Genome-wide association analysis identifies novel genetic loci and candidate genes associated with seed dormancy in cultivated peanut

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In this study, a genome-wide association analysis (GWAS) was conducted using 353 peanut accessions with diverse genetic backgrounds from various global regions. The germination index was evaluated across multiple years and environments to identify genomic loci regulating seed dormancy. Phenotypic evaluation revealed extensive variation in seed dormancy within the natural population. The broad-sense heritability was greater than 0.89, and both genotype and genotype-by-environment interactions showed significant effects. Numerous SNPs on chromosomes A02, A03, A04, A05, A07, A09, A11, and A19 were identified significantly associated with seed dormancy, with particularly consistent and stable associations on chromosomes A03 and A04. By integrating mutation type, expression level, allele mining and gene function analysis, the gene Arahy.5SR8FF , encoding HECT-type E3 ubiquitin-protein ligases, was considered as the key candidate for regulating seed dormancy in peanut. Additionally, two peak SNPs on chromosome A03 and A04 were validated as significantly linked to seed dormancy, which were developed into KASP markers and can be used for marker assisted breeding. Peanut Seed dormancy (SD) GWAS Kompetitive allele specific PCR (KASP) markers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Key Message Two significant regions on chromosomes A03 and A04, along with the candidate gene Arahy.5SR8FF , were identified to be associated with seed dormancy in peanut through GWAS. Two KASP markers were developed and validated, demonstrating their potential for use in marker-assisted breeding. Introduction Seeds are a crucial phase in the plant life cycle and serve as a primary source of global food supply. Seed dormancy, an important adaptive trait, refers to the phenomenon where viable seeds fail to germinate despite favorable environmental conditions. This evolutionary strategy is vital for wild plants, allowing them to avoid harsh seasonal conditions and delay germination until optimal conditions are present, thereby ensuring effective population propagation and dispersal (Chen et al. 2023 ). However, in agricultural systems, where seeds are harvested for consumption or planting, dormancy presents a double-edged sword. On one hand, moderate dormancy helps prevent pre-harvest sprouting (PHS), where seeds germinate prematurely on the mother plant under rainy conditions, leading to significant yield and quality losses (Xu et al. 2022 ; Kaur et al. 2023 ; Wei et al. 2023 ). On the other hand, excessive dormancy can interfere with crop breeding programs that depend on rapid generation turnover and limit farmers' ability to use freshly harvested seeds for subsequent planting (Ali et al. 2022 ). Peanut, as a crop primarily cultivated and harvested for its seeds, faces a similar dilemma in balancing dormancy and germination. Seed dormancy related traits, including seed dormancy (SD) (Wang et al. 2021b ), fresh seed germination (FSG) (Zhang et al. 2022 ), fresh seed dormancy (FSD) (Vishwakarma et al. 2016 ; Kumar et al. 2020 ), pre-harvest sprouting (PHS), and in situ germination, are polygenic quantitative traits controlled by complex gene networks and highly sensitive to environmental factors. Breeding peanut varieties with certain seed dormancy (SD) through molecular breeding is essential for reducing yield losses and quality degradation caused by premature germination at harvest, while also providing valuable germplasm resources for improving productivity in multi-cropping systems. Extensive quantitative trait loci (QTLs) associated with seed dormancy have been identified in peanut. Using an F 2 population derived from two Spanish-type parents exhibiting significant seed dormancy, the qfsd-1 and qfsd-2 were identified on chromosomes A05 and B06 for fresh seed dormancy (Vishwakarma et al. 2016 ). This population was further advanced to recombinant inbred lines (RILs), and additional QTLs qFSDA09 and qFSDB05 were mapped to chromosomes A09 and B05 through Bulk Segregant Analysis sequencing (BSA-seq) (Kumar et al. 2020 ). Further studies using biparental RIL populations identified additional QTLs, including qPD_A04-1 , qPD_A04-2 , qPD_A04-3 (Wang et al. 2021b ), and qFSGA04 (Zhang et al. 2022 ) on chromosome A04, as well as qPD_A05 on chromosome A05. More recently, a GWAS analyses involving 184 peanut accessions detected SNPs associated with fresh seed dormancy on chromosomes A01, A04, A08, A09, B02, B04, B05, B07, and B09, pinpointing potential candidate genomic regions for molecular breeding (Bomireddy et al. 2024 ). Seed dormancy is a complex agronomic trait regulated by the hormonal homeostasis but also by a combination of hormonal balance and epigenetic mechanisms (Shu et al. 2015 ), including chromatin remodeling (Tognacca and Botto 2021 ), protein phosphorylation (Baudouin et al. 2022 ), and cell cycle regulation (Greco et al. 2012 ). Among these regulatory layers, phytohormones, particularly the antagonistic actions of abscisic acid (ABA) and gibberellins (GA), play central roles in controlling the dormancy-germination transition. ABA is the primary promoter of dormancy induction and maintenance, whereas GA promotes germination (Shu et al. 2016 ). During seed development, ABA accumulation upregulates key regulators such as ABI3 and ABI5 (Kinoshita et al. 2010 ; Nakashima and Yamaguchi-Shinozaki 2013 ), driving maturation and preventing premature germination. This core signaling module involves ABA receptors ( PYR / PYL / RCAR ) (Zhao et al. 2020 ), clade A type 2C protein phosphatases (PP2Cs) (Née et al. 2017 ; Ghanizadeh et al. 2025 ), and sucrose non-fermenting-1-related protein kinase 2 (SnRK2s) (Wang et al. 2020 ), which together activate downstream effector genes to enforce dormancy. The activity of this module is finely tuned by various factors. Transcription factors from NAC (Shah et al. 2024 ), MYB (Singh and Roychoudhury 2023 ), and WRKY families (Zhou et al. 2025 ) bind directly to the promoters of PYL , PP2C , or SnRK2 genes, modulating their expression (Singh and Roychoudhury 2023 ; Zhou et al. 2025 ). Moreover, the ubiquitin-proteasome pathway also plays a crucial role, with RING-type (Koiwai et al. 2007 ) or U-box E3 ligases (such as ATL43 and SAUL1 in Arabidopsis ) (Raab et al. 2009 ) mediating the ubiquitination and degradation of PYL receptors or ABI5, thereby negatively regulating ABA signaling. Other phosphatases, including PP2A (Nakashima and Yamaguchi-Shinozaki 2013 ) and PP2C.E (Li et al. 2024 ; Ghanizadeh et al. 2025 ), can dephosphorylate SnRK2, further fine-tuning the ABA pathway. This intricate network ensures precise control over dormancy cycles. Understanding the dynamics of this regulatory system offers valuable insights for manipulating seed dormancy in crop breeding programs. This study aims to identify significant SNPs associated with seed dormancy in peanut by conducting GWAS using a panel of 353 cultivated peanut accessions, to predict the key candidate gene regulating seed dormancy by integrating mutation type, expression level and allele mining, and to develop significant linked KASP markers and validate for marker-assisted breeding. Materials and methods Plant materials This study utilized a natural germplasm panel comprising 353 cultivated peanut accessions, which spans a broad geographic distribution, originating from 18 Chinese provinces and 26 additional countries worldwide. The germplasm panel encompasses five botanical varieties (85 var. hypogaea , 12 var. hirsuta , 26 var. fastigiata , 84 var. vulgaris and two var. peruviana ) and two kinds of irregular types (100 irregular -hypogaea -type and 44 irregular- fastigiata -type) (Zheng et al. 2024 ). Whole-genome resequencing was performed on the natural germplasm panel with 29.00× mean depth and a total of 864,179 single-nucleotide polymorphisms (SNPs) and 71,052 insertions/deletions (InDels) were obtained and used for GWAS analysis (Zheng et al. 2024 ). Phenotype Evaluation The dormancy of 353 peanut germplasms was investigated using the seeds harvested from Zhengzhou (Henan province, in 2020), Shangqiu (Henan province, in 2023), and Xinxiang (Henan province, in 2023 and 2024). To ensure that differences in dormancy were more likely attributable to genetic factors rather than maturity variations, germplasm materials were categorized into three groups (early-maturing: 110-day growth period; medium-maturing: 125-day growth period; late-maturing: 140-day growth period) and harvested accordingly (Bomireddy et al. 2024 ). The harvested peanut pods were sun-dried for one week and then used for phenotyping. For each accession, seed selection was based on maturity, health, uniformity, and the presence of a dark brown inner shell to ensure consistent maturity. Ninety seeds meeting these criteria were divided into three petri dishes, with 30 seeds per dish for biological replication. The seeds were rehydrated and incubated in darkness at 28 ± 2°C (Vishwakarma et al. 2016 ; Kumar et al. 2020 ; Wang et al. 2021; Zhang et al. 2022 ). The number of germinated seeds were investigated at 7- and 14-days post-imbibition based on the criterion of radicle emergence through the seed coat. The Germination Index (GI) is calculated using the following formula: $$\:GI=\left[\frac{G7\times\:2+G14\times\:1}{(14+7)\times\:n}\right]\times\:100\%$$ where G7 and G14 stands for the number of germinated seeds at 7- and 14-day post-imbibition (DPI) respectively, and n stands for the total number of seeds (Kaur et al. 2023 ). Statistical analysis of phenotypic data The phenotypic data were normalized using the arcsine function: \(\:y\) = \(\:\:\frac{180}{{\pi\:}}\) ​⋅ \(\:arc\text{s}\text{i}\text{n}\) .( \(\:\sqrt{x/100}\) ), \(\:x\in\:\left[\text{0,100}\right]\) where \(\:x\) stands for the \(\:GI\) . The descriptive statistics of the normalized data were analyzed using the IBM SPSS Statistics software (v.22; IBM, USA). Analysis of variance (ANOVA) and correlation between environments were calculated using the AOV module in QTL IciMapping software (Meng et al. 2015 ). Broad-sense heritability was estimated as $$\:{H}^{2}={V}_{G}/[{V}_{G}+\left(\frac{1}{e}\right){V}_{GE}+\left(\frac{1}{re}\right){V}_{\epsilon\:}]$$ where \(\:e\) represents the number of environments and \(\:r\) represents the number of replicates. The Best Linear Unbiased Estimator (BLUE) values across four environments were calculated using the R package lme4 ( https://cran.r-project.org/ ): $$\:y=\:X\alpha\:\:+K\mu\:+e$$ where \(\:y\) denotes phenotype, \(\:X\) represents genotype, \(\:\alpha\:\) is a vector including fixed effects, \(\:K\) denotes relative kinship matrix, \(\:\mu\:\) is a vector of random additive genetic effects, and \(\:e\) represents unobserved vector of random residual. Genome-wide association analysis for seed dormancy Genome-wide association mapping was conducted using Genome Association and Prediction Integrated Tool (GAPIT) software and the Mixed Linear Model (MLM) method was used (Lipka et al. 2012 ). The Manhattan and Q-Q plots of association analysis results were visualized using the qqman package (version compatible with R 4.3.3; R Core Team, http://www.r-project.org/ ). KASP markers development and validation The significant associated SNPs linked with phenotypes were developed into KASP markers for marker-assisted breeding. Each KASP marker includes two allele-specific forward primers (FAM: 5′-GAAGGTGACCAAGTTCATGCT-3′, HEX: 5′-GAAGGTCGGAGTCAACGGATT-3′) and one common reverse primer. The KASP assays were performed using Hydrocycler and PHERAstar from the LGC