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Article

Identification of qAs1—A Minor-Effect QTL Controlling Grain Arsenic Accumulation in Rice Using Near-Isogenic Lines Under High-Arsenic and Flooded Conditions

by
Liang Guo
1,
Zheng Dong
1,
Haibo Xiong
2,
Xiaowu Pan
1,
Wenqiang Liu
1,
Zuwu Chen
1 and
Xiaoxiang Li
1,*
1
State Key Laboratory of Hybrid Rice, Hunan Hybrid Rice Research Center, Hunan Academy of Agricultural Sciences, Changsha 410125, China
2
Academy of Science and Technology, Chuxiong Normal University, Chuxiong 675000, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(12), 2699; https://doi.org/10.3390/agronomy15122699
Submission received: 22 October 2025 / Revised: 20 November 2025 / Accepted: 21 November 2025 / Published: 24 November 2025
(This article belongs to the Section Crop Breeding and Genetics)
Browse Figures
Versions Notes

Abstract

Arsenic (As) contamination in rice poses a serious risk to food safety and human health. Genetic dissection of As-related quantitative trait loci (QTLs) provides a sustainable strategy for breeding low-As cultivars. In this study, we aimed to improve the detection of minor-effect QTLs for total As accumulation by optimizing both environmental and genetic factors. A recombinant inbred line (RIL) population derived from the cross between Yuzhenxiang (YZX, indica) and YBK (Javanica) was used for initial QTL mapping, and a single locus, qAs1, was identified on chromosome1. To enhance As uptake and phenotypic differentiation, we conducted QTL validation and fine mapping under high-As and continuously flooded conditions using near-isogenic lines (NILs) to minimize background genetic interference. The effect of qAs1 was consistently validated across generations, and the locus was refined to a 159.5 kb genomic interval. Transcriptome analysis revealed three differentially expressed genes (LOC_Os01g52110, LOC_Os01g52214, and LOC_Os01g52260) involved in redox regulation and detoxification. These findings demonstrate the effectiveness of NIL-based fine mapping under optimized environmental conditions and provide promising targets for the genetic improvement of low-As rice cultivars.

1. Introduction

Arsenic (As) is a Group I carcinogen with no safe exposure threshold, posing a dual threat to ecosystem integrity and human health [1]. Through the soil–plant–food chain, As can readily enter the human body, and chronic ingestion of As-contaminated food has been associated with severe health disorders, including skin and lung cancers, as well as metabolic dysfunctions [2,3]. Severe As contamination is predominantly found in South and Southeast Asian countries, such as Bangladesh, India, Pakistan, Vietnam, and China, where groundwater irrigation and the use of As-rich soils aggravate human exposure risks [4].
Rice (Oryza sativa L.) is the major dietary source of As, accounting for approximately 60% of total human daily As intake [5]. Due to its unique physiological traits and flooded cultivation system, rice efficiently absorbs As from anaerobic paddy soils [6,7,8]. Consequently, As contamination poses a serious threat to both rice quality and yield, representing a critical challenge for global food security. Genetic improvement of low-As rice cultivars, leveraging the natural variation in grain As accumulation, offers one of the most effective and sustainable strategies to mitigate this problem.
Grain As concentration is a complex quantitative trait controlled by multiple loci with small effects and subject to strong environmental influence. To date, several quantitative trait loci (QTLs) associated with As-related traits, including grain, root, stem, and leaf As content, have been identified using F2, doubled haploid (DH), recombinant inbred line (RIL), and chromosome segment substitution line (CSSL) populations [9,10,11,12,13]. At least 16 QTLs related to grain As content have been reported, with additive effects ranging from 0.007 to 0.092 mg/kg. However, most studies have remained at the preliminary mapping stage, with limited reproducibility and no reports of fine mapping or gene cloning to date.
One major obstacle in dissecting As-related QTLs lies in the strong environmental dependency of trait expression. Field conditions, particularly soil As background and water management, can substantially alter phenotypic variance and QTL detectability. Norton et al. [10] failed to detect As-related QTLs under low-As field conditions, but later identified qAs8 and qAs10 when using the same RIL population grown in As-contaminated soil [11]. Similarly, Zhang et al. [13] found that QTLs were only detectable under continuous flooding, whereas they disappeared under intermittent irrigation. These findings underscore that environmental optimization—by enhancing arsenic uptake and phenotypic differentiation—is critical for successful detection of minor-effect loci.
At the same time, the use of genetically homogeneous materials such as near-isogenic lines (NILs) can effectively reduce background genetic noise and allow precise quantification of small-effect QTLs. Combining environmental optimization with NIL analysis thus provides a promising strategy for validating and fine-mapping environmentally sensitive QTLs. However, this integrated approach has rarely been applied in the context of arsenic accumulation, and its effectiveness for detecting minor-effect loci remains largely untested.
In this study, we sought to overcome the challenges of detecting minor-effect QTLs associated with As accumulation in rice by optimizing both environmental and genetic conditions. Using a RIL population derived from a cross between Yuzhenxiang (YZX, indica) and YBK (javanica), we identified a QTL controlling grain As concentration, designated qAs1. To enhance As uptake and increase phenotypic variation, QTL validation and fine mapping were conducted under high-As and continuously flooded field conditions, employing NILs to effectively minimize background genetic interference. As a result, qAs1 was refined to a 159.5 kb genomic interval containing three putative candidate genes associated with redox regulation and detoxification. This study demonstrates that integrating environmental and genetic optimization can significantly improve the resolution of QTL mapping and provides a practical framework for dissecting minor-effect loci that contribute to environmentally regulated traits in rice and other crops.

