Genetic mapping and diagnostic marker development for a co-localization interval conferring resistance to both Aspergillus flavus infection and aflatoxin production in peanut

Genetic mapping and diagnostic marker development for a co-localization interval conferring resistance to both Aspergillus flavus infection and aflatoxin production in peanut | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genetic mapping and diagnostic marker development for a co-localization interval conferring resistance to both Aspergillus flavus infection and aflatoxin production in peanut Gaorui Jin, Bolun Yu, Yingbin Ding, Li Huang, Taihua Yang, Huaiyong Luo, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8228412/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Aflatoxin contamination caused by Aspergillus flavus threatens the development of peanut industry, breeding aflatoxin-resistant peanut varieties are highly needed. In this study, a recombinant inbred line (RIL) population was developed from a cross between Zhonghua 16 and Kainong H03-3—a high oleic acid peanut line exhibiting resistance to both A. flavus infection and aflatoxin production. Using this RIL population, a high-density genetic map was constructed and employed to identify quantitative trait loci (QTLs) associated with resistance to A. flavus infection (described as percentage seed infection index, PSII) and aflatoxin production (described as aflatoxin content, ATC) across four environments. Three QTLs for PSII and ATC were detected, among which novel QTLs with major effect for both traits were co-localized in chromosome B06, explaining 20.71% and 22.73% of phenotypic variance for PSII and ATC, respectively. Within this co-localized genomic region, 13 candidate genes exhibited strong co-expression patterns linked to both PSII and ATC. Among them, Chr16g3738, harboring a non-synonymous variant in its exon, was used to develop a diagnostic KASP marker KASP-Chr16g3738. Validation of KASP-Chr16g3738 in a peanut germplasm panel and a breeding population (Jihua 6×Kaixuan 01–6) demonstrated that the favorable allele of the candidate gene was associated with a reduction of 15.27–56.67% in PSII and 27.59–72.76% in ATC. This study was the first report in identification of a genomic interval co-localized with QTLs conferring resistance to both A. flavus infection and aflatoxin production, and provides an efficient marker-assisted selection approach for accelerating improvement of resistance to aflatoxin in peanut. peanut Aspergillus flavus genetic linkage map QTL WGCNA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Peanut ( Arachis hypogaea L.) is an economically important crop globally and a major source of plant-derived oils and protein. In 2023, the global peanut planting area was 30.92 million hectares and the production was 54.27 million tons (FAO 2025). However, peanuts are highly susceptible to infection by Aspergillus flavus or A. parasitic during planting, harvesting and storage, resulting in aflatoxin contamination under induciable conditions (Yenew et al. 2025 ; Mbata et al. 2024 ; Jallow et al. 2021 ). Aflatoxins are classified as group I carcinogens by the International Agency for Research on Cancer (IARC) of the World Health Organization (WHO) (Sudakin D. L. 2003), posing significant health risks to both humans and animals, including liver damage (Sang et al. 2023 ; Fan et al. 2021 ) and developmental impairments in children (Nejad et al. 2023 ; Cao et al. 2022 ). Due to their chemical stability and high melting point (> 200°C), aflatoxins are resistant to degradation during food processing (Wang et al. 2023b ). Current detoxification strategies, such as alkaline hydrolysis, thermal processing, and ultrasonic irradiation, often reduce the nutritional value and taste quality of related products (Guan et al. 2021 ; Kumar et al. 2022 ). Although improvements in cultivation and storage practices can reduce contamination risks (Bhatnagar-Mathyu et al. 2015), their effectiveness is influenced by uncontrollable variables such as climatic factors (Crosta et al. 2025 ). Therefore, breeding and planting peanut varieties resistant to A. flavus is the most economical, effective and convenient strategy for prevention and control of the contamination. Peanut kernels exhibit two distinct mechanisms of resistance to A. flavus : infection resistance (preventing fungal colonization and penetration through the seed coat) and aflatoxin production resistance (inhibiting fungal proliferation and aflatoxin biosynthesis in infected kernels) (Soni et al. 2020 ). While A. flavus infection resistance offers partial protection, mechanical damage to seed coats during harvesting, shelling, and processing inevitably undermines this defense. Under such conditions, resistance to aflatoxin production becomes critical. Therefore, integrating both resistance mechanisms in breeding is essential for effectively mitigating aflatoxin contamination risks, but peanut germplasm accessions with resistance both to seed infection and toxin production have been rarely reported. The advancement of high-throughput sequencing technology has facilitated the assembly and annotation of several peanut reference genomes, including “Tifrunner” (Bertioli et al. 2019 ), “Shitouqi” (Chen et al. 2019 ), “Fuhuasheng” (Zhuang et al. 2019 ), and “Yuanza 9102” (Wang et al. 2025 ). These genomic resources have significantly accelerated the development of high-density genetic linkage maps and enhanced understanding of the genetic architecture of multiple traits in peanut including resistance to aflatoxin. Multiple major QTLs for A. flavus infection resistance, including qRAF-3-1, qRAF-14-1 (Khan et al. 2020 ), qPSIIA03 and qPSIIA10 (Yu et al. 2019 ), have been identified through genetic linkage analysis using high-density genetic maps constructed with SNP/Indel markers (Liang et al. 2009 ). For aflatoxin production resistance, major QTLs have been detected in chromosomes A05, A07, and B06 (Jin et al. 2023 ). From fine mapping of a major QTL qAFTRA07 , a candidate gene AhAftr1 encoding an NB-LRR-type disease resistance protein was identified (Yu et al. 2024 ). Moreover, integrative analyses combining genetic linkage analysis and weighted gene co-expression network analysis (WGCNA) identified 21 genes significantly correlated with aflatoxin production resistance on chromosomes A07 and B06 (Huai et al. 2025 ). These studies have laid a solid foundation for understanding the genetic mechanism of resistance to A. flavus in peanut. However, few QTLs or genes conferring resistance to both infection and aflatoxin production have been identified yet. In our previous study, a high-oleic-acid line, KNH03-3, was identified to possess consistent dual resistance to A. flavus infection and aflatoxin production across multiple environments. Nevertheless, the major genetic loci and underlying genes remain uncharacterized. In this study, a RIL population comprising 204 lines was developed from a cross between the high-yielding variety ZH16 with the dual-resistant line KNH03-3. Resistance to A. flavus infection (described as percentage seed infection index, PSII) and aflatoxin production (described as aflatoxin content, ATC) was evaluated in the RIL population across four environments. Whole-genome resequencing was employed to construct a high-density genetic map to precisely map major loci associated with A. flavus infection resistance and aflatoxin production resistance. In addition, a combination of weighted gene co-expression network analysis (WGCNA) and allelic variation analysis was performed to predict candidate genes linked to A. flavus infection and aflatoxin production resistance in peanut. Finally, a KASP marker was developed based on the allelic variation and validated in a peanut germplasm panel and breeding populations. This tudy provides novel insights into the genetic basis of dual resistance to A. flavus infection and aflatoxin production and pave a molecular foundation for marker-assisted breeding of high-oleic and aflatoxin-resistant peanut cultivars. Materials and Methods Plant materials A peanut population consisting of 204 F 7 -F 10 RILs was developed from a cross between ZH16 and KNH03-3 (ZK RIL) through single-seed descent method. The male parent (KNH03-3) is a high oleic acid line exhibiting dual resistance to A. flavus infection and aflatoxin production obtained from the Kaifeng Academy of Agricultural and Forestry Sciences. The female parent (ZH16) is a high-yielding variety from the Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences (OCRI-CAAS). The RIL population and parental lines were planted in a randomized complete block design with four replications. Field trials were conducted in Wuhan in 2020 (F 7 generation), in Wuhan and Quanzhou in 2021 (F 8 and F 9 generation), and in Wuhan in 2022 (F 10 generation). The four environments were designated as Wuhan 2020 (2020WH), Wuhan 2021 (2021WH), Quanzhou 2021 (2021QZ), and Wuhan 2022 (2022WH). A set of 87 germplasm accessions for KASP marker validation was planted in Wuhan in 2014 (2014WH), 2015 (2015WH), and 2016 (2016WH), respectively. The breeding populations for KASP verification derived from Kaixuan 01–6 (KX01-6) ×JH 6 were provided by the Hebei Academy of Agricultural and Forestry Sciences and planted in Hainan in 2024 (2024HN). KX01-6 is a variety closely related to KNH03-3, while JH6 is a variety relatively susceptible to A. flavus compared to KX01-6. Standard agronomic practices were followed for all field experiments, including irrigation, fertilization, and pest management. After harvest, peanut pods were immediately dried, and mature and healthy seeds were selected for further investigation. Phenotypic evaluation of infection resistance The A. flavus strain AF2202, isolated and preserved at OCRI-CAAS in 50% glycerol at -80°C, was used for artificial inoculation. The inoculation procedure followed the method of Jin et al. ( 2024 ). The AF2202 solution was cultured in PDA medium at 30℃ for 7 days, then spores were isolated with 0.1% Tween and diluted to a 2×10 6 CFU (colony forming units)/mL concentration spore solution. For each peanut line, 15 seeds were selected for disinfection, inoculated with 1 mL of spore suspension, and then evenly distributed in sterile Petri dishes. All dishes were incubated at 30℃ for 7 days. After incubation, the improved 0–8 evaluation method was used to score A. flavus infection. The percent seed infection index (PSII) was calculated according to the formula: PSII = (∑8 i = 0i×N i /N×9)×100%, where N0, N1, N2, N3, N4, N5, N6, N7, and N8 represent the number of seeds of levels 0–8 and N represents the total number of seeds. Phenotypic evaluation of aflatoxin production resistance Following the seed infection resistance assessment, spores were washed off from the inoculated seed surfaces with 75% ethanol. The samples were then dried at 121℃ for 3 h, and ground to a fine powder. 1 g of the powder was mixed with 5 ml of methanol and 1 ml of petroleum ether, and shaken on a shaker for 45 min at 190 rpm. Then the mixture was centrifuged in a centrifuge at 3000 rpm for 6 min, and 500 µL of methanol from the middle layer was taken and diluted 20-fold with 55% methanol. Aflatoxin content in each sample was quantified using high-performance liquid chromatography (Agilent Technologies, Santa Clara, CA, USA, 1200 series equipped with HPLC C18 4.6 mm 250 mm, 5 nm column). The mobile phase consisted of a mixture of methanol/water (45:55), with a flow rate of 0.7 mL/min, and a column temperature set at 30 ℃. The injection volume was 10 µL, and the injection time was 17 min. An aflatoxin standard solution (CRM46304, Sigma-Aldrich, Munich, Germany) was used to develop a standard curve. Statistical analysis of phenotypic data Phenotypic measurements were obtained from three biological replicates for all samples. Data analysis were performed using the IBM SPSS Statistics software (v.26; IBM, USA). Normality of data distributions was assessed using the Kolmogorov-Smirnov test. Significant phenotypic differences among genotypes were determined using Duncan’s Test and Independent-samples T-tests. The variance analysis was performed to evaluate significant differences among RILs, environments, and the interactions between genotype and environment. The broad-sense heritability was estimated as described by Huang et al. ( 2016 ).. Library construction and sequencing Genomic DNA was extracted from young leaves of F₇ plants of the RIL population using the cetyltrimethylammonium bromide (CTAB) method. Paired-end sequencing libraries (300–500 bp insert size) were prepared following the Illumina standard protocol for the 204 RILs and their parents. Library sequencing was performed on the Illumina HiSeq 2500 platform (Illumina Inc., San Diego, CA, USA). Raw reads were filtered to obtain high-quality clean reads by using