Discovery of Cold tolerance genes and Favorable Alleles in Kam Sweet Rice Across Various Growth Stages

Discovery of Cold tolerance genes and Favorable Alleles in Kam Sweet Rice Across Various Growth Stages | 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 Discovery of Cold tolerance genes and Favorable Alleles in Kam Sweet Rice Across Various Growth Stages Kunchi Yu, Chunhui Liu, Joohyun Lee, Xiaoding Ma, Bing Han, Zhengwu Zhao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7495419/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Cold sensitivity in rice leads to significant yield losses. Identifying cold-tolerant germplasm and uncovering cold tolerance genes are essential for developing resilient rice varieties. Kam Sweet Rice (KSR) is notable for strong cold tolerance, which is an invaluable genetic resource for identifying such genes. In this study, we phenotyped cold tolerance across various growth stages using 104 KSR accessions and 268 other rice landraces. Genome-wide association studies (GWAS) identified 89 loci significantly associated with cold tolerance: 57 loci at the germination stage, 9 at the bud stage, and 28 at the seedling stage, with 61 loci (69%) being newly discovered. Through association and selection sweep analyses, we identified two high-confidence candidate genes, OsCTD2 and OsLTPL159, associated with cold tolerance at both germination and seedling stages. Haplotype analysis revealed significant differences in cold tolerance grade and survival rate among various haplotypes of these genes, with superior haplotypes predominantly present in KSR. RNA-seq and qRT-PCR results showed that the superior haplotypes of OsCTD2 and OsLTPL159 exhibited significantly higher expression in cold-tolerant accessions under cold stress, whereas no significant differences were observed in cold-sensitive accessions with the inferior haplotypes. These results indicated that OsCTD2 and OsLTPL159 are involved in cold stress response in rice. Additionally, we identified two other promising candidate genes and their superior haplotypes for cold tolerance: OsGRS7 at the germination stage and OsBSR6 at the bud stage. Our findings provide a solid foundation for cloning cold tolerance genes and offer insights for designing molecular breeding strategies. Kam Sweet Rice Cold-tolerance Candidate gene Superior haplotype Various growth stages Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Key message Combined association and selection sweep analyses identified OsCTD2 and OsLTPL159 as key candidate genes associated with cold tolerance at both germination and seedling stages. Introduction Rice ( Oryza sativa ), a thermophilic crop originating from tropical and subtropical regions, is highly sensitive to low temperatures. Cold stress—one of the major global abiotic constraints—causes stage-specific damage during germination, seedling growth, and booting, leading to substantial regional yield losses, particularly in temperate japonica and high-altitude indica cultivation areas. In China, chilling damage alone results in estimated annual grain losses of 5.0–10.0 million metric tons, equivalent to 6–12% of total national production (National Agricultural Statistics, 2023). These losses underscore the urgency of breeding cold-tolerant rice varieties to safeguard food security and promote sustainable agriculture. Kam Sweet Rice (KSR), designated as a Specialty Rice by the FAO International Rice Commission (IRC), is a distinctive germplasm domesticated and preserved for millennia by the Dong ethnic community in Congjiang County, Qiandongnan Prefecture, Guizhou Province (Bedigian et al. 2003). Congjiang County lies on the southeastern Yunnan–Guizhou Plateau, transitioning from the Miaoling Mountains to the Guangxi hills. Mountainous and hilly terrain covers 98% of its area, with elevations from 137 m to 1,676 m, producing a topographic relief of 1,539 m. This altitudinal range creates diverse microclimates: higher elevations are foggy and cool with limited thermal accumulation, whereas lower areas are warm, rainy, and receive abundant precipitation. These terraced ecosystems provide ideal conditions for selecting indigenous KSR varieties. KSR