2. Materials and Methods

2.1. Plant Materials

Two types of genetic populations were used in this study. The first consisted of 157 RILs derived from the cross YZX/YBK, used for preliminary QTL mapping. The parental line YZX is a high-quality indica rice variety, while YBK is a javanica-type variety.
The second type comprised NILs, including three BC2F3 sets for QTL validation and seven BC2F5 sets for fine mapping (Figure 1). Based on resequencing data of the RIL population, one plant homozygous for the YBK allele at the qAs1 interval was identified and backcrossed twice with YZX. Marker-assisted selection (MAS) was performed in the BC2F1 generation to identify three plants carrying consecutive heterozygous segments within the interval G29951–G32775. These plants were self-pollinated to produce BC2F2 populations. From each population, homozygous plants for either the YZX or YBK allele were selected and selfed to generate three BC2F3 NIL sets, designated BC2F3-I, BC2F3-II, and BC2F3-III. The segregating regions for these sets spanned G29987–G30807, G29987–G31634, and G30893–G32543, respectively (Figure 2a).
For fine mapping, one BC2F2 plant heterozygous within the interval G29951–G30893 was selected. Among 1000 BC2F3 progeny, seven plants with smaller overlapping heterozygous segments were identified using interval-specific markers. Homozygous plants for either YZX or YBK alleles were then selected from their selfed progeny to develop seven BC2F5 NIL sets, designated BC2F5-I to BC2F5-VII, with segregating regions covering G29987–G30045, G29987–G30110, G29987–G30178, G29987–G30343, G30117–G30807, G30213–G30807, and G30407–G30807, respectively (Figure 2b). Each NIL set contained 15 lines homozygous for the YZX allele and 15 lines homozygous for the YBK allele.

2.2. Field Experiments

Field experiments were conducted over three consecutive years under natural conditions. The RIL population was grown in 2020 at the experimental station of the Hunan Rice Research Institute, Changsha, Hunan Province, China (soil total As content: 20 mg/kg). Three sets of BC2F3 NILs were planted in 2023, and seven sets of BC2F5 NILs were planted in 2024 at an As-contaminated field in Liuyang, Hunan Province (soil total As content: 109 mg/kg).
NIL trials were arranged in a randomized complete block design with two replications. Each line was grown in a single row of eight plants at a spacing of 16.7 cm × 20.0 cm under standard agronomic practices. Fields were continuously flooded (5–10 cm depth) throughout the growing season. At maturity, the middle five plants from each row were bulk-harvested for grain total As analysis.