SOAPnuke (Chen et al. 2018 ). The clean reads were aligned to the peanut reference genome (Zhonghua 5) using the BWA software (Li & Durbin, 2009 ). HaplotypeCaller and Genotype GVCFs in GATK were used to identify SNPs and InDels. Linkage map construction and QTL analysis Low-quality variants were filtered using the following criteria: (a) Minor allele frequency (MAF) ≥ 0.2; (b) Proportion of missing genotypes ≤ 0.1; (c) Relative heterozygosity rate ≤ 0.2. Missing genotypes, were imputed based on a hidden Markov model (HMM). Finally, a linkage map was constructed using MSTMap software, and the genetic distances between markers were calculated using the Kosambi mapping function. The QTL Cartographer 2.5 software (Wang et al. 2006 ) was used to perform QTL analysis via a composite interval mapping (CIM) model using a high-density bin genetic map. The control markers, window size, and walk speed were set to 5, 10, and 1 cM, respectively. The LOD threshold was set at 2.5 to detect additive QTLs. QTLs were named with an initial letter “q” followed by the abbreviated trait name and corresponding chromosome number. If multiple QTLs exhibit overlapping physical intervals within the same linkage group across multiple environments, they are considered as a single consistent QTL and are designated with the same nomenclature. Development and validation of KASP markers Based on QTL mapping results, KASP markers were developed for the SNP sequences flanking the major loci. KASP primers were designed using the PolyMarker Web site ( http://www.polymarker.info/ ) and synthesized by Wuhan Genoseq Technology Co., LTD. KASP assays were conducted using 2 × KASP Master Mix (2.5 µL) (LGC Biosearch Technologies), two forward primers (0.075 µL), one reverse primer (0.2 µL), genomic DNA (50 ng/µL, 1.5 µL), and ddH2O (0.65 µL). The fluorescence data were analyzed using the online software SNPway ( http://www.snpway.com ). Marker validation was performed in 300 RIL lines (including the 204 used for mapping), 59 lines from the JH6 × KX01-6 breeding population (JK population), and a germplasm panel consisting of 87 accessions. RNA extraction and transcriptome analysis RNA extraction and transcriptome analysis The peanut parental lines, ZH16 (Z) and KNH03-3 (K), were selected for RNA-seq analysis. For each genotype, a treatment group (inoculated with A. flavus ) and a control group (CK) were sampled and treated with an equal volume of 0.1% Tween solution. Samples were collected at 1, 3, 5, and 7 days (designated as 1D, 3D, 5D, and 7D, respectively) after inoculation. The resultant samples were designated as Z_1D, Z_3D, Z_5D, Z_7D, ZCK_1D, ZCK_3D, ZCK_5D, ZCK_7D, K_1D, K_3D, K_5D, K_7D, KCK_1D, KCK_3D, KCK_5D and KCK_7D. Total RNA was isolated using the Rneasy Plant Mini kit (QIAGEN). Library construction and sequencing were performed by the DNBSEQ-T7 platform. The sequencing data were deposited in the Genome Sequence Archive database under accession number CRA028108 (Chen et al. 2021 ). Raw reads were filtered by SOAPnuke software (v2.1.0), and clean sequencing data were mapped to the peanut reference genome (Zhonghua 5) using HISAT2. The number of reads from each sample compared to each transcript was converted into FPKM (fragments per kilobase per million bases) by RSEM to estimate gene expression levels. Results Evaluation of phenotypic variation in the RILs The PSII of the resistant male parent KNH03-3 ranged from 35.24% to 57.99% across four environments, significantly lower than that of the susceptible female parent ZH16 (76.43% to 88.33%, Table 1 ). In the RIL population, PSII values ranged from 5.64–87.50% in 2020WH, 11.03–95.36% in 2021WH, 31.92–94.83% in 2021QZ, and 30.83–92.97% in 2022WH, respectively. For the aflatoxin production resistance, KNH03-3 exhibited significantly lower aflatoxin content compared to ZH16 in artificial inoculation across all four environments (Table 1 , Fig. 1 ). The RIL population displayed transgressive segregation for aflatoxin content, with ATC values spanning 5.10–132.60 µg/g (2020WH), 8.15–311.41 µg/g (2021WH), 18.26–207.57 µg/g (2021QZ), and 7.72–173.36 µg/g (2022WH) (Table 1 , Fig. 1 ). Variance analysis revealed that genotype, environment, and genotype × environment interaction all significantly influenced seed infection resistance and aflatoxin production resistance. The estimated broad-sense heritability ( H 2 ) was 0.70 for PSII and 0.62 for ATC (Table 1 ). Correlation analysis across multiple environments showed there was a significantly positive association between PSII and ATC, with Pearson correlation coefficients ranging from 0.41 to 0.68 (Fig. 2 ). The hundred seeds weight (HSW) of the RIL population displayed broad phenotypic variability across three environments, ranging from 38.47-106.45 g (2021WH), 36.76-106.16 g (2021QZ), and 39.35–98.70 g (2022WH) (Table S1 , Fig. S1 ). For oleic acid content (OA), the RIL population displayed transgressive segregation with ranges of 44.10–82.38% (2020WH), 37.26–82.13% (2021WH), and 40.71–82.15% (2021QZ) (Table S1 , Fig. S1 ). From the RIL population, an elite high-oleic acid line (QZ835) with similar resistance to seed infection and aflatoxin production as the highly-resistant parent, KNH03-3. Furthermore, the HSW of QZ835 was significantly higher than that of KNH03-3, indicating a higher yield potential (Table 2 ). Table 1 Description of phenotypic data of PSII and ATC in the RIL population. Trait Env Parents RIL Population ZH16 KH03-3 Range Mean ± SD CV H 2 PSII(%) 2020WH 79.72 ± 6.14** 43.87 ± 6.69 5.64–87.50 37.59 ± 18.29 0.49 0.70 2021WH 76.43 ± 8.16** 35.24 ± 3.36 11.03–95.36 49.58 ± 16.98 0.34 2021QZ 88.33 ± 3.63** 57.99 ± 3.85 31.92–94.83 68.98 ± 13.83 0.20 2022WH 80.14 ± 3.49** 46.52 ± 5.94 30.83–92.97 69.05 ± 11.77 0.17 ATC(µg/g) 2020WH 110.43 ± 9.51** 18.58 ± 2.33 5.10-132.60 39.93 ± 25.11 0.63 0.62 2021WH 129.67 ± 32.49** 37.35 ± 9.65 8.15-311.41 93.99 ± 57.75 0.61 2021QZ 123.67 ± 16.62** 43.55 ± 9.55 18.26-207.57 77.74 ± 39.43 0.51 2022WH 120.09 ± 33.45* 38.43 ± 11.54 7.72-173.36 54.44 ± 29.61 0.54 Note: “*” indicates a significant different between ZH16 and KNH03-3 ( P < 0.05); “* *” indicates an extremely significant different ( P < 0.01) PSII for the percent seed infection index, ATC for aflatoxin content, 2020WH for 2020Wuhan, 2021WH for 2021Wuhan, 2021QZ for 2021Quanzhou, 2022WH for 2022Wuhan. Table 2 Detailed information of PSII, ATC, HSW and OA in a elite line and parents across multiple environments. Traits QZ835 ZH16 KNH03-3 PSII(%) 45.14 ± 11.26 b 81.16 ± 6.67 a 45.90 ± 9.56 b ATC(µg/g) 41.35 ± 14.42 b 120.97 ± 8.06 a 34.48 ± 10.94 b HSW(g) 74.00 ± 2.63 b 92.38 ± 4.13 a 60.40 ± 0.66 c OA(%) 78.13 ± 0.87 b 50.42 ± 0.77 c 81.41 ± 0.44 a PSII for the percent seed infection index, ATC for aflatoxin content, HSW for hundred seeds weight, OA for oleic acid content. Mapping QTLs for PSII and ATC A high-density genetic linkage map was constructed using 115,397 SNP/InDel markers. Those were consolidated into 1,985 recombination bin markers. The markers were arranged into 20 linkage groups (LGs) spanning a total of 1,030.76 cM. The high-resolution genetic map achieved a marker density of 1.93 bins per centiMorgan (cM). The average distances between bins in each linkage group ranged from 0.36 and 3.30 cM (Table 3 , Fig. S2). To evaluate the genetic map quality, we analyzed the chromosomal origins in each recombinant inbred line, revealing that chromosome segments inherited from distinct parents formed continuous fragments within individual RILs (Fig. S3). Furthermore, a co-linearity analysis between the constructed genetic linkage map and the reference genome demonstrated a strong alignment between the linkage groups (LGs) and their corresponding chromosomes (Fig. S4), thereby confirming the accuracy and uniformity of the genetic map for subsequent QTL analysis. Table 3 Detailed information on genetic linkage map. Chr Genetic linkage map Number of markers Number of bins Bin interval(cM) ChrA01 52.17 10395 113 0.47 ChrA02 35.34 1432 75 0.48 ChrA03 56.83 16438 113 0.51 ChrA04 44.94 13788 85 0.54 ChrA05 44.36 8116 104 0.43 ChrA06 49.32 2051 94 0.53 ChrA07 42.72 8500 98 0.44 ChrA08 61.41 3969 125 0.50 ChrA09 50.84 14465 107 0.48 ChrA10 60.85 1508 132 0.46 ChrB01 52.82 202 17 3.30 ChrB02 40.80 3316 114 0.36 ChrB03 67.89 2853 147 0.47 ChrB04 50.94 1658 84 0.61 ChrB05 50.44 975 62 0.83 ChrB06 62.33 3026 128 0.49 ChrB07 55.79 1545 103 0.55 ChrB08 40.75 13527 75 0.55 ChrB09 58.77 2722 91 0.65 ChrB10 51.47 4911 118 0.44 Whole 1030.76 115397 1985 0.66 A total of three QTLs were identified for PSII in 3 chromosomes (A01, A07, and B06), explaining 4.30–20.71% of phenotypic variation (PVE) in four environments. Among the three PSII QTLs identified, a major QTL, qPSIIB06 , was consistently detected in all the environments, explaining 5.12–20.71% of the phenotypic variation. For ATC, three QTLs, namely qATCB06 , qATCA01 , and qATCA08 , were identified, which explained 6.04–22.73% of the phenotypic variation (Table 4 , Fig. 3 ). Among them, the major QTL ( qATCB06 ) with additive effects ranging from − 7.61 to -29.68 was stably detected across all environments. Interestingly, the major and stable QTLs for ATC and PSII ( qATCB06 and qPSIIB06 ) were co-localized within a ~ 12 cM interval on chromosome B06 (Fig. 3 ). Table 4 QTLs for PSII and ATC in RIL population. Trait QTL LG Env CI Marker Interval LOD PVE(%) Add PSII qPSIIA01 A01 2021WH 20.5-22.62 c01b041-c01b046 3.09 4.30 -0.04 qPSIIA07 A07 2021QZ 2.13–17.74 c07b008-c07b036 4.86 8.19 0.04 qPSIIB06 B06 2020WH 60.75–61.54 c16b123-c16b125 2.64 5.12 -0.04 2021WH 50.41–62.07 c16b091-c16b127 13.24 20.71 -0.08 2021QZ 57.56–62.07 c16b115-c16b124 5.39 9.16 -0.04 2022WH 49.88–62.07 c16b089-c16b127 7.80 12.72 -0.04 ATC qATCA01 A01 2021WH 19.97–32.39 c01b039-c01b069 4.74 6.04 -15.33 qATCA08 A08 2022WH 32.69–44.36 c08b069-c08b105 4.86 7.85 9.45 qATCB06 B06 2020WH 50.41–62.07 c16b091-c16b127 4.14 7.79 -7.61 2021WH 50.67–62.07 c16b092-c16b127 12.69 22.73 -29.68 2021QZ 60.22–62.07 c16b121-c16b127 3.81 7.57 -13.91 2022WH 52.00-62.07 c16b096-c16b127 8.97 15.04 -13.09 PSII for the percent seed infection index, ATC for aflatoxin content, 2020WH for 2020Wuhan, 2021WH for 2021Wuhan, 2021QZ for 2021Quanzhou, 2022WH for 2022Wuhan. RNA-Seq analysis of seeds infected by A.flavus A total of 48 peanut samples were used for RNA-seq library construction, comprising 24 inoculated samples from both parental lines collected at 1, 3, 5, and 7 days after inoculation (DAI) and 24 corresponding control samples collected at the same time points. In total, 283.2 Gb of high-quality clean data were obtained, with each library yielding 4.35–11.37 Gb of clean data. After aligning the sequencing data to the reference genome, a total of 66,787 genes were detected as expressed genes, with the number of expressed genes per library ranging from 21,630 to 51,536 (Table S2). To elucidate regulatory modules associated with A. flavus resistance, a WGCNA was performed using transcriptome data. Consequently, a scale-free co-expression network comprising 16 modules was established, with gene counts in each module ranging from 39 to 2080 (Fig. 4 A; Fig. 4 B). To characterize the key modules associated with resistance in peanut seeds, the module-trait relationships (MTRs) were assessed. As shown in Fig. 4 B, two modules, including the brown (for PSII, r = 0.9, p < 0.001; for ATC, r = 0.74, p < 0.01) and the magenta module (for PSII, r = 0.49, p < 0.05; for ATC, r = 0.36, p < 0.05) were significantly positively correlated with A. flavus infection and aflatoxin production. The blue module (for PSII, r = -0.68, p < 0.01; for ATC, r = -0.54, p < 0.01) and the turquoise module (for PSII, r = -0.64, p < 0.01; for ATC, r = -0.46, p < 0.01) were significantly and positively correlated with infection resistance and aflatoxin production resistance. Gene Ontology (GO) enrichment analysis revealed that the brown module was significantly enriched in biological processes related to response to chemical stimulus and small molecule metabolic process(Fig. 4 C). The magenta module was significantly enriched with GO terms associated with aerobic respiration (Fig. 4 D). For the blue module, gene enrichment was observed in GO terms linked to intracellular anatomical structures, the nucleolus, and nucleic acid metabolic process (Fig. 4 E). The turquoise module showed significant enrichment in processes related to RNA biosynthetic process, biosynthetic process and regulation of biosynthetic process (Fig. 4 F). Prediction of candidate genes and development of diagnostic markers Based on the physical positions of the flanking molecular markers c16b092 and c16b128, which delimited the confidence intervals of qPSIIB06 and qATCB06 , the candidate genomic region conferring resistance to A. flavus infection and aflatoxin production resistance was mapped to a 5.3Mb physical interval