A New Genetic Architecture for PHS Resistance in Rice: Deciphering the Epistatic Interactions of Three Major QTLs

A New Genetic Architecture for PHS Resistance in Rice: Deciphering the Epistatic Interactions of Three Major QTLs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A New Genetic Architecture for PHS Resistance in Rice: Deciphering the Epistatic Interactions of Three Major QTLs Chang-Ju Lee, Tae-Heon Kim, Dong-Hyun Baek, Jingli Gao, Woo-Keun Park, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8227857/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Pre-harvest sprouting (PHS), thef premature germination of grains before harvest, threatens rice yield and quality under erratic climatic conditions. This study aims to investigate the genetic basis of PHS resistance by conducting a genome-wide association study (GWAS) on 182 diverse rice genetic resources representing multiple ecotypes using 289,569 high-quality single-nucleotide polymorphisms. Three major QTLs— qRPH7, qRPH8, and qRPH11 —were identified using the complementary multi-locus models, Bayesian information and Linkage disequilibrium, iteratively Nested Keyway and Multi-Locus Mixed Model. qRPH7 showed the strongest association, explaining up to 80% of phenotypic variance, and co-localized with SDR4 and qPH7 . Allelic combination analyses revealed that the qRPH7–SDR4 and qRPH7–qPH7 combinations conferred strong resistance, whereas qRPH7 alone was insufficient. In contrast, qRPH11 contributed additively to enhance resistance, while qRPH8 displayed antagonistic epistasis that reduced resistance stability. Overall, PHS resistance is governed by a polygenic architecture involving both additive and epistatic interactions. These findings establish a new genetic architecture underlying PHS resistance in rice and propose a targeted breeding strategy through pyramiding qRPH7 with SDR4, qPH7 , and qRPH11 . This study advances mechanistic insight into seed dormancy and sprouting while providing actionable resources to support marker-assisted selection and accelerate the development of PHS-resistant cultivars suited to climate change. PHS rice GWAS genetic architecture antagonistic epistasis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Climate change has intensified unpredictable abiotic stresses, including heat waves and erratic rainfall, resulting in major crop yield losses, necessitating urgent intervention from agricultural research institutions (Benitez-Alfonso et al. 2023 ; Lobell et al. 2011 ). In rice, these climatic shifts promote pre-harvest sprouting (PHS) under hot, humid conditions during grain filling, leading to substantial production losses (Baek et al. 2014; Sohn et al. 2021 ). PHS, characterized by premature seed germination, reduces yield, lowers milling recovery, and degrades grain quality, ultimately posing a critical threat to farmer income and global food security (Lee et al. 2020 ; Zhu et al. 2019 ). In temperate japonica rice-growing regions, including Japan, Korea, and California (USA), PHS is projected to incur significant cumulative losses of USD 8–10 billion under extreme conditions and USD 4–5 billion under milder scenarios over the next decade (Lee et al. 2021 ). Mitigating these risks requires the use of diverse rice genetic resources to advance resistance breeding. Specifically, identifying resistant genetic resources and uncovering quantitative trait loci (QTLs) and candidate genes offer promising breeding strategies to minimize the adverse effects of climate change on rice production and ensure a stable food supply (Li et al. 2011 ; Lobell et al. 2011 ; Mizuno et al. 2018 ). However, most studies rely on biparental populations or focus narrowly on a few major-effect loci, such as SDR4 and qPH7 (Lee et al. 2023 ; Sugimoto et al. 2010 ). Although these approaches have yielded valuable insights, they may not fully capture the full extent of natural genetic variation present in diverse rice genetic resources, potentially limiting the development of durable resistance to PHS. PHS resistance is closely linked to seed dormancy, a mechanism that prevents premature germination under unfavorable conditions (Bewley 1997 ). Seed germination is regulated by both environmental factors, including temperature, moisture, and oxygen availability, and intrinsic hormonal signals (Klupczyńska et al. 2021; Née et al. 2017 ; Penfield 2017 ). Abscisic acid (ABA) induces dormancy, whereas gibberellins (GA) stimulate germination, with the ABA–GA balance largely determining seed fate (Finch-Savage et al. 2006; Finkelstein et al. 2008 ). ABA signaling operates through the PYR/PYL–PP2C–SnRK2 module to maintain dormancy, while GA induces germination by promoting DELLA protein degradation (Ali et al. 2022 ; Lan et al. 2024 ; Tyler et al. 2004 ; Umezawa et al. 2010 ). Dormancy release occurs through processes such as after-ripening or dry storage, which reduces ABA sensitivity, enhances GA responsiveness, and is accompanied by reactive oxygen species (ROS) accumulation and chromatin modifications (Liu et al. 2014 ). Structural and biochemical properties of the seed coat, including inhibitory compounds and physical barriers to water or oxygen, further contribute to dormancy maintenance and PHS resistance (Debeaujon et al. 2000 ). The husk, pericarp, and testa restrict water uptake, oxygen diffusion, and embryo expansion, closely linking these barriers to PHS resistance in rice (Roberts 1961 ). Additionally, the seed coat contains germination-inhibitory compounds, such as phenolics and alkaloids, which reinforce dormancy through both physical and chemical inhibition (Chenyin et al. 2023 ). Numerous genes and QTLs linked to seed dormancy and PHS resistance have been identified in rice. Among them, Seed dormancy 4 ( SDR4 ) is a key regulator that integrates ABA and GA signaling to reinforce dormancy and shows strong associations with PHS resistance across diverse genetic resources (Sugimoto et al. 2010 ). More recently, a major QTL for PHS resistance, qPH7 , was identified using a recombinant inbred line population derived from Korean weedy rice, and fine-mapping localized it to a 210-kb interval (23.575–23.785 Mb) on chromosome 7 (Lee et al. 2023 ). Beyond these loci, additional genetic determinants have been identified. For instance, Rc (qSD7-1) , which controls seed coat pigmentation, is consistently linked to dormancy and PHS resistance (Gu et al. 2004 ; Gu et al. 2003 ). qSD12 , mapped in multiple biparental populations, contributes to natural variation in dormancy by promoting ABA accumulation in early developing seeds to induce primary dormancy (Gu et al. 2010 ; Gu et al. 2008 ). Carbohydrate metabolism-related loci such as PHS8/ISA1 further highlight the role of endosperm composition in PHS regulation (Du et al. 2018 ). Regulatory genes involved in hormonal signaling also contribute to dormancy control. OsVP1 functions as a central transcription factor coordinating ABA-mediated seed maturation and dormancy, while qSD1 - 2 / OsGA20ox2 encodes a GA biosynthesis enzyme that modulates GA levels and the dormancy-germination balance (Chen et al. 2021 ; Ye et al. 2015 ). The OsDOG1L gene family maintains dormancy through mechanisms similar to those of the Arabidopsis DOG1 pathway (Bentsink et al. 2006 ; Wang et al. 2020 ). The major QTL qLTG3-1 enhances low-temperature germinability by weakening embryonic tissues—improving germination under suboptimal temperatures (Fujino et al. 2008 ). Collectively, these studies show the polygenic complexity of PHS resistance in rice, integrating hormonal regulation, metabolic pathways, and structural seed traits that govern dormancy and germination. However, despite considerable progress in elucidating the genetic control of PHS resistance, studies largely focus on biparental populations or a few major-effect loci, limiting relevance to the broader genetic diversity of rice genetic resources. Furthermore, the polygenic and environmentally sensitive nature of PHS resistance, driven by the interplay of seed dormancy, hormone regulation, and structural traits, suggests that key components of its genetic architecture remain unresolved. Therefore, this study aims to investigate PHS resistance by conducting a genome-wide association study (GWAS) on 182 rice genetic resources representing multiple ecotypes to capture natural allelic variation beyond the resolution of conventional linkage mapping. This study identifies novel QTL through GWAS and systematically examines their genetic interactions with previously reported loci such as SDR4 and qPH7 , thereby clarifying the complex architecture underlying PHS resistance. By highlighting allelic combinations with practical breeding value, the findings could provide mechanistic insights and actionable resources for marker-assisted selection, supporting the development of rice cultivars with stable PHS resistance under diverse climatic conditions. Materials and methods Plant materials A panel of 182 rice genetic resources was used for phenotypic and genotypic evaluation to identify genomic regions associated with PHS resistance. The set of genetic resources comprised 106 Japonica , 35 Indica , 33 Admixed , 6 Aus , and 2 Aromatic types. Of the 182 rice genetic resources, 116 were obtained from the National Institute of Crop Science, and the remaining were sourced from the National Agrobiodiversity Center. Field management The experiment was conducted in 2024 at the Experimental Farm, College of Agriculture and Life Sciences, Kyungpook National University. Seedlings were transplanted at a spacing of 30 × 15 cm, with one seedling per hill. Fertilizer was applied at rates of 9.0–4.5–5.7 kg/10a (N–P₂O₅–K₂O), following national crop fertilizer guidelines (National Institute of Agricultural Sciences 2022 ). Pre-harvest sprouting evaluation PHS resistance was evaluated by recording the heading date of each rice genetic resource and harvesting the main panicle 40 days after heading, corresponding to an accumulated growing degree day value of 1,000°C (Kang et al. 2018 ). Only seed samples with normal germination rates above 70% under standard conditions were used to ensure phenotypic reliability. Three biological replicates were included per genetic resource. Panicles were fully wrapped in tissue paper to facilitate moisture absorption and placed in stainless steel trays (325 × 265 × 63 mm). Samples were incubated in a growth chamber at 25°C and 100% relative humidity for 7 days (Rural Development Administration 2012 ). After incubation, the germination rate was calculated as the percentage of germinated seeds among the total number of filled seeds per panicle. The mean value of three replicates was used to determine the final PHS rate. Seeds were considered germinated when the coleoptile visibly emerged from the hull, while unfilled or defective grains were excluded. Based on germination rates, PHS resistance was classified into five categories: degree 1 (≤ 20%), degree 3 (21–40%), degree 5 (40–60%), degree 7 (60–80%), and degree 9 (81% ≤) (Rural Development Administration 2012 ). Table 1 presents the classification criteria. Rice genetic resources with degrees 1 or 3 were considered resistant, while those with degrees 5, 7, or 9 were considered susceptible. Table 1 Criteria for the classification of evaluating PHS severity Degree Observation Tolerance 1 ≤ 20% Highly Tolerant 3 21–40% Tolerant 5 41–60% Moderately Tolerant 7 61–80% Susceptible 9 81% ≤ Highly Susceptible Table 2 Marker information used for the identification of PHS-related genes, SDR4 and qPH7 QTL/gene Chr Marker Sequence (5’-3’) Restriction enzyme Reference SDR4 7 SDR4-SacII F: GTGTCGGTGGTGGTCGTC R: CGAGAACCCCTTGCATGTCT SacII (Sugimoto et al. 2010 ) qPH7 7 PH_1_13 F: ATCTGTATGACTTAAGGCACG R: ACTAAACTGTGCTAAATTGCG DdeI (Lee et al. 2023 ) Genotyping data collection and processing Single-nucleotide polymorphism (SNP) genotyping was performed using the 580K Axiom Rice Genotyping Chip (580K_KNU chip), developed from eight genomic data sources (Kim et al. 2022 ). Genomic DNA samples were hybridized to the array and scanned on the GeneTitan® platform, Affymetrix, Santa Clara, CA, USA. SNP calling was conducted with Genotyping Console v4.2, Affymetrix, Santa Clara, CA, USA, and further refined using the SNPolisher R package v3.0. SNPs were aligned to the IRGSP-1.0 ( japonica ), MH63RS2 ( indica ), and Oryza rufipogon reference genomes. High-quality SNP markers were selected for GWAS. They were filtered using the following criteria: minor allele frequency (MAF) > 0.05, missing rate < 0.02, heterozygosity rate 10×. After filtering, 289,569 SNPs were retained for GWAS. Genome-wide association study GWAS was performed using two multi-locus models—Bayesian information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) and Multi-Locus Mixed Model (MLMM) implemented in the GAPIT package in R. Before association testing, population structure was assessed by principal component analysis (PCA), with the first three principal components included as covariates alongside a kinship matrix. MLMM iteratively incorporates significant markers as covariates, simultaneously detecting multiple loci contributing to phenotypic variation (Segura et al. 2012 ). BLINK filters redundant markers using linkage disequilibrium (LD) and applies the Bayesian Information Criterion (BIC) for model selection, enhancing statistical power while controlling false positives (Huang et al. 2019 ). For multiple testing correction, a Bonferroni adjustment at α = 0.05 was applied, yielding a genome-wide significance threshold of p < 1.726 × 10⁻⁷ (− log₁₀ p = 6.76). GWAS results were visualized using Manhattan and quantile–quantile (QQ) plots generated with the “qqman” R package (Turner 2018 ). Significant SNPs were annotated by assigning open reading frames (ORFs) within a ± 150-kb window around each SNP as candidate genes. Statistical analysis Variation in PHS among rice genetic resources was evaluated using one-way analysis of variance (ANOVA) in R statistical software, version 4.3.1; R Core Team, 2023. When ANOVA results were significant (p < 0.05), group comparisons were performed using Duncan’s multiple range test through the “agricolae” R package (de Mendiburu 2023 ) to identify statistically significant differences among genetic resources. Haplotype analysis Two-locus haplotype analysis of SDR4 and qPH7 was performed using PCR amplification with Solg™ e-Taq DNA Polymerase (SolGent, Daejeon, Korea). For the SDR4-SacII marker (Sugimoto et al. 2010 ), thermal cycling conditions included an initial denaturation at 94°C for 5 min; 35 cycles of 94°C for 20 s, 55°C for 25 s, and 72°C for 1 min; with a final extension at 72°C for 5 min. For the PH_1_13 marker (Lee et al. 2023 ), the same conditions were used except for the annealing step at 54°C for 45 s. PCR products for SDR4 and qPH7 were digested with the restriction enzymes Sac II and Dde I, respectively. Digested products were separated on a 1.5% agarose gel and stained with SYBR™ Safe DNA Gel Stain (Thermo Fisher Scientific, Waltham, MA, USA). Bands were visualized using the DAVINCH Gel Imager CG-550 (DAVINCH-K, Seoul, Korea). Results Pre-harvest sprouting phenotypic variation in diverse rice genetic resources PHS was evaluated in 182 rice genetic resources using the predefined criteria (Table 1 ). PHS rates ranged from 0% to 95.7%, with an average of 20.8 ± 25.2% (Fig. 1 ). The distribution was right-skewed (skewness = 1.36; kurtosis = 0.87). The median value was 8.5%, indicating that most rice genetic resources exhibited relatively low sprouting levels. The resistant control cultivar, Joun, showed an average PHS rate of 13.61 ± 4.23%, whereas the susceptible control, Jopyeong, exhibited a significantly higher rate of 44.23 ± 5.30%. PHS rates were classified into five degrees according to the established evaluation criteria (Table 1 ; Fig. 2 ). Among the 182 genetic resources, 20.9% were classified as susceptible (degree 5, 7, or 9), while the remaining 79.1% were classified as resistant (degree 1 or 3). The resistant and susceptible control cultivars corresponded