2.3. Determination of Total Arsenic

Rice grains were air-dried and stored at 4 °C until analysis. Total As concentration was determined following previously published protocols [14]. A total of 100 grains per line were ground into fine powder using an FS-II cyclone mill. Exactly 1.000 g of sample was weighed into a microwave digestion vessel, and 5 mL of HNO3 was added. The samples were pre-digested for 2 h, sealed, and digested using a microwave digestion system under the following temperature program: 120 °C for 10 min, 150 °C for 10 min, and 190 °C for 30 min, with 5 min heating ramps between each step.
After cooling, the digests were vented, rinsed with ultrapure water, evaporated on a hotplate (100 °C, 30 min), and diluted to 25 mL with ultrapure water. Blank samples were processed in parallel. Total As content was determined using an inductively coupled plasma mass spectrometer (ICP-MS, XSERIES 2, Thermo Fisher Scientific, Waltham, MA, USA). For samples with 0.1–1.0 mg/kg As, the difference between duplicate determinations did not exceed 15% of the mean value.

2.4. Genotyping of the RIL Population and Construction of the Genetic Linkage Map

At the tillering stage, young leaves were collected from the parental lines and 157 RILs. DNA extraction and whole-genome resequencing were performed by BGI Genomics Co., LTd. (Shenzhen, China).
High-quality genomic DNA was randomly fragmented using a Covaris ultrasonicator, followed by end repair, A-tailing, adapter ligation, and purification. The DNA fragments were PCR-amplified to construct sequencing libraries. Library quality was evaluated, and libraries were pooled according to their effective concentration and target sequencing depth. Paired-end sequencing was performed on an Illumina HiSeq/MiSeq platform to generate raw reads. The raw data were processed to remove sequencing adapters and low-quality reads, filter missing bases, and perform base-quality checks. The resulting clean reads were used for subsequent genomic analyses.
Based on the resequencing data of the 157 RILs, single-nucleotide polymorphisms (SNPs) were identified, and bin markers were constructed following the method of Huang et al. [15]. A genetic linkage map was generated using the R/qtl package (version 0.97) [16].

2.5. Preliminary QTL Mapping

QTL analysis for total As content was performed using QTL IciMapping with a PIN of 0.001, a step size of 1 cM, and the inclusive composite interval mapping (ICIM) method [17]. The significance threshold was determined by 1000 permutations (p = 0.05). Additive effects and the proportion of phenotypic variance explained were estimated at the peak bin marker. QTLs were named following the standard nomenclature conventions [18].

2.6. DNA Marker Genotyping and Fine Mapping

For NILs, DNA was extracted from pooled leaf samples of the middle five plants per line using the method of Zheng et al. [19]. A total of 18 insertion-deletion (Indel) markers were developed within the qAs1 region (Table S1). Marker design was based on sequence polymorphisms (≥20 bp indel length) identified between the two parental lines, YZX and YBK, using resequencing data. Markers were evenly distributed across the candidate interval to ensure sufficient coverage for detecting recombination breakpoints. PCR amplification was performed using standard protocols, and products were resolved on 2% agarose gels or 6% non-denaturing polyacrylamide gels (PAGE) [20].
Two-way analyses of variance (ANOVA) were performed to test phenotypic differences between the two homozygous genotypic groups within each NIL set using the GLM procedure in SAS (version 8) [21]. When significant differences (p < 0.05) were detected, additive effects and the proportion of variance explained were calculated accordingly.

2.7. RNA-Seq and RT-qPCR Analysis

Fourteen-day-old seedlings of BC2F5-I NILs homozygous for either the YZX allele (NIL-qAs1YZX) or the YBK allele (NIL-qAs1YBK) were used for transcriptome analysis. Seedlings were treated with 20 μmol/L NaAsO2 in nutrient solution, and leaf tissues were sampled at 0 h and 6 h under As stress, with three biological replicates per treatment.
Total RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). RNA quality and concentration were measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), and integrity was verified with an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). RNA-seq libraries were prepared with the VAHTS Universal V5 RNA-seq Library Prep Kit (Vazyme, Nanjing, China) and sequenced on the Illumina NovaSeq 6000 platform (Illumina, San Diego, CA, USA) by BGI Genomics Co., Ltd. (Shenzhen, China) to generate 150 bp paired-end reads. Low-quality reads were removed using fastp [22], and clean reads were aligned to the rice reference genome using HISAT2 [23]. Gene expression was quantified as FPKM values [24], and read counts were obtained using HTSeq-count [25]. Differentially expressed genes (DEGs) were identified using DESeq2 [26] with thresholds of q < 0.05 and |fold change| > 2.
For validation, three candidate genes were selected for RT-qPCR. Total RNA was extracted and reverse-transcribed using the HiScript® Q RT SuperMix for qPCR kit (Vazyme, Nanjing, China). Gene-specific primers were designed from mRNA sequences, and Ubiquitin was used as the internal control. Each reaction was performed in triplicate using an ABI 7500 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). Relative gene expression levels were calculated using the 2−ΔΔCT method. Primer sequences are listed in Table S2.

3. Results

3.1. Phenotypic Variation in the YZX/YBK RIL Population

Significant differences in total As concentration were observed between the parents, with 0.38 mg/kg in YZX and 0.19 mg/kg in YBK. The RIL population exhibited a continuous distribution of total As content, ranging from 0.16 to 0.44 mg/kg with a mean of 0.28 mg/kg and a coefficient of variation of 25.37% (Figure 3). This continuous variation indicates that total As accumulation in rice grain is a quantitative trait controlled by multiple genetic factors.

3.2. Preliminary QTL Mapping in the RIL Population

Whole-genome resequencing achieved mapping rates of 95.88% for YZX and 98.67% for YBK against the Oryza sativa cv. Nipponbare reference genome (IRGSP 1.0), with average sequencing depths of 28.47× and 29.43×, respectively. A total of 979 Gb of sequencing data were generated for the 157 RILs, corresponding to 3.06–5.51 Gb per line (average depth 13.30×). In total, 1,349,482 high-quality, homozygous SNPs were identified.
Using 667 bin markers, a genetic linkage map spanning 1301.62 cM was constructed across 12 chromosomes, with an average of 55.58 markers per chromosome and an average inter-marker distance of 1.95 cM (Figure 4a; Table S3). The mean correlation coefficient between genetic and physical distances was 0.945, confirming high collinearity (Table S4).
QTL analysis identified a single locus, qAs1, located between 29.97 and 32.34 Mb on chromosome 1 (Table 1). qAs1 had a LOD value of 4.85, explained 10.81% of the phenotypic variation, and carried an additive effect of −0.054 mg/kg, with the favorable allele derived from YBK.

3.3. QTL Effect Verification Using BC2F3 NILs

Because the effect of qAs1 detected in the RIL population was relatively small, further verification was performed using three BC2F3 NILs in the YZX background. To enhance As uptake and phenotypic differentiation, NILs were planted in As-contaminated fields under continuous flooding throughout the growth period.
According to the two-way ANOVA results (Table 2), significant differences in total As content were observed between the two homozygous in BC2F3-I and BC2F3-II (p < 0.01), with the YBK allele reducing total As concentration by 0.156–0.166 mg/kg. In contrast, BC2F3-III showed no significant difference between the alleles. The direction of allelic effect was consistent with the qAs1 effect in the RILs, indicating that qAs1 segregated in BC2F3-I and BC2F3-II but not in BC2F3-III.
By comparing the segregating regions among the three NIL sets (Figure 2a), BC2F3-I and BC2F3-II shared the overlapping interval G29987–G30807, whereas BC2F3-III segregated for a partially downstream region (G30893–G32543) and showed no significant difference (Table 2). This pattern indicates that qAs1 resides upstream of G30893, within the interval G29951–G30893 spanning approximately 942.6 kb. This defined region was subsequently used for fine mapping in advanced NIL generations.