from 139.50 Mb to 144.80 Mb on chromosome B06 (Fig. 3 ). According to gene annotations, a total of 295 genes are present within this interval, among which 200 genes were expressed during the infection process. Based on re-sequencing data of the two parental lines, a total of 420 variants were identified between parental lines within this interval, among which 17 variants caused non-synonymous mutations in 16 genes. Additionally, 47 SNPs/Indels located in the upstream/downstream regions of genes were identified in 43 genes (Table S3). Integrating the WGCNA results with the variant data, 13 genes were identified as high-confidence candidates, including 3 from the brown module, 5 from the yellow module, and 5 from the turquoise module. Variants were identified in the intergenic, exonic, upstream, and downstream regions of these 13 genes (Table 5 ), only Chr16g3738 contained a nonsynonymous SNP (C/A, position 144,475,090) within its coding sequence. This variant was therefore used to develop a resistance diagnostic KASP marker (KASP-Chr16g3738). Validation using the ZK RIL population revealed distinct allelic differentiation. The KASP-Chr16g3738 identified 169 lines carrying the CC genotype (ZH16 genotype) and 123 lines harboring the AA genotype (KNH03-3 genotype) (Fig. 5 A). Phenotypic analysis revealed significantly lower mean values for both PSII and ATC in lines with AA genotype compared to those with CC genotype (P < 0.01) (Fig. 6 A and B). Screening of a peanut germplasm panel further confirmed this trend (Fig. 5 B). Compared to the CC genotype, the mean values of PSII and ATC in the AA genotype were reduced by 22.98%–29.30% and 27.59%–29.87%, respectively (Fig. 6 C and D). When applied to the JK breeding population (derived from JH6 × KX01-6) (Fig. 5 C), KASP-Chr16g3738 effectively distinguished the resistant lines from the susceptible ones. The parents (JH6 and KX01-6) exhibited PSII values of 84.13% and 55.28% and ATC levels of 94.83 µg/g and 44.45 µg/g, respectively. The mean values of both PSII and ATC in the AA-genotype (PSII, 60.32 ± 9.34%; ATC, 42.65 ± 7.12 µg/g) lines were significantly lower than those of lines with the CC-genotype (PSII, 81.19 ± 12.07%; ATC, 75.91 ± 21.69 µg/g) (Fig. 6 E and F). Moreover, for the susceptible parent JH6, the PSII and ATC of the AA-genotype exhibited reductions of 15.27–56.67% and 46.12–72.76%, respectively. These results demonstrated that the diagnostic marker KASP-Chr16g3738 is reliable and effective for distinguishing resistant genotypes and can be directly applied in germplasm screening and marker-assisted selection in peanut breeding. Table 5 Module and functional annotation of candidate genes. Gene ID Module Annotation-location Function Chr16g3501 blue intergenic elongation factor G-2 Chr16g3519 blue intergenic myosin IB heavy chain Chr16g3534 blue intergenic/downstream uncharacterized RNA-binding protein C1827.05c Chr16g3707 blue intergenic selenium binding Chr16g3750 blue intergenic/downstream protein of unknown DUF642 Chr16g3469 brown intergenic/downstream inactive LRR receptor-like serine/threonine-protein kinase BIR2 Chr16g3541 brown intronic uncharacterized protein LOC100527040 isoform X4 Chr16g3585 brown intergenic uncharacterized protein LOC107605226 isoform X1 Chr16g3565 turquoise intergenic dihydrofolate synthetase isoform X1 Chr16g3666 turquoise upstream trihelix transcription factor ASR3 Chr16g3734 turquoise intergenic transcriptional corepressor LEUNIG isoform X1 Chr16g3738 turquoise exonic flavin-binding monooxygenase family protein Chr16g3758 turquoise intergenic peptidyl-prolyl cis-trans isomerase family protein Discussion The co-segregation phenomenon of dual resistance to A. flavus infection and aflatoxin production in RIL population In this study, a RIL population comprising 204 lines was developed by crossing a high-yielding peanut variety ZH16 with a high oleic acid line KNH03-3. Phenotypic data of PSII, ATC, HSW, and OA were collected from the RIL population across multiple environments. The male parent KNH03-3 was characterized with desirable dual resistance to seed infection and aflatoxin production across all environments, exhibiting relatively lower PSII and ATC values compared to the female parent ZH16. As in numerous reported research, resistance of peanut kernel against A. flavus can be categorized into two mechanisms including ‌A. flavus infection resistance‌ and ‌aflatoxin production resistance (Ding et al. 2022 ). Korani et al. ( 2017 ) demonstrated that ‌aflatoxin production resistance in peanut seeds was primarily ‌regulated by genotype‌, but exhibited ‌no correlation‌ with the ‌fungal biomass‌ observed on seed coats. In our previous study, a RIL population (ZH10 × ICG12625) was used to evaluate A. flavus infection resistance and aflatoxin production resistance‌. Although the resistant parent ICG12625 exhibited higher A. flavus ‌infection resistance‌ and ‌aflatoxin production resistance‌ compared to ZH10, no significant association between these two resistance traits in RIL (P > 0.05) was obsevered (Yu et al. 2019 ), suggesting independent inheritance of loci controlling each trait. However, in the ZH16 × KNH03-3 population, a significantly positive correlation was observed between seed infection resistance and aflatoxin production resistance, indicating that the underlying loci governing both traits may be tightly linked or pleiotropic. Hence, integrating the dual resistance mechanisms‌, infection resistance and aflatoxin production resistance, in peanut is highly promising and could contribute to more effective management of aflatoxin contamination‌. Breeding programs using resistant parents with ‌co-segregating dual resistance traits‌ are expected to generate superior progeny with better resistance through stable inheritance of synergistic defense pathways. A novel high-oleic acid peanut germplasm with aflatoxin resistance and high-yield potential High oleic acid has been an important quality trait in most peanut breeding programs and applied in industry, but the existing A. flavus resistant varieties such as J11, Kanghuang 1 and ZH6, all possess a normal oleic acid content (OA < 60%) and high oleate peanut variety with aflatoxin resistance has not been reported. In this study, a high-oleic acid elite line QZ835 exhibited ‌stable dual resistance to aflatoxin and higher HSW compared to the resistant parent KNH03-3 was identified, showing a great potential in integrating resistance to aflatoxin, high oleic acid and high yield components. Co-localized major-effect QTLs on chromosome B06 confer simultaneous resistance to A. flavus infection and aflatoxin production in peanut Most genetic studies on peanut resistance to A. flavus have focused on either infection resistance or aflatoxin production resistance. Reported QTLs for seed infection resistance have been mapped on chromosomes A03, A05, A08, A10, B01, B04, B08, and B10 (Khan et al. 2020 ; Jiang et al. 2021 ), while QTLs conferring resistance to aflatoxin production have been identified on chromosomes A05, A07, A08, B05, B06, and B09 (Yu et al. 2019 ; Yu et al. 2024 ; Huai et al. 2025 ). Huai et al. ( 2025 ) narrowed down qAFB1B06.2 on B06 to a physical interval of 9.24–21.55 Mb using an SNP-based linkage map. In this study, two novel and stable major-effect QTLs conferring resistance to A. flavus infection and aflatoxin production were co-localized on chromosome B06, suggesting the presence of either pleiotropic effects or closely linked causal genes that simultaneously regulate both resistance mechanisms within the same genomic interval. Prediction of candidate genes related to resistance to A. flavus infection and aflatoxin production and development of KASP markers Combining forward genetic analysis (such as genetic linkage analysis and GWAS) with transcriptome analysis enables the effective and rapid identification of candidate genes in crops (Vanshney et al. 2021). Through an integrative approach, QTL analysis, and RNA-sequencing technology, Derakhshani et al. ( 2020 ) successfully identified 16 candidate genes potentially regulating cadmium tolerance in barley. A total of 17 candidate genes were identified for pre-harvest sprouting in maize using WGCNA and QTL analysis, among which 3 were functionally validated using mutants (Ma et al. 2023 ). In this study, a 5.3 Mb genomic region conferring dual resistance to A. flavus infection and aflatoxin production was identified on chromosome B06 through QTL mapping. Furthermore, 13 genes in the target interval were grouped into modules associated with dual resistance using WGCNA. Among them, ‌only Chr16g3738 harbored a non-synonymous mutation between the two parents. This gene encodes a flavin-binding monooxygenase-like protein, ‌which has been confirmed to be associated with reactive oxygen species (ROS) homeostasis‌ regulation and ‌catalyzes flavonoid hydroxylation. In tomato, flavin monooxygenase family members (FMO1) interact with catalase (CAT2) in regulating ROS homeostasis (Wang et al. 2023b ). In Lotus japonicus , the flavin monooxygenase LjF8H catalyzes the hydroxylation of flavonoids to generate antimicrobial derivatives such as gossypetin (Hiraga et al. 2021 ). According to the allelic variation within Chr16g3738, we have developed a KASP diagnostic marker, KASP-Chr16g3738, tightly associated with resistance to both A. flavus infection and aflatoxin production. Marker validation in peanut germplasm panel and breeding populations demonstrated that the lines selected by KASP-Chr16g3738 possessed reduced PSII (by 25.70-26.12%) and ATC (by 29.07–29.52%), confirming its effectiveness in marker-assisted selection (MAS) for aflatoxin resistance. Conclusin In conclusion, KNH03-3 was identified to possess dual resistance to A. flavus infection and aflatoxin production. Two novel major effect QTLs conferring resistance to both A. flavus infection and aflatoxin production were co-localized within a 5.3 Mb genomic interval on chromosome B06. Through integration of WGCNA and allelic variation analysis, Chr16g3738 encoding a flavin-binding monooxygenase-like protein was identified as the most probable causal gene conferring the dual resistance. The KASP molecular marker (KASP-Chr16g3738) developed was validated to be applicable in selecting genotypes with reduced PSII and ATC. The above findings provideinsights of aflatoxin resistance and are meaningful for accelerating the genetic enhancement of resistance to aflatoxin in peanut. Declarations Funding This research work was financially supported by National Natural Science Foundation of China (32101708), National Key R&D Program of China (2023YFD1202800), the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences (No. CAAS-ASTIP-2021-OCRI), Central Public-interest Scientific Institution Basal Research Fund for Chinese Academy of Tropical Agricultural Sciences (No. 1610172024001), the Earmarked Fund for CARS (CARS-13), and the Central Public-interest Scientific Institution Basal Research Fund (No. Y2025YC112). CRediT authorship contribution statement Gaorui Jin: Writing - original draft, Data curation, Investigation. Bolun Yu: Writing - original draft. Yingbin Ding: Software,Investigation. Li Huang: Investigation. Taihuang Yang: Software. Huaiyong Luo: Funding acquisition. Jin Wang: Resources. Yong Lei: Resources. Huifang Jiang: Resources, Writing: review & editing. 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J Adv Res 62:15–26. https://doi.org/10.1016/j.jare.2023.09.014 Zhuang W, Chen H, Yang M, Wang J, Pandey MK, Zhang C et al (2019) The genome of cultivated peanut provides insight into legume karyotypes, polyploid evolution and crop domestication. Nat Genet 51(5):865–876. https://doi.org/10.1038/s41588-019-0402-2 Supplementary Files Fig.S1.pdf Fig.S2.pdf Fig.S3.pdf Fig.S4.pdf supplementarydata.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor revisions 17 Feb, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers invited by journal 08 Dec, 2025 Editor assigned by journal 29 Nov, 2025 First submitted to journal 27 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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06:37:10","extension":"xml","order_by":39,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":258510,"visible":true,"origin":"","legend":"","description":"","filename":"TAAGD25011150structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8228412/v1/803e731e4c6ead2011c65848.xml"},{"id":98037558,"identity":"e4cc3dc2-aa78-42ec-ab47-a6a65acf49e1","added_by":"auto","created_at":"2025-12-12 06:37:10","extension":"html","order_by":40,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":266401,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8228412/v1/e9eef87b0dff45e247f1865e.html"},{"id":98037521,"identity":"afe919a9-0a75-441b-86d9-9b8de3f127f6","added_by":"auto","created_at":"2025-12-12 06:37:10","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":452412,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic distribution of PSII and ATC in the RIL population in four environments. PSII for the percent seed infection index, ATC for aflatoxin content, 2020WH for 2020Wuhan, 2021WH for 2021Wuhan, 2021QZ for 2021Quanzhou, 2022WH for 2022Wuhan.