SNPline platform. PCR reaction system included 2 µL DNA, 1µL 1× KASP Master Mix, and 0.014 µL KASP Primer Mix. PCR reaction was carried out using the following program: initial denaturation at 94°C for 15 min, followed by 10 cycles of touchdown PCR (denaturation at 94°C for 20 s, with the annealing temperature starting at 61°C and decreasing by 0.6 per cycle, for 60 s per cycle), which was followed by 26 cycles of amplification (denaturation at 94°C for 20 s, and annealing at 55°C for 60 s) (Zheng et al. 2018 ). Cluster plots of the genotyping data were visualized using the SNPviewer Software supported by LGC Genomics. QTL detection and candidate genes prediction QTLs were designated based on their chromosomal locations and the presence of significant SNPs. Significant SNPs located within the linkage disequilibrium (LD) decay distance on a specific chromosome were grouped together and considered as a single QTL. The locations of significant SNPs within genes and the candidate genes were predicted according to the annotation of the reference genome A. hypogaea cv . Tifrunner (v1) in Peanutbase ( https://www.peanutbase.org/ ). Clustering analysis of gene expression profiles within the QTL regions was conducted using transcript abundance data from two independent datasets: one comprising samples from sequential seed developmental stages in three genetically distinct peanut accessions (Wang et al. 2021a ), and the other including samples collected at multiple time points after imbibition from paired dormant and non-dormant genotypes. RNA extraction and cDNA synthesis For expression analysis of the target candidate genes, mature seeds from two genotypes exhibiting contrasting dormancy phenotypes were collected, with three biological replicates (each replicate consisting of 30 seeds). Seeds were sampled after 0, 24, and 48 hours of rehydration, immediately frozen in liquid nitrogen, and stored at -80°C until further processing. Total RNA was extracted, treated with DNase and used for cDNA synthesis. The resulting cDNA was subsequently diluted 20-fold and used for quantitative real-time PCR (qRT-PCR) analysis of the candidate genes. Primer sequences and qPCR assay The primer sequences specific to the candidate genes were designed using Primer 3 software (Primer3 Input, version 0.4.0). Specificity of the primers for each candidate gene was verified by BLAST analysis against GenBank database ( https://blast.ncbi.nlm.nih.gov/Blast.cgi ), as well as by gel electrophoresis and melting curve analysis. Results Characterization of dormancy phenotype and heritability Seed dormancy was evaluated for 353 cultivated peanut accessions across four environments (2020ZZ: Zhengzhou, Henan, 2020; 2023SQ: Shangqiu, Henan, 2023; 2023XX: Xinxiang, Henan, 2023; 2024XX: Xinxiang, Henan, 2024). The GI displayed a continuous and wide range of phenotypic variations, ranging from 0% to 100%, with a skewed phenotypic distribution. Among all accessions, the largest group consisted of lines with the highest GI values (90–100%) ranked first, followed by those with the lowest GI values (0–10%) (Fig. 1 A–D, Table 1 ). Based on the seed GI of the natural population across the four environments, BLUE values for GI were estimated using a linear regression model. The correlated coefficients between the BLUE values and the phenotypes from the four environments were all greater than 0.9 (2020ZZ: 0.904, 2023SQ: 0.926, 2023XX: 0.917, 2024XX: 0.987) (Table S1 ). The phenotypic distribution of the BLUE values showed a similar trend to that observed in each environment, also displaying a skewed phenotypic distribution (Fig. 1 E). The correlation coefficients amount the four environments ranged from 0.860 to 0.926 (Table S1 ), indicating that the germination index is highly reliable and stable across different environments. Analysis of variance (ANOVA) of seed germination rate across the four environments revealed that genotype, environment, and genotype-by-environment interaction all had significant effects on GI (Table 2 ). The broad-sense heritability of GI exceeded 0.89, indicating that the genotypic factors play a predominant role in determining seed germination rate in peanut. Table 1 Descriptive statistics, coefficient of variation (CV), and broad-sense heritability of seed dormancy evaluated across 353 diverse peanut accessions Environment Minimum Maximum Mean ± SE SD CV% Skewness Kurtosis Heritability 2020ZZ 0 100 71.89 ± 1.98 35.34 49.08 -1.01 -0.6 0.97 2023SQ 0 100 75.41 ± 2.05 36.87 48.82 -1.16 -0.4 0.89 2023XX 0 100 73.09 ± 2.04 37.58 51.33 -1.06 -0.64 0.98 2024XX 0 100 77.14 ± 1.90 34.86 45.13 -1.4 0.26 0.99 BLUE 0 100 73.90 ± 1.84 34.59 46.74 -1.21 -0.18 0.96 Table 2 Summary of analysis of variance of the germination indices (GI) Source DF SS MS F- Value P >F Environment (E) 3 95773.37 31924.46 253.3565 0.00E + 00 Genotype (G) 352 4018502 11416.2 90.6004 0.00E + 00 G×E 979 683642.2 639802.6 5.1865 0.00E + 00 Error 2312 291326 653.5267 Total 3654 5050699 DF, degrees of freedom; SS, sum of square, MS, Mean sum of Squares; P, probability GWAS identified significant SNPs associated with seed dormancy High-resolution genome-wide association studies (GWAS) were performed for seed dormancy by integrating the genotypes and phenotypes of 353 peanut accessions. The genome-wide significance threshold was set as -log 10 (0.05/ n ) (where n = 935,231 represents the number of markers), resulting in a cutoff value of 7.27. Using this threshold, 182 SNPs significantly associated with seed dormancy were identified across chromosomes A02, A03, A04, A05, A07, A09, A11, and A19 (Fig. 2 ). Notably, the significant regions on chromosomes A03 and A04 were consistently detected across all four environments and the BLUE values, while the region on chromosome A07 was repeatedly identified in 2020ZZ, 2024XX, and the BLUE values (Fig. 2 A, D and E). The significant regions on chromosomes A02, A05 and A19 were detected in only a single environment (Fig. 2 ). On chromosome A03, 72 significant SNPs were identified and the peak SNP A03:5839362 was detected in the BLUE values, 2023SQ and 2023XX (Table 3 ). Two additional linked peak SNPs A03:5858205 and A03:6065070 were identified in 2020ZZ and 2024XX, respectively (Table 3 ). These markers exhibited -log₁₀( p ) value ranging from 7.61 to 11.77, with the effect size between 15.73 and 19.23 (Table 3 ). The repeated detection of this region across multiple environments indicates that it represents a stable quantitative trait locus. A total of 43 significant SNPs were detected on chromosome A04. The peak SNP A04:46136957 was detected in 2023SQ and 2024XX, with -log₁₀( p ) value of 7.66 and 7.60, and effect size of 22.25 and 20.25, respectively. The linked peak SNP A04:48714612 was detected in 2020ZZ, with -log₁₀( p ) value of 7.66 and effect size of 21.91. Another linked peak SNP A4:88396106 was identified in 2023XX and the BLUE values, with -log₁₀( p ) value of 7.44 and 7.58, and effect size of 19.54 and 23.25, respectively (Table 3 ). Additionally, the peak SNP A07:78500040 was identified in 2020ZZ, and the linked peak SNP A07:78616537 was identified in 2024XX and the BLUE values, with -log₁₀( p ) value ranged from 8.71 to 9.54, and effect size ranged from − 25.52 to -21.90 (Table 3 ). The peak SNPs on chromosomes A09 (A09:2502616) and A11 (A11:37521919) were detected in 2024XX data and the BLUE values (Fig. 2 , Table 3 ). The linkage between the peak SNPs and seed dormancy were illustrated using the violin and boxplots (Fig. 3 and Fig. S1 ). For the peak SNPs on chromosomes A03 and A04, the GI of the accessions with the minor alleles had significant differences with those with the major alleles across all four environments and the BLUE values (Fig. 3 ). In contrast, for the four peak SNPs on chromosomes A07, A09, and A11, there were no significant difference on GI between the accessions with different alleles (Fig. S1 ). Table 3 The peak SNP loci associated with seed dormancy on five chromosomes Chr Pos Major allele Minor allele Env − log( p ) MAF Effect A03 5839362 G A BLUE 11.77 0.16 18.41 2023SQ 8.39 0.19 16.68 2023XX 9.29 0.15 17.34 5858205 G A 2020ZZ 7.61 0.16 15.73 6065070 G C 2024XX 10.13 0.15 19.23 A04 46136957 G A 2023SQ 7.66 0.25 22.25 2024XX 7.60 0.27 20.25 48714612 T C 2020ZZ 7.66 0.26 21.91 88396106 G A BLUE 7.44 0.30 19.54 2023XX 7.58 0.26 23.25 A07 78500040 C G 2020ZZ 8.71 0.03 -23.11 78616537 C A BLUE 9.54 0.03 -21.90 2024XX 9.20 0.03 -25.52 A09 2502616 G A 2024XX 7.86 0.01 -32.31 BLUE 7.31 0.01 -26.81 A11 37521919 G A 2024XX 8.79 0.01 -37.57 BLUE 8.42 0.01 -33.07 Development and application of KASP markers Based on the peak SNPs A03:5839362 and A04:46136957, we developed two KASP markers, designated Tif1-A03-5839362 and Tif1-A04-46136957, to distinguish four haplotypes (Hap1: AA, Hap2: AG, Hap3: GA, and Hap4: GG) (Fig. 4 A, Table S2 ). Phenotypic analysis revealed a significant difference in germination index (GI) among haplotypes: materials carrying Hap4 showed a markedly lower average GI, ranging from 5.57% to 8.04%, compared to those with Hap1, which had an average GI between 90.02% and 93.67% (Fig. 4 B). These results demonstrate the potential utility of the developed KASP markers for marker-assisted selection in peanut breeding programs. Estimation of haplotypes The significant SNPs identified on chromosomes A03 and A04 were used for haplotype block analysis. SNPs within approximately 300 kb flanking each significant SNPs were extracted for this analysis. The candidate region on chromosome A03 was partitioned into five distinct blocks (Fig. 5 ). Notably, the peak SNPs A03:5839362, A03:5858205, and A03:6065070 were co-localized within Block 2 (Fig. 5 B-C), which spans a physical interval of 265.30 kb between A03:5839362 and A03:6104664. According to annotation of the reference genome Arachis hypogaea cv. Tifrunner (v1), 22 genes were annotated in Block 2 (Fig. 5 C, Table S3). The candidate region on chromosome A04 contained two haplotype blocks. The peak SNP A04:46136957 was located in Block 1, which spans 583.93 kb from A04:45848396 to A04:46432327 and contained five annotated genes (Fig. S2 and Table S3). The peak SNP A04:88396106 was located in Block 2, spanning 691.30 kb from A04:88004671 to A04:88695969 and contained six predicted genes (Fig. S2 and Table S3). Candidate genes identified for seed dormancy A total of 33 genes were annotated within the candidate intervals on chromosomes A03 and A04. Sequence analysis of the natural population identified 14 mutations across 11 of these candidate genes (Table 4 ). Nine mutations occurred in intronic regions, three were located in exonic regions and two were found in the 3’-UTR (Table 4 ). Among them, eight genes displayed relatively high expression levels based on the transcriptome sequencing data from seed development (25 to 85 days after flowering) (Fig. S3 A-C) and seed germination (0–48 hours after imbibition) (Fig. S3 D-E). By integrating mutation types, expression levels and gene functions associated with seed dormancy, Arahy.5SR8FF and Arahy.2PCK9I were identified as the key candidate genes, both of them encode E3 ubiquitin-protein ligases (Table 4 ). Haplotype analysis of Arahy.5SR8FF identified four distinct haplotypes (Fig. 6 A-B). The mean seed germination rate for Hap2 was significantly lower (22.30%–26.67%) than that of Hap1 (83.17%–88.61%) and Hap3 (53.30%–72.70%) across all environments (Fig. 6 C). In contrast, no significant difference in GI was observed between the two haplotypes of Arahy.2PCK9I (Fig. S4 A, C, E). Table 4 Genes with mutations identified in the natural population. Chr Pos Gene ID Start End Strand Mut type Structure type Function type Functional description A03 5869075 Arahy.JXV2U6 5863215 5870852 + G/A