to degree 1 and degree 5, respectively. To evaluate PHS variation among ecotypes within the population, the genetic resources were grouped into five ecotype categories and evaluated for PHS levels based on predefined criteria (Fig. S1 ). Japonica (group I) and Indica (group II) showed the widest PHS variation, with Indica showing a lower median value than that of Japonica. In contrast, Admixed (group III) predominantly exhibited low PHS rates. Genotypic profiling and population structure of rice genetic resources Overall, 289,569 SNPs were analyzed across 182 resources using an SNP chip and next-generation sequencing (NGS) (Table S1 ). SNP distribution varied across the 12 rice chromosomes, with an average of 24,131 SNPs per chromosome. Chromosome 1 showed the highest number (37,852), whereas chromosome 12 had the lowest (16,551). SNP density also differed among chromosomes, averaging 1.32 SNPs/Mb (Fig. S2). Chromosome 12 exhibited the highest density (1.66 SNPs/Mb), while chromosomes 2 and 3 showed the lowest (1.09 SNPs/Mb). A phylogenetic analysis of 182 rice genetic resources was performed using genome-wide SNP data to assess genetic relationships and population structure (Fig. S3a, b). The neighbor-joining tree analysis revealed five distinct ecotype groups based on genetic similarity: (i) Japonica , (ii) Indica , (iii) Admixed , (iv) Aus , and (v) Aromatic (Fig. S3a). These groups reflect unique genetic backgrounds and ecological adaptations, with pronounced divergence between Japonica and Indica . Genetic structure was further validated through PCA (Fig. S3b), which supported the same five-group clustering pattern. The PCA results showed clear genetic differentiation among ecotypes, providing complementary evidence to the phylogenetic analysis. This grouping establishes the basis for interpreting phenotypic variation in traits such as PHS resistance. Genome-wide association analysis for pre-harvest sprouting resistance GWAS were performed using genotypic and PHS rate data from 182 rice genetic resources to identify SNPs associated with PHS resistance. The GAPIT package in R was used to implement the BLINK and MLMM models. BLINK identified significant SNPs on chromosomes 7, 8, and 11, while MLMM detected lead SNPs on chromosomes 7 and 11 (Fig. 3 ). The SNPs on chromosomes 7 and 11 were detected at identical loci in both models, whereas the SNP on chromosome 8 was unique to BLINK. These lead SNPs on chromosomes 7, 8, and 11 were considered QTLs and designated as qRPH7, qRPH8, and qRPH11 , respectively (Table 3 ). Table 3 Summary of QTLs associated with PHS identified through GWAS QTLs SNP Chr Pos (bp) -log 10 ( p ) MAF PVE Model qRPH7 AX-115841304 7 23,799,472 8.01 0.08 43.69 BLINK qRPH8 AX-275910905 8 2,882,518 8.07 0.35 32.84 ” qRPH11 AX-115796079 11 2,374,434 7.85 0.13 13.07 ” qRPH7 AX-115841304 7 23,799,472 9.04 0.08 80.00 MLMM qRPH11 AX-115796079 11 2,374,434 6.72 0.13 33.89 ” Chr, chromosome; MAF, minor allele frequency; PVE: phenotype variance explained, QTL, quantitative trait loci; GWAS, genome-wide association study; PHS, pre-harvest sprouting; BLINK, Bayesian information and linkage disequilibrium iteratively nested keyway; MLMM, multi-locus mixed model; SNP, single nucleotide polymorphism qRPH7 showed strong statistical significance in both BLINK and MLMM, with –log₁₀( p ) values of 8.01 and 9.04 and phenotypic variance explained (PVE) values of 43.69% and 80.00%, respectively. qRPH11 showed –log₁₀( p ) values of 7.52 (BLINK) and 6.72 (MLMM), which were marginally below the significance threshold but remained close, with PVE values of 13.07% and 33.89%, respectively. qRPH8 was detected exclusively by BLINK, with a –log₁₀( p ) value of 8.07 and a PVE of 32.84%. Despite differences between the models, both BLINK and MLMM consistently identified three QTLs significantly associated with PHS resistance. To assess the effects of the identified QTLs, the 182 rice genetic resources plant materials were classified into five groups (I-V) based on their QTL combinations, and PHS was confirmed. Group I included all three QTLs— qRPH7 , qRPH8 , and qRPH11 . Groups II–IV each contained two QTLs: Group II possessed qRPH7 and qRPH11 ; Group III had qRPH7 and qRPH8 ; and Group IV included qRPH8 and qRPH11 . Group V included genetic resources carrying only qRPH7 . Group I showed the lowest mean PHS rate, and Groups I and II exhibited significantly lower PHS rates than those of other groups. Functional validation and genotypic effect of identified quantitative trait loci To identify candidate genes related to PHS resistance, ORFs located within ± 150 kb of the three QTLs ( qRPH7, qRPH8 , and qRPH11 ) identified through GWAS were examined. The analysis prioritized genes annotated in the Rice Annotation Project database and functionally related to PHS, germination, seed dormancy, and ABA/GA signaling pathways (Table S2). Within the qRPH7 interval, two major loci linked to PHS resistance— SDR4 and qPH7 —were identified (Fig. 5 a). Additionally, the qRPH11 region harbored OsPK1 , a gene implicated in hormonal regulation related to PHS resistance (Fig. 5 b). In contrast, no annotated ORFs with known roles in PHS, seed dormancy, or ABA/GA signaling were identified within the ± 150 kb region flanking qRPH8. Thus, leaving the functional candidate gene(s) underlying this QTL unresolved. Among the 182 rice genetic resources, 167 carrying qRPH7 were selected to analyze the genotypes of the candidate loci SDR4 and qPH7 (Fig. S4). Consequently, 53 genetic resources carried SDR4 , 42 carried qPH7 , and 39 possessed both genes. In contrast, 14 carried SDR4 alone, three carried only qPH7 , and 111 carried neither locus. Among the 167 rice genetic resources excluding group IV (Fig. 4 ), those carrying qRPH7 were classified into four combination types (A–D) based on the presence or absence of SDR4 and qPH7 , and their PHS rates were compared (Table 4 ). Type A ( SDR4 + qPH7 ) exhibited the lowest mean PHS rate (2.0 ± 2.5%), followed by Type C ( qPH7 , 3.8 ± 5.5%), Type B ( SDR4 , 6.1 ± 5.6%), and Type D (neither locus), which had the highest rate at 24.8 ± 24.7%. The overall mean PHS rate was 17.5 ± 22.7%. According to PHS classification criteria, Types A, B, C, and the overall mean were categorized as degree 1 (≤ 20%), whereas Type D corresponded to degree 3 (21–40%). Table 4 PHS rates based on qPH7 and SDR4 combinations in rice genetic resources carrying qRPH7 Genotypic combination type of qRPH7 No. of plants PHS (%) Degree qRPH7 A. SDR4 + qPH7 39 2.0 ± 2.5 1 B. SDR4 14 6.1 ± 5.6 1 C. qPH7 3 3.8 ± 5.5 1 D. None 111 24.8 ± 24.7 3 Total 167 17.5 ± 22.7 1 The effects of qRPH8 and qRPH11 were evaluated in 111 rice genetic resources belonging to the D subtype of qRPH7 (Table 5 ). Based on the presence or absence of qRPH8 and qRPH11 , these resources were further classified into four types (a–d), and their PHS rates were compared. Among these groups, type a ( qRPH11 + qRPH8 ) showed a mean PHS rate of 20.6 ± 26.4%, type b ( qRPH11 ) exhibited 18.3 ± 19.5%, type c ( qRPH8 ) recorded a markedly higher rate of 67.8 ± 27.1%, and type d ( none ) showed 46.0 ± 26.9%. The overall average PHS rate was 24.8 ± 24.7%, corresponding to degree 3 (21–40%). Based on the PHS classification criteria, type b was categorized as degree 1, type a as degree 3, type d as degree 5, and type c as degree 7. Duncan’s multiple range test (p < 0.05) revealed significant differences in PHS rates among types: types c and d were grouped as “a”, while types a and b were grouped as “b”, indicating that groups carrying qRPH11 exhibited significantly lower PHS rates. Table 5 Pre-harvest sprouting (PHS) rates based on combinations of qRPH11 and qRPH8 in the subtype of D carrying qRPH7 Subtype of D carrying qRPH7 No. of plants PHS (%) Degree a. qRPH11 + qRPH8 5 20.6 ± 26.4 b 3 b. qRPH11 83 18.3 ± 19.5 b 1 c. qRPH8 3 67.8 ± 27.1 a 7 d. None 20 46.0 ± 26.9 a 5 Total 111 24.8 ± 24.7 3 In total, 111 rice genetic resources classified as type D ( qRPH7 None ) were further divided into four subtypes based on the genotypic combinations of qRPH11 and qRPH8 , and their PHS rates were compared. According to Duncan’s multiple range test, significant differences among subtypes are indicated by different letters Integrative modeling of quantitative trait loci-based resistance mechanisms in rice The effects of different QTL combinations on PHS resistance were evaluated. Strong resistance was observed in genetic resources carrying qRPH7, qRPH8 , and qRPH11 simultaneously (groups 1–3), which showed low PHS rates of 2.1 ± 2.5%, 4.6 ± 8.4%, and 5.7 ± 6.4%, respectively. Similarly, genetic resources harboring qRPH7 and qRPH11 (groups 4–6) demonstrated very strong resistance, with PHS rates of 1.4 ± 2.7%, 6.7 ± 4.5%, and 0.2%, respectively. In contrast, group 7, which was comparable to groups 1–3 but lacked either SDR4 or qPH7 , and group 8, which was comparable to groups 4–6 but lacked SDR4 or qPH7 , exhibited moderate resistance, with PHS rates of 20.6 ± 26.4% and 18.3 ± 19.5%, respectively. However, both groups exhibited considerable phenotypic variation. By comparison, group 9 ( qRPH7 and qRPH8 ) and group 10 ( qRPH7 alone) exhibited high PHS rates of 67.8 ± 27.1% and 46.0 ± 26.9%, respectively. Similarly, group 11 ( qRPH8 and qRPH11 ) showed a high PHS rate of 55.6 ± 31.6%. In group 12, the additional of qRPH7 along with qRPH8 and qRPH11 did not reduce the PHS rate, which remained high at 57.4 ± 23.8%. Discussion Rice ( Oryza sativa ) is a critical global crop, but its productivity and quality are highly susceptible to environmental threats such as PHS. While previous studies identify loci associated with PHS resistance, most have focused on specific cultivars, leaving the broader molecular mechanisms unresolved. To address this gap, a GWAS was conducted, which identified three major QTLs— qRPH7 , qRPH8 , and qRPH11 —significantly associated with PHS resistance. Of these, the qRPH7 locus exhibited the highest statistical significance and explained the largest proportion of phenotypic variation. Based on these findings, genotypic interactions were further investigated, and a functional model of resistance expression was developed, which forms the basis for the following discussion. Quantitative trait loci identification through genome-wide association study In this study, genomic regions associated with PHS resistance in rice were identified using two complementary multi-locus models, BLINK and MLMM (Fig. 3 ; Table 3 ). These models, each with distinct strengths, were used to enhance both the precision and comprehensiveness of QTL detection. Based on the BIC, the BLINK model effectively reduces redundancy by accounting for LD among markers and selecting the most informative markers, thereby increasing statistical power while minimizing false positives (Huang et al. 2019 ). In contrast, the MLMM model incorporates significant markers as cofactors in a stepwise manner, allowing it to capture polygenic effects and control confounding factors, which improves accuracy and reproducibility in QTL detection (Segura et al. 2012 ). Using the BLINK model, three putative QTLs— qRPH7, qRPH8 , and qRPH11 —were detected. Among these, qRPH7 and qRPH11 were also identified by MLMM, with qRPH7 accounting for a high proportion of phenotypic variance (PVE = 80.0%). This overlap between models highlights the robustness of these loci. Conversely, qRPH8 was detected only by BLINK, and its inconsistent phenotypic association suggests a limited contribution to PHS resistance. Quantitative trait loci effects on pre-harvest sprouting within tested plants In rice genetic resources, GWAS identified three major QTLs— qRPH7 , qRPH8 , and qRPH11 —that exhibit additive cumulative effects on PHS resistance (Fig. 4 ). The lowest mean PHS incidence (4.5 ± 10.4%) was observed in Group I, which harbors all three QTLs, suggesting that the combination of these loci is highly effective in enhancing resistance. Among the three groups possessing two QTLs, Group III ( qRPH7 + qRPH8 ) and Group IV ( qRPH8 + qRPH11 ), both of which include qRPH8 , exhibited high PHS rates of 67.8 ± 27.1% and 57.1 ± 24.3%, respectively, suggesting that qRPH8 may negatively affect PHS resistance. In contrast, Group II ( qRPH7 and qRPH11 ) demonstrated a lower PHS incidence (16.1 ± 18.6%) and was classified as resistant; however, the wide phenotypic variance observed in this group suggests potential influence from genetic background or environmental factors. The effect of a single QTL was observed only in Group V, which contains qRPH7 alone. Since no genetic resource lines individually carried qRPH8 or qRPH11 , their single effects could not be evaluated. While qRPH7 exhibited the highest PVE, its solitary presence in Group V did not confer significant resistance. Identification of candidate genes within quantitative trait loci regions To identify candidate genes influencing PHS, ORFs within the three significant QTLs— qRPH7 , qRPH8 , and qRPH11 — were functionally annotated based on a comprehensive review. The analysis focused on ORFs annotated in the Rice Annotation Project database that are associated with PHS, general germination, seed dormancy, and ABA/GA signaling pathways (Table S2). Within the genomic region surrounding qRPH7 (± 150 kb), the presence of two major loci associated with PHS resistance— SDR4 and qPH7 —were confirmed (Fig. 5 ). Among the 167 rice genetic resources carrying qRPH7 , 56 (33.5%) possessed at least one of these two major loci, SDR4 or qPH7 (Fig. S4). Within this group, 14 carried only SDR4 , 3 carried only qPH7 , and 39 possessed both loci. Within the ± 150 kb genomic region surrounding qRPH11 , 52 ORFs were identified, among which OsPK1 —a gene known to regulate the balance between ABA and GA—was the only locus associated with PHS or seed germination (Fig. 5 b). In rice, OsPK1 is a metabolism-related gene that contributes to growth regulation and environmental adaptation (Zhang et al. 2012 ). By modulating the ABA/GA balance, OsPK1 integrates hormonal signaling and functions as a molecular link between stress responses and growth suppression. In contrast, no annotated ORFs with known functions associated with PHS, seed dormancy, or ABA/GA signaling were identified within the ± 150 kb region flanking qRPH8 . qRPH7 positively enhances PHS resistance by acting through two associated genes. In contrast, qRPH8 exhibits epistatic interactions that may hinder resistance rather than enhance it. This antagonistic effect complicates the functional interpretation of qRPH8 , particularly given its relatively ambiguous phenotypic expression. Synergistic and antagonistic effects of quantitative trait loci combinations on pre-harvest sprouting resistance in rice Among the 167 genetic resources carrying qRPH7 , four QTL combination types were classified based on the presence of SDR4, qPH7 , or both, with some combinations significantly associated with reduced PHS rates (Table 4 ). Genetic resources in types A, B, and C—each carrying either SDR4 , qPH7 , or both—consistently exhibited low PHS rates, indicating strong resistance. In contrast, type D, which possesses qRPH7 but lacks SDR4 and qPH7 , demonstrated a markedly broader distribution and higher mean PHS rates. This divergence suggests that qRPH7 alone is insufficient to confer stable resistance and highlights the significant individual and combined contributions of SDR4 and qPH7 in enhancing seed dormancy and suppressing PHS. In the Type D subtype, the roles of qRPH8 and qRPH11 were investigated to further dissect the genetic architecture underlying PHS resistance in the absence of SDR4 and qPH7 (Table 5 ). Among the four genotypic combinations evaluated (types a–d), types a and b, carrying qRPH11 , consistently exhibited lower PHS rates compared to those that lack this locus (types c and d). Type b, carrying qRPH11 alone, exhibited the lowest PHS rate (18.3 ± 19.5%) and was classified as degree 1 resistance, suggesting that qRPH11 positively contributes to resistance, either independently or in combination. Type c, which carries only qRPH8 , exhibited the highest PHS rate (67.8 ± 27.1%), suggesting that in certain genetic backgrounds, qRPH8 may function as an epistatic gene, suppressing the effects of other resistance loci or even promoting susceptibility. Duncan’s multiple range test results further support the significant contribution of qRPH11 to PHS resistance, with types a and b forming distinct statistical groups compared to those of types c and d. These findings suggest an additive effect of qRPH11 , while the apparent lack of beneficial effect from qRPH8 raises concerns about its utility in breeding programs and warrants further functional characterization. In this study, one genetic resource exhibited a PHS rate of 18.0% despite lacking the qRPH7 locus. This finding indicates the presence of alternative genetic factors contributing to PHS resistance independent of qRPH7 . Developing a segregating population from this genetic resource would facilitate further investigation of the underlying mechanisms. Figure 4 illustrates two genetic resources in Group I that appear as outliers, exhibiting high PHS rates of 49.2% and 49.8%, respectively. These genetic resources lacked SDR4 and qPH7 , despite carrying qRPH7 and qRPH11 , suggesting that the absence of SDR4 and qPH7 may have a greater effect on PHS susceptibility than the presence of qRPH7 and qRPH11 . Therefore, functional analysis incorporating SDR4 and qPH7 will be essential to elucidate the genetic interactions among these loci. Overall, these results highlight how qRPH7 , qRPH8 , and qRPH11 interact synergistically and antagonistically in modulating PHS resistance. To further contextualize these findings within the broader genetic framework, all 12 possible QTL–loci combinations were analyzed (Fig. 6 ), which revealed the complex genetic architecture underlying PHS resistance. Based on these observations, multi-locus combinations were then examined to determine how additive and epistatic interactions collectively shape PHS resistance. Complex genetic architecture of pre-harvest sprouting resistance In this study, PHS resistance was evaluated using 12 allelic combinations derived from three QTLs ( qRPH7, qRPH8 , and qRPH11 ) and two loci ( SDR4, qPH7 ) (Fig. 6 ). The results indicate that both the additive effects of individual QTLs and their genetic interactions are essential for determining PHS resistance. Strong resistance was observed in groups harboring all three QTLs (Groups 1–3) and in those carrying qRPH7 together with qRPH11 (Groups 4–6). In contrast, Groups 7 and 8, which had the same QTL combinations as Groups 1–3 and 4–6, respectively, but lacked SDR4 and qPH7 , exhibited lower average resistance and greater variation, indicating that the effect of qRPH7 depends on the presence of SDR4 and qPH7 . A comparison between Groups 8 and 10 further supports this finding: Group 10 ( qRPH7 alone) exhibited high susceptibility, while Group 8 ( qRPH7 + qRPH11 without SDR4 and qPH7 ) showed overall resistance, suggesting that qRPH11 acts additively to enhance the effect of qRPH7 . More specifically, Group 7 comprised three highly resistant genetic resources (≤ 20% PHS) and two susceptible (> 40%), while Group 8 included 83 genetic resources, of which 55 were highly resistant (≤ 20%), 17 moderately resistant (≤ 40%), and 11 susceptible. These findings suggest that in the absence of SDR4 and qPH7 , qRPH11 , in combination with qRPH7 , contributes to resistance. However, some genetic resources remain susceptible, indicating that minor QTLs or background genetic variation may also influence PHS. The most notable finding was the antagonistic epistasis of qRPH8 . Among combinations lacking SDR4 and qPH7 , groups carrying qRPH7 with qRPH8 or qRPH8 with qRPH11 (Groups 9, 11, 12) exhibited high PHS rates. In contrast, resistance was observed when all three QTLs ( qRPH7, qRPH8, qRPH11 ) were present (Groups 1–3, 7). These findings suggest that qRPH8 suppresses the effect of qRPH7 or qRPH11 when present individually, leading to susceptibility, but when all three QTLs are combined, this antagonistic effect is neutralized. Thus, qRPH8 may function as an antagonistic regulator, modulating the effects of other major QTLs rather than acting only as a minor contributor. Collectively, these findings indicate that PHS resistance is regulated by complex interactions among qRPH7–SDR4–qPH7, qRPH11 , and qRPH8 , rather than by a single major locus. Specifically, qRPH7 functions in an SDR4 - and qPH7 -dependent manner and is further enhanced by qRPH11 , while qRPH8 exerts antagonistic epistasis by suppressing or modifying the effects of the other loci. These findings highlight that PHS is a typical polygenic trait, governed by additive effects and complex interactions among multiple loci. Overall, our results show a complex genetic interplay among multiple loci contributing to PHS resistance in rice. The consistent effects of SDR4 , qPH7 , and qRPH11 suggest that combining these loci through marker-assisted selection could substantially enhance resistance. In contrast, the effects of qRPH8 are inconsistent or adverse, highlighting the need for careful interpretation of its role. Future studies should investigate potential epistatic interactions among these loci and account for environmental influences that may affect the phenotypic expression of resistance. In breeding, these findings provide a practical framework for improving PHS resistance in rice. We propose a targeted pyramiding strategy incorporating SDR4 , qPH7 , and qRPH11 to develop rice cultivars with enhanced PHS resistance. The inclusion of qRPH8 in breeding programs should be carefully considered, as its phenotypic effects are inconsistent. Using these validated loci in marker-assisted selection may accelerate the development of resilient varieties, particularly under humid and warm conditions that increase PHS risk. Moreover, integrating genotype-by-environment interaction analyses will be essential to ensure stable resistance across diverse cultivation settings. Conclusion This study elucidates the genetic basis of PHS resistance in rice through phenotypic evaluation and genome-wide association analysis of 182 genetic resources. Three major QTLs— qRPH7 , qRPH8 , and qRPH11 —were identified, with qRPH7 exhibiting the strongest association and harboring two previously reported dormancy-related loci, SDR4 and qPH7 . Beyond identifying individual loci, a comprehensive genetic model was constructed integrating qRPH7 , qRPH8, qRPH11, SDR4 , and qPH7 , which accounted for phenotypic variation across the entire panel. Analysis of genotypic combinations revealed that pyramiding qRPH7 with SDR4, qPH7 , and qRPH11 confers robust resistance, while qRPH8 exhibits antagonistic interactions that limit its utility in breeding. These findings indicate that PHS resistance is controlled by polygenic architecture rather than a single major locus, shaped by additive and epistatic interactions. In breeding, these findings establish a practical framework for improving resistance. Marker-assisted selection targeting qRPH7 in combination with SDR4, qPH7 , and qRPH11 provides an effective strategy to accelerate the development of resilient varieties, particularly under humid and warm conditions where PHS risk is high. Moreover, integrating genotype-by-environment interaction analyses is essential to ensure durable resistance across diverse cultivation settings. This study advances the conceptual understanding of complex stress-resistance traits by demonstrating how additive and antagonistic epistasis collectively influence phenotypic outcomes, thereby shifting the focus from single-locus mapping to a systems-level understanding of trait regulation. The validated loci and their combinations provide breeders with actionable targets for genetic pyramiding, thereby bridging fundamental genetics with breeding. Ultimately, these insights strengthen rice resilience and contribute to sustaining global grain yield and quality under changing climate conditions. Abbreviations PHS Pre-harvest sprouting GWAS Genome-wide association study QTLs Quantitative trait loci ABA Abscisic acid GA Gibberellins ROS Reactive oxygen species MAF Minor allele frequency BLINK Bayesian-information and linkage-disequilibrium iteratively nested keyway MLMM Multi-Locus mixed model LD Linkage disequilibrium BIC Bayesian information criterion QQ Quantile–quantile ORFs Open reading frames ANOVA One-way analysis of variance NGS Next-generation sequencing PCA Principal component analysis SNP Single-nucleotide polymorphism Declarations Author information Chang-Ju Lee and Tae-Heon Kim have contributed equally to this work. Author Contributions C.L.: Data curation, Validation, Investigation, Visualization, Writing-original draft, Funding. acquisition. T.K.: Methodology, Validation, Writing-review and editing, Funding acquisition. D.B.: Investigation, Visualization. J.G.: Investigation, Data curation. W.P.: Investigation, Datacuration. S.K.: Conceptualization, Methodology, Writing-review and editing, Funding acquisition. Funding This research was supported by the Regional Innovation System & Education (RISE) program through the Gyeongsangbuk-do RISE Center, funded by the Ministry of Education (MOE) and Gyeongsangbuk-do, Republic of Korea (2024-RISE-00-000). Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate Not applicable. Consent for publication : Not applicable. Competing interests The authors declare that they have no competing interests. References Ali F, Qanmber G, Li F, Wang Z (2022) Updated role of ABA in seed maturation, dormancy, and germination. Journal of Advanced Research 35:199-214. doi:10.1016/j.jare.2021.03.011 Baek J-S, Chung N-J (2014) Influence of rainfall during the ripening stage on pre-harvest sprouting, seed quality, and longevity of rice (Oryza sativa L.). Korean J Crop Sci 59:406-12 Benitez-Alfonso Y, Soanes BK, Zimba S, Sinanaj B, German L, Sharma V, Bohra A, Kolesnikova A, Dunn JA, Martin AC (2023) Enhancing climate change resilience in agricultural crops. Curr Biol 33:R1246-R61. doi:10.1016/j.cub.2023.10.028 Bentsink L, Jowett J, Hanhart CJ, Koornneef M (2006) Cloning of DOG1, a quantitative trait locus controlling seed dormancy in Arabidopsis. PNAS 103:17042-7. doi:10.1073/pnas.0607877103 Bewley JD (1997) Seed germination and dormancy. The plant cell 9:1055. doi:10.1105/tpc.9.7.1055 Chen W, Wang W, Lyu Y, Wu Y, Huang P, Hu S, Wei X, Jiao G, Sheng Z, Tang S (2021) OsVP1 activates Sdr4 expression to control rice seed dormancy via the ABA signaling pathway. The Crop Journal 9:68-78. doi:10.1016/j.cj.2020.06.005 Chenyin P, Yu W, Fenghou S, Yongbao S (2023) Review of the current research progress of seed germination inhibitors. Horticulturae 9:462. doi:htps://doi.org/10.3390/horticulturae9040462 de Mendiburu F (2023) agricolae: Statistical Procedures for Agricultural Research R package version 1.3-7 https://CRAN.R-project.org/package=agricolae Debeaujon I, Leon-Kloosterziel KM, Koornneef M (2000) Influence of the testa on seed dormancy, germination, and longevity in Arabidopsis. Plant Physiol 122:403-14. doi:10.1104/pp.122.2.403 Du L, Xu F, Fang J, Gao S, Tang J, Fang S, Wang H, Tong H, Zhang F, Chu J (2018) Endosperm sugar accumulation caused by mutation of PHS 8/ISA 1 leads to pre‐harvest sprouting in rice. The Plant Journal 95:545-56. doi:10.1111/tpj.13970 Finch‐Savage WE, Leubner‐Metzger G (2006) Seed dormancy and the control of germination. New Phytol 171:501-23. doi:10.1111/j.1469-8137.2006.01787.x Finkelstein R, Reeves W, Ariizumi T, Steber C (2008) Molecular aspects of seed dormancy. Annu Rev Plant Biol 59:387-415. doi:10.1146/annurev.arplant.59.032607.092740 Fujino K, Sekiguchi H, Matsuda Y, Sugimoto K, Ono K, Yano M (2008) Molecular identification of a major quantitative trait locus, qLTG3–1, controlling low-temperature germinability in rice. PNAS 105:12623-8. doi:10.1073/pnas.0805303105 Gu X-Y, Kianian SF, Foley ME (2004) Multiple loci and epistases control genetic variation for seed dormancy in weedy rice (Oryza sativa). Genetics 166:1503-16. doi:10.1534/genetics.166.3.1503 Gu X-Y, Liu T, Feng J, Suttle JC, Gibbons J (2010) The qSD12 underlying gene promotes abscisic acid accumulation in early developing seeds to induce primary dormancy in rice. Plant Mol Biol 73:97-104. doi:10.1007/s11103-009-9555-1 Gu X-Y, Turnipseed EB, Foley ME (2008) The qSD12 locus controls offspring tissue-imposed seed dormancy in rice. Genetics 179:2263-73. doi:10.1534/genetics.108.092007 Gu XY, Chen ZX, Foley ME (2003) Inheritance of seed dormancy in weedy rice. Crop Sci 43:835-43. doi:https://doi.org/10.2135/cropsci2003.8350 Huang M, Liu X, Zhou Y, Summers RM, Zhang Z (2019) BLINK: a package for the next level of genome-wide association studies with both individuals and markers in the millions. Gigascience 8:giy154. doi:10.1093/gigascience/giy154 Kang S, Shon J, Kim H-S, Kim S-J, Choi J-S, Park J-H, Yoon Y, Sim J, Yang W (2018) Analysis of genetic variation in pre-harvest sprouting at different cumulative temperatures after heading of rice. Korean J Crop Sci 63:8-17. doi:https://doi.org/10.7740/kjcs.2018.63.1.008 Kim K-W, Nawade B, Nam J, Chu S-H, Ha J, Park Y-J (2022) Development of an inclusive 580K SNP array and its application for genomic selection and genome-wide association studies in rice. Front Plant Sci 13:1036177. doi:10.3389/fpls.2022.1036177 Klupczyńska EA, Pawłowski TA (2021) Regulation of seed dormancy and germination mechanisms in a changing environment. Int J Mol Sci 22:1357. doi:10.3390/ijms22031357 Lan Y, Song Y, Liu M, Luo D (2024) Genome-wide identification, phylogenetic, structural and functional evolution of the core components of ABA signaling in plant species: A focus on rice. Planta 260:58. doi:10.1007/s00425-024-04475-2 Lee C-M, Park H-S, Baek M-K, Jeong O-Y, Seo J, Kim S-M (2023) QTL mapping and improvement of pre-harvest sprouting resistance using japonica weedy rice. Front Plant Sci 14:1194058. doi:10.3389/fpls.2023.1194058 Lee H, Lee Y, Hwang W, Jeong J, Yang S, Lee C, Choi M (2020) Investigation of Changes in Grain Quality and Physicochemical Properties of Rice According to the Temperature during the Ripening Stage and Preharvest Sprouting. Korean J Crop Sci 65:294-302. doi:https://doi.org/10.7740/kjcs.2020.65.4.294 Lee J-S, Chebotarov D, McNally KL, Pede V, Setiyono TD, Raquid R, Hyun W-J, Jeung J-U, Kohli A, Mo Y (2021) Novel sources of pre-harvest sprouting resistance for japonica rice improvement. Plants 10:1709. doi:10.3390/plants10081709 Li W, Xu L, Bai X, Xing Y (2011) Quantitative trait loci for seed dormancy in rice. Euphytica 178:427-35. doi:10.1007/s10681-010-0327-4 Liu Y, Fang J, Xu F, Chu J, Yan C, Schläppi MR, Wang Y, Chu C (2014) Expression patterns of ABA and GA metabolism genes and hormone levels during rice seed development and imbibition: a comparison of dormant and non-dormant rice cultivars. J Genet Genomics 41:327-38. doi:10.1016/j.jgg.2014.04.004 Lobell DB, Schlenker W, Costa-Roberts J (2011) Climate trends and global crop production since 1980. Science 333:616-20. doi:10.1126/science.1204531 Mizuno Y, Yamanouchi U, Hoshino T, Nonoue Y, Nagata K, Fukuoka S, Ando T, Yano M, Sugimoto K (2018) Genetic dissection of pre-harvest sprouting resistance in an upland rice cultivar. Breed Sci 68:200-9. doi:10.1270/jsbbs.17062 National Institute of Agricultural Sciences (2022) Fertilizer recommendation for crops. National Institute of Agricultural Sciences, Wanju. Née G, Xiang Y, Soppe WJ (2017) The release of dormancy, a wake-up call for seeds to germinate. Curr Opin Plant Biol 35:8-14. doi:10.1016/j.pbi.2016.09.002 Penfield S (2017) Seed dormancy and germination. Curr Biol 27:R874-R8 Roberts E (1961) Dormancy in rice seed II: The influence of covering structures. J Exp Bot 12:430-45 Rural Development Administration (2012) Manual for standard evaluation method in agricultural experiment and research. RDA, Jeonju Segura V, Vilhjálmsson BJ, Platt A, Korte A, Seren Ü, Long Q, Nordborg M (2012) An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat Genet 44:825-30. doi:10.1038/ng.2314 Sohn S-I, Pandian S, Kumar