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8228412/v1/f546482952894a7f329f7950.jpg"},{"id":98427044,"identity":"f3aeb940-de6e-4844-9fd9-484aea09113f","added_by":"auto","created_at":"2025-12-17 16:39:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1438629,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation analysis between PSII and ATC. PSII for the percent seed infection index, ATC for aflatoxin content, 2020WH for 2020Wuhan, 2021WH for 2021Wuhan, 2021QZ for 2021Quanzhou, 2022WH for 2022Wuhan, “\u003csup\u003e* *\u003c/sup\u003e” indicates an significant correlation (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8228412/v1/ac4a3a1a512d56b1989026c6.jpg"},{"id":98426849,"identity":"ce9969cf-ce04-4116-ae0e-0b98fd600943","added_by":"auto","created_at":"2025-12-17 16:38:50","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":392634,"visible":true,"origin":"","legend":"\u003cp\u003eCo-location of QTLs associated with PSII and ATC on Chr B06 in four environments. PSII for the percent seed infection index, ATC for aflatoxincontent, 2020WH for 2020Wuhan, 2021WH for 2021Wuhan, 2021QZ for 2021Quanzhou, 2022WH for 2022Wuhan.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8228412/v1/5cc66dc1ff4cf3af7373ec7a.jpg"},{"id":98037523,"identity":"031b66b5-387e-409f-b304-f06650718179","added_by":"auto","created_at":"2025-12-12 06:37:10","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2234349,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA analysis of all expressed genes and GO analysis of the four module genes. A: Cluster dendrogram; B: Module-trait relationships of all expressed genes. C: GO analysis of the brown module genes; D: GO analysis of the magenta module genes; E: GO analysis of the blue module genes; F: GO analysis of the turquoise module genes.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8228412/v1/1ab7e5281049a6bccd7841ae.jpg"},{"id":98037530,"identity":"be40dbe5-387c-4df3-9538-6c6830f75d31","added_by":"auto","created_at":"2025-12-12 06:37:10","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":353325,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots of KASP marker KASP-Chr16g3738. A: Genotyping of RIL; B: Genotyping of germplasm accessions; C: Genotyping of JK breeding populations.\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8228412/v1/e30cbb1de0eea3a50fc2a125.jpg"},{"id":98037528,"identity":"f049103c-5de7-428f-a5c2-c3bedfeb0062","added_by":"auto","created_at":"2025-12-12 06:37:10","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1439639,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic comparison between two genotypes of KASP-Chr16g3738. A and B: comparison in the ZK RIL Populations for PSII and ATC, respectively; C and D: comparison in the germplasm accessions; E and F: comparison in the JK breeding populations. PSII for the percent seed infection index, ATC for aflatoxin content, 2020WH for 2020Wuhan, 2021WH for 2021Wuhan, 2021QZ for 2021Quanzhou, 2022WH for 2022Wuhan, 2014WH for 2014Wuhan, 20215WH for 2015Wuhan, 2016WH for 2016Wuhan, 2024HN for 2024Hainan. “**” indicates significant at \u003cem\u003ep \u003c/em\u003e≤ 0.01.\u003c/p\u003e","description":"","filename":"fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8228412/v1/1ce765611d80e723ba042613.jpg"},{"id":98774631,"identity":"ee0bcf16-9a34-418e-98a6-d682eb0f36b7","added_by":"auto","created_at":"2025-12-22 12:06:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7672471,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8228412/v1/7fb3e3a9-0b58-412b-9149-e8670c732b46.pdf"},{"id":98037525,"identity":"ccfbdc5c-722b-48c8-95b5-7e7b15338361","added_by":"auto","created_at":"2025-12-12 06:37:10","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5640,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8228412/v1/d0a5507b3623a9db7a153c23.pdf"},{"id":98426929,"identity":"a299af52-5a21-420a-b3e7-779443baa6ce","added_by":"auto","created_at":"2025-12-17 16:39:02","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":443582,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8228412/v1/3a79db806c441fe6f20be521.pdf"},{"id":98427098,"identity":"77f39d37-ff5f-45f1-b481-c75ac67f0320","added_by":"auto","created_at":"2025-12-17 16:39:33","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":899603,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8228412/v1/ab57eecb21d2aa2aab6c4055.pdf"},{"id":98037527,"identity":"0978f7ce-9415-4387-b242-8db0fe68d07e","added_by":"auto","created_at":"2025-12-12 06:37:10","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":701465,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8228412/v1/3960330454a893211d16df39.pdf"},{"id":98426468,"identity":"ee450e39-eded-485d-a942-bcc8d05c469a","added_by":"auto","created_at":"2025-12-17 16:36:26","extension":"xlsx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":3063605,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarydata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8228412/v1/18dac7b5e2c001f908f79caf.xlsx"}],"financialInterests":"","formattedTitle":"Genetic mapping and diagnostic marker development for a co-localization interval conferring resistance to both Aspergillus flavus infection and aflatoxin production in peanut","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePeanut (\u003cem\u003eArachis hypogaea\u003c/em\u003e L.) is an economically important crop globally and a major source of plant-derived oils and protein. In 2023, the global peanut planting area was 30.92\u0026nbsp;million hectares and the production was 54.27\u0026nbsp;million tons (FAO 2025). However, peanuts are highly susceptible to infection by \u003cem\u003eAspergillus flavus\u003c/em\u003e or \u003cem\u003eA. parasitic\u003c/em\u003e during planting, harvesting and storage, resulting in aflatoxin contamination under induciable conditions (Yenew et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mbata et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jallow et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Aflatoxins are classified as group I carcinogens by the International Agency for Research on Cancer (IARC) of the World Health Organization (WHO) (Sudakin D. L. 2003), posing significant health risks to both humans and animals, including liver damage (Sang et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Fan et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and developmental impairments in children (Nejad et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Cao et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Due to their chemical stability and high melting point (\u0026gt;\u0026thinsp;200\u0026deg;C), aflatoxins are resistant to degradation during food processing (Wang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). Current detoxification strategies, such as alkaline hydrolysis, thermal processing, and ultrasonic irradiation, often reduce the nutritional value and taste quality of related products (Guan et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although improvements in cultivation and storage practices can reduce contamination risks (Bhatnagar-Mathyu et al. 2015), their effectiveness is influenced by uncontrollable variables such as climatic factors (Crosta et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, breeding and planting peanut varieties resistant to \u003cem\u003eA. flavus\u003c/em\u003e is the most economical, effective and convenient strategy for prevention and control of the contamination.\u003c/p\u003e\u003cp\u003ePeanut kernels exhibit two distinct mechanisms of resistance to \u003cem\u003eA. flavus\u003c/em\u003e: infection resistance (preventing fungal colonization and penetration through the seed coat) and aflatoxin production resistance (inhibiting fungal proliferation and aflatoxin biosynthesis in infected kernels) (Soni et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While \u003cem\u003eA. flavus\u003c/em\u003e infection resistance offers partial protection, mechanical damage to seed coats during harvesting, shelling, and processing inevitably undermines this defense. Under such conditions, resistance to aflatoxin production becomes critical. Therefore, integrating both resistance mechanisms in breeding is essential for effectively mitigating aflatoxin contamination risks, but peanut germplasm accessions with resistance both to seed infection and toxin production have been rarely reported.\u003c/p\u003e\u003cp\u003eThe advancement of high-throughput sequencing technology has facilitated the assembly and annotation of several peanut reference genomes, including \u0026ldquo;Tifrunner\u0026rdquo; (Bertioli et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), \u0026ldquo;Shitouqi\u0026rdquo; (Chen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), \u0026ldquo;Fuhuasheng\u0026rdquo; (Zhuang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and \u0026ldquo;Yuanza 9102\u0026rdquo; (Wang et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These genomic resources have significantly accelerated the development of high-density genetic linkage maps and enhanced understanding of the genetic architecture of multiple traits in peanut including resistance to aflatoxin. Multiple major QTLs for \u003cem\u003eA. flavus\u003c/em\u003e infection resistance, including \u003cem\u003eqRAF-3-1, qRAF-14-1\u003c/em\u003e (Khan et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), \u003cem\u003eqPSIIA03\u003c/em\u003e and \u003cem\u003eqPSIIA10\u003c/em\u003e (Yu et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), have been identified through genetic linkage analysis using high-density genetic maps constructed with SNP/Indel markers (Liang et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For aflatoxin production resistance, major QTLs have been detected in chromosomes A05, A07, and B06 (Jin et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). From fine mapping of a major QTL \u003cem\u003eqAFTRA07\u003c/em\u003e, a candidate gene \u003cem\u003eAhAftr1\u003c/em\u003e encoding an NB-LRR-type disease resistance protein was identified (Yu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, integrative analyses combining genetic linkage analysis and weighted gene co-expression network analysis (WGCNA) identified 21 genes significantly correlated with aflatoxin production resistance on chromosomes A07 and B06 (Huai et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These studies have laid a solid foundation for understanding the genetic mechanism of resistance to \u003cem\u003eA. flavus\u003c/em\u003e in peanut. However, few QTLs or genes conferring resistance to both infection and aflatoxin production have been identified yet. In our previous study, a high-oleic-acid line, KNH03-3, was identified to possess consistent dual resistance to \u003cem\u003eA. flavus\u003c/em\u003e infection and aflatoxin production across multiple environments. Nevertheless, the major genetic loci and underlying genes remain uncharacterized.\u003c/p\u003e\u003cp\u003eIn this study, a RIL population comprising 204 lines was developed from a cross between the high-yielding variety ZH16 with the dual-resistant line KNH03-3. Resistance to \u003cem\u003eA. flavus\u003c/em\u003e infection (described as percentage seed infection index, PSII) and aflatoxin production (described as aflatoxin content, ATC) was evaluated in the RIL population across four environments. Whole-genome resequencing was employed to construct a high-density genetic map to precisely map major loci associated with \u003cem\u003eA. flavus\u003c/em\u003e infection resistance and aflatoxin production resistance. In addition, a combination of weighted gene co-expression network analysis (WGCNA) and allelic variation analysis was performed to predict candidate genes linked to \u003cem\u003eA. flavus\u003c/em\u003e infection and aflatoxin production resistance in peanut. Finally, a KASP marker was developed based on the allelic variation and validated in a peanut germplasm panel and breeding populations. This tudy provides novel insights into the genetic basis of dual resistance to \u003cem\u003eA. flavus\u003c/em\u003e infection and aflatoxin production and pave a molecular foundation for marker-assisted breeding of high-oleic and aflatoxin-resistant peanut cultivars.