exonic nonsynonymous SNV (V/I) Plasma-membrane choline transporter family protein A03 5905893 Arahy.8M9DZW 5904097 5906751 + G/T intronic EARLY-RESPONSIVE TO DEHYDRATION 7 A03 5983859 Arahy.5SR8FF 5983287 5994026 + G/A exonic nonsynonymous SNV (S/N) E3 ubiquitin-protein ligase UPL3 A03 5985661 5983287 5994026 + TT/-- intronic A03 6006994 Arahy.VVQ74P 6004716 6010401 + T/C intronic Rac-like GTP-binding protein A03 6025606 Arahy.RN7VB5 6022994 6027593 + A/C intronic Receptor-like cytosolic serine/threonine-protein kinase A03 6049104 Arahy.181IYB 6047123 6049962 - C/A 3‘-UTR Heavy metal-associated isoprenylated plant protein 26 A03 6076384 Arahy.2PCK9I 6074470 6076761 + C/A exonic stop BOI-related E3 ubiquitin-protein ligase 3 A03 6102760 Arahy.YQYF3H 6092964 6102931 + C/T 3‘-UTR Pentatricopeptide repeat-containing protein MRL1 A04 46238622 Arahy.6W4GGI 46237198 46240239 - G/T intronic cytochrome b5-like heme/steroid-binding domain protein A04 46239808 Arahy.6W4GGI 46237198 46240239 - G/A intronic A04 88149562 Arahy.JR6N4B 88146430 88158072 + A/G intronic chitinase-like protein A04 88156593 88146430 88158072 + A/C intronic Transmembrane amino acid transporter family protein A04 88193131 Arahy.V8HTKV 88192700 88198943 - T/- intronic laccase 17 Expression analysis of candidate genes To determine whether the candidate gene Arahy.5SR8FF exhibits differential expression during seed germination among haplotypes, accessions representing Hap1 (N420, N728, N741) and Hap2 (N539, N666, N668) were selected. Seed samples from these six accessions were collected at three germination stages (0h, 24h, and 48h after imbibition), with three biological replicates per time point. Relative expression levels were quantified using qRT-PCR (Table S4, Fig. 7 ). Although Arahy.5SR8FF showed generally low expression across all accessions, its expression was consistently lower in Hap1 than in Hap2. Moreover, in weak dormancy accessions (Hap1: N420, N728, N741), expression decreased progressively during germination, whereas in strong dormancy accessions (Hap2: N539, N666, N668), expression gradually increased over the same period. Discussion Seed dormancy represents a critical agronomic trait in peanut production. Insufficient dormancy can lead to pre-harvest germination of seeds in soil, significantly compromising yield and quality, while excessively prolonged dormancy may hinder breeding efficiency and limit agricultural productivity. The development and application of molecular markers for breeding new peanut varieties with certain seed dormancy constitutes a promising strategy for genetic improvement. Identifying dormancy associated QTLs and SNPs associated with dormancy is the first step in breeding target peanuts varieties. In this study, QTLs, SNPs and candidate genes involved in seed dormancy were identified by analyzing whole genomic resequencing data from 353 peanut accessions, along with phenotypic data collected across four environments. In this study, two significant regions on chromosomes A03 and A04 were consistently detected for seed dormancy across all four environments and the BLUE values by GWAS. Notably, similar results were reported by Zhang et al ( 2022 ) and Wang et al (2022), who identified major QTLs for seed dormancy on chromosome A04 using different recombinant inbred line (RIL) populations (Wang et al. 2021b ; Zhang et al. 2022 ). This convergence of findings was further validated by Bomireddy et al ( 2024 ) in GWAS of 184 peanut accessions (Bomireddy et al. 2024 ), adding credibility to our results. However, previous studies on the genetic control of peanut seed dormancy have also identified associated loci on other chromosomes, including Arahy. A01, A08, A09, B02, B04, B05, B07, and B09, through QTL mapping or GWAS. In contrast, our GWAS analysis revealed a novel locus on chromosome A03, which had not been previously reported. The genomic positions of SNPs associated with peanut seed dormancy identified on chromosome A04 in this study exhibit some discrepancies with previous reports. Our multi-environmental analysis revealed significant SNPs spanning a broad physical interval from 20 Mb to 100 Mb on A04, with peak associations primarily localized around 46 Mb and 88 Mb. This observation is consistent with findings from Wang et al. (2022), who identified seed dormancy-related QTLs ( qPD_A04-1 , qPD_A04-2 , and qPD_A04-3 ) within a similarly wide genomic region (27,963,468 bp − 117,729,029 bp) on chromosome A04. Similarly, Zhang et al. ( 2022 ) mapped a QTL for fresh seed germination ( qFSGA04 ) to position 100,806,014 on A04, and Bomireddy et al. ( 2024 ) reported qFSD_A04-1 at 119,365,410 bp. Subsequent linkage disequilibrium (LD) block analysis of our target intervals revealed a limited number of annotated genes. The broad physical range covered by the significant SNPs poses challenges for precise candidate gene prediction. The novel significant locus identified on chromosome A03 in this study, was consistently detected across multiple environments with a high -log( p ) peak value. Its haplotypes demonstrated a strong ability to differentiate phenotypic variation, making it the primary target for candidate gene investigation. Within this interval, the haplotypes of gene Arahy.5SR8FF significantly stratified the phenotypes. Annotation and sequence analysis revealed that Arahy.5SR8FF encodes a HECT (Homologous to E6-AP C-Terminus) E3 ubiquitin ligase. Previous studies have implicated HECT-type E3 ligases in ABA-mediated seed germination. For instance, HECT-type E3 Ligases UPL1 and UPL4 regulate ABA-dependent seed germination, where UPL1 promotes ABA-induced germination inhibition, while UPL4 represses it (Tajdel-Zielińska et al. 2024 ). Moreover, the RING-type E3 ligase AIRP3 forms an E2-E3 pair with UBC27, promoting the ubiquitination and degradation of ABI1 and thereby enhancing ABA-mediated germination inhibition (Pan et al. 2020 ). Similarly, the U-box E3 ubiquitin ligase PLANT U-BOX 35 (PUB35) cooperate with AFP1 to negatively regulate ABI5 protein degradation, fine-tuning seed germination and post-germinative seedling growth (Du et al. 2024 ). An analogous negative regulatory role was reported in wheat, where the E3 ligase TaPUB1 interacts with TaPYL4 and TaABI5, acting as a negative regulator in ABA signaling (Zhang et al. 2021 ). Additionally, quantitative expression analysis during seed germination revealed that the relative expression level of haplotype Hap1 was significantly lower than that of Hap2. Therefore, we hypothesize that the candidate gene Arahy.5SR8FF may function as a negative regulator in peanut seed dormancy. However, further experimental validation is required to elucidate its precise molecular function. Conclusion In summary, two significant regions on chromosomes A03 and A4 associated with seed dormancy were identified through GWAS, of which the region on chromosome A03 is considered as a novel and major QTL. Accordingly, two peak SNPs in the two significant regions were validated to be linked with seed dormancy and were developed into KASP markers for assisted breeding. The Arahy.5SR8FF , encoding HECT-type E3 ubiquitin-protein ligases, was identified as the key candidate gene regulating seed dormancy in peanut. Declarations Supplementary Information Supplementary material 1 (Supplementary Fig S.docx); Supplementary material 2 (Supplementary Table S.docx). Conflict of interest The authors declare that they have no conflict of interest. Funding This work was supported by National Key Research and Development Program (2023YFD1200200), Special Project for National Supercomputing Zhengzhou Center Innovation Ecosystem Construction (201400210600), China Agriculture Research System of Ministry of Finance People's Republic of China (MOF) and Ministry of Agriculture and Rural Affairs (MARA) (CARS-13), Henan Provincial Agriculture Research System, China (S2012-5), Fundamental work project of Henan Academy of Agricultural Sciences (2025JC03). Author contribution statement MZ performed field experiments, laboratory experiments, data analysis and wrote the manuscript. ZS and FQ developed the population, designed the experiments, and assisted in manuscript preparation. LQ and PD designed experiments and provided help in laboratory experiments. CL performed field experiments, laboratory experiments and assisted in manuscript preparation. JW assisted in genotypic analysis. ZZ assisted in data analysis. JX and HL performed field experiments. LM , LS , SH , BH and WD provided help in laboratory experiments. JW and MZ supervised field experiments at Shangqiu. ZZ and XZ conceived and designed the experiments, facilitated the project, and assisted in manuscript preparation. All authors read and approved the final manuscript. Data availability All relevant data can be found within the manuscript and in Supplementary Material. 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Int J Biol Macromol 305:141223. https://doi.org/10.1016/j.ijbiomac.2025.141223 Supplementary Files SupplementaryFigS.docx SupplementaryTableS.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor revisions 27 Mar, 2026 Reviewers agreed at journal 10 Dec, 2025 Reviewers invited by journal 09 Dec, 2025 Editor assigned by journal 26 Nov, 2025 First submitted to journal 24 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8200443","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":557840913,"identity":"b74031f7-005c-455c-8d64-c1a7e06e72fa","order_by":0,"name":"Maoning 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12:29:34","extension":"html","order_by":44,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":186579,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8200443/v1/b3088c1c726a7e15f00a3418.html"},{"id":98225591,"identity":"7a818195-0e26-4961-a52a-24247e567078","added_by":"auto","created_at":"2025-12-15 12:29:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":139747,"visible":true,"origin":"","legend":"\u003cp\u003eThe frequency distribution of germination indices (GI) across four environments. \u003cstrong\u003eA\u003c/strong\u003e: 2020, Zhengzhou, Henan (2020ZZ); \u003cstrong\u003eB\u003c/strong\u003e: 2023, Shangqiu, Hainan (2023SQ); \u003cstrong\u003eC\u003c/strong\u003e: 2023, Xinxiang, Henan (2023XX); \u003cstrong\u003eD\u003c/strong\u003e: 2024, Xinxiang, Henan (2024XX); \u003cstrong\u003eE\u003c/strong\u003e: BLUE.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-8200443/v1/d1969dcbcd68eb42d6e9b157.png"},{"id":98434176,"identity":"3db93a40-a0bd-4760-80f9-efd6c2c28544","added_by":"auto","created_at":"2025-12-17 16:51:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":269343,"visible":true,"origin":"","legend":"\u003cp\u003eGWAS signals for GI across five environments. Manhattan plot and quantile–quantile (Q–Q) plot for the GWAS for GI at \u003cstrong\u003eA\u003c/strong\u003e 2020ZZ, \u003cstrong\u003eB\u003c/strong\u003e 2023SQ, \u003cstrong\u003eC\u003c/strong\u003e2023XX, \u003cstrong\u003eD\u003c/strong\u003e 2024XX, \u003cstrong\u003eE\u003c/strong\u003e BLUE. The red lines on the \u003cem\u003eY\u003c/em\u003e-axis designate the significance threshold (− log10 \u003cem\u003eP \u003c/em\u003e\u0026lt; 7.23). The numbers on the \u003cem\u003eX\u003c/em\u003e-axis represent peanut chromosomes. Red dashed line represents the Bonferroni correction threshold. Blue dashed line represents − log10 \u003cem\u003eP \u003c/em\u003e= 5.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-8200443/v1/fa30f7a4e65046c06663cf01.png"},{"id":98225590,"identity":"35b394fb-265e-4573-b904-4d022fa64b3f","added_by":"auto","created_at":"2025-12-15 12:29:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":408938,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plots and boxplots showed significant difference on GI between the accessions with genotypes AA (n = 56) and GG (n = 295) at the A03. 