TS, Zoclanclounon YAB, Muthuramalingam P, Shilpha J, Satish L, Ramesh M (2021) Seed dormancy and pre-harvest sprouting in rice—an updated overview. Int J Mol Sci 22:11804. doi:10.3390/ijms222111804 Sugimoto K, Takeuchi Y, Ebana K, Miyao A, Hirochika H, Hara N, Ishiyama K, Kobayashi M, Ban Y, Hattori T (2010) Molecular cloning of Sdr4, a regulator involved in seed dormancy and domestication of rice. PNAS 107:5792-7. doi:10.1073/pnas.0911965107 Turner SD (2018) qqman: an R package for visualizing GWAS results using QQ and manhattan plots. JOSS 3(25):731. doi:https://doi.org/10.21105/joss.00731 Tyler L, Thomas SG, Hu J, Dill A, Alonso JM, Ecker JR, Sun T-p (2004) DELLA proteins and gibberellin-regulated seed germination and floral development in Arabidopsis. Plant Physiol 135:1008-19. doi:10.1104/pp.104.039578 Umezawa T, Nakashima K, Miyakawa T, Kuromori T, Tanokura M, Shinozaki K, Yamaguchi-Shinozaki K (2010) Molecular basis of the core regulatory network in ABA responses: sensing, signaling and transport. Plant Cell Physiol 51:1821-39. doi:10.1093/pcp/pcq156 Wang Q, Lin Q, Wu T, Duan E, Huang Y, Yang C, Mou C, Lan J, Zhou C, Xie K (2020) OsDOG1L-3 regulates seed dormancy through the abscisic acid pathway in rice. Plant Sci 298:110570. doi:10.1016/j.plantsci.2020.110570 Ye H, Feng J, Zhang L, Zhang J, Mispan MS, Cao Z, Beighley DH, Yang J, Gu X-Y (2015) Map-based cloning of seed dormancy1-2 identified a gibberellin synthesis gene regulating the development of endosperm-imposed dormancy in rice. Plant Physiol 169:2152-65. doi:10.1104/pp.15.01202 Zhang Y, Feng F, He C (2012) Downregulation of OsPK1 contributes to oxidative stress and the variations in ABA/GA balance in rice. Plant Mol Biol Rep 30:1006-13. doi:10.1007/s11105-011-0386-2 Zhu D, Qian Z, Wei H, Guo B, Xu K, Dai Q, Zhang H, Huo Z (2019) The effects of field pre-harvest sprouting on the morphological structure and physicochemical properties of rice (Oryza sativa L.) starch. Food Chem 278:10-6. doi:10.1016/j.foodchem.2018.11.017 Additional Declarations No competing interests reported. Supplementary Files Additionalfile1Supplementaryfile.docx Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8227857","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":558078453,"identity":"bf0212c4-fddc-4264-a3a2-67e96d059373","order_by":0,"name":"Chang-Ju Lee","email":"","orcid":"","institution":"Kyungpook National University","correspondingAuthor":false,"prefix":"","firstName":"Chang-Ju","middleName":"","lastName":"Lee","suffix":""},{"id":558078455,"identity":"4916ae5e-70ff-4714-9255-a808f26a0b32","order_by":1,"name":"Tae-Heon Kim","email":"","orcid":"","institution":"Kyungpook National 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16:59:27","extension":"html","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":171980,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8227857/v1/4b6cb8500335054ccc2240cd.html"},{"id":98348772,"identity":"b7eaf43f-8920-4737-9b6e-5c018566c789","added_by":"auto","created_at":"2025-12-16 19:54:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75172,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of PHS rates in 182 rice genetic resources, with the median and PHS rate of the reference cultivars indicated. PHS, pre-harvest sprouting; CK_R, Joun; CK_S, Jopyeong\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8227857/v1/a6c6bd27bd78af0345ab083b.png"},{"id":98348775,"identity":"3e68c394-2fcc-4e34-8442-18144679a794","added_by":"auto","created_at":"2025-12-16 19:54:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":183915,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative images of pre-harvest sprouting severity at classification degrees 1 to 9in 182 rice genetic resources\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8227857/v1/d441548c42f35829ebd95b4f.png"},{"id":98439252,"identity":"c2f53916-cfa7-49f6-ac08-43c9be76f2bf","added_by":"auto","created_at":"2025-12-17 17:01:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":192360,"visible":true,"origin":"","legend":"\u003cp\u003eGWAS of PHS in 182 rice genetic resources.\u003cstrong\u003e \u003c/strong\u003eaQQ and Manhattan plot generated using BLINK. The green horizontal line represents the genome-wide significance threshold. b QQ and Manhattan plot generated using MLMM, as in a. GWAS, genome-wide association study; PHS, pre-harvest sprouting; BLINK, Bayesian information and linkage disequilibrium iteratively nested keyway; MLMM, multi-locus mixed model; QQ, quantile–quantile\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8227857/v1/1a15f813e53d29f8186cbba8.png"},{"id":98348777,"identity":"33d6e626-5c44-4048-a218-8466873731b9","added_by":"auto","created_at":"2025-12-16 19:54:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":56943,"visible":true,"origin":"","legend":"\u003cp\u003ePre-harvest sprouting (PHS) rate of rice genetic resources grouped based on allelic combinations at three QTLs (\u003cem\u003eqRPH7, qRPH8, qRPH11\u003c/em\u003e). Different letters denote significant differences among groups based on Duncan’s multiple range test\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8227857/v1/451d118bade739c144210d0b.png"},{"id":98348781,"identity":"6813ce63-aa94-4ff5-bb17-1b783cf80c6c","added_by":"auto","created_at":"2025-12-16 19:54:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":228806,"visible":true,"origin":"","legend":"\u003cp\u003ea \u003cem\u003eqRPH7\u003c/em\u003eon chromosome 7 (red arrow) and surrounding genes, with previously reported PHS-related genes, \u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e, highlighted in red. b \u003cem\u003eqRPH11\u003c/em\u003e on chromosome 11 (red arrow) and surrounding genes, with previously reported ABA/GA-related gene, \u003cem\u003eOsPK1\u003c/em\u003e, highlighted in red. PHS, pre-harvest sprouting; ABA, Abscisic acid; GA, gibberellins\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8227857/v1/6d54914baba1a785e50f75e0.png"},{"id":98439789,"identity":"7318baba-f5db-4fa4-b351-183379eca2b2","added_by":"auto","created_at":"2025-12-17 17:02:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":193614,"visible":true,"origin":"","legend":"\u003cp\u003eClassification of 182 rice genetic resources into 12 allelic combinations based on two previously reported loci and three QTLs\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8227857/v1/554bb3c11b2614601df245a7.png"},{"id":98774925,"identity":"aba90c64-5844-4fcd-b38e-aa9810eebe4b","added_by":"auto","created_at":"2025-12-22 12:17:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2148858,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8227857/v1/b6c6fa2f-ce90-49bf-a60b-88580cbd98b8.pdf"},{"id":98348773,"identity":"6e3c11ff-a55f-4e1f-bdb2-37e43ec35b9e","added_by":"auto","created_at":"2025-12-16 19:54:57","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":325632,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-8227857/v1/688cd28369f2b585439be88b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A New Genetic Architecture for PHS Resistance in Rice: Deciphering the Epistatic Interactions of Three Major QTLs","fulltext":[{"header":"Background","content":"\u003cp\u003eClimate change has intensified unpredictable abiotic stresses, including heat waves and erratic rainfall, resulting in major crop yield losses, necessitating urgent intervention from agricultural research institutions (Benitez-Alfonso et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lobell et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In rice, these climatic shifts promote pre-harvest sprouting (PHS) under hot, humid conditions during grain filling, leading to substantial production losses (Baek et al. 2014; Sohn et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). PHS, characterized by premature seed germination, reduces yield, lowers milling recovery, and degrades grain quality, ultimately posing a critical threat to farmer income and global food security (Lee et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In temperate \u003cem\u003ejaponica\u003c/em\u003e rice-growing regions, including Japan, Korea, and California (USA), PHS is projected to incur significant cumulative losses of USD 8\u0026ndash;10\u0026nbsp;billion under extreme conditions and USD 4\u0026ndash;5\u0026nbsp;billion under milder scenarios over the next decade (Lee et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Mitigating these risks requires the use of diverse rice genetic resources to advance resistance breeding. Specifically, identifying resistant genetic resources and uncovering quantitative trait loci (QTLs) and candidate genes offer promising breeding strategies to minimize the adverse effects of climate change on rice production and ensure a stable food supply (Li et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lobell et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mizuno et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, most studies rely on biparental populations or focus narrowly on a few major-effect loci, such as \u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e (Lee et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sugimoto et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Although these approaches have yielded valuable insights, they may not fully capture the full extent of natural genetic variation present in diverse rice genetic resources, potentially limiting the development of durable resistance to PHS.\u003c/p\u003e\u003cp\u003ePHS resistance is closely linked to seed dormancy, a mechanism that prevents premature germination under unfavorable conditions (Bewley \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Seed germination is regulated by both environmental factors, including temperature, moisture, and oxygen availability, and intrinsic hormonal signals (Klupczyńska et al. 2021; N\u0026eacute;e et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Penfield \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Abscisic acid (ABA) induces dormancy, whereas gibberellins (GA) stimulate germination, with the ABA\u0026ndash;GA balance largely determining seed fate (Finch-Savage et al. 2006; Finkelstein et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). ABA signaling operates through the PYR/PYL\u0026ndash;PP2C\u0026ndash;SnRK2 module to maintain dormancy, while GA induces germination by promoting DELLA protein degradation (Ali et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lan et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tyler et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Umezawa et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Dormancy release occurs through processes such as after-ripening or dry storage, which reduces ABA sensitivity, enhances GA responsiveness, and is accompanied by reactive oxygen species (ROS) accumulation and chromatin modifications (Liu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Structural and biochemical properties of the seed coat, including inhibitory compounds and physical barriers to water or oxygen, further contribute to dormancy maintenance and PHS resistance (Debeaujon et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The husk, pericarp, and testa restrict water uptake, oxygen diffusion, and embryo expansion, closely linking these barriers to PHS resistance in rice (Roberts \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1961\u003c/span\u003e). Additionally, the seed coat contains germination-inhibitory compounds, such as phenolics and alkaloids, which reinforce dormancy through both physical and chemical inhibition (Chenyin et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNumerous genes and QTLs linked to seed dormancy and PHS resistance have been identified in rice. Among them, \u003cem\u003eSeed dormancy 4\u003c/em\u003e (\u003cem\u003eSDR4\u003c/em\u003e) is a key regulator that integrates ABA and GA signaling to reinforce dormancy and shows strong associations with PHS resistance across diverse genetic resources (Sugimoto et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). More recently, a major QTL for PHS resistance, \u003cem\u003eqPH7\u003c/em\u003e, was identified using a recombinant inbred line population derived from Korean weedy rice, and fine-mapping localized it to a 210-kb interval (23.575\u0026ndash;23.785 Mb) on chromosome 7 (Lee et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBeyond these loci, additional genetic determinants have been identified. For instance, \u003cem\u003eRc (qSD7-1)\u003c/em\u003e, which controls seed coat pigmentation, is consistently linked to dormancy and PHS resistance (Gu et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Gu et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). \u003cem\u003eqSD12\u003c/em\u003e, mapped in multiple biparental populations, contributes to natural variation in dormancy by promoting ABA accumulation in early developing seeds to induce primary dormancy (Gu et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Gu et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Carbohydrate metabolism-related loci such as \u003cem\u003ePHS8/ISA1\u003c/em\u003e further highlight the role of endosperm composition in PHS regulation (Du et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Regulatory genes involved in hormonal signaling also contribute to dormancy control. \u003cem\u003eOsVP1\u003c/em\u003e functions as a central transcription factor coordinating ABA-mediated seed maturation and dormancy, while \u003cem\u003eqSD1\u003c/em\u003e-\u003cem\u003e2\u003c/em\u003e/\u003cem\u003eOsGA20ox2\u003c/em\u003e encodes a GA biosynthesis enzyme that modulates GA levels and the dormancy-germination balance (Chen et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ye et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The \u003cem\u003eOsDOG1L\u003c/em\u003e gene family maintains dormancy through mechanisms similar to those of the Arabidopsis \u003cem\u003eDOG1\u003c/em\u003e pathway (Bentsink et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The major QTL \u003cem\u003eqLTG3-1\u003c/em\u003e enhances low-temperature germinability by weakening embryonic tissues\u0026mdash;improving germination under suboptimal temperatures (Fujino et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Collectively, these studies show the polygenic complexity of PHS resistance in rice, integrating hormonal regulation, metabolic pathways, and structural seed traits that govern dormancy and germination. However, despite considerable progress in elucidating the genetic control of PHS resistance, studies largely focus on biparental populations or a few major-effect loci, limiting relevance to the broader genetic diversity of rice genetic resources. Furthermore, the polygenic and environmentally sensitive nature of PHS resistance, driven by the interplay of seed dormancy, hormone regulation, and structural traits, suggests that key components of its genetic architecture remain unresolved.\u003c/p\u003e\u003cp\u003eTherefore, this study aims to investigate PHS resistance by conducting a genome-wide association study (GWAS) on 182 rice genetic resources representing multiple ecotypes to capture natural allelic variation beyond the resolution of conventional linkage mapping. This study identifies novel QTL through GWAS and systematically examines their genetic interactions with previously reported loci such as \u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e, thereby clarifying the complex architecture underlying PHS resistance. By highlighting allelic combinations with practical breeding value, the findings could provide mechanistic insights and actionable resources for marker-assisted selection, supporting the development of rice cultivars with stable PHS resistance under diverse climatic conditions.