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePlant materials\u003c/h2\u003e\u003cp\u003eA peanut population consisting of 204 F\u003csub\u003e7\u003c/sub\u003e-F\u003csub\u003e10\u003c/sub\u003e RILs was developed from a cross between ZH16 and KNH03-3 (ZK RIL) through single-seed descent method. The male parent (KNH03-3) is a high oleic acid line exhibiting dual resistance to \u003cem\u003eA. flavus\u003c/em\u003e infection and aflatoxin production obtained from the Kaifeng Academy of Agricultural and Forestry Sciences. The female parent (ZH16) is a high-yielding variety from the Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences (OCRI-CAAS).\u003c/p\u003e\u003cp\u003eThe RIL population and parental lines were planted in a randomized complete block design with four replications. Field trials were conducted in Wuhan in 2020 (F\u003csub\u003e7\u003c/sub\u003e generation), in Wuhan and Quanzhou in 2021 (F\u003csub\u003e8\u003c/sub\u003e and F\u003csub\u003e9\u003c/sub\u003e generation), and in Wuhan in 2022 (F\u003csub\u003e10\u003c/sub\u003e generation). The four environments were designated as Wuhan 2020 (2020WH), Wuhan 2021 (2021WH), Quanzhou 2021 (2021QZ), and Wuhan 2022 (2022WH). A set of 87 germplasm accessions for KASP marker validation was planted in Wuhan in 2014 (2014WH), 2015 (2015WH), and 2016 (2016WH), respectively. The breeding populations for KASP verification derived from Kaixuan 01\u0026ndash;6 (KX01-6) \u0026times;JH 6 were provided by the Hebei Academy of Agricultural and Forestry Sciences and planted in Hainan in 2024 (2024HN). KX01-6 is a variety closely related to KNH03-3, while JH6 is a variety relatively susceptible to \u003cem\u003eA. flavus\u003c/em\u003e compared to KX01-6. Standard agronomic practices were followed for all field experiments, including irrigation, fertilization, and pest management. After harvest, peanut pods were immediately dried, and mature and healthy seeds were selected for further investigation.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePhenotypic evaluation of infection resistance\u003c/h3\u003e\n\u003cp\u003eThe \u003cem\u003eA. flavus\u003c/em\u003e strain AF2202, isolated and preserved at OCRI-CAAS in 50% glycerol at -80\u0026deg;C, was used for artificial inoculation. The inoculation procedure followed the method of Jin et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The AF2202 solution was cultured in PDA medium at 30℃ for 7 days, then spores were isolated with 0.1% Tween and diluted to a 2\u0026times;10\u003csup\u003e6\u003c/sup\u003e CFU (colony forming units)/mL concentration spore solution. For each peanut line, 15 seeds were selected for disinfection, inoculated with 1 mL of spore suspension, and then evenly distributed in sterile Petri dishes. All dishes were incubated at 30℃ for 7 days. After incubation, the improved 0\u0026ndash;8 evaluation method was used to score \u003cem\u003eA. flavus\u003c/em\u003e infection. The percent seed infection index (PSII) was calculated according to the formula: PSII = (\u0026sum;8 i\u0026thinsp;=\u0026thinsp;0i\u0026times;N\u003csub\u003ei\u003c/sub\u003e/N\u0026times;9)\u0026times;100%, where N0, N1, N2, N3, N4, N5, N6, N7, and N8 represent the number of seeds of levels 0\u0026ndash;8 and N represents the total number of seeds.\u003c/p\u003e\n\u003ch3\u003ePhenotypic evaluation of aflatoxin production resistance\u003c/h3\u003e\n\u003cp\u003eFollowing the seed infection resistance assessment, spores were washed off from the inoculated seed surfaces with 75% ethanol. The samples were then dried at 121℃ for 3 h, and ground to a fine powder. 1 g of the powder was mixed with 5 ml of methanol and 1 ml of petroleum ether, and shaken on a shaker for 45 min at 190 rpm. Then the mixture was centrifuged in a centrifuge at 3000 rpm for 6 min, and 500 \u0026micro;L of methanol from the middle layer was taken and diluted 20-fold with 55% methanol. Aflatoxin content in each sample was quantified using high-performance liquid chromatography (Agilent Technologies, Santa Clara, CA, USA, 1200 series equipped with HPLC C18 4.6 mm 250 mm, 5 nm column). The mobile phase consisted of a mixture of methanol/water (45:55), with a flow rate of 0.7 mL/min, and a column temperature set at 30 ℃. The injection volume was 10 \u0026micro;L, and the injection time was 17 min. An aflatoxin standard solution (CRM46304, Sigma-Aldrich, Munich, Germany) was used to develop a standard curve.\u003c/p\u003e\n\u003ch3\u003eStatistical analysis of phenotypic data\u003c/h3\u003e\n\u003cp\u003ePhenotypic measurements were obtained from three biological replicates for all samples. Data analysis were performed using the IBM SPSS Statistics software (v.26; IBM, USA). Normality of data distributions was assessed using the Kolmogorov-Smirnov test. Significant phenotypic differences among genotypes were determined using Duncan\u0026rsquo;s Test and Independent-samples T-tests. The variance analysis was performed to evaluate significant differences among RILs, environments, and the interactions between genotype and environment. The broad-sense heritability was estimated as described by Huang et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)..\u003c/p\u003e\n\u003ch3\u003eLibrary construction and sequencing\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was extracted from young leaves of F₇ plants of the RIL population using the cetyltrimethylammonium bromide (CTAB) method. Paired-end sequencing libraries (300\u0026ndash;500 bp insert size) were prepared following the Illumina standard protocol for the 204 RILs and their parents. Library sequencing was performed on the Illumina HiSeq 2500 platform (Illumina Inc., San Diego, CA, USA). Raw reads were filtered to obtain high-quality clean reads by using SOAPnuke (Chen et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The clean reads were aligned to the peanut reference genome (Zhonghua 5) using the BWA software (Li \u0026amp; Durbin, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). HaplotypeCaller and Genotype GVCFs in GATK were used to identify SNPs and InDels.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eLinkage map construction and QTL analysis\u003c/h2\u003e\u003cp\u003eLow-quality variants were filtered using the following criteria: (a) Minor allele frequency (MAF)\u0026thinsp;\u0026ge;\u0026thinsp;0.2; (b) Proportion of missing genotypes\u0026thinsp;\u0026le;\u0026thinsp;0.1; (c) Relative heterozygosity rate\u0026thinsp;\u0026le;\u0026thinsp;0.2. Missing genotypes, were imputed based on a hidden Markov model (HMM). Finally, a linkage map was constructed using MSTMap software, and the genetic distances between markers were calculated using the \u003cem\u003eKosambi\u003c/em\u003e mapping function. The QTL Cartographer 2.5 software (Wang et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) was used to perform QTL analysis via a composite interval mapping (CIM) model using a high-density bin genetic map. The control markers, window size, and walk speed were set to 5, 10, and 1 cM, respectively. The LOD threshold was set at 2.5 to detect additive QTLs. QTLs were named with an initial letter \u0026ldquo;q\u0026rdquo; followed by the abbreviated trait name and corresponding chromosome number. If multiple QTLs exhibit overlapping physical intervals within the same linkage group across multiple environments, they are considered as a single consistent QTL and are designated with the same nomenclature.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDevelopment and validation of KASP markers\u003c/h3\u003e\n\u003cp\u003eBased on QTL mapping results, KASP markers were developed for the SNP sequences flanking the major loci. KASP primers were designed using the PolyMarker Web site (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.polymarker.info/\u003c/span\u003e\u003cspan address=\"http://www.polymarker.info/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and synthesized by Wuhan Genoseq Technology Co., LTD. KASP assays were conducted using 2 \u0026times; KASP Master Mix (2.5 \u0026micro;L) (LGC Biosearch Technologies), two forward primers (0.075 \u0026micro;L), one reverse primer (0.2 \u0026micro;L), genomic DNA (50 ng/\u0026micro;L, 1.5 \u0026micro;L), and ddH2O (0.65 \u0026micro;L). The fluorescence data were analyzed using the online software SNPway (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.snpway.com\u003c/span\u003e\u003cspan address=\"http://www.snpway.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Marker validation was performed in 300 RIL lines (including the 204 used for mapping), 59 lines from the JH6 \u0026times; KX01-6 breeding population (JK population), and a germplasm panel consisting of 87 accessions.\u003c/p\u003e\n\u003ch3\u003eRNA extraction and transcriptome analysis\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eRNA extraction and transcriptome analysis\u003c/div\u003e\u003cp\u003eThe peanut parental lines, ZH16 (Z) and KNH03-3 (K), were selected for RNA-seq analysis. For each genotype, a treatment group (inoculated with \u003cem\u003eA. flavus\u003c/em\u003e) and a control group (CK) were sampled and treated with an equal volume of 0.1% Tween solution. Samples were collected at 1, 3, 5, and 7 days (designated as 1D, 3D, 5D, and 7D, respectively) after inoculation. The resultant samples were designated as Z_1D, Z_3D, Z_5D, Z_7D, ZCK_1D, ZCK_3D, ZCK_5D, ZCK_7D, K_1D, K_3D, K_5D, K_7D, KCK_1D, KCK_3D, KCK_5D and KCK_7D. Total RNA was isolated using the Rneasy Plant Mini kit (QIAGEN). Library construction and sequencing were performed by the DNBSEQ-T7 platform. The sequencing data were deposited in the Genome Sequence Archive database under accession number CRA028108 (Chen et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Raw reads were filtered by SOAPnuke software (v2.1.0), and clean sequencing data were mapped to the peanut reference genome (Zhonghua 5) using HISAT2. The number of reads from each sample compared to each transcript was converted into FPKM (fragments per kilobase per million bases) by RSEM to estimate gene expression levels.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eEvaluation of phenotypic variation in the RILs\u003c/h2\u003e\u003cp\u003eThe PSII of the resistant male parent KNH03-3 ranged from 35.24% to 57.99% across four environments, significantly lower than that of the susceptible female parent ZH16 (76.43% to 88.33%, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the RIL population, PSII values ranged from 5.64\u0026ndash;87.50% in 2020WH, 11.03\u0026ndash;95.36% in 2021WH, 31.92\u0026ndash;94.83% in 2021QZ, and 30.83\u0026ndash;92.97% in 2022WH, respectively. For the aflatoxin production resistance, KNH03-3 exhibited significantly lower aflatoxin content compared to ZH16 in artificial inoculation across all four environments (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The RIL population displayed transgressive segregation for aflatoxin content, with ATC values spanning 5.10\u0026ndash;132.60 \u0026micro;g/g (2020WH), 8.15\u0026ndash;311.41 \u0026micro;g/g (2021WH), 18.26\u0026ndash;207.57 \u0026micro;g/g (2021QZ), and 7.72\u0026ndash;173.36 \u0026micro;g/g (2022WH) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Variance analysis revealed that genotype, environment, and genotype \u0026times; environment interaction all significantly influenced seed infection resistance and aflatoxin production resistance. The estimated broad-sense heritability (\u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) was 0.70 for PSII and 0.62 for ATC (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Correlation analysis across multiple environments showed there was a significantly positive association between PSII and ATC, with Pearson correlation coefficients ranging from 0.41 to 0.68 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The hundred seeds weight (HSW) of the RIL population displayed broad phenotypic variability across three environments, ranging from 38.47-106.45 g (2021WH), 36.76-106.16 g (2021QZ), and 39.35\u0026ndash;98.70 g (2022WH) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). For oleic acid content (OA), the RIL population displayed transgressive segregation with ranges of 44.10\u0026ndash;82.38% (2020WH), 37.26\u0026ndash;82.13% (2021WH), and 40.71\u0026ndash;82.15% (2021QZ) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). From the RIL population, an elite high-oleic acid line (QZ835) with similar resistance to seed infection and aflatoxin production as the highly-resistant parent, KNH03-3. Furthermore, the HSW of QZ835 was significantly higher than that of KNH03-3, indicating a higher yield potential (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescription of phenotypic data of PSII and ATC in the RIL population.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEnv\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eParents\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRIL Population\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZH16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKH03-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePSII(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2020WH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.72\u0026thinsp;\u0026plusmn;\u0026thinsp;6.14**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.87\u0026thinsp;\u0026plusmn;\u0026thinsp;6.