5839362 locus (\u003cstrong\u003eA\u003c/strong\u003e), genotypes AA (n = 49) and GG (n = 286) at the A03. 5858205 locus (\u003cstrong\u003eB\u003c/strong\u003e), genotypes CC (n = 57) and GG (n =293) at the A03. 6065070 locus (\u003cstrong\u003eC\u003c/strong\u003e), genotypes AA (n = 100) and GG (n = 247) at the A04. 46136957 locus (\u003cstrong\u003eD\u003c/strong\u003e), genotypes CC (n = 92) and TT (n = 253) at the A04. 48714612 locus (\u003cstrong\u003eE\u003c/strong\u003e), and genotypes AA (n = 105) and GG (n = 245) at the A04. 88396106 locus (\u003cstrong\u003eF\u003c/strong\u003e) across four environments and the BLUE values in different accessions.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-8200443/v1/fe76ff8e4c1bd803f597628d.png"},{"id":98432996,"identity":"7e6125c7-cb6b-4303-bf91-d3209bcc9acb","added_by":"auto","created_at":"2025-12-17 16:50:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":112841,"visible":true,"origin":"","legend":"\u003cp\u003eThe boxplot for the haplotypes based on the predicted value of GI in the combined environment. \u003cstrong\u003eA\u003c/strong\u003e Four haplotypes of the peak SNPs A03:5839362 and A04:46136957. \u003cstrong\u003eB\u003c/strong\u003e Violin plots and boxplots describing the GI distribution in different accessions with genotypes Hap1, Hap2, Hap3, Hap4 at two\u003cem\u003e \u003c/em\u003epeak SNPs.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-8200443/v1/8c572dfa56c1d92451f1fb03.png"},{"id":98225593,"identity":"763c8323-8824-4000-82f3-7dcc7e47d684","added_by":"auto","created_at":"2025-12-15 12:29:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":945610,"visible":true,"origin":"","legend":"\u003cp\u003eGWAS signal, haplotype block analysis, and candidate genes for seed dormancy on Chromosome A03. \u003cstrong\u003eA\u003c/strong\u003e GWAS signal for GI on Chromosome A03. Red line represents the Bonferroni correction threshold. \u003cstrong\u003eB\u003c/strong\u003e Haplotype analysis of significant SNPs on Chromosome A03. \u003cstrong\u003eC\u003c/strong\u003e The candidate genes in Block 2.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-8200443/v1/968226c574847bbc3b8d1bd9.png"},{"id":98431552,"identity":"b7887b0b-cba5-48c2-a8ac-ac7de1946d56","added_by":"auto","created_at":"2025-12-17 16:47:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":139084,"visible":true,"origin":"","legend":"\u003cp\u003eThe boxplot for the haplotypes based on the predicted value of GI in the combined environment. \u003cstrong\u003eA\u003c/strong\u003e Gene structure of the candidate gene \u003cem\u003eArahy.5SR8FF\u003c/em\u003e. \u003cstrong\u003eB\u003c/strong\u003e Four haplotypes of the candidate gene \u003cem\u003eArahy.5SR8FF\u003c/em\u003e. \u003cstrong\u003eC\u003c/strong\u003e Violin plots and boxplots describing the GI distribution in different accessions with genotypes Hap1, Hap2, Hap3 at \u003cem\u003eArahy.5SR8FF\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-8200443/v1/f11aa4e6d6a79fefba2aaa0e.png"},{"id":98225595,"identity":"12eda34f-8387-452e-9a56-5599af70a07e","added_by":"auto","created_at":"2025-12-15 12:29:33","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":431378,"visible":true,"origin":"","legend":"\u003cp\u003eRelative transcript levels of \u003cem\u003eArahy.5SR8FF\u003c/em\u003e of the non-dormant and dormant genotypes. \u003cstrong\u003eA\u003c/strong\u003e Relative expression of \u003cem\u003eArahy.5SR8FF\u003c/em\u003e within 0-24 hours after imbibition. \u003cstrong\u003eB\u003c/strong\u003e Relative expression of \u003cem\u003eArahy.5SR8FF\u003c/em\u003e within 24-48 hours after imbibition. The transcript level for \u003cem\u003eArahy.5SR8FF\u003c/em\u003e was determined using \u003cem\u003eADH3-acting\u003c/em\u003eas a reference gene, which was set to 1. Data shown are means of three biological replicates ± SE.\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-8200443/v1/9a03ba0d234b7b9c8abb246c.png"},{"id":98444935,"identity":"3003508b-ce86-4d89-8e81-ac9f770db97c","added_by":"auto","created_at":"2025-12-17 17:18:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3360939,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8200443/v1/bb83561f-dc36-4552-bf1e-52de07d1ba32.pdf"},{"id":98433186,"identity":"8a5a902c-bf2d-48cb-bb8b-a59f5ef094a4","added_by":"auto","created_at":"2025-12-17 16:50:24","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":1625587,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigS.docx","url":"https://assets-eu.researchsquare.com/files/rs-8200443/v1/80242f797204707bf3329412.docx"},{"id":98225605,"identity":"a83c9e6c-be1b-4868-8057-8841be251ce5","added_by":"auto","created_at":"2025-12-15 12:29:33","extension":"docx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":24232,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS.docx","url":"https://assets-eu.researchsquare.com/files/rs-8200443/v1/899dcaf8232c2004e026de6e.docx"}],"financialInterests":"","formattedTitle":"Genome-wide association analysis identifies novel genetic loci and candidate genes associated with seed dormancy in cultivated peanut","fulltext":[{"header":"Key Message","content":"\u003cp\u003eTwo significant regions on chromosomes A03 and A04, along with the candidate gene \u003cem\u003eArahy.5SR8FF\u003c/em\u003e, were identified to be associated with seed dormancy in peanut through GWAS. Two KASP markers were developed and validated, demonstrating their potential for use in marker-assisted breeding.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eSeeds are a crucial phase in the plant life cycle and serve as a primary source of global food supply. Seed dormancy, an important adaptive trait, refers to the phenomenon where viable seeds fail to germinate despite favorable environmental conditions. This evolutionary strategy is vital for wild plants, allowing them to avoid harsh seasonal conditions and delay germination until optimal conditions are present, thereby ensuring effective population propagation and dispersal (Chen et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, in agricultural systems, where seeds are harvested for consumption or planting, dormancy presents a double-edged sword. On one hand, moderate dormancy helps prevent pre-harvest sprouting (PHS), where seeds germinate prematurely on the mother plant under rainy conditions, leading to significant yield and quality losses (Xu et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kaur et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wei et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). On the other hand, excessive dormancy can interfere with crop breeding programs that depend on rapid generation turnover and limit farmers' ability to use freshly harvested seeds for subsequent planting (Ali et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Peanut, as a crop primarily cultivated and harvested for its seeds, faces a similar dilemma in balancing dormancy and germination. Seed dormancy related traits, including seed dormancy (SD) (Wang et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e), fresh seed germination (FSG) (Zhang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), fresh seed dormancy (FSD) (Vishwakarma et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), pre-harvest sprouting (PHS), and in situ germination, are polygenic quantitative traits controlled by complex gene networks and highly sensitive to environmental factors. Breeding peanut varieties with certain seed dormancy (SD) through molecular breeding is essential for reducing yield losses and quality degradation caused by premature germination at harvest, while also providing valuable germplasm resources for improving productivity in multi-cropping systems.\u003c/p\u003e\u003cp\u003eExtensive quantitative trait loci (QTLs) associated with seed dormancy have been identified in peanut. Using an F\u003csub\u003e2\u003c/sub\u003e population derived from two Spanish-type parents exhibiting significant seed dormancy, the \u003cem\u003eqfsd-1\u003c/em\u003e and \u003cem\u003eqfsd-2\u003c/em\u003e were identified on chromosomes A05 and B06 for fresh seed dormancy (Vishwakarma et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This population was further advanced to recombinant inbred lines (RILs), and additional QTLs \u003cem\u003eqFSDA09\u003c/em\u003e and \u003cem\u003eqFSDB05\u003c/em\u003e were mapped to chromosomes A09 and B05 through Bulk Segregant Analysis sequencing (BSA-seq) (Kumar et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Further studies using biparental RIL populations identified additional QTLs, including \u003cem\u003eqPD_A04-1\u003c/em\u003e, \u003cem\u003eqPD_A04-2\u003c/em\u003e, \u003cem\u003eqPD_A04-3\u003c/em\u003e (Wang et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e), and \u003cem\u003eqFSGA04\u003c/em\u003e (Zhang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) on chromosome A04, as well as qPD_A05 on chromosome A05. More recently, a GWAS analyses involving 184 peanut accessions detected SNPs associated with fresh seed dormancy on chromosomes A01, A04, A08, A09, B02, B04, B05, B07, and B09, pinpointing potential candidate genomic regions for molecular breeding (Bomireddy et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeed dormancy is a complex agronomic trait regulated by the hormonal homeostasis but also by a combination of hormonal balance and epigenetic mechanisms (Shu et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), including chromatin remodeling (Tognacca and Botto \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), protein phosphorylation (Baudouin et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and cell cycle regulation (Greco et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Among these regulatory layers, phytohormones, particularly the antagonistic actions of abscisic acid (ABA) and gibberellins (GA), play central roles in controlling the dormancy-germination transition. ABA is the primary promoter of dormancy induction and maintenance, whereas GA promotes germination (Shu et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). During seed development, ABA accumulation upregulates key regulators such as ABI3 and ABI5 (Kinoshita et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nakashima and Yamaguchi-Shinozaki \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), driving maturation and preventing premature germination. This core signaling module involves ABA receptors (\u003cem\u003ePYR\u003c/em\u003e/\u003cem\u003ePYL\u003c/em\u003e/\u003cem\u003eRCAR\u003c/em\u003e) (Zhao et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), clade A type 2C protein phosphatases (PP2Cs) (N\u0026eacute;e et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ghanizadeh et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and sucrose non-fermenting-1-related protein kinase 2 (SnRK2s) (Wang et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which together activate downstream effector genes to enforce dormancy. The activity of this module is finely tuned by various factors. Transcription factors from \u003cem\u003eNAC\u003c/em\u003e (Shah et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), \u003cem\u003eMYB\u003c/em\u003e (Singh and Roychoudhury \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and \u003cem\u003eWRKY\u003c/em\u003e families (Zhou et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) bind directly to the promoters of \u003cem\u003ePYL\u003c/em\u003e, \u003cem\u003ePP2C\u003c/em\u003e, or \u003cem\u003eSnRK2\u003c/em\u003e genes, modulating their expression (Singh and Roychoudhury \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhou et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Moreover, the ubiquitin-proteasome pathway also plays a crucial role, with RING-type (Koiwai et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) or U-box E3 ligases (such as ATL43 and SAUL1 in \u003cem\u003eArabidopsis\u003c/em\u003e) (Raab et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) mediating the ubiquitination and degradation of PYL receptors or ABI5, thereby negatively regulating ABA signaling. Other phosphatases, including PP2A (Nakashima and Yamaguchi-Shinozaki \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and PP2C.E (Li et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ghanizadeh et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), can dephosphorylate SnRK2, further fine-tuning the ABA pathway. This intricate network ensures precise control over dormancy cycles. Understanding the dynamics of this regulatory system offers valuable insights for manipulating seed dormancy in crop breeding programs.