZM2G005633Endochitinase BexpressTure
GRMZM2G006428NAexpressTure
GRMZM2G006216S-adenosyl-L-methionine-dependent methyltransferase superfamily proteinexpressTure
GRMZM2G005939NAexpressTure
Notes: All QTL with overlapping QTL regions were categorized as a locus. a. ID of loci was numbered according to chromosome and position by an ascending order method; b. Physical position of each SNP based on B73 RefGen_v2; c. p value of the corresponding trait calculated by Q model; d. The phenotypic variance explained by the corresponding locus; e. A plausible biological candidate gene in the locus or the nearest annotated gene to the lead SNP. f. Whether the gene is expressed or not; g. validation of the correlation analysis of gene expression level.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Gao, J.; Li, J.; Zhang, J.; Sun, Y.; Ju, X.; Li, W.; Duan, H.; Xue, Z.; Sun, L.; Hussain Sahito, J.; et al. Identification of Novel QTL for Mercury Accumulation in Maize Using an Enlarged SNP Panel. Genes 2024, 15, 257. https://doi.org/10.3390/genes15020257

AMA Style

Gao J, Li J, Zhang J, Sun Y, Ju X, Li W, Duan H, Xue Z, Sun L, Hussain Sahito J, et al. Identification of Novel QTL for Mercury Accumulation in Maize Using an Enlarged SNP Panel. Genes. 2024; 15(2):257. https://doi.org/10.3390/genes15020257

Chicago/Turabian Style

Gao, Jionghao, Jianxin Li, Jihong Zhang, Yan Sun, Xiaolong Ju, Wenlong Li, Haiyang Duan, Zhengjie Xue, Li Sun, Javed Hussain Sahito, and et al. 2024. "Identification of Novel QTL for Mercury Accumulation in Maize Using an Enlarged SNP Panel" Genes 15, no. 2: 257. https://doi.org/10.3390/genes15020257

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop