Identification of two QTLs for web blotch resistance in peanut (Arachis hypogaea L.) based on BSA-seq

Abstract

Background

Peanut (Arachis hypogaea L.) is a globally important oilseed and cash crop. Web blotch is one of the most important peanut foliar diseases, causing severe yield losses worldwide.

Results

In this study, an F₆ population was used to identify quantitative trait loci (QTLs) for peanut web blotch resistance, based on bulked segregant analysis (BSA). Kompetitive Allele-Specific PCR (KASP) markers were developed and used to further narrow QTL intervals and detect candidate genes. Two major QTLs, qWBRA05 and qWBRA08 were identified, spanning physical intervals of 465.75 Kb and 434.83 Kb, and explaining percentages of phenotypic variation (PVE) of 8.79% and 15.09%, respectively. Moreover, two KASP markers were developed within the QTL interval effectively distinguished between web blotch resistance and web blotch susceptible materials.

Conclusions

The QTLs identified and two molecular markers closely linked to web blotch resistance were developed within the QTL interval, which are potentially valuable in peanut breeding.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-024-05930-8.

Background

Peanut is an important oilseed and cash crop grown in more than 100 countries worldwide. In 2022, China ranked second for global peanut cultivated area, with 4.8 million hectares (accounting 16.2% of the total), and first for production, with a total output of 18.3 million tons (accounting for 36.5% of the total) (www.stats.gov.cn/sj/ndsj/2022/indexch.htm). However current peanut varieties provide yield far below the required yield. Peanut pests and diseases, such as web blotch disease, bacterial wilt, Scleotium rolfsii, rust, grubs, have caused more than 20% yield reductions in some areas [1], seriously affecting peanut production.

Peanut web blotch (also known as brown streak disease, smut blotch) is a fungal infestation caused by Didymella arachidicola, which mainly affects leaves, leading to massive defoliation of plants and affecting the growth and development of peanut pods. Web blotch disease can occur throughout the entire reproductive period of peanut and is the most severe in the middle and late stages, causing more than 30% reduction in peanut yield in severe cases [2]. Breeding disease-resistant peanut varieties is the most economical and effective method to control peanut web blotch.

Genetic dissection of the resistance mechanism to web blotch will provide a theoretical basis for breeding resistant varieties. Up to now, some resistant quantitative trait loci (QTLs) were identified for peanut web blotch. A QTL on linkage group 7 and associated with a proportion of variance explained (PVE) of 5.76% was identified using SSR markers [3]. Eight resistant QTLs with PVE ranging from 2.8 to 15.1% were identified based on whole genome sequencing of recombinant inbred line (RIL) populations [4]. Using F_2:3 lines and RILs, a major QTL was mapped on a 169 kb physical interval on chromosome 16, which was associated with a logarithm of odds (LOD) score of 13.18 and PVE of 13.20% [5]. The resistance mechanism of peanut web blotch was studied at the cytological level using different resistant materials [6].

BSA-seq, which combines the bulked segregant analysis (BSA) method and next-generation sequencing (NGS) technology, can rapidly localize genes associated with a target trait [7]. BSA-seq is based on sequencing data, from parental genotypes and pools of individuals displaying contrasting phenotypes, and association statistics such as single nucleotide polymorphism index (SNP-index) [8], Euclidean distance (ED) [9], G’-value [7], and P-value [10]. In recent years, following the release of wild [11] and cultivated [12] peanut genomes, BSA-seq has been successfully used to map economically important traits in peanut, such as seed coat colour [13], pod shell thickness [14], web blotch resistance [5], and bacterial wilt resistance [15].

Kompetitive Allele-Specific PCR (KASP) is a technique for allele genotyping based on SNPs and InDels, which is widely used for the development of molecular markers [16] and allele mining [17, 18] due to its low cost, high throughput, and high accuracy. In recent studies, QTLs for traits such as anemone-type flowers in chrysanthemum [19], cold hardiness in maize [20], and sucrose content in peanut [21] were identified using BSA-seq in combination with KASP markers development.

In this study, BSA-seq was used to locate QTL for peanut web blotch resistance using the F₆ population derived from the cross between the parental genotypes NZ and YH22, and candidate intervals were further narrowed down through the developed KASP markers. Finally, candidate resistance genes for peanut web blotch were screened based on the polymorphic SNPs between the two parents within the target interval, and molecular markers were provided to detect the web blotch-resistant peanut varieties.