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePlant materials\u003c/h2\u003e\u003cp\u003eA panel of 182 rice genetic resources was used for phenotypic and genotypic evaluation to identify genomic regions associated with PHS resistance. The set of genetic resources comprised 106 \u003cem\u003eJaponica\u003c/em\u003e, 35 \u003cem\u003eIndica\u003c/em\u003e, 33 \u003cem\u003eAdmixed\u003c/em\u003e, 6 \u003cem\u003eAus\u003c/em\u003e, and 2 \u003cem\u003eAromatic\u003c/em\u003e types. Of the 182 rice genetic resources, 116 were obtained from the National Institute of Crop Science, and the remaining were sourced from the National Agrobiodiversity Center.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eField management\u003c/h3\u003e\n\u003cp\u003eThe experiment was conducted in 2024 at the Experimental Farm, College of Agriculture and Life Sciences, Kyungpook National University. Seedlings were transplanted at a spacing of 30 \u0026times; 15 cm, with one seedling per hill. Fertilizer was applied at rates of 9.0\u0026ndash;4.5\u0026ndash;5.7 kg/10a (N\u0026ndash;P₂O₅\u0026ndash;K₂O), following national crop fertilizer guidelines (National Institute of Agricultural Sciences \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003ePre-harvest sprouting evaluation\u003c/h3\u003e\n\u003cp\u003ePHS resistance was evaluated by recording the heading date of each rice genetic resource and harvesting the main panicle 40 days after heading, corresponding to an accumulated growing degree day value of 1,000\u0026deg;C (Kang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Only seed samples with normal germination rates above 70% under standard conditions were used to ensure phenotypic reliability. Three biological replicates were included per genetic resource. Panicles were fully wrapped in tissue paper to facilitate moisture absorption and placed in stainless steel trays (325 \u0026times; 265 \u0026times; 63 mm). Samples were incubated in a growth chamber at 25\u0026deg;C and 100% relative humidity for 7 days (Rural Development Administration \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). After incubation, the germination rate was calculated as the percentage of germinated seeds among the total number of filled seeds per panicle. The mean value of three replicates was used to determine the final PHS rate.\u003c/p\u003e\u003cp\u003eSeeds were considered germinated when the coleoptile visibly emerged from the hull, while unfilled or defective grains were excluded. Based on germination rates, PHS resistance was classified into five categories: degree 1 (\u0026le;\u0026thinsp;20%), degree 3 (21\u0026ndash;40%), degree 5 (40\u0026ndash;60%), degree 7 (60\u0026ndash;80%), and degree 9 (81% \u0026le;) (Rural Development Administration \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the classification criteria. Rice genetic resources with degrees 1 or 3 were considered resistant, while those with degrees 5, 7, or 9 were considered susceptible.\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\u003eCriteria for the classification of evaluating PHS severity\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDegree\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObservation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTolerance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHighly Tolerant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u0026ndash;40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTolerant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41\u0026ndash;60%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerately Tolerant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61\u0026ndash;80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSusceptible\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81% \u0026le;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHighly Susceptible\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\u003eMarker information used for the identification of PHS-related genes, \u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e\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=\"char\" char=\".\" 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\u003eQTL/gene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChr\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMarker\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSequence (5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRestriction enzyme\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSDR4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSDR4-SacII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF: GTGTCGGTGGTGGTCGTC\u003c/p\u003e\u003cp\u003eR: CGAGAACCCCTTGCATGTCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eSacII\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(Sugimoto et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eqPH7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePH_1_13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF: ATCTGTATGACTTAAGGCACG\u003c/p\u003e\u003cp\u003eR: ACTAAACTGTGCTAAATTGCG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eDdeI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(Lee et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eGenotyping data collection and processing\u003c/h3\u003e\n\u003cp\u003eSingle-nucleotide polymorphism (SNP) genotyping was performed using the 580K Axiom Rice Genotyping Chip (580K_KNU chip), developed from eight genomic data sources (Kim et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Genomic DNA samples were hybridized to the array and scanned on the GeneTitan\u0026reg; platform, Affymetrix, Santa Clara, CA, USA. SNP calling was conducted with Genotyping Console v4.2, Affymetrix, Santa Clara, CA, USA, and further refined using the SNPolisher R package v3.0. SNPs were aligned to the IRGSP-1.0 (\u003cem\u003ejaponica\u003c/em\u003e), MH63RS2 (\u003cem\u003eindica\u003c/em\u003e), and \u003cem\u003eOryza rufipogon\u003c/em\u003e reference genomes. High-quality SNP markers were selected for GWAS. They were filtered using the following criteria: minor allele frequency (MAF)\u0026thinsp;\u0026gt;\u0026thinsp;0.05, missing rate\u0026thinsp;\u0026lt;\u0026thinsp;0.02, heterozygosity rate\u0026thinsp;\u0026lt;\u0026thinsp;0.05, removal of non-polymorphic SNPs, and sequencing depth\u0026thinsp;\u0026gt;\u0026thinsp;10\u0026times;. After filtering, 289,569 SNPs were retained for GWAS.\u003c/p\u003e\n\u003ch3\u003eGenome-wide association study\u003c/h3\u003e\n\u003cp\u003eGWAS was performed using two multi-locus models\u0026mdash;Bayesian information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) and Multi-Locus Mixed Model (MLMM) implemented in the GAPIT package in R. Before association testing, population structure was assessed by principal component analysis (PCA), with the first three principal components included as covariates alongside a kinship matrix. MLMM iteratively incorporates significant markers as covariates, simultaneously detecting multiple loci contributing to phenotypic variation (Segura et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). BLINK filters redundant markers using linkage disequilibrium (LD) and applies the Bayesian Information Criterion (BIC) for model selection, enhancing statistical power while controlling false positives (Huang et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For multiple testing correction, a Bonferroni adjustment at α\u0026thinsp;=\u0026thinsp;0.05 was applied, yielding a genome-wide significance threshold of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1.726 \u0026times; 10⁻⁷ (\u0026minus;\u0026thinsp;log₁₀ \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.76). GWAS results were visualized using Manhattan and quantile\u0026ndash;quantile (QQ) plots generated with the \u0026ldquo;qqman\u0026rdquo; R package (Turner \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Significant SNPs were annotated by assigning open reading frames (ORFs) within a\u0026thinsp;\u0026plusmn;\u0026thinsp;150-kb window around each SNP as candidate genes.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eVariation in PHS among rice genetic resources was evaluated using one-way analysis of variance (ANOVA) in R statistical software, version 4.3.1; R Core Team, 2023. When ANOVA results were significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), group comparisons were performed using Duncan\u0026rsquo;s multiple range test through the \u0026ldquo;agricolae\u0026rdquo; R package (de Mendiburu \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) to identify statistically significant differences among genetic resources.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eHaplotype analysis\u003c/h3\u003e\n\u003cp\u003eTwo-locus haplotype analysis of \u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e was performed using PCR amplification with Solg\u0026trade; e-Taq DNA Polymerase (SolGent, Daejeon, Korea). For the SDR4-SacII marker (Sugimoto et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), thermal cycling conditions included an initial denaturation at 94\u0026deg;C for 5 min; 35 cycles of 94\u0026deg;C for 20 s, 55\u0026deg;C for 25 s, and 72\u0026deg;C for 1 min; with a final extension at 72\u0026deg;C for 5 min. For the PH_1_13 marker (Lee et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the same conditions were used except for the annealing step at 54\u0026deg;C for 45 s. PCR products for \u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e were digested with the restriction enzymes \u003cem\u003eSac\u003c/em\u003eII and \u003cem\u003eDde\u003c/em\u003eI, respectively. Digested products were separated on a 1.5% agarose gel and stained with SYBR\u0026trade; Safe DNA Gel Stain (Thermo Fisher Scientific, Waltham, MA, USA). Bands were visualized using the DAVINCH Gel Imager CG-550 (DAVINCH-K, Seoul, Korea).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePre-harvest sprouting phenotypic variation in diverse rice genetic resources\u003c/h2\u003e\u003cp\u003ePHS was evaluated in 182 rice genetic resources using the predefined criteria (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). PHS rates ranged from 0% to 95.7%, with an average of 20.8\u0026thinsp;\u0026plusmn;\u0026thinsp;25.2% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The distribution was right-skewed (skewness\u0026thinsp;=\u0026thinsp;1.36; kurtosis\u0026thinsp;=\u0026thinsp;0.87). The median value was 8.5%, indicating that most rice genetic resources exhibited relatively low sprouting levels. The resistant control cultivar, Joun, showed an average PHS rate of 13.61\u0026thinsp;\u0026plusmn;\u0026thinsp;4.23%, whereas the susceptible control, Jopyeong, exhibited a significantly higher rate of 44.23\u0026thinsp;\u0026plusmn;\u0026thinsp;5.30%.\u003c/p\u003e\u003cp\u003ePHS rates were classified into five degrees according to the established evaluation criteria (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among the 182 genetic resources, 20.9% were classified as susceptible (degree 5, 7, or 9), while the remaining 79.1% were classified as resistant (degree 1 or 3). The resistant and susceptible control cultivars corresponded to degree 1 and degree 5, respectively. To evaluate PHS variation among ecotypes within the population, the genetic resources were grouped into five ecotype categories and evaluated for PHS levels based on predefined criteria (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). \u003cem\u003eJaponica\u003c/em\u003e (group I) and \u003cem\u003eIndica\u003c/em\u003e (group II) showed the widest PHS variation, with Indica showing a lower median value than that of Japonica. In contrast, \u003cem\u003eAdmixed\u003c/em\u003e (group III) predominantly exhibited low PHS rates.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eGenotypic profiling and population structure of rice genetic resources\u003c/h2\u003e\u003cp\u003eOverall, 289,569 SNPs were analyzed across 182 resources using an SNP chip and next-generation sequencing (NGS) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). SNP distribution varied across the 12 rice chromosomes, with an average of 24,131 SNPs per chromosome. Chromosome 1 showed the highest number (37,852), whereas chromosome 12 had the lowest (16,551). SNP density also differed among chromosomes, averaging 1.32 SNPs/Mb (Fig. S2). Chromosome 12 exhibited the highest density (1.66 SNPs/Mb), while chromosomes 2 and 3 showed the lowest (1.09 SNPs/Mb).\u003c/p\u003e\u003cp\u003eA phylogenetic analysis of 182 rice genetic resources was performed using genome-wide SNP data to assess genetic relationships and population structure (Fig. S3a, b). The neighbor-joining tree analysis revealed five distinct ecotype groups based on genetic similarity: (i) \u003cem\u003eJaponica\u003c/em\u003e, (ii) \u003cem\u003eIndica\u003c/em\u003e, (iii) \u003cem\u003eAdmixed\u003c/em\u003e, (iv) \u003cem\u003eAus\u003c/em\u003e, and (v) \u003cem\u003eAromatic\u003c/em\u003e (Fig. S3a). These groups reflect unique genetic backgrounds and ecological adaptations, with pronounced divergence between \u003cem\u003eJaponica\u003c/em\u003e and \u003cem\u003eIndica\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eGenetic structure was further validated through PCA (Fig. S3b), which supported the same five-group clustering pattern. The PCA results showed clear genetic differentiation among ecotypes, providing complementary evidence to the phylogenetic analysis. This grouping establishes the basis for interpreting phenotypic variation in traits such as PHS resistance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eGenome-wide association analysis for pre-harvest sprouting resistance\u003c/h2\u003e\u003cp\u003eGWAS were performed using genotypic and PHS rate data from 182 rice genetic resources to identify SNPs associated with PHS resistance. The GAPIT package in R was used to implement the BLINK and MLMM models. BLINK identified significant SNPs on chromosomes 7, 8, and 11, while MLMM detected lead SNPs on chromosomes 7 and 11 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The SNPs on chromosomes 7 and 11 were detected at identical loci in both models, whereas the SNP on chromosome 8 was unique to BLINK. These lead SNPs on chromosomes 7, 8, and 11 were considered QTLs and designated as \u003cem\u003eqRPH7, qRPH8, and qRPH11\u003c/em\u003e, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\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\u003eSummary of QTLs associated with PHS identified through GWAS\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\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=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQTLs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChr\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePos (bp)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-log\u003csub\u003e10\u003c/sub\u003e(\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMAF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePVE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eqRPH7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAX-115841304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23,799,472\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e43.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBLINK\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eqRPH8\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAX-275910905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,882,518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e32.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eqRPH11\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAX-115796079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,374,434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e13.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eqRPH7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAX-115841304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23,799,472\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMLMM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eqRPH11\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAX-115796079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,374,434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e33.