combines high grain quality with resilience to low soil fertility, drought, waterlogging, and shade, and is particularly notable for strong cold tolerance. Our previous population structure analysis showed that KSR accessions from Qiandongnan (QDN) form a distinct genetic cluster, with significantly lower genetic diversity (π) and higher genetic differentiation ( F ST ) compared to accessions from Central China (CC), East China (EC), South China (SC), Southwest China (SW), and non-QDN Guizhou cultivars (GZ) (Liu et al. 2023). This distinct genomic profile makes KSR an exceptional resource for identifying cold tolerance genes. Cold tolerance in rice is a quantitative trait regulated by multiple genetic factors (Lv et al. 2019; Mishra et al. 1996). Mapping quantitative trait loci (QTLs) and identifying functional genes associated with cold tolerance provide a targeted strategy for developing resilient cultivars through molecular breeding. Recent advances in QTL mapping and genome-wide association studies (GWAS) have uncovered numerous cold tolerance–related QTLs across key developmental stages, including germination, seedling, and booting, with several now cloned. At the germination stage, OsNAC5 (Li et al. 2024) and OsbZIP23 (Sun et al. 2024) have been identified. Seedling-stage genes include HAN1 (Mao et al. 2019), COLD1 (Luo et al. 2021), COLD11 (Li et al. 2023), COG1 (Xia et al. 2023), COG2 (Feng et al. 2023), OsSEH1 (Gu et al. 2023), COG4 (Sun et al. 2024), COLD6 (Luo et al. 2024) and CTK1 (Wu et al. 2024) and OsbZIP72 (Gu et al. 2024). At booting, notable genes include Ctb1 (Saito et al. 2010; Saito et al. 2004; Saito et al. 2001), OsLea9 - OsMAPK3 (Lou et al. 2022), CTB3 (Li et al. 2025), CTB6 (Gao et al. 2025), and CTF1 (Dong et al. 2025). Several genes, such as CTB4a (Li et al. 2021; Zhang et al. 2017) , bZIP73 (Liu et al. 2018; Liu et al. 2019), CTB2 (Li et al. 2021), COG3 (Liu et al. 2024), OsSRO1c (Hu et al. 2024a; You et al. 2014), and CTB5 (Guo et al. 2025), contribute to cold tolerance at both the seedling and booting stages. Previous GWAS analyses have identified several cold tolerance–associated genes, including OsLea9–OsMAPK3 , CTK1 , OsSRO1c , OsbZIP72 , and CTB5 . For example, Lou et al. (Lou et al. 2022) selected four rice varieties with contrasting cold response phenotypes and identified two key reproductive-stage regulators, OsLea9 and OsMAPK3 , by integrating RNA-seq data with GWAS results. The superior haplotype combination OsMAPK3 ^Geng/ OsLEA9 ^KL enhances low-temperature adaptation in temperate japonica cultivated in high-altitude environments. Similarly, Dong et al. (Dong et al. 2025) identified OsCTK1 as a seedling-stage cold tolerance regulator through a GWAS scan of 413 diverse accessions. Three putative substrates—acidic ribosomal protein OsP3B , cyclic nucleotide-gated ion channel OsCNGC9 , and dual-specific mitogen-activated protein kinase phosphatase OsMKP1 —are each involved in chilling tolerance. Hu et al. (Hu et al. 2024a; You et al. 2014) identified the pleiotropic gene OsSRO1c via a GWAS of seedling cold tolerance in a core set of 529 accessions. The OsSRO1c protein forms a heteromeric complex with OsDREB2B , co-regulating transcription of the pivotal cold tolerance gene COLD1 , thereby enhancing adaptation at both seedling and heading stages. Gu et al., (Gu et al. 2024) working with 540 accessions, detected OsbZIP72 within the significant locus qCTS9.1 ; functional analysis showed it enhances cold tolerance by modulating reactive oxygen species (ROS) accumulation. Guo et al. (Guo et al. 2025) identified the transcription factor CTB5 through QTL mapping in biparental populations and GWAS of 155 accessions, revealing its dual regulatory role at the booting and seedling stages. CTB5 directly regulates PYL9 and improves seedling cold tolerance by reducing ROS accumulation. Despite these advances, the number of identified cold tolerance genes remains limited, and novel genes applicable to molecular breeding are still lacking. In this study, we conducted a comprehensive evaluation of cold tolerance in 104 KSR accessions and 268 other rice landraces across the germination, bud, and seedling stages. GWAS