3.4. Fine Mapping Using BC2F5 NILs

To further delimit qAs1, seven BC2F5 NIL sets were developed, each carrying smaller and partially overlapping heterozygous segments within the qAs1 candidate interval (Figure 2b).
As shown in Table 2, significant differences in total As content were detected in BC2F5-I (p < 0.001), which segregated for the interval G29987–G30045, with an additive effect of –0.157 mg/kg and a phenotypic variance explained of 44.2%. Similarly, BC2F5-II, BC2F5-III, and BC2F5-IV—all carrying overlapping intervals extending from G29987 to G30045—also showed significant allelic effects (p < 0.001), with the YBK allele consistently associated with lower As accumulation. In contrast, BC2F5-V, BC2F5-VI, and BC2F5-VII, which segregated for downstream intervals starting at G30117 or later, showed no significant differences between genotypes (Table 2).
Combining the genotypic breakpoints with the phenotypic results (Table 2; Figure 2b), the qAs1 locus was therefore refined to a 159.5 kb interval (G29951–G30110), which is shared among all four informative NIL sets (BC2F5-I to BC2F5-IV), showing significant segregation but absent in non-significant sets. This logic-based refinement precisely defines the most likely genomic region harboring the causal gene for qAs1.

3.5. Identification of Candidate Genes Within the qAs1 Interval

According to the Rice Genome Annotation Project (https://rice.uga.edu, accessed on 10 October 2025), the 159.5 kb target interval in the Nipponbare genome harbors 26 annotated genes (Table S5). Among them, 19 encode known proteins, six encode expressed proteins, and one encodes a hypothetical protein.
To explore associations between gene expression and As accumulation, RNA-seq analysis was performed on seedlings of NIL-qAs1YZX and NIL-qAs1YBK under As stress conditions. Of the 26 genes, only three (LOC_Os01g52110, LOC_Os01g52214, and LOC_Os01g52260) showed significant differential expression in NIL-qAs1YBK compared with NIL-qAs1YZX under arsenic stress, while 21 genes showed no differential expression, and two were undetectable (Figure 5a).
RT-qPCR validation confirmed the RNA-seq results. After As treatment, LOC_Os01g52214 was upregulated in NIL-qAs1YBK, whereas LOC_Os01g52260 and LOC_Os01g52110 were downregulated compared with NIL-qAs1YZX (Figure 5b). These consistent expression trends verified the reliability of the transcriptome data.