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eChr, chromosome; MAF, minor allele frequency; PVE: phenotype variance explained, QTL, quantitative trait loci; GWAS, genome-wide association study; PHS, pre-harvest sprouting; BLINK, Bayesian information and linkage disequilibrium iteratively nested keyway; MLMM, multi-locus mixed model; SNP, single nucleotide polymorphism\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eqRPH7\u003c/em\u003e showed strong statistical significance in both BLINK and MLMM, with \u0026ndash;log₁₀(\u003cem\u003ep\u003c/em\u003e) values of 8.01 and 9.04 and phenotypic variance explained (PVE) values of 43.69% and 80.00%, respectively. \u003cem\u003eqRPH11\u003c/em\u003e showed \u0026ndash;log₁₀(\u003cem\u003ep\u003c/em\u003e) values of 7.52 (BLINK) and 6.72 (MLMM), which were marginally below the significance threshold but remained close, with PVE values of 13.07% and 33.89%, respectively. \u003cem\u003eqRPH8\u003c/em\u003e was detected exclusively by BLINK, with a \u0026ndash;log₁₀(\u003cem\u003ep\u003c/em\u003e) value of 8.07 and a PVE of 32.84%. Despite differences between the models, both BLINK and MLMM consistently identified three QTLs significantly associated with PHS resistance.\u003c/p\u003e\u003cp\u003eTo assess the effects of the identified QTLs, the 182 rice genetic resources plant materials were classified into five groups (I-V) based on their QTL combinations, and PHS was confirmed. Group I included all three QTLs\u0026mdash;\u003cem\u003eqRPH7\u003c/em\u003e, \u003cem\u003eqRPH8\u003c/em\u003e, and \u003cem\u003eqRPH11\u003c/em\u003e. Groups II\u0026ndash;IV each contained two QTLs: Group II possessed \u003cem\u003eqRPH7\u003c/em\u003e and \u003cem\u003eqRPH11\u003c/em\u003e; Group III had \u003cem\u003eqRPH7\u003c/em\u003e and \u003cem\u003eqRPH8\u003c/em\u003e; and Group IV included \u003cem\u003eqRPH8\u003c/em\u003e and \u003cem\u003eqRPH11\u003c/em\u003e. Group V included genetic resources carrying only \u003cem\u003eqRPH7\u003c/em\u003e. Group I showed the lowest mean PHS rate, and Groups I and II exhibited significantly lower PHS rates than those of other groups.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eFunctional validation and genotypic effect of identified quantitative trait loci\u003c/h2\u003e\u003cp\u003eTo identify candidate genes related to PHS resistance, ORFs located within \u0026plusmn;\u0026thinsp;150 kb of the three QTLs (\u003cem\u003eqRPH7, qRPH8\u003c/em\u003e, and \u003cem\u003eqRPH11\u003c/em\u003e) identified through GWAS were examined. The analysis prioritized genes annotated in the Rice Annotation Project database and functionally related to PHS, germination, seed dormancy, and ABA/GA signaling pathways (Table S2). Within the \u003cem\u003eqRPH7\u003c/em\u003e interval, two major loci linked to PHS resistance\u0026mdash;\u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e\u0026mdash;were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Additionally, the \u003cem\u003eqRPH11\u003c/em\u003e region harbored \u003cem\u003eOsPK1\u003c/em\u003e, a gene implicated in hormonal regulation related to PHS resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eIn contrast, no annotated ORFs with known roles in PHS, seed dormancy, or ABA/GA signaling were identified within the \u0026plusmn;\u0026thinsp;150 kb region flanking qRPH8. Thus, leaving the functional candidate gene(s) underlying this QTL unresolved. Among the 182 rice genetic resources, 167 carrying \u003cem\u003eqRPH7\u003c/em\u003e were selected to analyze the genotypes of the candidate loci \u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e (Fig. S4). Consequently, 53 genetic resources carried \u003cem\u003eSDR4\u003c/em\u003e, 42 carried \u003cem\u003eqPH7\u003c/em\u003e, and 39 possessed both genes. In contrast, 14 carried \u003cem\u003eSDR4\u003c/em\u003e alone, three carried only \u003cem\u003eqPH7\u003c/em\u003e, and 111 carried neither locus.\u003c/p\u003e\u003cp\u003eAmong the 167 rice genetic resources excluding group IV (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e), those carrying \u003cem\u003eqRPH7\u003c/em\u003e were classified into four combination types (A\u0026ndash;D) based on the presence or absence of \u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e, and their PHS rates were compared (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Type A (\u003cem\u003eSDR4\u0026thinsp;+\u0026thinsp;qPH7\u003c/em\u003e) exhibited the lowest mean PHS rate (2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5%), followed by Type C (\u003cem\u003eqPH7\u003c/em\u003e, 3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5%), Type B (\u003cem\u003eSDR4\u003c/em\u003e, 6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6%), and Type D (neither locus), which had the highest rate at 24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;24.7%. The overall mean PHS rate was 17.5\u0026thinsp;\u0026plusmn;\u0026thinsp;22.7%. According to PHS classification criteria, Types A, B, C, and the overall mean were categorized as degree 1 (\u0026le;\u0026thinsp;20%), whereas Type D corresponded to degree 3 (21\u0026ndash;40%).\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\u003ePHS rates based on \u003cem\u003eqPH7\u003c/em\u003e and \u003cem\u003eSDR4\u003c/em\u003e combinations in rice genetic resources carrying \u003cem\u003eqRPH7\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotypic combination type of \u003cem\u003eqRPH7\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo. of plants\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePHS (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDegree\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eqRPH7\u003c/em\u003e\u003c/p\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA. \u003cem\u003eSDR4\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eqPH7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB. \u003cem\u003eSDR4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC. \u003cem\u003eqPH7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD. \u003cem\u003eNone\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;24.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e17.5\u0026thinsp;\u0026plusmn;\u0026thinsp;22.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\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\u003eThe effects of \u003cem\u003eqRPH8\u003c/em\u003e and \u003cem\u003eqRPH11\u003c/em\u003e were evaluated in 111 rice genetic resources belonging to the D subtype of \u003cem\u003eqRPH7\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Based on the presence or absence of \u003cem\u003eqRPH8\u003c/em\u003e and \u003cem\u003eqRPH11\u003c/em\u003e, these resources were further classified into four types (a\u0026ndash;d), and their PHS rates were compared. Among these groups, type a (\u003cem\u003eqRPH11\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eqRPH8\u003c/em\u003e) showed a mean PHS rate of 20.6\u0026thinsp;\u0026plusmn;\u0026thinsp;26.4%, type b (\u003cem\u003eqRPH11\u003c/em\u003e) exhibited 18.3\u0026thinsp;\u0026plusmn;\u0026thinsp;19.5%, type c (\u003cem\u003eqRPH8\u003c/em\u003e) recorded a markedly higher rate of 67.8\u0026thinsp;\u0026plusmn;\u0026thinsp;27.1%, and type d (\u003cem\u003enone\u003c/em\u003e) showed 46.0\u0026thinsp;\u0026plusmn;\u0026thinsp;26.9%. The overall average PHS rate was 24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;24.7%, corresponding to degree 3 (21\u0026ndash;40%). Based on the PHS classification criteria, type b was categorized as degree 1, type a as degree 3, type d as degree 5, and type c as degree 7. Duncan\u0026rsquo;s multiple range test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) revealed significant differences in PHS rates among types: types c and d were grouped as \u0026ldquo;a\u0026rdquo;, while types a and b were grouped as \u0026ldquo;b\u0026rdquo;, indicating that groups carrying \u003cem\u003eqRPH11\u003c/em\u003e exhibited significantly lower PHS rates.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePre-harvest sprouting (PHS) rates based on combinations of \u003cem\u003eqRPH11\u003c/em\u003e and \u003cem\u003eqRPH8\u003c/em\u003e in the subtype of D carrying \u003cem\u003eqRPH7\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubtype of D carrying \u003cem\u003eqRPH7\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo. of plants\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePHS (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDegree\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ea. \u003cem\u003eqRPH11\u0026thinsp;+\u0026thinsp;qRPH8\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.6\u0026thinsp;\u0026plusmn;\u0026thinsp;26.4\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eb. \u003cem\u003eqRPH11\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.3\u0026thinsp;\u0026plusmn;\u0026thinsp;19.5\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ec. \u003cem\u003eqRPH8\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67.8\u0026thinsp;\u0026plusmn;\u0026thinsp;27.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ed. \u003cem\u003eNone\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.0\u0026thinsp;\u0026plusmn;\u0026thinsp;26.9\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;24.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eIn total, 111 rice genetic resources classified as type D (\u003cem\u003eqRPH7\u003c/em\u003e\u003csub\u003e\u003cem\u003eNone\u003c/em\u003e\u003c/sub\u003e) were further divided into four subtypes based on the genotypic combinations of \u003cem\u003eqRPH11\u003c/em\u003e and \u003cem\u003eqRPH8\u003c/em\u003e, and their PHS rates were compared. According to Duncan\u0026rsquo;s multiple range test, significant differences among subtypes are indicated by different letters\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eIntegrative modeling of quantitative trait loci-based resistance mechanisms in rice\u003c/h2\u003e\u003cp\u003eThe effects of different QTL combinations on PHS resistance were evaluated. Strong resistance was observed in genetic resources carrying \u003cem\u003eqRPH7, qRPH8\u003c/em\u003e, and \u003cem\u003eqRPH11\u003c/em\u003e simultaneously (groups 1\u0026ndash;3), which showed low PHS rates of 2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5%, 4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4%, and 5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4%, respectively. Similarly, genetic resources harboring \u003cem\u003eqRPH7\u003c/em\u003e and \u003cem\u003eqRPH11\u003c/em\u003e (groups 4\u0026ndash;6) demonstrated very strong resistance, with PHS rates of 1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7%, 6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5%, and 0.2%, respectively. In contrast, group 7, which was comparable to groups 1\u0026ndash;3 but lacked either \u003cem\u003eSDR4\u003c/em\u003e or \u003cem\u003eqPH7\u003c/em\u003e, and group 8, which was comparable to groups 4\u0026ndash;6 but lacked \u003cem\u003eSDR4\u003c/em\u003e or \u003cem\u003eqPH7\u003c/em\u003e, exhibited moderate resistance, with PHS rates of 20.6\u0026thinsp;\u0026plusmn;\u0026thinsp;26.4% and 18.3\u0026thinsp;\u0026plusmn;\u0026thinsp;19.5%, respectively. However, both groups exhibited considerable phenotypic variation. By comparison, group 9 (\u003cem\u003eqRPH7\u003c/em\u003e and \u003cem\u003eqRPH8\u003c/em\u003e) and group 10 (\u003cem\u003eqRPH7\u003c/em\u003e alone) exhibited high PHS rates of 67.8\u0026thinsp;\u0026plusmn;\u0026thinsp;27.1% and 46.0\u0026thinsp;\u0026plusmn;\u0026thinsp;26.9%, respectively. Similarly, group 11 (\u003cem\u003eqRPH8\u003c/em\u003e and \u003cem\u003eqRPH11\u003c/em\u003e) showed a high PHS rate of 55.6\u0026thinsp;\u0026plusmn;\u0026thinsp;31.6%. In group 12, the additional of \u003cem\u003eqRPH7\u003c/em\u003e along with \u003cem\u003eqRPH8\u003c/em\u003e and \u003cem\u003eqRPH11\u003c/em\u003e did not reduce the PHS rate, which remained high at 57.4\u0026thinsp;\u0026plusmn;\u0026thinsp;23.8%.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eRice (\u003cem\u003eOryza sativa\u003c/em\u003e) is a critical global crop, but its productivity and quality are highly susceptible to environmental threats such as PHS. While previous studies identify loci associated with PHS resistance, most have focused on specific cultivars, leaving the broader molecular mechanisms unresolved. To address this gap, a GWAS was conducted, which identified three major QTLs\u0026mdash;\u003cem\u003eqRPH7\u003c/em\u003e, \u003cem\u003eqRPH8\u003c/em\u003e, and \u003cem\u003eqRPH11\u003c/em\u003e\u0026mdash;significantly associated with PHS resistance. Of these, the \u003cem\u003eqRPH7\u003c/em\u003e locus exhibited the highest statistical significance and explained the largest proportion of phenotypic variation. Based on these findings, genotypic interactions were further investigated, and a functional model of resistance expression was developed, which forms the basis for the following discussion.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eQuantitative trait loci identification through genome-wide association study\u003c/h2\u003e\u003cp\u003eIn this study, genomic regions associated with PHS resistance in rice were identified using two complementary multi-locus models, BLINK and MLMM (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These models, each with distinct strengths, were used to enhance both the precision and comprehensiveness of QTL detection. Based on the BIC, the BLINK model effectively reduces redundancy by accounting for LD among markers and selecting the most informative markers, thereby increasing statistical power while minimizing false positives (Huang et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In contrast, the MLMM model incorporates significant markers as cofactors in a stepwise manner, allowing it to capture polygenic effects and control confounding factors, which improves accuracy and reproducibility in QTL detection (Segura et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Using the BLINK model, three putative QTLs\u0026mdash;\u003cem\u003eqRPH7, qRPH8\u003c/em\u003e, and \u003cem\u003eqRPH11\u003c/em\u003e\u0026mdash;were detected. Among these, \u003cem\u003eqRPH7\u003c/em\u003e and \u003cem\u003eqRPH11\u003c/em\u003e were also identified by MLMM, with \u003cem\u003eqRPH7\u003c/em\u003e accounting for a high proportion of phenotypic variance (PVE\u0026thinsp;=\u0026thinsp;80.0%). This overlap between models highlights the robustness of these loci. Conversely, \u003cem\u003eqRPH8\u003c/em\u003e was detected only by BLINK, and its inconsistent phenotypic association suggests a limited contribution to PHS resistance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eQuantitative trait loci effects on pre-harvest sprouting within tested plants\u003c/h2\u003e\u003cp\u003eIn rice genetic resources, GWAS identified three major QTLs\u0026mdash;\u003cem\u003eqRPH7\u003c/em\u003e, \u003cem\u003eqRPH8\u003c/em\u003e, and \u003cem\u003eqRPH11\u003c/em\u003e\u0026mdash;that exhibit additive cumulative effects on PHS resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The lowest mean PHS incidence (4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4%) was observed in Group I, which harbors all three QTLs, suggesting that the combination of these loci is highly effective in enhancing resistance. Among the three groups possessing two QTLs, Group III (\u003cem\u003eqRPH7\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eqRPH8\u003c/em\u003e) and Group IV (\u003cem\u003eqRPH8\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eqRPH11\u003c/em\u003e), both of which include \u003cem\u003eqRPH8\u003c/em\u003e, exhibited high PHS rates of 67.8\u0026thinsp;\u0026plusmn;\u0026thinsp;27.1% and 57.1\u0026thinsp;\u0026plusmn;\u0026thinsp;24.3%, respectively, suggesting that \u003cem\u003eqRPH8\u003c/em\u003e may negatively affect PHS resistance. In contrast, Group II (\u003cem\u003eqRPH7\u003c/em\u003e and \u003cem\u003eqRPH11\u003c/em\u003e) demonstrated a lower PHS incidence (16.1\u0026thinsp;\u0026plusmn;\u0026thinsp;18.6%) and was classified as resistant; however, the wide phenotypic variance observed in this group suggests potential influence from genetic background or environmental factors. The effect of a single QTL was observed only in Group V, which contains \u003cem\u003eqRPH7\u003c/em\u003e alone. Since no genetic resource lines individually carried \u003cem\u003eqRPH8\u003c/em\u003e or \u003cem\u003eqRPH11\u003c/em\u003e, their single effects could not be evaluated. While \u003cem\u003eqRPH7\u003c/em\u003e exhibited the highest PVE, its solitary presence in Group V did not confer significant resistance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of candidate genes within quantitative trait loci regions\u003c/h2\u003e\u003cp\u003eTo identify candidate genes influencing PHS, ORFs within the three significant QTLs\u0026mdash;\u003cem\u003eqRPH7\u003c/em\u003e, \u003cem\u003eqRPH8\u003c/em\u003e, and \u003cem\u003eqRPH11\u003c/em\u003e\u0026mdash; were functionally annotated based on a comprehensive review. The analysis focused on ORFs annotated in the Rice Annotation Project database that are associated with PHS, general germination, seed dormancy, and ABA/GA signaling pathways (Table S2). Within the genomic region surrounding \u003cem\u003eqRPH7\u003c/em\u003e (\u0026plusmn;\u0026thinsp;150 kb), the presence of two major loci associated with PHS resistance\u0026mdash;\u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e\u0026mdash;were confirmed (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Among the 167 rice genetic resources carrying \u003cem\u003eqRPH7\u003c/em\u003e, 56 (33.5%) possessed at least one of these two major loci, \u003cem\u003eSDR4\u003c/em\u003e or \u003cem\u003eqPH7\u003c/em\u003e (Fig. S4). Within this group, 14 carried only \u003cem\u003eSDR4\u003c/em\u003e, 3 carried only \u003cem\u003eqPH7\u003c/em\u003e, and 39 possessed both loci.