Results

Phenotypic evaluation

The proportion of the total leaf area covered by lesion was used to estimate the level of resistance of the two parental genotypes NZ and YH22 and the F₆ lines. A total of 301 and 201 lines were phenotyped for the NZ/YH22 and YH22/NZ populations, respectively. NZ and YH22 displayed contrasting response to web blotch (Fig. 1A and B), as their disease indexes were 12.65% and 79.93%, respectively. Disease indexes among the F₆ lines displayed large variation (Fig. 1C and D). The resistance for each line was evaluated based on the disease index and the distribution of resistance classification for the NZ/YH22 and YH22/NZ population were illustrated in Supplementary Fig. S1.

Fig. 1.

Phenotypes of the resistant parent NZ and the susceptible parent YH22 inoculated with YY187. (A) The symptom of NZ at 14 days after inoculation. (B) The symptom of YH22 at 14 days after inoculation. (C) The frequency distribution of the disease index for the NZ/YH22 derived F₆ population. (D) The frequency distribution of the disease index for the YH22/NZ derived F₆ population

Based on the NZ/YH22 population, 28 highly resistant lines with the disease index less than 20 and 30 highly susceptible lines with a disease index greater than 75 (Supplementary Table S1) were selected to construct the R-pool and S-pool, respectively. A t-test was performed, revealing a significant difference between the two groups, with a P value of less than 0.0001.

BSA-seq analysis

After filtering and processing raw sequencing reads from the two parents and pools, a total of 303.7 Gb clean data was obtained. The G + C content ranged from 36.86 to 37.80% and the Q30 values exceeded 89.10%. The coverage rate ranged between 98.40% and 99.10% and the average sequencing depth ranged between 34.50× and 39.40× (Table 1). A total of 190,867 high-quality SNPs were obtained after filtering.

Table 1.

Summary of the sequencing quality and alignment results for BSA-seq analysis

Sample	Clean Base (Gb)	Q30 (%)	GC Content (%)	Mapped (%)	Coverage Rate (%)	Mean Depth
NZ	72.42	93.55	37.34	97.80	98.40	34.80×
YH22	72.83	92.72	37.80	99.20	98.80	39.40×
R-pool	76.91	92.69	36.86	99.20	99.00	38.60×
S-pool	81.62	89.10	36.95	99.50	99.10	34.50×

Four statistics were used to conduct SNP association analysis: Euclidean Distance (ED), Fisher-exact test (FET), G-statistic (G), Δ (SNP-index). A total of four candidate intervals were identified on chromosomes 5, 8, and 18 with confidence level higher than 99% (Fig. 2; Table 2). The candidate interval on chromosome 5, spanning 4.95 Mb (Chr.05: 7200001–12150000) was detected using the ED and G statistics. The candidate interval on chromosome 8, spanning 10.61 Mb (Chr.08: 38550001–49160000) was detected using all the four statistics. Two candidate intervals were detected using the ED, FET and G statistics on chromosome 18, spanning 4.67 Mb and 2.40 Mb (Chr.18: 114590001–119260000 and Chr.18: 125530001–127930000).

Fig. 2.

QTL analysis for the peanut web blotch resistance based on BSA-seq. Distribution of the values of ED² (A), -log10(P) (B), G-value (C) and Δ(SNP-index) (D) across the whole genome. The green/blue and red lines represent 95% and 99% confidence intervals, respectively. The significant genomic regions related to WBR are indicated by red rectangles

Table 2.

Candidate regions associated with web blotch resistance based on BSA-seq analysis

Algorithm	Chromosome	Start (bp)	Start (bp)	Peak
Euclidean distance	Arahy.05	7,200,001	12,460,000	0.375395
	Arahy.08	38,550,001	49,160,000	0.89273
	Arahy.18	115,960,001	119,260,000	0.307904
Fisher-exact test	Arahy.08	41,450,001	46,690,000	0.002439
	Arahy.18	114,690,001	119,260,000	0.004141
	Arahy.18	125,630,001	127,930,000	0.006565
G-statistic	Arahy.05	7,370,001	12,150,000	14.32138
	Arahy.08	38,550,001	49,160,000	37.20801
	Arahy.18	114,590,001	119,260,000	18.27514
	Arahy.18	125,530,001	127,930,000	13.88328
Δ(SNP-index)	Arahy.08	40,120,001	48,670,000	0.654464

Development of KASP markers and genetic mapping

Polymorphic SNPs and InDels between the two parents within the candidate interval were used to design KASP marker assays. A total of 10, 9 and 2 assays were developed for the candidate intervals on chromosome 5, 8 and 18, respectively (Supplementary Table S2) and used to perform linkage and QTL mapping. Two QTLs were detected on chromosomes 5 and 8, which were named qWBRA05 and qWBRA08, respectively, whereas no QTL was detected on chromosome 18 (Figs. 3A and 4A). Using the NZ/YH22 derived population, qWBRA05 (PVE 8.79%, LOD score 5.95) was mapped between positions Chr.05:9232073 and Chr.05:9536505, whereas qWBRA08 (PVE 15.09%, LOD score 10.78) was mapped between positions Chr.08:42510958 and Chr.08:42935578. Using the YH22/NZ derived population, qWBRA05 (PVE 18.52%, LOD score 9.21) was mapped between positions Chr.05.8852790 and Chr.05.9232073, and qWBRA08 (PVE 17.93%, LOD score 8.29) was mapped between positions Chr.08.42510958 and Chr.08.42935578.

Fig. 3.

Fine mapping of qWBRA05 related to peanut web blotch resistance. (A) QTL mapping for peanut web blotch resistance using the developed KASP markers on chromosomes 5. (B) The candidate interval was deemed as between Chr.05.9071757 and Chr.05.9536505 based on the genotype and phenotype of the recombinant lines selected for qWBRA05 with the resistant genotype for qWBRA08

Fig. 4.

Fine mapping of qWBRA08 related to peanut web blotch resistance. (A) QTL mapping for peanut web blotch resistance using the developed KASP markers on chromosomes 8. (B) The candidate interval was deem as between Chr.08.42386841 and Chr.08.42821666 based on the genotype and phenotype of the recombinant lines selected for qWBRA08 with the resistant genotype for qWBRA05

To further validate the qWBRA05 and qWBRA08 physical intervals, recombinant lines were selected from the NZ/YH22 populations based on the genotyping results of KASP markers. Ten and four recombinant lines were selected for qWBRA05 and qWBRA08, respectively (Figs. 3B and 4B). The QTL qWBRA05 was confirmed to be located between Chr.05.9071757 and Chr.05.9536505 based on recombination events between the markers Chr.05.9071757 and Chr.05.9232073 (lines NY039 and NY046) and between the markers Chr.05.9232073 and Chr.05.9536505 (line NY016) (Fig. 3B). The QTL qWBRA08 was positioned between the markers Chr.08.42386841 and Chr.08.42821666, based on recombination events between the markers Chr.08.42386841 and Chr.08.42510958 (line NY104) and between the markers Chr.08.42510958 and Chr.08.42821666 (line NY077) (Fig. 4B).

The 301 lines of the NZ/YH22 F₆ population and the two parents were grouped according to the genotyping results of two KASP markers Chr.05.9232073 and Chr.08.42601522 (Supplementary Fig. S2). At the Chr.08.42601522 locus, homozygosity for the NZ allele (genotype AA) was associated with a significantly lower disease index than homozygosity for the YH22 allele (genotype aa); similarly, at the Chr.05.9232073 locus, homozygosity for the NZ allele (genotype BB) was associated with a significantly lower disease index than homozygosity for the YH22 allele (genotype bb) (Supplementary Fig. S3). In addition, AABB and aabb lines displayed the lowest and highest disease indexes, respectively, whereas AAbb and aaBB lines were associated with intermediate disease indexes, indicating additivity between the two QTLs (Supplementary Fig. S4).

Candidate gene prediction

A total of 44 genes were annotated within the candidate intervals of qWBRA05 and qWBRA08, using the annotation of the A. hypogaea cv. Tifrunner reference genome 2.0 (https://www.peanutbase.org/peanut_genome). The resistant and susceptible parents displayed SNPs in the coding regions of six genes, namely Arahy.13KD3B, Arahy.DWSH38, Arahy.TIS67U, Arahy.68QIGD, Arahy.GLQ2UZ, and Arahy.67Y9C9, which resulted in missense mutations and, in one case, the extension of the coding sequence (Table 3).

Table 3.

Candidate genes with SNPs in the coding regions covered by qWBRA05 and qWBRA08

Gene ID	Position (5’to3’)	Functional annotation	Base variation	Location	Amino acid variation
Arahy. 13KD3B	Chr.05: 9,115,352–9,117,974	serine/threonine-protein phosphatase	G-A	CDS	S-N
Arahy. DWSH38	Chr.05: 9,429,122–9,435,150	BEL1-like homeodomain protein	C-G	CDS	R-G
Arahy. TIS67U	Chr.08: 42,564,489–42,583,931	SWIM zinc finger family protein	C-T、C-A	3’UTR、CDS	A-D
Arahy. 68QIGD	Chr.08: 42,657,415–42,662,448	Sec14p-like phosphatidylinositol transfer family protein	T-C	CDS	stop codon–R
Arahy. GLQ2UZ	Chr.08: 42,784,851–42,786,662	MLP-like protein 43	A-G	CDS	E-G
Arahy. 67Y9C9	Chr.08: 42,804,889–42,806,667	MLP-like protein 31	C-G、C-A、A-T、G-C、 T-G	CDS	I-M、D-E、M-L、 S-T、 I-M

Discussion

QTL mapping for web blotch resistance and development of KASP markers suitable for assisted selection

Peanut web blotch is one of the fast-spreading and harmful peanut leaf spot diseases, causing serious yield losses. Breeding disease-resistant peanut varieties is the most cost-effective method to control peanut web blotch. At present, some resistance QTLs had identified using GWAS [22] and BSA-seq [5], but this information was not exploited in practical breeding. Therefore, there is a need for further research on peanut web blotch resistance and the development of breeder-friendly markers suitable to carry out assisted selection for this trait.

In this study, a BSA-seq approach was successfully used to identify QTLs for peanut web blotch resistance. The use of BSA-seq allows for the rapid identification of target genes, which is made possible by the linkage between genetic markers and target traits. Such as, seed coat color in mung bean [23], early-maturity traits in upland cotton [24]. The genomic intervals associated with two QTLs, qWBRA05 and qWBRA08, were narrowed down to 464.75 Kb and 434.83 Kb, respectively, through the development of KASP markers and genotyping of F₆ segregating lines. In comparison to alternative markers, KASP markers offer the benefits of high-throughput, high accuracy and rapidity. In addition, the intervals were validated by genotyping of recombinant lines. Both QTLs identified in this study were newly discovered.

Candidate genes prediction for peanut web blotch resistance

A total of six candidate genes were predicted based on the sequence difference between the two parents. Arahy.13KD3B encodes a serine/threonine-protein phosphatase that plays an essential role in plant adversity signaling [25]. Arahy.DWSH38 encodes BEL1-like homeodomain protein involved in the regulation of plant development and environmental response processes [26]. Arahy.TIS67U encodes a zinc finger protein that plays an important role in the regulation of gene expression, cell differentiation, embryonic development and plant stress resistance [27]. Arahy.68QIGD encodes a phosphatidylinositol transporter protein that performs important functions in plants, including osmoregulation [28], cell polarity growth [29], protein transport [30], plant immunomodulation [31], and virus interactions [32]. Arahy.GLQ2UZ and Arahy.67Y9C9 encode major latex proteins (MLPs). MLP belongs to a subfamily of the Bet v1 protein family that responds to both biotic and abiotic stresses and plays an important role in plant disease resistance. Arahy.13KD3B encodes serine/threonine protein phosphatase that regulates plant adversity signaling pathways by catalysing the dephosphorylation of substrate proteins. It was shown that overexpression of OsBIPP2C2a in transgenic tobacco plants resulted in increased disease resistance against tobacco mosaic virus and Phytophthora parasitica var. nicotianae [33].

Arahy.68QIGD encodes phosphatidylinositol transport proteins (PITPs), which is a lipid-binding protein prevalent in eukaryotes. Studies have shown that such genes are involved in signaling processes in plant immune response and osmotic stress [34]. The SEC14-only protein gene (Arahy.68QIGD) has been reported to be involved in the immune response of Nicotiana benthamiana to Ralstonia solanacearum. NbSEC14 was up-regulated when Nicotiana benthamiana was infested by Ralstonia solanacearum, and the Nicotiana benthamiana plants with silenced NbSEC14 exhibit reduced resistance to Ralstonia solanacearum [31]. Moreover, there was one base (T/C) difference between the two parents in the CDS region, which resulted in a stop codon mutation that lengthened the gene code by 138 bp.

Arahy.GLQ2UZ and Arahy.67Y9C9 encode major latex proteins (MLP). MLP is involved in both abiotic and biotic stress resistance in plants. MLP interacts with ERFs, the transcription factors that induce PR genes, and induces the expression of PR genes encoding disease resistance proteins by enhancing the binding of ERFs to the promoters of PR genes, leading to resistance in plants [35]. MLP gene expression is regulated by phytohormones [36] and is involved in drought and salt stress tolerance through the ABA signaling pathway [37]. MLPs can express resistance to pathogens through ET or JA signaling pathways [38]. Studies in N. benthamiana have shown that, NbMLP28 is highly expressed in the jasmonic acid (JA) signaling pathway following infestation with potato Y virus and silencing of NbMLP28 by VIGS made tobacco plants more susceptible to potato Y virus [39]. The main disease resistance mechanism of MLP is through the induction of PR genes, which indirectly cause plants to develop resistance [35]. Comparison of the Arahy.GLQ2UZ and Arahy.67Y9C9 sequences in NZ and YH22 revealed a base mutation in the CDS region of Arahy.GLQ2UZ, which resulted in the change of coded amino acid. There are five base mutations in the CDS region of Arahy.67Y9C9, all resulting in coding amino acid changes. Whether these mutations in the gene are associated with differences in the web blotch resistance between NZ and YH22 remains unclear.

In this study, two developed KASP markers (Chr.08.42601522 and Chr.05.9232073) were closed linked with the resistance of peanut web blotch, which can be used in molecular marker-assisted breeding.

Conclusion

In this study, two major QTLs (qWBRA05 and qWBRA08) for web blotch resistance were identified using the BSA-seq method. The candidate interval of qWBRA05 was narrowed to 464.75 Kb between Chr.05.9071757 and Chr.05.9536505. The candidate interval of qWBRA08 was narrowed to 434.83 Kb between Chr.08.42386841 and Chr.08.42821666. In addition, two molecular markers closely linked to web blotch resistance were developed within the QTL interval, which are potentially valuable in peanut breeding.

Materials and methods

Plant and fungal materials

Two F₆ populations were derived from reciprocal crosses between the resistant peanut germplasm NZ and the susceptible variety YuHua22 (YH22), which were named NZ/YH22 (NZ female parent, including 305 lines) and YH22/NZ (YH22 female parent, including 209 lines). The two parents and two populations are maintained at the Henan Academy of Agricultural Science (Zhengzhou, China).

The D. arachidicola strain used in this study (YY187) was isolated from diseased peanut leaves at the experimental base in Yuanyang (Henan, China).

Pathogen inoculation and disease tests

The parental and F₆ lines above described were grown in climate chambers at 26℃, 50% humidity, 16/8 of light/darkness, according to an experimental design with three replicates and three plants per replicate. When plants reached the 6–8 leaf stage, the 3rd leaf from the top was labelled and inoculated by spraying a YY187 spore suspension (2.0 × 10⁶ mL^− 1) + 0.1% Tween 20, according to the artificial inoculation method of Zhang et al. [40]. After inoculation, humidity was adjusted to 85%, and the dark culture was overlaid for 48 h followed by normal light culture.

Inoculated leaves were evaluated 14 days post-inoculation (dpi) using an Epson Perfection V39 scanner (Hangzhou Wanshen Testing Technology, China). The spot area of each diseased leaf was calculated using the Leaf Area Meter Software (v.2.3.0.3; WanShen LA-S, China). Response to peanut web blotch was assessed based on the proportion of the total leaf area covered by lesion, according to the following disease grades: Class 1, 0–1%; Class 2, 1–3%; Class 3, 3–5%; Class 4, 5–7%; Class 5, 7–10%; Class 6, 10–15%; Class 7, 15–18%; Class 8, 18–20%; Class 9, ≥ 20%. A disease index was calculated as follows: ∑(number of diseased leaves × disease grade)/(Total number of diseased leaves×9)×100. Based on the disease index, the peanut material was classified as: immune (0), highly resistant (0–20), resistant (20–40), moderately susceptible (40–60), susceptible (60–80) and highly susceptible (80–100).