4. Discussion

Detecting minor-effect QTLs for complex traits such as As accumulation in rice is challenging because their small genetic effects are often masked by environmental variation and background noise. In this study, we addressed this limitation by integrating two complementary strategies: conducting experiments under high-As and continuously flooded conditions to enhance phenotypic variation, and using NILs to minimize background genetic interference. This approach significantly enhanced phenotypic differentiation and enabled the stable detection and fine mapping of qAs1 to a 159.5 kb genomic interval, confirming the effectiveness of optimizing both environmental and genetic factors for dissecting minor-effect QTLs.
The NIL-based validation demonstrated that the genetic effect of qAs1 was stable across generations, while its contribution to phenotypic variance increased as the genetic background became more homogeneous. In the RIL population grown under low-As conditions, qAs1 explained 10.8% of the phenotypic variance with an additive effect of −0.054 mg/kg, typical of a minor-effect locus. However, under high-As and continuously flooded conditions, the same locus exhibited an additive effect of approximately −0.15 mg/kg, accounting for up to 44.2% of the variance in NILs. This amplification likely reflects both the genetic uniformity of NILs and enhanced As uptake under flooded anaerobic conditions, which increase As(III) solubility. The genetic uniformity of NILs also minimizes background noise, leading to more accurate and stable QTL effect estimation. Nevertheless, because the RIL and NIL trials were conducted in different locations and years with distinct soil As levels (20 vs. 109 mg/kg), the stronger effect observed in NILs likely reflects a combined influence of genetic uniformity and gene-by-environment (G × E) interaction. Together, these factors explain the larger apparent effect of qAs1 observed under optimized conditions and underscore the value of NILs in validating and refining minor-effect QTLs that are otherwise difficult to detect in segregating populations. Similar findings have been reported in studies of other complex traits, where NILs provide a powerful framework for the validation, dissection, and eventual cloning of minor-effect QTLs [27,28,29,30,31,32]. Thus, future multi-location and multi-year experiments are required to confirm the stability of qAs1 across environmental conditions.
Within the 159.5 kb interval harboring qAs1, three putative candidate genes were identified based on their significant differential expression between NIL-qAs1YZX and NIL-qAs1YBK under As stress. LOC_Os01g52110, encoding a RING finger and CHY zinc finger domain-containing protein (CHYR1), has been characterized in Arabidopsis as an E3 ubiquitin ligase involved in abscisic acid (ABA) signaling and reactive oxygen species (ROS) regulation [33,34,35]. CHYR1 mediates SnRK2.6-dependent phosphorylation to regulate stomatal closure and ROS accumulation, thereby enhancing drought tolerance [34]. CHYR1-related ubiquitin ligases also regulate the degradation of phosphorylated WRKY70, balancing oxidative and immune responses [35]. Although CHYR1 has not been directly linked to arsenic metabolism, its role in ABA and redox signaling suggests an indirect contribution to arsenic tolerance through modulation of oxidative stress pathways. LOC_Os01g52214, encoding ubiquinone oxidoreductase (Complex I), is a key component of the mitochondrial electron transport chain, transferring electrons from NADH to ubiquinone [36]. Complex I is a major source of mitochondrial ROS and plays a central role in maintaining cellular redox balance during abiotic stress [37]. Because redox signaling is tightly linked to heavy metal detoxification processes such as glutathione recycling and thiol-mediated sequestration, LOC_Os01g52214 may affect As accumulation indirectly by regulating ROS homeostasis and antioxidant defense rather than controlling As uptake. LOC_Os01g52260, encoding serine acetyltransferase (SAT), catalyzes the formation of O-acetylserine, the precursor for cysteine biosynthesis and a signaling molecule in sulfur assimilation [38]. SAT activity determines cysteine availability for glutathione (GSH) and phytochelatin synthesis, which are essential for As(III) chelation and detoxification [39]. Therefore, LOC_Os01g52260 likely contributes to As detoxification by enhancing thiol metabolism and promoting the sequestration of arsenic into less toxic complexes.
Taken together, these three genes represent strong candidate loci underlying qAs1. They are more likely to regulate arsenic detoxification and redox homeostasis rather than direct uptake. The differential expression of these genes between contrasting NILs supports this hypothesis. Based on these results, we propose the following physiological model: (1) CHYR1 may regulate As-induced oxidative signaling and ABA-mediated stress responses; (2) Ubiquinone oxidoreductase may modulate mitochondrial ROS production, maintaining redox balance under As stress; (3) SAT may enhance cysteine and glutathione biosynthesis, thereby facilitating As detoxification.
Although these mechanisms are biologically plausible, the current expression data cannot establish causality. The observed differential expression may be a consequence of differential As accumulation rather than its cause. To verify this, future work should include haplotype analysis of LOC_Os01g52110, LOC_Os01g52214, and LOC_Os01g52260, identifying SNPs or InDels in their coding and promoter regions between YZX and YBK. Functional verification should also be pursued through CRISPR/Cas9 knockout in YBK (low-As parent), overexpression in YZX (high-As parent), and heterologous expression in yeast to directly assess their effects on As tolerance and accumulation.
Furthermore, the current NIL-based study focused only on grain total As content, which limits mechanistic inference. Future research should integrate biochemical phenotyping of related physiological parameters: for example, quantifying cysteine, glutathione, and phytochelatins for LOC_Os01g52260; ROS and antioxidant enzyme activities for LOC_Os01g52214; and ABA-related responses or root structure traits for LOC_Os01g52110. Such data will provide a crucial biochemical bridge linking gene expression to physiological detoxification mechanisms and strengthen the causal understanding of qAs1.
In summary, qAs1 likely reduces grain As accumulation by enhancing detoxification and redox regulation rather than limiting root As uptake. The integration of NILs, transcriptomics, and future functional genomics will allow further dissection of this locus and provide valuable targets for breeding low-As rice cultivars.