identified cold tolerance genes and their superior haplotypes across various growth stages. These findings provide a strong foundation for precision cloning of cold tolerance genes and support the development of molecular breeding strategies to produce cold-tolerant rice varieties. Materials and methods Materials and genomic data We analyzed a core panel of 372 rice accessions from previous studies, comprising 104 KSR accessions and 268 other landraces collected from 12 provinces. The curated dataset contained 3,566,872 quality single-nucleotide polymorphisms (SNPs) with an average sequencing depth of ~12.43× per accession (Liu et al. 2023, Table S1). Identification of cold tolerance in rice at the germination stage Following the Description Specification and Data Standard of Rice Germplasm Resources (Han, 2006) and the method of Han et al. (Han and Zhang, 2004) cold tolerance was assessed at the germination, bud, and seedling stages. The experiment followed a completely randomized design with three biological replicates per treatment. Each replicate contained 50 surface-sterilized seeds, treated with 0.5% sodium hypochlorite (NaClO) for 15 minutes and rinsed three times with sterile distilled water. Seeds were aseptically placed on moistened filter paper in 90 mm Petri dishes. After 24 hours of imbibition in darkness at 25 ± 1 °C, the dishes were transferred to an illuminated growth chamber maintained at 14 ± 0.5 °C for a 20-day germination period. Germination was considered successful when both the radicle (>1 mm) and coleoptile had emerged. Germination energy and germination rate were calculated as: Germination energy (%) = (Total seeds tested/Number of seeds germinated within specified days) ×100% Germination rate (%) = (Total seeds tested/Total germinated seeds) ×100% Identification of cold tolerance in rice at the bud stage Three biological replicates were established, each containing 50 surface-sterilized seeds (treated with 0.5% NaClO for 15 minutes, followed by triple rinsing with sterile distilled water). After standard germination treatment, 30 seeds with uniform coleoptile length (approximately 5.0 mm) were selected per replicate. The Seeds were then subjected to low-temperature stress at 5 °C for 10 days in darkness, followed by transfer to a controlled environment (20–30 °C) for 7 days. After 7 days, the number of dead seedlings was recorded, and the survival rate of seedlings was calculated as follows: Survival rate (%) = [1 - (Number of dead seedlings/Total seedlings)] × 100% Identification of cold tolerance in rice at the seedling stage The seedlings were surface-sterilized following standard protocols, soaked, and germinated. After germination, the seedlings were grown in a greenhouse or outdoor environment, with temperatures maintained between 20–30 °C. Upon reaching the three- to four-leaf stage, seedlings were subjected to a 7-day low-temperature treatment at 10°C. Following the cold treatment, seedlings were transferred to outdoor recovery conditions (20–30 °C) for a 7 days. Cold tolerance was evaluated based on a leaf withering grade scale: Grade 1: Strong cold tolerance, all leaves remain green or nearly green. Grade 3: Strong cold tolerance, leaves show slight decolorization or yellowing. Grade 5: moderate cold tolerance, most leaves are yellowed. Grade 7: weak cold tolerance, leaves dry out, and some seedlings die. Grade 9: Very weak cold tolerance, most or all seedlings are dead. The statistical analysis of phenotypic data All experiments followed a completely randomized design with triple replication. Mean values, standard deviations, ranges, and coefficients of variation (CV) were analyzed using ANOVA with Duncan's multiple range test ( p < 0.05) in IBM SPSS v26. The diversity index (H’) was calculated as described by Xue et al. (Xue et al. 2020) using Excel 2019. Student's t -tests, with Bonferroni correction, were employed to analyze variations in cold tolerance values across various growth stages among the subpopulations, using the ‘ggstatsplot’ and ‘ggplot2’ packages in R. Additionally, the variances in cold tolerance-related trait values between haplotypes of each gene were assessed using the same statistical