\u003c/p\u003e\u003cp\u003eThis study aims to identify significant SNPs associated with seed dormancy in peanut by conducting GWAS using a panel of 353 cultivated peanut accessions, to predict the key candidate gene regulating seed dormancy by integrating mutation type, expression level and allele mining, and to develop significant linked KASP markers and validate for marker-assisted breeding.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePlant materials\u003c/h2\u003e\u003cp\u003eThis study utilized a natural germplasm panel comprising 353 cultivated peanut accessions, which spans a broad geographic distribution, originating from 18 Chinese provinces and 26 additional countries worldwide. The germplasm panel encompasses five botanical varieties (85 \u003cem\u003evar. hypogaea\u003c/em\u003e, 12 \u003cem\u003evar. hirsuta\u003c/em\u003e, 26 \u003cem\u003evar. fastigiata\u003c/em\u003e, 84 \u003cem\u003evar. vulgaris\u003c/em\u003e and two \u003cem\u003evar. peruviana\u003c/em\u003e) and two kinds of irregular types (100 irregular\u003cem\u003e-hypogaea\u003c/em\u003e-type and 44 irregular-\u003cem\u003efastigiata\u003c/em\u003e-type) (Zheng et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Whole-genome resequencing was performed on the natural germplasm panel with 29.00\u0026times; mean depth and a total of 864,179 single-nucleotide polymorphisms (SNPs) and 71,052 insertions/deletions (InDels) were obtained and used for GWAS analysis (Zheng et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePhenotype Evaluation\u003c/h3\u003e\n\u003cp\u003eThe dormancy of 353 peanut germplasms was investigated using the seeds harvested from Zhengzhou (Henan province, in 2020), Shangqiu (Henan province, in 2023), and Xinxiang (Henan province, in 2023 and 2024). To ensure that differences in dormancy were more likely attributable to genetic factors rather than maturity variations, germplasm materials were categorized into three groups (early-maturing: 110-day growth period; medium-maturing: 125-day growth period; late-maturing: 140-day growth period) and harvested accordingly (Bomireddy et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The harvested peanut pods were sun-dried for one week and then used for phenotyping. For each accession, seed selection was based on maturity, health, uniformity, and the presence of a dark brown inner shell to ensure consistent maturity. Ninety seeds meeting these criteria were divided into three petri dishes, with 30 seeds per dish for biological replication. The seeds were rehydrated and incubated in darkness at 28\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C (Vishwakarma et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al. 2021; Zhang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The number of germinated seeds were investigated at 7- and 14-days post-imbibition based on the criterion of radicle emergence through the seed coat. The Germination Index (GI) is calculated using the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:GI=\\left[\\frac{G7\\times\\:2+G14\\times\\:1}{(14+7)\\times\\:n}\\right]\\times\\:100\\%$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eG7\u003c/em\u003e and \u003cem\u003eG14\u003c/em\u003e stands for the number of germinated seeds at 7- and 14-day post-imbibition (DPI) respectively, and n stands for the total number of seeds (Kaur et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eStatistical analysis of phenotypic data\u003c/h3\u003e\n\u003cp\u003eThe phenotypic data were normalized using the arcsine function:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:y\\)\u003c/span\u003e\u003c/span\u003e =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\frac{180}{{\\pi\\:}}\\)\u003c/span\u003e\u003c/span\u003e​\u0026sdot;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:arc\\text{s}\\text{i}\\text{n}\\)\u003c/span\u003e\u003c/span\u003e.(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sqrt{x/100}\\)\u003c/span\u003e\u003c/span\u003e), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\in\\:\\left[\\text{0,100}\\right]\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e stands for the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:GI\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe descriptive statistics of the normalized data were analyzed using the IBM SPSS Statistics software (v.22; IBM, USA). Analysis of variance (ANOVA) and correlation between environments were calculated using the AOV module in QTL IciMapping software (Meng et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Broad-sense heritability was estimated as\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{H}^{2}={V}_{G}/[{V}_{G}+\\left(\\frac{1}{e}\\right){V}_{GE}+\\left(\\frac{1}{re}\\right){V}_{\\epsilon\\:}]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:e\\)\u003c/span\u003e\u003c/span\u003e represents the number of environments and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003e represents the number of replicates. The Best Linear Unbiased Estimator (BLUE) values across four environments were calculated using the R package lme4 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e):\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:y=\\:X\\alpha\\:\\:+K\\mu\\:+e$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:y\\)\u003c/span\u003e\u003c/span\u003e denotes phenotype, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:X\\)\u003c/span\u003e\u003c/span\u003e represents genotype, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\alpha\\:\\)\u003c/span\u003e\u003c/span\u003e is a vector including fixed effects, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:K\\)\u003c/span\u003e\u003c/span\u003e denotes relative kinship matrix, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:\\)\u003c/span\u003e\u003c/span\u003e is a vector of random additive genetic effects, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:e\\)\u003c/span\u003e\u003c/span\u003e represents unobserved vector of random residual.\u003c/p\u003e\n\u003ch3\u003eGenome-wide association analysis for seed dormancy\u003c/h3\u003e\n\u003cp\u003eGenome-wide association mapping was conducted using Genome Association and Prediction Integrated Tool (GAPIT) software and the Mixed Linear Model (MLM) method was used (Lipka et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The Manhattan and Q-Q plots of association analysis results were visualized using the qqman package (version compatible with R 4.3.3; R Core Team, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org/\u003c/span\u003e\u003cspan address=\"http://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eKASP markers development and validation\u003c/h3\u003e\n\u003cp\u003eThe significant associated SNPs linked with phenotypes were developed into KASP markers for marker-assisted breeding. Each KASP marker includes two allele-specific forward primers (FAM: 5\u0026prime;-GAAGGTGACCAAGTTCATGCT-3\u0026prime;, HEX: 5\u0026prime;-GAAGGTCGGAGTCAACGGATT-3\u0026prime;) and one common reverse primer. The KASP assays were performed using Hydrocycler and PHERAstar from the LGC SNPline platform. PCR reaction system included 2 \u0026micro;L DNA, 1\u0026micro;L 1\u0026times; KASP Master Mix, and 0.014 \u0026micro;L KASP Primer Mix. PCR reaction was carried out using the following program: initial denaturation at 94\u0026deg;C for 15 min, followed by 10 cycles of touchdown PCR (denaturation at 94\u0026deg;C for 20 s, with the annealing temperature starting at 61\u0026deg;C and decreasing by 0.6 per cycle, for 60 s per cycle), which was followed by 26 cycles of amplification (denaturation at 94\u0026deg;C for 20 s, and annealing at 55\u0026deg;C for 60 s) (Zheng et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Cluster plots of the genotyping data were visualized using the SNPviewer Software supported by LGC Genomics.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eQTL detection and candidate genes prediction\u003c/h2\u003e\u003cp\u003eQTLs were designated based on their chromosomal locations and the presence of significant SNPs. Significant SNPs located within the linkage disequilibrium (LD) decay distance on a specific chromosome were grouped together and considered as a single QTL. The locations of significant SNPs within genes and the candidate genes were predicted according to the annotation of the reference genome \u003cem\u003eA. hypogaea cv\u003c/em\u003e. Tifrunner (v1) in Peanutbase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.peanutbase.org/\u003c/span\u003e\u003cspan address=\"https://www.peanutbase.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eClustering analysis of gene expression profiles within the QTL regions was conducted using transcript abundance data from two independent datasets: one comprising samples from sequential seed developmental stages in three genetically distinct peanut accessions (Wang et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e), and the other including samples collected at multiple time points after imbibition from paired dormant and non-dormant genotypes.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRNA extraction and cDNA synthesis\u003c/h3\u003e\n\u003cp\u003eFor expression analysis of the target candidate genes, mature seeds from two genotypes exhibiting contrasting dormancy phenotypes were collected, with three biological replicates (each replicate consisting of 30 seeds). Seeds were sampled after 0, 24, and 48 hours of rehydration, immediately frozen in liquid nitrogen, and stored at -80\u0026deg;C until further processing. Total RNA was extracted, treated with DNase and used for cDNA synthesis. The resulting cDNA was subsequently diluted 20-fold and used for quantitative real-time PCR (qRT-PCR) analysis of the candidate genes.