Construction of resistance and susceptibility pools and resequencing

DNA was extracted using the Plant Genomic DNA Extraction Kit (Tiangen, Beijing, China). Based on the results of the disease test, equal amounts of DNA from 30 highly susceptible and 30 highly resistant lines of the NZ/YH22 population were mixed to construct the S and R pool, respectively. Sequencing libraries were constructed for the two parents and the two pools, which were then sequenced to generate 150 bp paired-end reads using the BGISEQ platform at the Beijing Genomics Institute (Beijing, China). After evaluating the quality of the sequencing data, the Soapnuke software [41] was used to filter the raw data by removing adapters and low-quality reads. High-quality clean reads were aligned to the reference genome of the peanut cultivar Tifrunner (version 2) (https://www.peanutbase.org/peanut_genome) using the Mem algorithm in the Burrows–Wheeler Aligner software [42]. SNPs and InDels were detected using the GATK4.1.2.0 Software [43] and annotated using the ANNOVAR Software [44].

BSA-seq analysis

In this study, four statistics, namely the Euclidean distance (ED), Fisher-exact test (FET), G-statistic (G), and Δ (SNP-index), were used to detect the association between variants and peanut web blotch resistance. Sliding window analysis were conducted at 1 Mb intervals with 10 kb steps, using a 99% confidence level as threshold. Overlapping intervals identified by all the four association tests were considered as regions associated with web blotch resistance QTLs.

Development KASP marker assays

Based on the candidate intervals obtained by BSA-seq, polymorphic SNPs and Indels between the two parents were selected, and the 100 nucleotides upstream and downstream of the target site were extracted and aligned to the reference genome. Unique sequences were used to design KASP primers using the Primer Premier 5.0 software, based on the different sites between the target sequence and its homologous sequence. The fluorescent labels FAM (5’-GAAGGTGACCAAGTTCATGCT-3’) and HEX (5’-GAAGGTCGGAGTCAACGGATT-3’) were added to the 5’ end of the two forward primers. Primers were synthesized by Tsingke Biotechnology Co., Ltd. The KASP assays were performed using the SNP Line platform (LGC Co., Ltd.). Cluster plots for genotyping results were obtained using the software SNPviewer.

Linkage map construction and QTL mapping

To narrow down the candidate genomic regions associated with web blotch resistance, the two F₆ populations were genotyped using the KASP marker assays above described. Phenotypic and genotypic data from each population were used to construct a linkage map and identify web blotch resistance-related QTLs, using QTL IciMapping 4.2 [45]. Genetic distances among markers were calculated using the Kosambi function [46]. The inclusive composite interval mapping method was used for QTL mapping, and the logarithm of odds (LOD) threshold for significant association was set equal to 2.5. Candidate intervals were validated according to the phenotype and KASP marker genotype of recombinant lines selected from the NZ/YH22 populations.

Candidate genes in the target region and their functions were predicted using the peanut genome (Tifrunner.gnm2) available at the Peanut Genomics Database (https://www.peanutbase.org/peanut_genome).

Electronic supplementary material

Below is the link to the electronic supplementary material.

Abbreviations

QTL

Quantitative Trait Loci

BSA-seq

Bulked Sergeant Analysis Sequencing

KASP

Kompetitive Allele-specific PCR

PVE

Phenotypic Variance Explained

SNPs

Single Nucleotide Polymorphisms

Indels

Insertion–deletion

RIL

Recombinant Inbred Line

LOD

Logarithm of Odds

Author contributions

MBZ conducted experiments, analyzed the data, and wrote the manuscript. ZQS and SP revised the manuscript. FYQ also performed the data analysis. HL construct the population. JW performed the genotyping experiments. LYF, GQC, FPZ, HFL, XHW, PYQ and WZD provide help in phenotype analysis. ZZ and XYZ conceived and designed the experiments. All authors read and approved the final manuscript.

Funding

This work was supported by the Key Research Project of the Shennong Laboratory (SN01-2022-03), National Natural Science Foundation of China (32401883), Supported by China Agriculture Research System of MOF and MARA (CARS-13), Henan Provincial Agriculture Research System, China (S2012-5), Major Science and Technology Projects of Henan Province (221100110300), Fundamental work project of Henan Academy of Agricultural Sciences (2024JC03), the Independent Innovation Project of the Henan Academy of Agricultural Sciences, China (2024ZC025), Fund for Distinguished Young Scholars from Henan Academy of Agricultural Sciences (2023JQ02, 2024JQ06), Science and Technology Project of Henan Province (232102110234), Special Program of Germplasm Innovation for grain storage in Liaoning province (2023JH1/10200002).

Data availability

The clean data of the two parents and two bulked pools obtained in this study have been submitted to the BioProject database at NCBI under the BioProject ID: PRJCA031578.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Clinical trial number

Not applicable.

Competing interests

The authors declare no competing interests.