methods. Scatter plots, violin plots, and stacked column graphs were generated using the ‘ggpubr’ and ‘ggplot2’ packages in R. Detection of selective sweeps To identify regions of selective scanning in each subpopulation, Likelihood ratio (CLR) values for each window were calculated using the command '-CLR -grid 4000' (where 'grid 4000' partitions each chromosome into 4000 windows). The top 5% of windows with the highest CLR values were identified as regions of selective scanning. Peak maps of CLR were generated using the 'CMplot' package in R. GWAS Cold tolerance across various growth stages (germination, bud, and seedling) was evaluated using the mixed linear model (MLM) available in the Tassel (v5.0) software (PJ et al., 2007). Based on the results of previous principal component analysis (PCA), the top three PCs were selected as covariates for inclusion in downstream analyses. The significance threshold for GWAS was set at p < 1 × 10⁻⁴ through Bonferroni correction, based on the effective number of independent SNPs (Li et al. 2012). To functionally annotate significant SNP markers, SNPeff software was used, following established protocols (Cingolani et al. 2012; Cui et al. 2022). Candidate gene identification involved scanning genomic regions 100 kb upstream and downstream of significant SNPs. Data visualization was performed using two approaches: 1) Manhattan plots generated with the ‘CMplot’ package in the R environment, and 2) linkage disequilibrium patterns illustrated through heatmaps rendered by LDBlockShow (v1.40) (Dong et al. 2020). RNA-seq data analysis Two varieties, Gouhuanggang (cold-tolerant) and Heigu (cold-sensitive), were subjected to a 7 °C low-temperature treatment at the seedling stage. Leaf samples were collected before the low-temperature treatment and 8 hours after treatment. Three biological replicates were performed. Total RNA was extracted using a RNeasy Plant Mini Kit (Qiagen). Sequencing was conducted on the Illumina platform (Illumina, San Diego, CA, USA), generating Approximately 6 Gb of 150-bp paired-end reads per sample. Sequence alignment was performed using TopHat2 (v2.1.1) to map quality-filtered RNA-seq reads against the japonica rice reference genome assembly IRGSP-1.0 (Trapnell et al. 2012). Differential gene expression analysis was performed with DESeq2 (Love et al. 2014), based on the read count data from the gene expression analysis. Weighted gene co-expression network analysis (WGCNA) was conducted using Cytoscape (v3.9.1) (Shannon et al. 2003). The transcriptomic sequencing data of this study was deposited in the NCBI Sequence Read Archive under the accession number: PRJNA1181572. qRT-PCR Total RNA was extracted using TRIzol reagent (Invitrogen, Cat#. AM1912). RNA (2 µg) was reverse transcribed to cDNA using a cDNA Synthesis SuperMix (TransGen, Cat#. AU311-02). qRT-PCR was performed using THUNDERBIRD SYBR qPCR Mix without Rox reagent (TOYOBO, Cat#. QPS-20(-)). The total reaction mix consisted of 10 μL of SYBR qPCR Mix, 7.2 µL of double-distilled water, 2 μL of complementary DNA (cDNA) template, and 0.4 μL of each upstream and downstream primer. The 2 −∆∆Ct method was used to evaluate transcript levels of candidate genes from three biological replicates, analyzed using Prism v10. UBI was used as the internal control, and the primers for qRT-PCR are listed in the Supplementary Table (Table S2). Results Phenotypic diversity We analyzed four cold tolerance–related traits in 104 KSR accessions and 268 other rice landraces after low-temperature treatments at different growth stages (Table S3). At germination, germination energy ranged from 0% to 96.67% (mean: 24.37%), while germination rate ranged from 3.33% to 100% (mean: 82.13%). at the bud stage, survival rate varied from 0% to 97.78%, averaging 40.73%. At the seedling stage, cold tolerance grade ranged from 2 to 8, with a mean of 5.76. The coefficient of variation (CV) for these traits ranged from 24.02% to 92.74%, with the lowest CV observed for cold tolerance grade at the seedling stage (24.02%) and the highest for germination energy at the germination stage (92.74%). the diversity index (H′) also varied among traits, with the lowest value recorded