\u003c/p\u003e\n\u003ch3\u003ePrimer sequences and qPCR assay\u003c/h3\u003e\n\u003cp\u003eThe primer sequences specific to the candidate genes were designed using Primer 3 software (Primer3 Input, version 0.4.0). Specificity of the primers for each candidate gene was verified by BLAST analysis against GenBank database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://blast.ncbi.nlm.nih.gov/Blast.cgi\u003c/span\u003e\u003cspan address=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), as well as by gel electrophoresis and melting curve analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCharacterization of dormancy phenotype and heritability\u003c/h2\u003e\u003cp\u003eSeed dormancy was evaluated for 353 cultivated peanut accessions across four environments (2020ZZ: Zhengzhou, Henan, 2020; 2023SQ: Shangqiu, Henan, 2023; 2023XX: Xinxiang, Henan, 2023; 2024XX: Xinxiang, Henan, 2024). The GI displayed a continuous and wide range of phenotypic variations, ranging from 0% to 100%, with a skewed phenotypic distribution. Among all accessions, the largest group consisted of lines with the highest GI values (90\u0026ndash;100%) ranked first, followed by those with the lowest GI values (0\u0026ndash;10%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u0026ndash;D, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBased on the seed GI of the natural population across the four environments, BLUE values for GI were estimated using a linear regression model. The correlated coefficients between the BLUE values and the phenotypes from the four environments were all greater than 0.9 (2020ZZ: 0.904, 2023SQ: 0.926, 2023XX: 0.917, 2024XX: 0.987) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The phenotypic distribution of the BLUE values showed a similar trend to that observed in each environment, also displaying a skewed phenotypic distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). The correlation coefficients amount the four environments ranged from 0.860 to 0.926 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), indicating that the germination index is highly reliable and stable across different environments.\u003c/p\u003e\u003cp\u003eAnalysis of variance (ANOVA) of seed germination rate across the four environments revealed that genotype, environment, and genotype-by-environment interaction all had significant effects on GI (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The broad-sense heritability of GI exceeded 0.89, indicating that the genotypic factors play a predominant role in determining seed germination rate in peanut.\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\u003eDescriptive statistics, coefficient of variation (CV), and broad-sense heritability of seed dormancy evaluated across 353 diverse peanut accessions\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=\"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=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\u003eEnvironment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCV%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSkewness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eKurtosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHeritability\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2020ZZ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e71.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e35.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e49.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2023SQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e75.41\u0026thinsp;\u0026plusmn;\u0026thinsp;2.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e36.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e48.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2023XX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e73.09\u0026thinsp;\u0026plusmn;\u0026thinsp;2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e51.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2024XX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e77.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e34.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e45.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBLUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e73.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e34.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e46.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.96\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\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\u003eSummary of analysis of variance of the germination indices (GI)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eF-\u003c/em\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026gt;F\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnvironment (E)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95773.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31924.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e253.3565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotype (G)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4018502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11416.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e90.6004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG\u0026times;E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e683642.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e639802.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.1865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eError\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e291326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e653.5267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5050699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eDF, degrees of freedom; SS, sum of square, MS, Mean sum of Squares; P, probability\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\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eGWAS identified significant SNPs associated with seed dormancy\u003c/h2\u003e\u003cp\u003eHigh-resolution genome-wide association studies (GWAS) were performed for seed dormancy by integrating the genotypes and phenotypes of 353 peanut accessions. The genome-wide significance threshold was set as -log\u003csub\u003e10\u003c/sub\u003e(0.05/\u003cem\u003en\u003c/em\u003e) (where \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;935,231 represents the number of markers), resulting in a cutoff value of 7.27. Using this threshold, 182 SNPs significantly associated with seed dormancy were identified across chromosomes A02, A03, A04, A05, A07, A09, A11, and A19 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, the significant regions on chromosomes A03 and A04 were consistently detected across all four environments and the BLUE values, while the region on chromosome A07 was repeatedly identified in 2020ZZ, 2024XX, and the BLUE values (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, D and E). The significant regions on chromosomes A02, A05 and A19 were detected in only a single environment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn chromosome A03, 72 significant SNPs were identified and the peak SNP A03:5839362 was detected in the BLUE values, 2023SQ and 2023XX (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Two additional linked peak SNPs A03:5858205 and A03:6065070 were identified in 2020ZZ and 2024XX, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These markers exhibited -log₁₀(\u003cem\u003ep\u003c/em\u003e) value ranging from 7.61 to 11.77, with the effect size between 15.73 and 19.23 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The repeated detection of this region across multiple environments indicates that it represents a stable quantitative trait locus.\u003c/p\u003e\u003cp\u003eA total of 43 significant SNPs were detected on chromosome A04. The peak SNP A04:46136957 was detected in 2023SQ and 2024XX, with -log₁₀(\u003cem\u003ep\u003c/em\u003e) value of 7.66 and 7.60, and effect size of 22.25 and 20.25, respectively. The linked peak SNP A04:48714612 was detected in 2020ZZ, with -log₁₀(\u003cem\u003ep\u003c/em\u003e) value of 7.66 and effect size of 21.91. Another linked peak SNP A4:88396106 was identified in 2023XX and the BLUE values, with -log₁₀(\u003cem\u003ep\u003c/em\u003e) value of 7.44 and 7.58, and effect size of 19.54 and 23.25, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, the peak SNP A07:78500040 was identified in 2020ZZ, and the linked peak SNP A07:78616537 was identified in 2024XX and the BLUE values, with -log₁₀(\u003cem\u003ep\u003c/em\u003e) value ranged from 8.71 to 9.54, and effect size ranged from \u0026minus;\u0026thinsp;25.52 to -21.90 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The peak SNPs on chromosomes A09 (A09:2502616) and A11 (A11:37521919) were detected in 2024XX data and the BLUE values (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe linkage between the peak SNPs and seed dormancy were illustrated using the violin and boxplots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). For the peak SNPs on chromosomes A03 and A04, the GI of the accessions with the minor alleles had significant differences with those with the major alleles across all four environments and the BLUE values (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast, for the four peak SNPs on chromosomes A07, A09, and A11, there were no significant difference on GI between the accessions with different alleles (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\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\u003eThe peak SNP loci associated with seed dormancy on five chromosomes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\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\u003ePos\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMajor allele\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMinor allele\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEnv\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus; log(\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMAF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eEffect\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5839362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBLUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.41\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2023SQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16.68\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2023XX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17.34\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\u003e5858205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2020ZZ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15.73\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\u003e6065070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2024XX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46136957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2023SQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e22.25\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2024XX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.25\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\u003e48714612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2020ZZ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21.91\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\u003cp\u003e88396106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBLUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19.54\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2023XX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78500040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2020ZZ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-23.11\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\u003e78616537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBLUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-21.90\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2024XX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-25.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2502616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2024XX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-32.31\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBLUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-26.