\u003c/p\u003e\u003cp\u003eWithin the \u0026plusmn;\u0026thinsp;150 kb genomic region surrounding \u003cem\u003eqRPH11\u003c/em\u003e, 52 ORFs were identified, among which \u003cem\u003eOsPK1\u003c/em\u003e\u0026mdash;a gene known to regulate the balance between ABA and GA\u0026mdash;was the only locus associated with PHS or seed germination (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). In rice, \u003cem\u003eOsPK1\u003c/em\u003e is a metabolism-related gene that contributes to growth regulation and environmental adaptation (Zhang et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). By modulating the ABA/GA balance, \u003cem\u003eOsPK1\u003c/em\u003e integrates hormonal signaling and functions as a molecular link between stress responses and growth suppression. In contrast, no annotated ORFs with known functions associated with PHS, seed dormancy, or ABA/GA signaling were identified within the \u0026plusmn;\u0026thinsp;150 kb region flanking \u003cem\u003eqRPH8\u003c/em\u003e. \u003cem\u003eqRPH7\u003c/em\u003e positively enhances PHS resistance by acting through two associated genes. In contrast, \u003cem\u003eqRPH8\u003c/em\u003e exhibits epistatic interactions that may hinder resistance rather than enhance it. This antagonistic effect complicates the functional interpretation of \u003cem\u003eqRPH8\u003c/em\u003e, particularly given its relatively ambiguous phenotypic expression.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eSynergistic and antagonistic effects of quantitative trait loci combinations on pre-harvest sprouting resistance in rice\u003c/h2\u003e\u003cp\u003eAmong the 167 genetic resources carrying \u003cem\u003eqRPH7\u003c/em\u003e, four QTL combination types were classified based on the presence of \u003cem\u003eSDR4, qPH7\u003c/em\u003e, or both, with some combinations significantly associated with reduced PHS rates (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Genetic resources in types A, B, and C\u0026mdash;each carrying either \u003cem\u003eSDR4\u003c/em\u003e, \u003cem\u003eqPH7\u003c/em\u003e, or both\u0026mdash;consistently exhibited low PHS rates, indicating strong resistance. In contrast, type D, which possesses \u003cem\u003eqRPH7\u003c/em\u003e but lacks \u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e, demonstrated a markedly broader distribution and higher mean PHS rates. This divergence suggests that \u003cem\u003eqRPH7\u003c/em\u003e alone is insufficient to confer stable resistance and highlights the significant individual and combined contributions of \u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e in enhancing seed dormancy and suppressing PHS.\u003c/p\u003e\u003cp\u003eIn the Type D subtype, the roles of \u003cem\u003eqRPH8\u003c/em\u003e and \u003cem\u003eqRPH11\u003c/em\u003e were investigated to further dissect the genetic architecture underlying PHS resistance in the absence of \u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Among the four genotypic combinations evaluated (types a\u0026ndash;d), types a and b, carrying \u003cem\u003eqRPH11\u003c/em\u003e, consistently exhibited lower PHS rates compared to those that lack this locus (types c and d). Type b, carrying \u003cem\u003eqRPH11\u003c/em\u003e alone, exhibited the lowest PHS rate (18.3\u0026thinsp;\u0026plusmn;\u0026thinsp;19.5%) and was classified as degree 1 resistance, suggesting that \u003cem\u003eqRPH11\u003c/em\u003e positively contributes to resistance, either independently or in combination. Type c, which carries only \u003cem\u003eqRPH8\u003c/em\u003e, exhibited the highest PHS rate (67.8\u0026thinsp;\u0026plusmn;\u0026thinsp;27.1%), suggesting that in certain genetic backgrounds, \u003cem\u003eqRPH8\u003c/em\u003e may function as an epistatic gene, suppressing the effects of other resistance loci or even promoting susceptibility. Duncan\u0026rsquo;s multiple range test results further support the significant contribution of \u003cem\u003eqRPH11\u003c/em\u003e to PHS resistance, with types a and b forming distinct statistical groups compared to those of types c and d. These findings suggest an additive effect of \u003cem\u003eqRPH11\u003c/em\u003e, while the apparent lack of beneficial effect from \u003cem\u003eqRPH8\u003c/em\u003e raises concerns about its utility in breeding programs and warrants further functional characterization.\u003c/p\u003e\u003cp\u003eIn this study, one genetic resource exhibited a PHS rate of 18.0% despite lacking the \u003cem\u003eqRPH7\u003c/em\u003e locus. This finding indicates the presence of alternative genetic factors contributing to PHS resistance independent of \u003cem\u003eqRPH7\u003c/em\u003e. Developing a segregating population from this genetic resource would facilitate further investigation of the underlying mechanisms. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates two genetic resources in Group I that appear as outliers, exhibiting high PHS rates of 49.2% and 49.8%, respectively. These genetic resources lacked \u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e, despite carrying \u003cem\u003eqRPH7\u003c/em\u003e and \u003cem\u003eqRPH11\u003c/em\u003e, suggesting that the absence of \u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e may have a greater effect on PHS susceptibility than the presence of \u003cem\u003eqRPH7\u003c/em\u003e and \u003cem\u003eqRPH11\u003c/em\u003e. Therefore, functional analysis incorporating \u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e will be essential to elucidate the genetic interactions among these loci.\u003c/p\u003e\u003cp\u003eOverall, these results highlight how \u003cem\u003eqRPH7\u003c/em\u003e, \u003cem\u003eqRPH8\u003c/em\u003e, and \u003cem\u003eqRPH11\u003c/em\u003e interact synergistically and antagonistically in modulating PHS resistance. To further contextualize these findings within the broader genetic framework, all 12 possible QTL\u0026ndash;loci combinations were analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003e), which revealed the complex genetic architecture underlying PHS resistance. Based on these observations, multi-locus combinations were then examined to determine how additive and epistatic interactions collectively shape PHS resistance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eComplex genetic architecture of pre-harvest sprouting resistance\u003c/h2\u003e\u003cp\u003eIn this study, PHS resistance was evaluated using 12 allelic combinations derived from three QTLs (\u003cem\u003eqRPH7, qRPH8\u003c/em\u003e, and \u003cem\u003eqRPH11\u003c/em\u003e) and two loci (\u003cem\u003eSDR4, qPH7\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The results indicate that both the additive effects of individual QTLs and their genetic interactions are essential for determining PHS resistance. Strong resistance was observed in groups harboring all three QTLs (Groups 1\u0026ndash;3) and in those carrying \u003cem\u003eqRPH7\u003c/em\u003e together with \u003cem\u003eqRPH11\u003c/em\u003e (Groups 4\u0026ndash;6). In contrast, Groups 7 and 8, which had the same QTL combinations as Groups 1\u0026ndash;3 and 4\u0026ndash;6, respectively, but lacked \u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e, exhibited lower average resistance and greater variation, indicating that the effect of \u003cem\u003eqRPH7\u003c/em\u003e depends on the presence of \u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e. A comparison between Groups 8 and 10 further supports this finding: Group 10 (\u003cem\u003eqRPH7\u003c/em\u003e alone) exhibited high susceptibility, while Group 8 (\u003cem\u003eqRPH7\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eqRPH11\u003c/em\u003e without \u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e) showed overall resistance, suggesting that \u003cem\u003eqRPH11\u003c/em\u003e acts additively to enhance the effect of \u003cem\u003eqRPH7\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eMore specifically, Group 7 comprised three highly resistant genetic resources (\u0026le;\u0026thinsp;20% PHS) and two susceptible (\u0026gt;\u0026thinsp;40%), while Group 8 included 83 genetic resources, of which 55 were highly resistant (\u0026le;\u0026thinsp;20%), 17 moderately resistant (\u0026le;\u0026thinsp;40%), and 11 susceptible. These findings suggest that in the absence of \u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e, \u003cem\u003eqRPH11\u003c/em\u003e, in combination with \u003cem\u003eqRPH7\u003c/em\u003e, contributes to resistance. However, some genetic resources remain susceptible, indicating that minor QTLs or background genetic variation may also influence PHS. The most notable finding was the antagonistic epistasis of \u003cem\u003eqRPH8\u003c/em\u003e. Among combinations lacking \u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e, groups carrying \u003cem\u003eqRPH7\u003c/em\u003e with \u003cem\u003eqRPH8\u003c/em\u003e or \u003cem\u003eqRPH8\u003c/em\u003e with \u003cem\u003eqRPH11\u003c/em\u003e (Groups 9, 11, 12) exhibited high PHS rates. In contrast, resistance was observed when all three QTLs (\u003cem\u003eqRPH7, qRPH8, qRPH11\u003c/em\u003e) were present (Groups 1\u0026ndash;3, 7). These findings suggest that \u003cem\u003eqRPH8\u003c/em\u003e suppresses the effect of \u003cem\u003eqRPH7\u003c/em\u003e or \u003cem\u003eqRPH11\u003c/em\u003e when present individually, leading to susceptibility, but when all three QTLs are combined, this antagonistic effect is neutralized. Thus, \u003cem\u003eqRPH8\u003c/em\u003e may function as an antagonistic regulator, modulating the effects of other major QTLs rather than acting only as a minor contributor.\u003c/p\u003e\u003cp\u003eCollectively, these findings indicate that PHS resistance is regulated by complex interactions among \u003cem\u003eqRPH7\u0026ndash;SDR4\u0026ndash;qPH7, qRPH11\u003c/em\u003e, and \u003cem\u003eqRPH8\u003c/em\u003e, rather than by a single major locus. Specifically, \u003cem\u003eqRPH7\u003c/em\u003e functions in an \u003cem\u003eSDR4\u003c/em\u003e- and \u003cem\u003eqPH7\u003c/em\u003e-dependent manner and is further enhanced by \u003cem\u003eqRPH11\u003c/em\u003e, while \u003cem\u003eqRPH8\u003c/em\u003e exerts antagonistic epistasis by suppressing or modifying the effects of the other loci. These findings highlight that PHS is a typical polygenic trait, governed by additive effects and complex interactions among multiple loci.\u003c/p\u003e\u003cp\u003eOverall, our results show a complex genetic interplay among multiple loci contributing to PHS resistance in rice. The consistent effects of \u003cem\u003eSDR4\u003c/em\u003e, \u003cem\u003eqPH7\u003c/em\u003e, and \u003cem\u003eqRPH11\u003c/em\u003e suggest that combining these loci through marker-assisted selection could substantially enhance resistance. In contrast, the effects of \u003cem\u003eqRPH8\u003c/em\u003e are inconsistent or adverse, highlighting the need for careful interpretation of its role. Future studies should investigate potential epistatic interactions among these loci and account for environmental influences that may affect the phenotypic expression of resistance.\u003c/p\u003e\u003cp\u003eIn breeding, these findings provide a practical framework for improving PHS resistance in rice. We propose a targeted pyramiding strategy incorporating \u003cem\u003eSDR4\u003c/em\u003e, \u003cem\u003eqPH7\u003c/em\u003e, and \u003cem\u003eqRPH11\u003c/em\u003e to develop rice cultivars with enhanced PHS resistance. The inclusion of \u003cem\u003eqRPH8\u003c/em\u003e in breeding programs should be carefully considered, as its phenotypic effects are inconsistent. Using these validated loci in marker-assisted selection may accelerate the development of resilient varieties, particularly under humid and warm conditions that increase PHS risk. Moreover, integrating genotype-by-environment interaction analyses will be essential to ensure stable resistance across diverse cultivation settings.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study elucidates the genetic basis of PHS resistance in rice through phenotypic evaluation and genome-wide association analysis of 182 genetic resources. Three major QTLs\u0026mdash;\u003cem\u003eqRPH7\u003c/em\u003e, \u003cem\u003eqRPH8\u003c/em\u003e, and \u003cem\u003eqRPH11\u003c/em\u003e\u0026mdash;were identified, with \u003cem\u003eqRPH7\u003c/em\u003e exhibiting the strongest association and harboring two previously reported dormancy-related loci, \u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eBeyond identifying individual loci, a comprehensive genetic model was constructed integrating \u003cem\u003eqRPH7\u003c/em\u003e, \u003cem\u003eqRPH8, qRPH11, SDR4\u003c/em\u003e, and \u003cem\u003eqPH7\u003c/em\u003e, which accounted for phenotypic variation across the entire panel. Analysis of genotypic combinations revealed that pyramiding \u003cem\u003eqRPH7\u003c/em\u003e with \u003cem\u003eSDR4, qPH7\u003c/em\u003e, and \u003cem\u003eqRPH11\u003c/em\u003e confers robust resistance, while \u003cem\u003eqRPH8\u003c/em\u003e exhibits antagonistic interactions that limit its utility in breeding. These findings indicate that PHS resistance is controlled by polygenic architecture rather than a single major locus, shaped by additive and epistatic interactions.\u003c/p\u003e\u003cp\u003eIn breeding, these findings establish a practical framework for improving resistance. Marker-assisted selection targeting \u003cem\u003eqRPH7\u003c/em\u003e in combination with \u003cem\u003eSDR4, qPH7\u003c/em\u003e, and \u003cem\u003eqRPH11\u003c/em\u003e provides an effective strategy to accelerate the development of resilient varieties, particularly under humid and warm conditions where PHS risk is high. Moreover, integrating genotype-by-environment interaction analyses is essential to ensure durable resistance across diverse cultivation settings.