for cold tolerance grade at the seedling stage (0.30) and the highest for survival rate at the bud stage (0.88). Phenotypic variation within subpopulations Previous studies classified the 372 accessions into six subpopulations based on genetic structure and geographical origin: Kam Sweet Rice (KSR), Central China (CC), East China (EC), South China (SC), Southwest China (SW), and non-QDN Guizhou cultivars (GZ) (Liu et al. 2023). Our results show that KSR consistently outperformed at least two other subpopulations in cold tolerance across growth stages (Fig. 1A-D). After cold stress, KSR had a germination energy of 35.6% and a germination rate of 94.0%, both significantly higher than CC (13.0% and 68.7%; p < 0.01), EC (20.9% and 74.6%; p < 0.01), SC (19.3% and 73.6%; p < 0.01), and GZ (15.9% and 77.7%; p < 0.01) (Fig. 1A-B). At the bud stage, KSR’s survival rate was 42.2%, significantly higher than SW (27.4%, p < 0.01) and GZ (36.8%, p < 0.05), and slightly higher than SC (41.0%) (Fig. 1C). At the seedling stage, KSR recorded a cold tolerance grade of 5.0, significantly better than EC (5.5, p < 0.05), SC (5.6, p < 0.05), SW (6.7, p < 0.01), and GZ (6.3, p < 0.01), and marginally better than CC (5.2) (Fig. 1D). These findings confirm KSR as an important genetic resource for cold tolerance improvement. Identification of selective signals of six subpopulations To better understand selective footprints in KSR and the 268 other accessions, we performed a genome scan using a composite likelihood ratio (CLR) approach to detect putative selection signatures across the six subpopulations: KSR, CC, EC, SC, SW, and GZ (Fig. 2, Fig.S1, Table S4). In total, we identified 8,268 potential selective regions containing 5,954 genes, with an average size of 7.77 kb—smaller than previously reported (Fig 2A-B; Table S4) (Liu et al. 2025). Among these, 684 common selective sweeps containing 566 genes were present in at least four subpopulations, representing strong candidates for selection during domestication (Figure 2A-B and Table S4). Notably, 43.3% of these sweeps overlapped with previously reported genomic regions associated with breeding improvements from different eras, indicating that they underwent selection during both domestication and later improvement (Cui et al. 2022). These regions represent priority targets for efficiently mining favorable alleles. As expected, several cloned genes directly involved in cold tolerance regulation were detected in KSR selective sweeps, including LTG1 (Lu et al. 2014), HAN1 (Mao et al. 2019), CTB4a (Li et al. 2021; Zhang et al. 2017), COLD1 (Luo et al. 2021), and CTB2 (Li et al. 2021) (Fig 2C). In addition, HAN1 and COLD1 appeared in the selective sweeps of SC and GZ, while CTB2 was found in CC. HAN1 encodes an oxidase that catalyzes the conversion of the bioactive jasmonoyl-L-isoleucine (JA-Ile) to its inactive form, 12-hydroxy-JA-Ile (12OH-JA-Ile), fine-tuning the jasmonic acid–mediated chilling response. In this study, we observed that the three haplotypes of HAN1 differed significantly in cold tolerance grade at the seedling stage and in survival rate at the bud stage (Fig. 2D-F). the superior haplotype, Hap1, was most frequent in KSR (83%), with lower frequencies in CC (56%), EC (54%), SW (14%), SC (43%), and GZ (54%) (Fig. 2G). These findings suggest that HAN1 underwent selection during KSR domestication, consistent with previous reports showing that the superior haplotype is mainly distributed in rice from northern China, Japan, and the Yunnan–Guizhou Plateau (Mao et al. 2019). Similarly, we found that the frequency of the superior haplotype (conferring cold tolerance at seedling and bud stages) of CTB4a was highest in KSR (63%), compared with CC (45%), EC (50%), SC (53%), SW (8%), and GZ (35%) (Fig S2). Furthermore, numerous cloned genes associated with yield-related traits were detected within selective sweeps. In KSR, these included GIF1 (Wang et al. 2008), GAD1 (Jin et al. 2016), GNP1 (Wu et al. 2016), GW5 (Liu et al. 2017), qTGW3 (Hu et al. 2018), GW2 (Hao et al. 2021), and An-1 (Song et al. 2022) (Fig. 2C). Several of these genes— GW2 , qTGW3 , An-1 , GIF1, and GNP1 —were also present in the selective