5. Conclusions

This study identified and fine-mapped the rice grain arsenic accumulation QTL qAs1 using NILs under high-As and continuously flooded conditions. These optimized environments and genetic settings enhanced As uptake and phenotypic variation, enabling the stable detection of this minor-effect locus. qAs1 was refined to a 159.5 kb genomic interval containing three candidate genes (LOC_Os01g52110, LOC_Os01g52214, and LOC_Os01g52260) involved in redox regulation and detoxification pathways. The findings highlight that optimizing both environmental conditions and genetic background is an effective strategy for dissecting minor-effect QTLs that are otherwise difficult to detect in segregating populations. Although the causal gene of qAs1 remains to be functionally validated, the candidate genes identified here provide valuable molecular targets for understanding the genetic basis of arsenic tolerance and accumulation in rice. These results lay the groundwork for future functional validation and molecular breeding of low-As rice cultivars.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy15122699/s1, Table S1: Indel markers developed for QTL mapping; Table S2: The primers used for gene expression analysis; Table S3: Distribution of the markers in the genetic linkage map of the RIL population; Table S4: The correlation between the linkage marker on the genetic map and the physical map; Table S5: Annotation putative genes at the qAs1 in Nipponbare reference genome.

Author Contributions

Conceptualization, X.L.; investigation, L.G., Z.D., H.X., X.P., W.L. and Z.C.; writing—original draft preparation, L.G.; writing—review and editing, L.G. and X.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Natural Science Foundation Program of Hunan Province, grant number 2023JJ40390, the Agricultural Science and Technology Innovation Fund Project of Hunan Province, grant number 2024CX120; and the Science and Technology Plan Project of Hunan Province, grant number 2025RC1063.

Data Availability Statement

The datasets generated and analyzed during the current study are available within the article and its Supplementary Materials. Additional information can be obtained from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AsArsenic
QTLQuantitative trait locus
RILRecombinant inbred line
YZXYuzhenxiang
NILNear-isogenic line
DHDoubled haploid
CSSLChromosome segment substitution line
MASMarker-assisted selection
ICIMInclusive composite interval mapping
PAGEPolyacrylamide gel electrophoresis
ANOVAAnalyses of variance
CHYR1RING finger and CHY zinc finger domain-containing protein
ABAAbscisic acid
ROSReactive oxygen species
SATSerine acetyltransferase