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.64\u0026ndash;87.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e37.59\u0026thinsp;\u0026plusmn;\u0026thinsp;18.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2021WH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76.43\u0026thinsp;\u0026plusmn;\u0026thinsp;8.16**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.24\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.03\u0026ndash;95.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e49.58\u0026thinsp;\u0026plusmn;\u0026thinsp;16.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2021QZ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.63**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57.99\u0026thinsp;\u0026plusmn;\u0026thinsp;3.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31.92\u0026ndash;94.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e68.98\u0026thinsp;\u0026plusmn;\u0026thinsp;13.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2022WH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80.14\u0026thinsp;\u0026plusmn;\u0026thinsp;3.49**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.52\u0026thinsp;\u0026plusmn;\u0026thinsp;5.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30.83\u0026ndash;92.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e69.05\u0026thinsp;\u0026plusmn;\u0026thinsp;11.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eATC(\u0026micro;g/g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2020WH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110.43\u0026thinsp;\u0026plusmn;\u0026thinsp;9.51**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.58\u0026thinsp;\u0026plusmn;\u0026thinsp;2.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.10-132.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e39.93\u0026thinsp;\u0026plusmn;\u0026thinsp;25.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2021WH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e129.67\u0026thinsp;\u0026plusmn;\u0026thinsp;32.49**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.35\u0026thinsp;\u0026plusmn;\u0026thinsp;9.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.15-311.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e93.99\u0026thinsp;\u0026plusmn;\u0026thinsp;57.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2021QZ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123.67\u0026thinsp;\u0026plusmn;\u0026thinsp;16.62**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.55\u0026thinsp;\u0026plusmn;\u0026thinsp;9.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.26-207.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e77.74\u0026thinsp;\u0026plusmn;\u0026thinsp;39.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2022WH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120.09\u0026thinsp;\u0026plusmn;\u0026thinsp;33.45*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.43\u0026thinsp;\u0026plusmn;\u0026thinsp;11.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.72-173.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e54.44\u0026thinsp;\u0026plusmn;\u0026thinsp;29.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: \u0026ldquo;*\u0026rdquo; indicates a significant different between ZH16 and KNH03-3 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05);\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u0026ldquo;* *\u0026rdquo; indicates an extremely significant different (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003ePSII for the percent seed infection index, ATC for aflatoxin content, 2020WH for 2020Wuhan, 2021WH for 2021Wuhan, 2021QZ for 2021Quanzhou, 2022WH for 2022Wuhan.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDetailed information of PSII, ATC, HSW and OA in a elite line and parents across multiple environments.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQZ835\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZH16\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKNH03-3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePSII(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.14\u0026thinsp;\u0026plusmn;\u0026thinsp;11.26\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81.16\u0026thinsp;\u0026plusmn;\u0026thinsp;6.67\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.90\u0026thinsp;\u0026plusmn;\u0026thinsp;9.56\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eATC(\u0026micro;g/g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.35\u0026thinsp;\u0026plusmn;\u0026thinsp;14.42\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120.97\u0026thinsp;\u0026plusmn;\u0026thinsp;8.06\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.48\u0026thinsp;\u0026plusmn;\u0026thinsp;10.94\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSW(g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.63\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92.38\u0026thinsp;\u0026plusmn;\u0026thinsp;4.13\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOA(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003ePSII for the percent seed infection index, ATC for aflatoxin content, HSW for hundred seeds weight, OA for oleic acid content.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eMapping QTLs for PSII and ATC\u003c/h2\u003e\u003cp\u003eA high-density genetic linkage map was constructed using 115,397 SNP/InDel markers. Those were consolidated into 1,985 recombination bin markers. The markers were arranged into 20 linkage groups (LGs) spanning a total of 1,030.76 cM. The high-resolution genetic map achieved a marker density of 1.93 bins per centiMorgan (cM). The average distances between bins in each linkage group ranged from 0.36 and 3.30 cM (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig. S2). To evaluate the genetic map quality, we analyzed the chromosomal origins in each recombinant inbred line, revealing that chromosome segments inherited from distinct parents formed continuous fragments within individual RILs (Fig. S3). Furthermore, a co-linearity analysis between the constructed genetic linkage map and the reference genome demonstrated a strong alignment between the linkage groups (LGs) and their corresponding chromosomes (Fig. S4), thereby confirming the accuracy and uniformity of the genetic map for subsequent QTL analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDetailed information on genetic linkage map.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChr\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGenetic linkage map\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of markers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumber of bins\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBin interval(cM)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChrA01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChrA02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChrA03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e56.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChrA04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChrA05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChrA06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChrA07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChrA08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3969\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChrA09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChrA10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChrB01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChrB02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChrB03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChrB04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChrB05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChrB06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChrB07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChrB08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChrB09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChrB10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhole\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1030.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e115397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA total of three QTLs were identified for PSII in 3 chromosomes (A01, A07, and B06), explaining 4.30\u0026ndash;20.71% of phenotypic variation (PVE) in four environments. Among the three PSII QTLs identified, a major QTL, \u003cem\u003eqPSIIB06\u003c/em\u003e, was consistently detected in all the environments, explaining 5.12\u0026ndash;20.71% of the phenotypic variation. For ATC, three QTLs, namely \u003cem\u003eqATCB06\u003c/em\u003e, \u003cem\u003eqATCA01\u003c/em\u003e, and \u003cem\u003eqATCA08\u003c/em\u003e, were identified, which explained 6.04\u0026ndash;22.73% of the phenotypic variation (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among them, the major QTL (\u003cem\u003eqATCB06\u003c/em\u003e) with additive effects ranging from \u0026minus;\u0026thinsp;7.61 to -29.68 was stably detected across all environments. Interestingly, the major and stable QTLs for ATC and PSII (\u003cem\u003eqATCB06\u003c/em\u003e and \u003cem\u003eqPSIIB06\u003c/em\u003e) were co-localized within a\u0026thinsp;~\u0026thinsp;12 cM interval on chromosome B06 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eQTLs for PSII and ATC in RIL population.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEnv\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMarker Interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLOD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePVE(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAdd\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqPSIIA01\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2021WH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.5-22.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ec01b041-c01b046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqPSIIA07\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2021QZ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.13\u0026ndash;17.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ec07b008-c07b036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e8.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqPSIIB06\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2020WH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60.75\u0026ndash;61.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ec16b123-c16b125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2021WH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.41\u0026ndash;62.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ec16b091-c16b127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e13.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e20.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2021QZ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57.56\u0026ndash;62.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ec16b115-c16b124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2022WH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49.88\u0026ndash;62.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ec16b089-c16b127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e12.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eATC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqATCA01\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2021WH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.97\u0026ndash;32.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ec01b039-c01b069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-15.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqATCA08\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2022WH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32.69\u0026ndash;44.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ec08b069-c08b105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e9.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqATCB06\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2020WH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.41\u0026ndash;62.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ec16b091-c16b127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-7.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2021WH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.67\u0026ndash;62.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ec16b092-c16b127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e12.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e22.