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37521919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2024XX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-37.57\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBLUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-33.07\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\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eDevelopment and application of KASP markers\u003c/h2\u003e\u003cp\u003eBased on the peak SNPs A03:5839362 and A04:46136957, we developed two KASP markers, designated Tif1-A03-5839362 and Tif1-A04-46136957, to distinguish four haplotypes (Hap1: AA, Hap2: AG, Hap3: GA, and Hap4: GG) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Phenotypic analysis revealed a significant difference in germination index (GI) among haplotypes: materials carrying Hap4 showed a markedly lower average GI, ranging from 5.57% to 8.04%, compared to those with Hap1, which had an average GI between 90.02% and 93.67% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). These results demonstrate the potential utility of the developed KASP markers for marker-assisted selection in peanut breeding programs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eEstimation of haplotypes\u003c/h2\u003e\u003cp\u003eThe significant SNPs identified on chromosomes A03 and A04 were used for haplotype block analysis. SNPs within approximately 300 kb flanking each significant SNPs were extracted for this analysis. The candidate region on chromosome A03 was partitioned into five distinct blocks (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Notably, the peak SNPs A03:5839362, A03:5858205, and A03:6065070 were co-localized within Block 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB-C), which spans a physical interval of 265.30 kb between A03:5839362 and A03:6104664. According to annotation of the reference genome \u003cem\u003eArachis hypogaea\u003c/em\u003e cv. \u003cem\u003eTifrunner\u003c/em\u003e (v1), 22 genes were annotated in Block 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, Table S3). The candidate region on chromosome A04 contained two haplotype blocks. The peak SNP A04:46136957 was located in Block 1, which spans 583.93 kb from A04:45848396 to A04:46432327 and contained five annotated genes (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e and Table S3). The peak SNP A04:88396106 was located in Block 2, spanning 691.30 kb from A04:88004671 to A04:88695969 and contained six predicted genes (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e and Table S3).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eCandidate genes identified for seed dormancy\u003c/h2\u003e\u003cp\u003eA total of 33 genes were annotated within the candidate intervals on chromosomes A03 and A04. Sequence analysis of the natural population identified 14 mutations across 11 of these candidate genes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Nine mutations occurred in intronic regions, three were located in exonic regions and two were found in the 3\u0026rsquo;-UTR (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Among them, eight genes displayed relatively high expression levels based on the transcriptome sequencing data from seed development (25 to 85 days after flowering) (Fig. S3 A-C) and seed germination (0\u0026ndash;48 hours after imbibition) (Fig. S3 D-E). By integrating mutation types, expression levels and gene functions associated with seed dormancy, \u003cem\u003eArahy.5SR8FF\u003c/em\u003e and \u003cem\u003eArahy.2PCK9I\u003c/em\u003e were identified as the key candidate genes, both of them encode E3 ubiquitin-protein ligases (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHaplotype analysis of \u003cem\u003eArahy.5SR8FF\u003c/em\u003e identified four distinct haplotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). The mean seed germination rate for Hap2 was significantly lower (22.30%\u0026ndash;26.67%) than that of Hap1 (83.17%\u0026ndash;88.61%) and Hap3 (53.30%\u0026ndash;72.70%) across all environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). In contrast, no significant difference in GI was observed between the two haplotypes of \u003cem\u003eArahy.2PCK9I\u003c/em\u003e (Fig. S4 A, C, E).\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\u003eGenes with mutations identified in the natural population.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\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\u003ePos\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGene ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStart\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEnd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStrand\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMut type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStructure type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eFunction type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eFunctional description\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5869075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eArahy.JXV2U6\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5863215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5870852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eG/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eexonic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003enonsynonymous SNV (V/I)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ePlasma-membrane choline transporter family protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5905893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eArahy.8M9DZW\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5904097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5906751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eG/T\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eintronic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eEARLY-RESPONSIVE TO DEHYDRATION 7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5983859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eArahy.5SR8FF\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5983287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5994026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eG/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eexonic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003enonsynonymous SNV (S/N)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eE3 ubiquitin-protein ligase UPL3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5985661\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5983287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5994026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTT/--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eintronic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6006994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eArahy.VVQ74P\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6004716\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6010401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT/C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eintronic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eRac-like GTP-binding protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6025606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eArahy.RN7VB5\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6022994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6027593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eA/C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eintronic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eReceptor-like cytosolic serine/threonine-protein kinase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6049104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eArahy.181IYB\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6047123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6049962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eC/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3\u0026lsquo;-UTR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eHeavy metal-associated isoprenylated plant protein 26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6076384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eArahy.2PCK9I\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6074470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6076761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eC/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eexonic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003estop\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eBOI-related E3 ubiquitin-protein ligase 3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6102760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eArahy.YQYF3H\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6092964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6102931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eC/T\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3\u0026lsquo;-UTR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ePentatricopeptide repeat-containing protein MRL1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46238622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eArahy.6W4GGI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46237198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46240239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eG/T\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eintronic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ecytochrome b5-like heme/steroid-binding domain protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46239808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eArahy.6W4GGI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46237198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46240239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eG/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eintronic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88149562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eArahy.JR6N4B\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88146430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88158072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eA/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eintronic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003echitinase-like protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88156593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88146430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88158072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eA/C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eintronic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eTransmembrane amino acid transporter family protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88193131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eArahy.V8HTKV\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88192700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88198943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT/-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eintronic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003elaccase 17\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\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eExpression analysis of candidate genes\u003c/h2\u003e\u003cp\u003eTo determine whether the candidate gene \u003cem\u003eArahy.5SR8FF\u003c/em\u003e exhibits differential expression during seed germination among haplotypes, accessions representing Hap1 (N420, N728, N741) and Hap2 (N539, N666, N668) were selected. Seed samples from these six accessions were collected at three germination stages (0h, 24h, and 48h after imbibition), with three biological replicates per time point. Relative expression levels were quantified using qRT-PCR (Table S4, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Although \u003cem\u003eArahy.5SR8FF\u003c/em\u003e showed generally low expression across all accessions, its expression was consistently lower in Hap1 than in Hap2. Moreover, in weak dormancy accessions (Hap1: N420, N728, N741), expression decreased progressively during germination, whereas in strong dormancy accessions (Hap2: N539, N666, N668), expression gradually increased over the same period.