\u003c/p\u003e\u003cp\u003eThis study advances the conceptual understanding of complex stress-resistance traits by demonstrating how additive and antagonistic epistasis collectively influence phenotypic outcomes, thereby shifting the focus from single-locus mapping to a systems-level understanding of trait regulation. The validated loci and their combinations provide breeders with actionable targets for genetic pyramiding, thereby bridging fundamental genetics with breeding. Ultimately, these insights strengthen rice resilience and contribute to sustaining global grain yield and quality under changing climate conditions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePHS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePre-harvest sprouting\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGenome-wide association study\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eQTLs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eQuantitative trait loci\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eABA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAbscisic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGibberellins\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReactive oxygen species\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMAF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMinor allele frequency\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBLINK\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBayesian-information and linkage-disequilibrium iteratively nested keyway\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMLMM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMulti-Locus mixed model\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLinkage disequilibrium\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBIC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBayesian information criterion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eQQ\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eQuantile\u0026ndash;quantile\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eORFs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOpen reading frames\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eANOVA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOne-way analysis of variance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNGS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNext-generation sequencing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrincipal component analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSingle-nucleotide polymorphism\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChang-Ju Lee and Tae-Heon Kim have contributed equally to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.L.: Data curation, Validation, Investigation, Visualization, Writing-original draft, Funding. acquisition. T.K.: Methodology, Validation, Writing-review and editing, Funding acquisition. D.B.: Investigation, Visualization. J.G.: Investigation, Data curation. W.P.: Investigation, Datacuration. S.K.: Conceptualization, Methodology, Writing-review and editing, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Regional Innovation System \u0026amp; Education (RISE) program through the Gyeongsangbuk-do RISE Center, funded by the Ministry of Education (MOE) and Gyeongsangbuk-do, Republic of Korea (2024-RISE-00-000).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAli F, Qanmber G, Li F, Wang Z (2022) Updated role of ABA in seed maturation, dormancy, and germination. Journal of Advanced Research 35:199-214. doi:10.1016/j.jare.2021.03.011\u003c/li\u003e\n \u003cli\u003eBaek J-S, Chung N-J (2014) Influence of rainfall during the ripening stage on pre-harvest sprouting, seed quality, and longevity of rice (Oryza sativa L.). Korean J Crop Sci 59:406-12\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBenitez-Alfonso Y, Soanes BK, Zimba S, Sinanaj B, German L, Sharma V, Bohra A, Kolesnikova A, Dunn JA, Martin AC (2023) Enhancing climate change resilience in agricultural crops. Curr Biol 33:R1246-R61. doi:10.1016/j.cub.2023.10.028\u003c/li\u003e\n \u003cli\u003eBentsink L, Jowett J, Hanhart CJ, Koornneef M (2006) Cloning of DOG1, a quantitative trait locus controlling seed dormancy in Arabidopsis. PNAS 103:17042-7. doi:10.1073/pnas.0607877103\u003c/li\u003e\n \u003cli\u003eBewley JD (1997) Seed germination and dormancy. The plant cell 9:1055. doi:10.1105/tpc.9.7.1055\u003c/li\u003e\n \u003cli\u003eChen W, Wang W, Lyu Y, Wu Y, Huang P, Hu S, Wei X, Jiao G, Sheng Z, Tang S (2021) OsVP1 activates Sdr4 expression to control rice seed dormancy via the ABA signaling pathway. The Crop Journal 9:68-78. doi:10.1016/j.cj.2020.06.005\u003c/li\u003e\n \u003cli\u003eChenyin P, Yu W, Fenghou S, Yongbao S (2023) Review of the current research progress of seed germination inhibitors. Horticulturae 9:462. doi:htps://doi.org/10.3390/horticulturae9040462\u003c/li\u003e\n \u003cli\u003ede Mendiburu F (2023) agricolae: Statistical Procedures for Agricultural Research R package version 1.3-7 https://CRAN.R-project.org/package=agricolae\u003c/li\u003e\n \u003cli\u003eDebeaujon I, Leon-Kloosterziel KM, Koornneef M (2000) Influence of the testa on seed dormancy, germination, and longevity in Arabidopsis. Plant Physiol 122:403-14. doi:10.1104/pp.122.2.403\u003c/li\u003e\n \u003cli\u003eDu L, Xu F, Fang J, Gao S, Tang J, Fang S, Wang H, Tong H, Zhang F, Chu J (2018) Endosperm sugar accumulation caused by mutation of PHS 8/ISA 1 leads to pre‐harvest sprouting in rice. The Plant Journal 95:545-56. doi:10.1111/tpj.13970\u003c/li\u003e\n \u003cli\u003eFinch‐Savage WE, Leubner‐Metzger G (2006) Seed dormancy and the control of germination. New Phytol 171:501-23. doi:10.1111/j.1469-8137.2006.01787.x\u003c/li\u003e\n \u003cli\u003eFinkelstein R, Reeves W, Ariizumi T, Steber C (2008) Molecular aspects of seed dormancy. Annu Rev Plant Biol 59:387-415. doi:10.1146/annurev.arplant.59.032607.092740\u003c/li\u003e\n \u003cli\u003eFujino K, Sekiguchi H, Matsuda Y, Sugimoto K, Ono K, Yano M (2008) Molecular identification of a major quantitative trait locus, qLTG3\u0026ndash;1, controlling low-temperature germinability in rice. PNAS 105:12623-8. doi:10.1073/pnas.0805303105\u003c/li\u003e\n \u003cli\u003eGu X-Y, Kianian SF, Foley ME (2004) Multiple loci and epistases control genetic variation for seed dormancy in weedy rice (Oryza sativa). Genetics 166:1503-16. doi:10.1534/genetics.166.3.1503\u003c/li\u003e\n \u003cli\u003eGu X-Y, Liu T, Feng J, Suttle JC, Gibbons J (2010) The qSD12 underlying gene promotes abscisic acid accumulation in early developing seeds to induce primary dormancy in rice. Plant Mol Biol 73:97-104. doi:10.1007/s11103-009-9555-1\u003c/li\u003e\n \u003cli\u003eGu X-Y, Turnipseed EB, Foley ME (2008) The qSD12 locus controls offspring tissue-imposed seed dormancy in rice. Genetics 179:2263-73. doi:10.1534/genetics.108.092007\u003c/li\u003e\n \u003cli\u003eGu XY, Chen ZX, Foley ME (2003) Inheritance of seed dormancy in weedy rice. Crop Sci 43:835-43. doi:https://doi.org/10.2135/cropsci2003.8350\u003c/li\u003e\n \u003cli\u003eHuang M, Liu X, Zhou Y, Summers RM, Zhang Z (2019) BLINK: a package for the next level of genome-wide association studies with both individuals and markers in the millions. Gigascience 8:giy154. doi:10.1093/gigascience/giy154\u003c/li\u003e\n \u003cli\u003eKang S, Shon J, Kim H-S, Kim S-J, Choi J-S, Park J-H, Yoon Y, Sim J, Yang W (2018) Analysis of genetic variation in pre-harvest sprouting at different cumulative temperatures after heading of rice. Korean J Crop Sci 63:8-17. doi:https://doi.org/10.7740/kjcs.2018.63.1.008\u003c/li\u003e\n \u003cli\u003eKim K-W, Nawade B, Nam J, Chu S-H, Ha J, Park Y-J (2022) Development of an inclusive 580K SNP array and its application for genomic selection and genome-wide association studies in rice. Front Plant Sci 13:1036177. doi:10.3389/fpls.2022.1036177\u003c/li\u003e\n \u003cli\u003eKlupczyńska EA, Pawłowski TA (2021) Regulation of seed dormancy and germination mechanisms in a changing environment. Int J Mol Sci 22:1357. doi:10.3390/ijms22031357\u003c/li\u003e\n \u003cli\u003eLan Y, Song Y, Liu M, Luo D (2024) Genome-wide identification, phylogenetic, structural and functional evolution of the core components of ABA signaling in plant species: A focus on rice. Planta 260:58. doi:10.1007/s00425-024-04475-2\u003c/li\u003e\n \u003cli\u003eLee C-M, Park H-S, Baek M-K, Jeong O-Y, Seo J, Kim S-M (2023) QTL mapping and improvement of pre-harvest sprouting resistance using japonica weedy rice. Front Plant Sci 14:1194058. doi:10.3389/fpls.2023.1194058\u003c/li\u003e\n \u003cli\u003eLee H, Lee Y, Hwang W, Jeong J, Yang S, Lee C, Choi M (2020) Investigation of Changes in Grain Quality and Physicochemical Properties of Rice According to the Temperature during the Ripening Stage and Preharvest Sprouting. Korean J Crop Sci 65:294-302. doi:https://doi.org/10.7740/kjcs.2020.65.4.294\u003c/li\u003e\n \u003cli\u003eLee J-S, Chebotarov D, McNally KL, Pede V, Setiyono TD, Raquid R, Hyun W-J, Jeung J-U, Kohli A, Mo Y (2021) Novel sources of pre-harvest sprouting resistance for japonica rice improvement. Plants 10:1709. doi:10.3390/plants10081709\u003c/li\u003e\n \u003cli\u003eLi W, Xu L, Bai X, Xing Y (2011) Quantitative trait loci for seed dormancy in rice. Euphytica 178:427-35. doi:10.1007/s10681-010-0327-4\u003c/li\u003e\n \u003cli\u003eLiu Y, Fang J, Xu F, Chu J, Yan C, Schl\u0026auml;ppi MR, Wang Y, Chu C (2014) Expression patterns of ABA and GA metabolism genes and hormone levels during rice seed development and imbibition: a comparison of dormant and non-dormant rice cultivars. J Genet Genomics 41:327-38. doi:10.1016/j.jgg.2014.04.004\u003c/li\u003e\n \u003cli\u003eLobell DB, Schlenker W, Costa-Roberts J (2011) Climate trends and global crop production since 1980. Science 333:616-20. doi:10.1126/science.1204531\u003c/li\u003e\n \u003cli\u003eMizuno Y, Yamanouchi U, Hoshino T, Nonoue Y, Nagata K, Fukuoka S, Ando T, Yano M, Sugimoto K (2018) Genetic dissection of pre-harvest sprouting resistance in an upland rice cultivar. Breed Sci 68:200-9. doi:10.1270/jsbbs.17062\u003c/li\u003e\n \u003cli\u003eNational Institute of Agricultural Sciences (2022) Fertilizer recommendation for crops. National Institute of Agricultural Sciences, Wanju.\u003c/li\u003e\n \u003cli\u003eN\u0026eacute;e G, Xiang Y, Soppe WJ (2017) The release of dormancy, a wake-up call for seeds to germinate. Curr Opin Plant Biol 35:8-14. doi:10.1016/j.pbi.2016.09.002\u003c/li\u003e\n \u003cli\u003ePenfield S (2017) Seed dormancy and germination. Curr Biol 27:R874-R8\u003c/li\u003e\n \u003cli\u003eRoberts E (1961) Dormancy in rice seed II: The influence of covering structures. J Exp Bot 12:430-45\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRural Development Administration (2012) Manual for standard evaluation method in agricultural experiment and research. RDA, Jeonju\u003c/li\u003e\n \u003cli\u003eSegura V, Vilhj\u0026aacute;lmsson BJ, Platt A, Korte A, Seren \u0026Uuml;, Long Q, Nordborg M (2012) An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat Genet 44:825-30. doi:10.1038/ng.2314\u003c/li\u003e\n \u003cli\u003eSohn S-I, Pandian S, Kumar TS, Zoclanclounon YAB, Muthuramalingam P, Shilpha J, Satish L, Ramesh M (2021) Seed dormancy and pre-harvest sprouting in rice\u0026mdash;an updated overview. Int J Mol Sci 22:11804. doi:10.3390/ijms222111804\u003c/li\u003e\n \u003cli\u003eSugimoto K, Takeuchi Y, Ebana K, Miyao A, Hirochika H, Hara N, Ishiyama K, Kobayashi M, Ban Y, Hattori T (2010) Molecular cloning of Sdr4, a regulator involved in seed dormancy and domestication of rice. PNAS 107:5792-7. doi:10.1073/pnas.0911965107\u003c/li\u003e\n \u003cli\u003eTurner SD (2018) qqman: an R package for visualizing GWAS results using QQ and manhattan plots. JOSS 3(25):731. doi:https://doi.org/10.21105/joss.00731\u003c/li\u003e\n \u003cli\u003eTyler L, Thomas SG, Hu J, Dill A, Alonso JM, Ecker JR, Sun T-p (2004) DELLA proteins and gibberellin-regulated seed germination and floral development in Arabidopsis. Plant Physiol 135:1008-19. doi:10.1104/pp.104.039578\u003c/li\u003e\n \u003cli\u003eUmezawa T, Nakashima K, Miyakawa T, Kuromori T, Tanokura M, Shinozaki K, Yamaguchi-Shinozaki K (2010) Molecular basis of the core regulatory network in ABA responses: sensing, signaling and transport. Plant Cell Physiol 51:1821-39. doi:10.1093/pcp/pcq156\u003c/li\u003e\n \u003cli\u003eWang Q, Lin Q, Wu T, Duan E, Huang Y, Yang C, Mou C, Lan J, Zhou C, Xie K (2020) OsDOG1L-3 regulates seed dormancy through the abscisic acid pathway in rice. Plant Sci 298:110570. doi:10.1016/j.plantsci.2020.110570\u003c/li\u003e\n \u003cli\u003eYe H, Feng J, Zhang L, Zhang J, Mispan MS, Cao Z, Beighley DH, Yang J, Gu X-Y (2015) Map-based cloning of seed dormancy1-2 identified a gibberellin synthesis gene regulating the development of endosperm-imposed dormancy in rice. Plant Physiol 169:2152-65. doi:10.1104/pp.15.01202\u003c/li\u003e\n \u003cli\u003eZhang Y, Feng F, He C (2012) Downregulation of OsPK1 contributes to oxidative stress and the variations in ABA/GA balance in rice. Plant Mol Biol Rep 30:1006-13. doi:10.1007/s11105-011-0386-2\u003c/li\u003e\n \u003cli\u003eZhu D, Qian Z, Wei H, Guo B, Xu K, Dai Q, Zhang H, Huo Z (2019) The effects of field pre-harvest sprouting on the morphological structure and physicochemical properties of rice (Oryza sativa L.) starch. Food Chem 278:10-6. doi:10.1016/j.foodchem.2018.11.017\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"PHS, rice, GWAS, genetic architecture, antagonistic epistasis","lastPublishedDoi":"10.21203/rs.3.rs-8227857/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8227857/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePre-harvest sprouting (PHS), thef premature germination of grains before harvest, threatens rice yield and quality under erratic climatic conditions. This study aims to investigate the genetic basis of PHS resistance by conducting a genome-wide association study (GWAS) on 182 diverse rice genetic resources representing multiple ecotypes using 289,569 high-quality single-nucleotide polymorphisms. Three major QTLs\u0026mdash;\u003cem\u003eqRPH7, qRPH8, and qRPH11\u003c/em\u003e\u0026mdash;were identified using the complementary multi-locus models, Bayesian information and Linkage disequilibrium, iteratively Nested Keyway and Multi-Locus Mixed Model. \u003cem\u003eqRPH7\u003c/em\u003e showed the strongest association, explaining up to 80% of phenotypic variance, and co-localized with \u003cem\u003eSDR4\u003c/em\u003e and \u003cem\u003eqPH7\u003c/em\u003e. Allelic combination analyses revealed that the \u003cem\u003eqRPH7\u0026ndash;SDR4\u003c/em\u003e and \u003cem\u003eqRPH7\u0026ndash;qPH7\u003c/em\u003e combinations conferred strong resistance, whereas \u003cem\u003eqRPH7\u003c/em\u003e alone was insufficient. In contrast, \u003cem\u003eqRPH11\u003c/em\u003e contributed additively to enhance resistance, while \u003cem\u003eqRPH8\u003c/em\u003e displayed antagonistic epistasis that reduced resistance stability. Overall, PHS resistance is governed by a polygenic architecture involving both additive and epistatic interactions. These findings establish a new genetic architecture underlying PHS resistance in rice and propose a targeted breeding strategy through pyramiding \u003cem\u003eqRPH7\u003c/em\u003e with \u003cem\u003eSDR4, qPH7\u003c/em\u003e, and \u003cem\u003eqRPH11\u003c/em\u003e. This study advances mechanistic insight into seed dormancy and sprouting while providing actionable resources to support marker-assisted selection and accelerate the development of PHS-resistant cultivars suited to climate change.\u003c/p\u003e","manuscriptTitle":"A New Genetic Architecture for PHS Resistance in Rice: Deciphering the Epistatic Interactions of Three Major QTLs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-16 19:54:52","doi":"10.21203/rs.3.rs-8227857/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"68a50529-7685-4378-8250-8c1f0c5b4df5","owner":[],"postedDate":"December 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T03:24:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-16 19:54:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8227857","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8227857","identity":"rs-8227857","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}