sweeps of SC and GZ (Fig S1). In addition, PROG1 (Tan et al. 2008), GS5 (Li et al. 2011), GL3.1 (Qi et al. 2012), and BG1 (Liu et al. 2015) were identified in the selective sweeps of CC and GZ (Fig S1). As expected, multiple disease resistance genes— Xa26 (Wang et al. 2005), EBR1 (You et al. 2016), Bph6 (Wu et al. 2022), and Bph14 (Guo et al. 2023)—were located within selective sweeps spanning at least two subpopulations, including KSR (Fig. 2C, Fig S1). These genes likely played important roles in the domestication and adaptation of KSR and other rice populations. Gene-co expression network analysis RNA-seq analysis was conducted on the cold-tolerant accession Gouhuanggang and the cold-sensitive accession Heigu under both control and cold stress conditions. Compared with the control, Gouhuanggang exhibited 4,923 differentially expressed genes (DEGs), including 2,447 upregulated and 2,476 downregulated genes, while Heigu showed 6,090 DEGs, with 3,467 upregulated and 2,623 downregulated (Fig S3A-B; Table S5). Of these, 815 DEGs were commonly upregulated and 645 were commonly downregulated in both accessions (Fig S3C-D; Table S5). Our weighted gene co-expression network analysis (WGCNA) identified 26 co-expressed gene modules (Fig S4A-B). The blue module ( r = 0.98, p < 0.01) and the magenta module ( r = 1, p < 0.01) were strongly correlated with cold treatment in Gouhuanggang and together contained 5,145 genes (Fig S4C). these modules included several known cold tolerance genes, such as COLD11 (Luo et al. 2021), OsMAPK3 (Xia et al. 2021), and OsRBCS3 (Hu et al. 2024b),. Gene Ontology (GO) annotation showed that genes in these modules primarily regulate cellular activities, including membrane stabilization, signal perception, transcriptional regulation, and nitrogen metabolism homeostasis—processes that collectively contribute to cold adaptation (Fig S5). Within the gene expression networks of these two modules, 37 hub genes were identified (Table S6). Integrating functional annotation with differential expression analysis, we pinpointed five high-confidence hub genes associated with cold tolerance: LOC_Os01g42280 , LOC_Os02g12840 , LOC_Os04g55159 , LOC_Os06g06790 , and LOC_Os07g48730 (Fig S6, Table S6). GWAS of cold tolerance We performed a genome-wide association study (GWAS) to identify loci associated with cold tolerance across rice growth stages, detecting 89 significant loci ( p < 1 × 10⁻⁵; Fig. 3; Table S7). Of these, 17 were associated with germination energy and 40 with germination rate at the germination stage; 9 loci were linked to seedling survival rate at the bud stage; and 28 were associated with cold tolerance grade at the seedling stage. Notably, four significant loci, qCTS2.1 (Chr2: 6,479,097– 6,858,939), qCTS4.2 (Chr4: 32,044,990–32,775,406), qCTS6.5 (Chr6: 16,541,073‒16,924,542), and qCTS7.2 (Chr7: 20,437,771– 20,759,260), were identified across two various growth stages, suggesting their role in regulating cold tolerance at various growth stages in rice simultaneously (Table S7). In total, 28 loci (31%) overlapped with previously reported quantitative trait nucleotides (QTNs) or known cold tolerance genes, including COLD1 (Luo et al. 2021), OsWRKY53 (Tang et al. 2022), and COLD6 (Luo et al. 2024) (Fig. 3, Table S7). Notably, 56 loci (59.6%) overlapped with regions under selective sweep. Among these, two loci— qCTS2.1 (Chr2: 6,479,097– 6,858,939) and qCTS4.2 (Chr4: 32,044,990–32,775,406)—located within selective sweep regions in at least five subpopulations, including KSR (Table S7). This overlap highlights their potential as key genomic targets for domestication and provides a solid foundation for further exploration and mining of cold tolerance candidate genes. Candidate genes for cold tolerance and their superior haplotypes across various growth stages The significant locus qCTS2.1 , located at 6.47–6.85 Mb on chromosome 2, was associated with cold tolerance at germination and seedling stages. This region falls within a linkage disequilibrium (LD) block (>200 kb) containing 11 genes with nonsynonymous mutations (Fig. 4A). Among these, OsCTD2 ( LOC_Os02g12840 ), a 7,071 bp gene encoding a DEAD-box ATP-dependent RNA