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Agronomy 15 02699 g001
Figure 1. Procedure for the Development of NILs.
Figure 1. Procedure for the Development of NILs.
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Figure 2. Segregating regions of NILs. (a) Three sets of BC2F3 NILs; (b) Seven sets of BC2F5 NILs.
Figure 2. Segregating regions of NILs. (a) Three sets of BC2F3 NILs; (b) Seven sets of BC2F5 NILs.
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Figure 3. Frequency distribution of total As content in the RIL population.
Figure 3. Frequency distribution of total As content in the RIL population.
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Figure 4. QTL analysis of total As content in the RIL population. (a) Genetic linkage map using bin markers; (b) Genome-wide LOD score profiles across chromosomes.
Figure 4. QTL analysis of total As content in the RIL population. (a) Genetic linkage map using bin markers; (b) Genome-wide LOD score profiles across chromosomes.
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Figure 5. Transcriptomic analysis of genes within the qAs1 region between NIL-qAs1YZX and NIL-qAs1YBK under arsenic stress. (a) Expression profiles of 26 genes within the qAs1 interval based on RNA-seq data; (b) Validation of three strong candidate genes by RT-qPCR. Red font in (a) indicates genes showing significant differential expression between the two lines under arsenic stress.
Figure 5. Transcriptomic analysis of genes within the qAs1 region between NIL-qAs1YZX and NIL-qAs1YBK under arsenic stress. (a) Expression profiles of 26 genes within the qAs1 interval based on RNA-seq data; (b) Validation of three strong candidate genes by RT-qPCR. Red font in (a) indicates genes showing significant differential expression between the two lines under arsenic stress.
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Table 1. QTL associated with total As content in the RIL population.
Table 1. QTL associated with total As content in the RIL population.
QTLChromosomeRegion (Mb)LODAR2 (%)
qAs1129.97–32.344.85−0.05410.81
A, additive effect of replacing a YZX allele by a YBK allele; R2, the proportion of phenotypic variance explained by the QTL effect.
Table 2. QTL analysis for grain total As content in three BC2F3 and seven BC2F5 populations.
Table 2. QTL analysis for grain total As content in three BC2F3 and seven BC2F5 populations.
PopulationSegregating RegionPhenotypic Mean (mg/kg)pAR2 (%)
YZXYBK
BC2F3-IG29987-G308070.6180.3070.0019−0.15625.9
BC2F3-IIG29987-G316340.6530.3210.0013−0.16628.7
BC2F3-IIIG30893-G325430.5860.6090.5644
BC2F5-IG29987-G300450.6480.334<0.0001−0.15744.2
BC2F5-IIG29987-G301100.5800.309<0.0001−0.13639.9
BC2F5-IIIG29987-G301780.5640.290<0.0001−0.13742.0
BC2F5-IVG29987-G303430.6010.342<0.0001−0.13037.3
BC2F5-VG30117-G308070.6130.5390.9765
BC2F5-VIIG30213-G308070.6280.6490.7709
BC2F5-VIIIG30407-G308070.6650.5810.9003
YZX, Yuzhenxiang; A, additive effect of replacing a YZX allele by a YBK allele; R2, the proportion of phenotypic variance explained by the QTL effect.
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MDPI and ACS Style

Guo, L.; Dong, Z.; Xiong, H.; Pan, X.; Liu, W.; Chen, Z.; Li, X. Identification of qAs1—A Minor-Effect QTL Controlling Grain Arsenic Accumulation in Rice Using Near-Isogenic Lines Under High-Arsenic and Flooded Conditions. Agronomy 2025, 15, 2699. https://doi.org/10.3390/agronomy15122699

AMA Style

Guo L, Dong Z, Xiong H, Pan X, Liu W, Chen Z, Li X. Identification of qAs1—A Minor-Effect QTL Controlling Grain Arsenic Accumulation in Rice Using Near-Isogenic Lines Under High-Arsenic and Flooded Conditions. Agronomy. 2025; 15(12):2699. https://doi.org/10.3390/agronomy15122699

Chicago/Turabian Style

Guo, Liang, Zheng Dong, Haibo Xiong, Xiaowu Pan, Wenqiang Liu, Zuwu Chen, and Xiaoxiang Li. 2025. "Identification of qAs1—A Minor-Effect QTL Controlling Grain Arsenic Accumulation in Rice Using Near-Isogenic Lines Under High-Arsenic and Flooded Conditions" Agronomy 15, no. 12: 2699. https://doi.org/10.3390/agronomy15122699

APA Style

Guo, L., Dong, Z., Xiong, H., Pan, X., Liu, W., Chen, Z., & Li, X. (2025). Identification of qAs1—A Minor-Effect QTL Controlling Grain Arsenic Accumulation in Rice Using Near-Isogenic Lines Under High-Arsenic and Flooded Conditions. Agronomy, 15(12), 2699. https://doi.org/10.3390/agronomy15122699

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