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSeed dormancy represents a critical agronomic trait in peanut production. Insufficient dormancy can lead to pre-harvest germination of seeds in soil, significantly compromising yield and quality, while excessively prolonged dormancy may hinder breeding efficiency and limit agricultural productivity. The development and application of molecular markers for breeding new peanut varieties with certain seed dormancy constitutes a promising strategy for genetic improvement. Identifying dormancy associated QTLs and SNPs associated with dormancy is the first step in breeding target peanuts varieties. In this study, QTLs, SNPs and candidate genes involved in seed dormancy were identified by analyzing whole genomic resequencing data from 353 peanut accessions, along with phenotypic data collected across four environments.\u003c/p\u003e\u003cp\u003eIn this study, two significant regions on chromosomes A03 and A04 were consistently detected for seed dormancy across all four environments and the BLUE values by GWAS. Notably, similar results were reported by Zhang et al (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Wang et al (2022), who identified major QTLs for seed dormancy on chromosome A04 using different recombinant inbred line (RIL) populations (Wang et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This convergence of findings was further validated by Bomireddy et al (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) in GWAS of 184 peanut accessions (Bomireddy et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), adding credibility to our results. However, previous studies on the genetic control of peanut seed dormancy have also identified associated loci on other chromosomes, including Arahy. A01, A08, A09, B02, B04, B05, B07, and B09, through QTL mapping or GWAS. In contrast, our GWAS analysis revealed a novel locus on chromosome A03, which had not been previously reported.\u003c/p\u003e\u003cp\u003eThe genomic positions of SNPs associated with peanut seed dormancy identified on chromosome A04 in this study exhibit some discrepancies with previous reports. Our multi-environmental analysis revealed significant SNPs spanning a broad physical interval from 20 Mb to 100 Mb on A04, with peak associations primarily localized around 46 Mb and 88 Mb. This observation is consistent with findings from Wang et al. (2022), who identified seed dormancy-related QTLs (\u003cem\u003eqPD_A04-1\u003c/em\u003e, \u003cem\u003eqPD_A04-2\u003c/em\u003e, and \u003cem\u003eqPD_A04-3\u003c/em\u003e) within a similarly wide genomic region (27,963,468 bp \u0026minus;\u0026thinsp;117,729,029 bp) on chromosome A04. Similarly, Zhang et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) mapped a QTL for fresh seed germination (\u003cem\u003eqFSGA04\u003c/em\u003e) to position 100,806,014 on A04, and Bomireddy et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported \u003cem\u003eqFSD_A04-1\u003c/em\u003e at 119,365,410 bp. Subsequent linkage disequilibrium (LD) block analysis of our target intervals revealed a limited number of annotated genes. The broad physical range covered by the significant SNPs poses challenges for precise candidate gene prediction.\u003c/p\u003e\u003cp\u003eThe novel significant locus identified on chromosome A03 in this study, was consistently detected across multiple environments with a high -log(\u003cem\u003ep\u003c/em\u003e) peak value. Its haplotypes demonstrated a strong ability to differentiate phenotypic variation, making it the primary target for candidate gene investigation. Within this interval, the haplotypes of gene Arahy.5SR8FF significantly stratified the phenotypes. Annotation and sequence analysis revealed that Arahy.5SR8FF encodes a HECT (Homologous to E6-AP C-Terminus) E3 ubiquitin ligase. Previous studies have implicated HECT-type E3 ligases in ABA-mediated seed germination. For instance, HECT-type E3 Ligases UPL1 and UPL4 regulate ABA-dependent seed germination, where UPL1 promotes ABA-induced germination inhibition, while UPL4 represses it (Tajdel-Zielińska et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, the RING-type E3 ligase AIRP3 forms an E2-E3 pair with UBC27, promoting the ubiquitination and degradation of ABI1 and thereby enhancing ABA-mediated germination inhibition (Pan et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, the U-box E3 ubiquitin ligase PLANT U-BOX 35 (PUB35) cooperate with AFP1 to negatively regulate ABI5 protein degradation, fine-tuning seed germination and post-germinative seedling growth (Du et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). An analogous negative regulatory role was reported in wheat, where the E3 ligase TaPUB1 interacts with TaPYL4 and TaABI5, acting as a negative regulator in ABA signaling (Zhang et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, quantitative expression analysis during seed germination revealed that the relative expression level of haplotype Hap1 was significantly lower than that of Hap2. Therefore, we hypothesize that the candidate gene Arahy.5SR8FF may function as a negative regulator in peanut seed dormancy. However, further experimental validation is required to elucidate its precise molecular function.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, two significant regions on chromosomes A03 and A4 associated with seed dormancy were identified through GWAS, of which the region on chromosome A03 is considered as a novel and major QTL. Accordingly, two peak SNPs in the two significant regions were validated to be linked with seed dormancy and were developed into KASP markers for assisted breeding. The \u003cem\u003eArahy.5SR8FF\u003c/em\u003e, encoding HECT-type E3 ubiquitin-protein ligases, was identified as the key candidate gene regulating seed dormancy in peanut.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eSupplementary Information\u003c/h2\u003e\u003cp\u003eSupplementary material 1 (Supplementary Fig S.docx); Supplementary material 2 (Supplementary Table S.docx).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by National Key Research and Development Program (2023YFD1200200), Special Project for National Supercomputing Zhengzhou Center Innovation Ecosystem Construction (201400210600), China Agriculture Research System of Ministry of Finance People's Republic of China (MOF) and Ministry of Agriculture and Rural Affairs (MARA) (CARS-13), Henan Provincial Agriculture Research System, China (S2012-5), Fundamental work project of Henan Academy of Agricultural Sciences (2025JC03).\u003c/p\u003e\u003ch2\u003eAuthor contribution\u003c/h2\u003e\u003cp\u003e\u003cb\u003estatement MZ\u003c/b\u003e performed field experiments, laboratory experiments, data analysis and wrote the manuscript. \u003cb\u003eZS\u003c/b\u003e and \u003cb\u003eFQ\u003c/b\u003e developed the population, designed the experiments, and assisted in manuscript preparation. \u003cb\u003eLQ\u003c/b\u003e and \u003cb\u003ePD\u003c/b\u003e designed experiments and provided help in laboratory experiments. \u003cb\u003eCL\u003c/b\u003e performed field experiments, laboratory experiments and assisted in manuscript preparation. \u003cb\u003eJW\u003c/b\u003e assisted in genotypic analysis. \u003cb\u003eZZ\u003c/b\u003e assisted in data analysis. \u003cb\u003eJX\u003c/b\u003e and \u003cb\u003eHL\u003c/b\u003e performed field experiments. \u003cb\u003eLM\u003c/b\u003e, \u003cb\u003eLS\u003c/b\u003e, \u003cb\u003eSH\u003c/b\u003e, \u003cb\u003eBH\u003c/b\u003e and \u003cb\u003eWD\u003c/b\u003e provided help in laboratory experiments. \u003cb\u003eJW\u003c/b\u003e and \u003cb\u003eMZ\u003c/b\u003e supervised field experiments at Shangqiu. \u003cb\u003eZZ\u003c/b\u003e and \u003cb\u003eXZ\u003c/b\u003e conceived and designed the experiments, facilitated the project, and assisted in manuscript preparation. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eAll relevant data can be found within the manuscript and in Supplementary Material.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAli F, Qanmber G, Li F, Wang Z (2022) Updated role of ABA in seed maturation, dormancy, and germination. J Adv Res 35:199\u0026ndash;214. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jare.2021.03.011\u003c/span\u003e\u003cspan address=\"10.1016/j.jare.2021.03.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaudouin E, Puyaubert J, Meimoun P et al (2022) Dynamics of protein phosphorylation during \u003cem\u003eArabidopsis\u003c/em\u003e seed germination. 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Int J Biol Macromol 305:141223. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijbiomac.2025.141223\u003c/span\u003e\u003cspan address=\"10.1016/j.ijbiomac.2025.141223\" 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, Seed dormancy (SD), GWAS, Kompetitive allele specific PCR (KASP) markers","lastPublishedDoi":"10.21203/rs.3.rs-8200443/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8200443/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSeed dormancy is a crucial trait that can influence both peanut yield and quality. In this study, a genome-wide association analysis (GWAS) was conducted using 353 peanut accessions with diverse genetic backgrounds from various global regions. The germination index was evaluated across multiple years and environments to identify genomic loci regulating seed dormancy. Phenotypic evaluation revealed extensive variation in seed dormancy within the natural population. The broad-sense heritability was greater than 0.89, and both genotype and genotype-by-environment interactions showed significant effects. Numerous SNPs on chromosomes A02, A03, A04, A05, A07, A09, A11, and A19 were identified significantly associated with seed dormancy, with particularly consistent and stable associations on chromosomes A03 and A04. By integrating mutation type, expression level, allele mining and gene function analysis, the gene \u003cem\u003eArahy.5SR8FF\u003c/em\u003e, encoding HECT-type E3 ubiquitin-protein ligases, was considered as the key candidate for regulating seed dormancy in peanut. Additionally, two peak SNPs on chromosome A03 and A04 were validated as significantly linked to seed dormancy, which were developed into KASP markers and can be used for marker assisted breeding.\u003c/p\u003e","manuscriptTitle":"Genome-wide association analysis identifies novel genetic loci and candidate genes associated with seed dormancy in cultivated peanut","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 12:29:28","doi":"10.21203/rs.3.rs-8200443/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revisions","date":"2026-03-27T15:37:45+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-12-10T07:12:15+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-09T20:16:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-26T15:28:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2025-11-25T02:52:23+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":"63474cdd-9c4c-4252-a971-d06f26b2b008","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T16:09:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-15 12:29:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8200443","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8200443","identity":"rs-8200443","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}