helicase, emerged as a strong candidate. In the same protein family, TCD33 is known to regulate chloroplast development in rice seedlings under cold stress (Xiaomei et al. 2020). gene co-expression network analysis identified OsCTD2 as a high-confidence hub gene for cold tolerance (Table S6). RNA-seq analysis showed that OsCTD2 was significantly upregulated in the cold-tolerant accession Gouhuanggang under cold stress, with no significant change observed in the cold-sensitive accession Heigu (Fig S6). These findings support OsCTD2 as a robust candidate gene. Across the 372 rice accessions, 20 nonsynonymous mutations in the OsCTD2 coding region defined two haplotypes (Fig. 4B). Hap1 was identified as the superior haplotype, displaying significantly lower cold tolerance grades at the seedling stage than Hap2 ( p < 0.01) and higher survival rates at the bud stage ( p < 0.01) (Fig. 4C-D). Furthermore, the Hap1 showed slightly higher average values for germination rate and germination energy at germination stage compared to Hap2 (Fig. 4E-F). qRT-PCR confirmed that Hap1 accessions exhibited significantly increased OsCTD2 expression under cold stress compared with control conditions, whereas Hap2 accessions showed no significant expression changes—consistent with RNA-seq results (Fig. 4G). Hap1 frequency varied markedly among subpopulations, being highest in KSR (80%), followed by CC (52%), EC (54%), SC (48%), SW (16%), and GZ (52%) (Fig. 4H). This distribution suggests that the superior Hap1 of OsCTD2 provides a selective advantage in KSR and represents a promising target for improving seedling-stage cold tolerance in rice breeding programs. The germination and seedling cold-tolerance locus qCTS4.2 (Chr4: 32.20–32.77 Mb, p 200-kb LD block containing 21 genes with nonsynonymous SNPs (Fig. 5A). Among these, OsLTPL125 ( LOC_Os04g55159 ), a 1,963 bp gene encoding a lipid transfer protein (LTP) family protein, was identified as a strong candidate. Related LTP family members, including OsLTPL159 , Psd1 , and OsHyPRP05 , have been shown to mediate cold tolerance in rice (Deng et al. 2019; Fujino and Sekiguchi 2011; Zhao et al. 2020). gene co-expression network analysis confirmed OsLTPL125 as a high-confidence hub gene associated with cold tolerance (Table S6). RNA-seq analysis revealed significant upregulation of Os LTPL125 in the cold-tolerant accession Gouhuanggang under cold stress compared with control conditions, whereas no significant change was observed in the cold-sensitive accession Heigu (Fig S6). Haplotype analysis based on four SNPs identified three OsLTPL159 haplotypes (Fig. 5B). Hap1 conferred superior cold tolerance, showing significantly lower cold tolerance grades at the seedling stage than Hap2 ( p < 0.05) and Hap3 ( p < 0.01), as well as higher bud-stage survival rates ( p < 0.01) (Fig. 5C-D). In addition, Hap1 exhibited significantly higher germination rate and germination energy than Hap2 ( p < 0.01) at the germination stage, with its mean values also marginally exceeding those of Hap3 (Fig. 5E-F). qRT-PCR analysis supported these results, showing that OsLTPL159 expression in Hap1 accessions increased significantly after cold stress, whereas Hap3 accessions exhibited no significant change—consistent with RNA-seq findings (Fig. 5G). Hap1 frequency was highest in KSR (80%), followed by EC (56%), CC (47%), SC (42%), GZ (40%), and SW (16%) (Fig. 5H). This distribution pattern highlights Hap1 as a promising genetic resource for developing cold-tolerant rice varieties. Furthermore, integrated haplotype analysis of genes with nonsynonymous mutations within QTL intervals, combined with functional annotation, identified two novel high-confidence candidates: OsGRS7 ( LOC_Os07g37210 ), associated with cold tolerance at the germination stage, and OsBSR6 ( LOC_Os06g34430 ), linked to bud-stage cold tolerance (Fig S7; Fig S8). Together, these findings expand the set of stage-specific cold tolerance genes and their superior haplotypes, providing a robust framework for targeted gene cloning and precision breeding to enhance cold resilience in rice. Discussion KSR is an invaluable genetic resource for improving cold tolerance in rice. Previous studies have