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-29.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2021QZ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60.22\u0026ndash;62.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ec16b121-c16b127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-13.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2022WH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52.00-62.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ec16b096-c16b127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e15.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-13.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003ePSII for the percent seed infection index, ATC for aflatoxin content, 2020WH for 2020Wuhan, 2021WH for 2021Wuhan, 2021QZ for 2021Quanzhou, 2022WH for 2022Wuhan.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRNA-Seq analysis of seeds infected by\u003c/b\u003e \u003cb\u003eA.flavus\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 48 peanut samples were used for RNA-seq library construction, comprising 24 inoculated samples from both parental lines collected at 1, 3, 5, and 7 days after inoculation (DAI) and 24 corresponding control samples collected at the same time points. In total, 283.2 Gb of high-quality clean data were obtained, with each library yielding 4.35\u0026ndash;11.37 Gb of clean data. After aligning the sequencing data to the reference genome, a total of 66,787 genes were detected as expressed genes, with the number of expressed genes per library ranging from 21,630 to 51,536 (Table S2).\u003c/p\u003e\u003cp\u003eTo elucidate regulatory modules associated with \u003cem\u003eA. flavus\u003c/em\u003e resistance, a WGCNA was performed using transcriptome data. Consequently, a scale-free co-expression network comprising 16 modules was established, with gene counts in each module ranging from 39 to 2080 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). To characterize the key modules associated with resistance in peanut seeds, the module-trait relationships (MTRs) were assessed. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, two modules, including the brown (for PSII, r\u0026thinsp;=\u0026thinsp;0.9, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; for ATC, r\u0026thinsp;=\u0026thinsp;0.74, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and the magenta module (for PSII, r\u0026thinsp;=\u0026thinsp;0.49, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; for ATC, r\u0026thinsp;=\u0026thinsp;0.36, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were significantly positively correlated with \u003cem\u003eA. flavus\u003c/em\u003e infection and aflatoxin production. The blue module (for PSII, r = -0.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; for ATC, r = -0.54, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and the turquoise module (for PSII, r = -0.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; for ATC, r = -0.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were significantly and positively correlated with infection resistance and aflatoxin production resistance. Gene Ontology (GO) enrichment analysis revealed that the brown module was significantly enriched in biological processes related to response to chemical stimulus and small molecule metabolic process(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The magenta module was significantly enriched with GO terms associated with aerobic respiration (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). For the blue module, gene enrichment was observed in GO terms linked to intracellular anatomical structures, the nucleolus, and nucleic acid metabolic process (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). The turquoise module showed significant enrichment in processes related to RNA biosynthetic process, biosynthetic process and regulation of biosynthetic process (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003ePrediction of candidate genes and development of diagnostic markers\u003c/h2\u003e\u003cp\u003eBased on the physical positions of the flanking molecular markers c16b092 and c16b128, which delimited the confidence intervals of \u003cem\u003eqPSIIB06\u003c/em\u003e and \u003cem\u003eqATCB06\u003c/em\u003e, the candidate genomic region conferring resistance to \u003cem\u003eA. flavus\u003c/em\u003e infection and aflatoxin production resistance was mapped to a 5.3Mb physical interval from 139.50 Mb to 144.80 Mb on chromosome B06 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). According to gene annotations, a total of 295 genes are present within this interval, among which 200 genes were expressed during the infection process. Based on re-sequencing data of the two parental lines, a total of 420 variants were identified between parental lines within this interval, among which 17 variants caused non-synonymous mutations in 16 genes. Additionally, 47 SNPs/Indels located in the upstream/downstream regions of genes were identified in 43 genes (Table S3). Integrating the WGCNA results with the variant data, 13 genes were identified as high-confidence candidates, including 3 from the brown module, 5 from the yellow module, and 5 from the turquoise module.\u003c/p\u003e\u003cp\u003eVariants were identified in the intergenic, exonic, upstream, and downstream regions of these 13 genes (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), only \u003cem\u003eChr16g3738\u003c/em\u003e contained a nonsynonymous SNP (C/A, position 144,475,090) within its coding sequence. This variant was therefore used to develop a resistance diagnostic KASP marker (KASP-Chr16g3738). Validation using the ZK RIL population revealed distinct allelic differentiation. The KASP-Chr16g3738 identified 169 lines carrying the CC genotype (ZH16 genotype) and 123 lines harboring the AA genotype (KNH03-3 genotype) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Phenotypic analysis revealed significantly lower mean values for both PSII and ATC in lines with AA genotype compared to those with CC genotype (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and B). Screening of a peanut germplasm panel further confirmed this trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Compared to the CC genotype, the mean values of PSII and ATC in the AA genotype were reduced by 22.98%\u0026ndash;29.30% and 27.59%\u0026ndash;29.87%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC and D). When applied to the JK breeding population (derived from JH6 \u0026times; KX01-6) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), KASP-Chr16g3738 effectively distinguished the resistant lines from the susceptible ones. The parents (JH6 and KX01-6) exhibited PSII values of 84.13% and 55.28% and ATC levels of 94.83 \u0026micro;g/g and 44.45 \u0026micro;g/g, respectively. The mean values of both PSII and ATC in the AA-genotype (PSII, 60.32\u0026thinsp;\u0026plusmn;\u0026thinsp;9.34%; ATC, 42.65\u0026thinsp;\u0026plusmn;\u0026thinsp;7.12 \u0026micro;g/g) lines were significantly lower than those of lines with the CC-genotype (PSII, 81.19\u0026thinsp;\u0026plusmn;\u0026thinsp;12.07%; ATC, 75.91\u0026thinsp;\u0026plusmn;\u0026thinsp;21.69 \u0026micro;g/g) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE and F). Moreover, for the susceptible parent JH6, the PSII and ATC of the AA-genotype exhibited reductions of 15.27\u0026ndash;56.67% and 46.12\u0026ndash;72.76%, respectively. These results demonstrated that the diagnostic marker KASP-Chr16g3738 is reliable and effective for distinguishing resistant genotypes and can be directly applied in germplasm screening and marker-assisted selection in peanut breeding.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModule and functional annotation of candidate genes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModule\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnnotation-location\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFunction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eChr16g3501\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eblue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eintergenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eelongation factor G-2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eChr16g3519\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eblue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eintergenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003emyosin IB heavy chain\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eChr16g3534\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eblue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eintergenic/downstream\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003euncharacterized RNA-binding protein C1827.05c\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eChr16g3707\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eblue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eintergenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eselenium binding\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eChr16g3750\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eblue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eintergenic/downstream\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eprotein of unknown DUF642\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eChr16g3469\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebrown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eintergenic/downstream\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003einactive LRR receptor-like serine/threonine-protein kinase BIR2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eChr16g3541\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebrown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eintronic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003euncharacterized protein LOC100527040 isoform X4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eChr16g3585\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebrown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eintergenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003euncharacterized protein LOC107605226 isoform X1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eChr16g3565\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eturquoise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eintergenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003edihydrofolate synthetase isoform X1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eChr16g3666\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eturquoise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eupstream\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003etrihelix transcription factor ASR3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eChr16g3734\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eturquoise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eintergenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003etranscriptional corepressor LEUNIG isoform X1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eChr16g3738\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eturquoise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eexonic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eflavin-binding monooxygenase family protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eChr16g3758\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eturquoise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eintergenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003epeptidyl-prolyl cis-trans isomerase family protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cb\u003eThe co-segregation phenomenon of dual resistance to\u003c/b\u003e \u003cb\u003eA. flavus\u003c/b\u003e \u003cb\u003einfection and aflatoxin production in RIL population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study, a RIL population comprising 204 lines was developed by crossing a high-yielding peanut variety ZH16 with a high oleic acid line KNH03-3. Phenotypic data of PSII, ATC, HSW, and OA were collected from the RIL population across multiple environments. The male parent KNH03-3 was characterized with desirable dual resistance to seed infection and aflatoxin production across all environments, exhibiting relatively lower PSII and ATC values compared to the female parent ZH16. As in numerous reported research, resistance of peanut kernel against \u003cem\u003eA. flavus\u003c/em\u003e can be categorized into two mechanisms including \u003cem\u003e\u0026zwnj;A. flavus\u003c/em\u003e infection resistance\u0026zwnj; and \u0026zwnj;aflatoxin production resistance (Ding et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Korani et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) demonstrated that \u0026zwnj;aflatoxin production resistance in peanut seeds was primarily \u0026zwnj;regulated by genotype\u0026zwnj;, but exhibited \u0026zwnj;no correlation\u0026zwnj; with the \u0026zwnj;fungal biomass\u0026zwnj; observed on seed coats. In our previous study, a RIL population (ZH10 \u0026times; ICG12625) was used to evaluate \u003cem\u003eA. flavus\u003c/em\u003e infection resistance and aflatoxin production resistance\u0026zwnj;. Although the resistant parent ICG12625 exhibited higher \u003cem\u003eA. flavus\u003c/em\u003e \u0026zwnj;infection resistance\u0026zwnj; and \u0026zwnj;aflatoxin production resistance\u0026zwnj; compared to ZH10, no significant association between these two resistance traits in RIL (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) was obsevered (Yu et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), suggesting independent inheritance of loci controlling each trait. However, in the ZH16 \u0026times; KNH03-3 population, a significantly positive correlation was observed between seed infection resistance and aflatoxin production resistance, indicating that the underlying loci governing both traits may be tightly linked or pleiotropic. Hence, integrating the dual resistance mechanisms\u0026zwnj;, infection resistance and aflatoxin production resistance, in peanut is highly promising and could contribute to more effective management of aflatoxin contamination\u0026zwnj;. Breeding programs using resistant parents with \u0026zwnj;co-segregating dual resistance traits\u0026zwnj; are expected to generate superior progeny with better resistance through stable inheritance of synergistic defense pathways.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eA novel high-oleic acid peanut germplasm with aflatoxin resistance and high-yield potential\u003c/h2\u003e\u003cp\u003eHigh oleic acid has been an important quality trait in most peanut breeding programs and applied in industry, but the existing \u003cem\u003eA. flavus\u003c/em\u003e resistant varieties such as J11, Kanghuang 1 and ZH6, all possess a normal oleic acid content (OA\u0026thinsp;\u0026lt;\u0026thinsp;60%) and high oleate peanut variety with aflatoxin resistance has not been reported. In this study, a high-oleic acid elite line QZ835 exhibited \u0026zwnj;stable dual resistance to aflatoxin and higher HSW compared to the resistant parent KNH03-3 was identified, showing a great potential in integrating resistance to aflatoxin, high oleic acid and high yield components.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCo-localized major-effect QTLs on chromosome B06 confer simultaneous resistance to\u003c/b\u003e \u003cb\u003eA. flavus\u003c/b\u003e \u003cb\u003einfection and aflatoxin production in peanut\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMost genetic studies on peanut resistance to \u003cem\u003eA. flavus\u003c/em\u003e have focused on either infection resistance or aflatoxin production resistance. Reported QTLs for seed infection resistance have been mapped on chromosomes A03, A05, A08, A10, B01, B04, B08, and B10 (Khan et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jiang et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while QTLs conferring resistance to aflatoxin production have been identified on chromosomes A05, A07, A08, B05, B06, and B09 (Yu et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Huai et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Huai et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) narrowed down \u003cem\u003eqAFB1B06.2\u003c/em\u003e on B06 to a physical interval of 9.24\u0026ndash;21.55 Mb using an SNP-based linkage map. In this study, two novel and stable major-effect QTLs conferring resistance to \u003cem\u003eA. flavus\u003c/em\u003e infection and aflatoxin production were co-localized on chromosome B06, suggesting the presence of either pleiotropic effects or closely linked causal genes that simultaneously regulate both resistance mechanisms within the same genomic interval.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrediction of candidate genes related to resistance to\u003c/b\u003e \u003cb\u003eA. flavus\u003c/b\u003e \u003cb\u003einfection and aflatoxin production and development of KASP markers\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCombining forward genetic analysis (such as genetic linkage analysis and GWAS) with transcriptome analysis enables the effective and rapid identification of candidate genes in crops (Vanshney et al. 2021). Through an integrative approach, QTL analysis, and RNA-sequencing technology, Derakhshani et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) successfully identified 16 candidate genes potentially regulating cadmium tolerance in barley. A total of 17 candidate genes were identified for pre-harvest sprouting in maize using WGCNA and QTL analysis, among which 3 were functionally validated using mutants (Ma et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, a 5.3 Mb genomic region conferring dual resistance to \u003cem\u003eA. flavus\u003c/em\u003e infection and aflatoxin production was identified on chromosome B06 through QTL mapping. Furthermore, 13 genes in the target interval were grouped into modules associated with dual resistance using WGCNA. Among them, \u0026zwnj;only Chr16g3738 harbored a non-synonymous mutation between the two parents. This gene encodes a flavin-binding monooxygenase-like protein, \u0026zwnj;which has been confirmed to be associated with reactive oxygen species (ROS) homeostasis\u0026zwnj; regulation and \u0026zwnj;catalyzes flavonoid hydroxylation. In tomato, flavin monooxygenase family members (FMO1) interact with catalase (CAT2) in regulating ROS homeostasis (Wang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). In \u003cem\u003eLotus japonicus\u003c/em\u003e, the flavin monooxygenase LjF8H catalyzes the hydroxylation of flavonoids to generate antimicrobial derivatives such as gossypetin (Hiraga et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). According to the allelic variation within Chr16g3738, we have developed a KASP diagnostic marker, KASP-Chr16g3738, tightly associated with resistance to both \u003cem\u003eA. flavus\u003c/em\u003e infection and aflatoxin production. Marker validation in peanut germplasm panel and breeding populations demonstrated that the lines selected by KASP-Chr16g3738 possessed reduced PSII (by 25.70-26.12%) and ATC (by 29.07\u0026ndash;29.52%), confirming its effectiveness in marker-assisted selection (MAS) for aflatoxin resistance.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusin","content":"\u003cp\u003eIn conclusion, KNH03-3 was identified to possess dual resistance to \u003cem\u003eA. flavus\u003c/em\u003e infection and aflatoxin production. Two novel major effect QTLs conferring resistance to both \u003cem\u003eA. flavus\u003c/em\u003e infection and aflatoxin production were co-localized within a 5.3 Mb genomic interval on chromosome B06. Through integration of WGCNA and allelic variation analysis, \u003cem\u003eChr16g3738\u003c/em\u003e encoding a flavin-binding monooxygenase-like protein was identified as the most probable causal gene conferring the dual resistance. The KASP molecular marker (KASP-Chr16g3738) developed was validated to be applicable in selecting genotypes with reduced PSII and ATC. The above findings provideinsights of aflatoxin resistance and are meaningful for accelerating the genetic enhancement of resistance to aflatoxin in peanut.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research work was financially supported by National Natural Science Foundation of China (32101708), National Key R\u0026amp;D Program of China (2023YFD1202800), the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences (No. CAAS-ASTIP-2021-OCRI), Central Public-interest Scientific Institution Basal Research Fund for Chinese Academy of Tropical Agricultural Sciences (No. 1610172024001), the Earmarked Fund for CARS (CARS-13), and the Central Public-interest Scientific Institution Basal Research Fund (No. Y2025YC112).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGaorui Jin:\u003c/strong\u003e Writing - original draft, Data curation, Investigation.\u003cstrong\u003e\u0026nbsp;Bolun Yu:\u0026nbsp;\u003c/strong\u003eWriting - original draft.\u0026nbsp;\u003cstrong\u003eYingbin Ding:\u0026nbsp;\u003c/strong\u003eSoftware,Investigation.\u003cstrong\u003e\u0026nbsp;Li Huang:\u0026nbsp;\u003c/strong\u003eInvestigation.\u0026nbsp;\u003cstrong\u003eTaihuang Yang:\u003c/strong\u003e Software.\u0026nbsp;\u003cstrong\u003eHuaiyong Luo:\u0026nbsp;\u003c/strong\u003eFunding acquisition.\u003cstrong\u003e\u0026nbsp;Jin Wang:\u0026nbsp;\u003c/strong\u003eResources.\u003cstrong\u003e\u0026nbsp;Yong Lei:\u0026nbsp;\u003c/strong\u003eResources.\u0026nbsp;\u003cstrong\u003eHuifang Jiang:\u0026nbsp;\u003c/strong\u003eResources, Writing: review \u0026amp; editing.\u003cstrong\u003e\u0026nbsp;Boshou Liao:\u0026nbsp;\u003c/strong\u003eWriting: review \u0026amp; editing, Project administration.\u003cstrong\u003e\u0026nbsp;Jinxiong Shen:\u0026nbsp;\u003c/strong\u003eSupervision.\u003cstrong\u003e\u0026nbsp;Nian Liua:\u0026nbsp;\u003c/strong\u003eWriting: review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBertioli DJ, Jenkins J, Clevenger J, Dudchenko O, Gao D, Seijo G et al (2019) The genome sequence of segmental allotetraploid peanut \u003cem\u003eArachis hypogaea\u003c/em\u003e. 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Nat Genet 51(5):865\u0026ndash;876. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41588-019-0402-2\u003c/span\u003e\u003cspan address=\"10.1038/s41588-019-0402-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"peanut, Aspergillus flavus, genetic linkage map, QTL, WGCNA","lastPublishedDoi":"10.21203/rs.3.rs-8228412/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8228412/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAflatoxin contamination caused by \u003cem\u003eAspergillus flavus\u003c/em\u003e threatens the development of peanut industry, breeding aflatoxin-resistant peanut varieties are highly needed. In this study, a recombinant inbred line (RIL) population was developed from a cross between Zhonghua 16 and Kainong H03-3\u0026mdash;a high oleic acid peanut line exhibiting resistance to both \u003cem\u003eA. flavus\u003c/em\u003e infection and aflatoxin production. Using this RIL population, a high-density genetic map was constructed and employed to identify quantitative trait loci (QTLs) associated with resistance to \u003cem\u003eA. flavus\u003c/em\u003e infection (described as percentage seed infection index, PSII) and aflatoxin production (described as aflatoxin content, ATC) across four environments. Three QTLs for PSII and ATC were detected, among which novel QTLs with major effect for both traits were co-localized in chromosome B06, explaining 20.71% and 22.73% of phenotypic variance for PSII and ATC, respectively. Within this co-localized genomic region, 13 candidate genes exhibited strong co-expression patterns linked to both PSII and ATC. Among them, Chr16g3738, harboring a non-synonymous variant in its exon, was used to develop a diagnostic KASP marker KASP-Chr16g3738. Validation of KASP-Chr16g3738 in a peanut germplasm panel and a breeding population (Jihua 6\u0026times;Kaixuan 01\u0026ndash;6) demonstrated that the favorable allele of the candidate gene was associated with a reduction of 15.27\u0026ndash;56.67% in PSII and 27.59\u0026ndash;72.76% in ATC. This study was the first report in identification of a genomic interval co-localized with QTLs conferring resistance to both \u003cem\u003eA. flavus\u003c/em\u003e infection and aflatoxin production, and provides an efficient marker-assisted selection approach for accelerating improvement of resistance to aflatoxin in peanut.\u003c/p\u003e","manuscriptTitle":"Genetic mapping and diagnostic marker development for a co-localization interval conferring resistance to both Aspergillus flavus infection and aflatoxin production in peanut","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-12 06:37:03","doi":"10.21203/rs.3.rs-8228412/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revisions","date":"2026-02-17T18:53:58+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2026-01-08T14:36:58+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-09T02:53:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-29T09:07:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2025-11-28T03:52:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"33365292-a564-41bc-a0e1-678b69a51c77","owner":[],"postedDate":"December 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-07T02:44:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-12 06:37:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8228412","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8228412","identity":"rs-8228412","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}