confirmed its exceptional resilience, enabling adaptation to cold mountainous and valley environments. Unlike many varieties that fail to complete their life cycle in cold-water paddies, KSR maintains normal growth and produces viable yields under these challenging conditions. For example, Chen et al. (Chen et al et al. 1999) evaluated bud-stage cold tolerance in 286 KSR accessions and found that 179 (62.58%) exhibited moderate-to-high resistance. In the present study, we assessed cold tolerance in 104 KSR accessions and 268 other rice landraces across the germination, bud, and seedling stages. comparative analysis among six subpopulations (CC, EC, SC, SW, GZ, and KSR) revealed marked phenotypic divergence, with KSR outperforming all other groups at every growth stage—consistent with earlier findings. As expected, selective footprint analysis revealed several key cold tolerance genes in KSR, including Osmyb4 (Vannini et al. 2004), qLTG3-1 (Fujino and Sekiguchi 2011), LTG1 (Lu et al. 2014), HAN1 (Mao et al. 2019), CTB4a (Li et al. 2021; Zhang et al. 2017), CTB2 (Li et al. 2021), and COLD1 (Luo et al. 2021). Among these, COLD1 and CTB4a are major-effect genes. COLD1 interacts with RGA1 to enhance GTPase activity, while CTB4a binds to AtpB to stimulate ATP activity—both contributing to improved cold tolerance. CTB2 has pleiotropic effects, enhancing resilience at the germination, seedling, and booting stages through dehydrin-mediated cellular protection. qLTG3-1 is specifically expressed in the aleurone layer of the seed coat and the epiblast covering the coleoptile, where it may enhance germination vigor at low temperatures by promoting cellular vacuolization and tissue relaxation. LTG1 improves low-temperature germination by modulating ABA/GA homeostasis and has been identified in previous genomic studies as a domestication target under selection in rice landraces. Collectively, these genes have played pivotal roles in the domestication of KSR and reinforce its value as a rich source of cold tolerance alleles. GWAS identified 89 significant loci associated with cold tolerance, of which 61 (69%) were novel, providing a valuable genetic basis for cloning new cold tolerance genes. The remaining 28 loci (31%) overlapped with previously reported QTLs or functional genes, supporting the reliability of our localization results. For example, Yang et al. (Thapa et al. 2020) conducted a GWAS on low-temperature germination using 200 indica rice accessions and identified 159 loci, among which qGI-5-1 overlaps with our locus qCTB5 (Chr1: 651,903–851,903). Using QTL-seq and linkage mapping, Yang et al. (Yang et al. 2021) also identified qCTS6 on chromosome 6, associated with seedling-stage cold tolerance; In our study, qCTS6.6 (Chr6: 28,113,785–28,439,348) overlapped with this QTL. In another study, Yang et al. (Yang et al. 2023) mapped cold tolerance at the bud stage in an RIL population, identifying seven loci, including qSR-6 , which overlaps with our qCTR6.4 (Chr6: 10,059,539–10,259,539). Similarly, Liu et al. (Liu et al. 2025) performed a GWAS on cold tolerance across various growth stages in 166 Chinese rice mini-core accessions, identifying 63 loci. four of these— qCTSs2 , qCTSs6 , qCTBs8 , and qCTBs12-1 —correspond to our loci qCTS2.1 (Chr2: 6,495,466–6,733,833), qCTS6.5 (Chr6: 16,724,542–16,924,542), and qCTR8.2 (Chr8: 21,603,782–21,803,782), respectively. Rastogi et al. (Rastogi et al. 2025) using a novel diversity panel of 238 accessions genotyped with the 7K SNP Cornell-IR LD Rice (C7AIR) array, detected 77 loci linked to 21 cold tolerance and related seedling-stage traits; among these, qCTScold12 overlaps with our qCTE12.1 (Chr12: 1,626,962–1,826,962). Notably, several known cold tolerance genes were also identified in our dataset, including OsTB1 (Chen et al., 2018), COLD1 (Luo et al. 2021), OsWRKY53 (Tang et al. 2022), OsDREB1C (Wang et al., 2022), and COLD6 (Luo et al. 2024). COLD1 encodes a regulator of G-protein signaling localized to the plasma membrane and endoplasmic reticulum, with a key SNP distinguishing cold perception sensitivity between japonica and indica subspecies. This SNP confers cold tolerance in japonica by modulating COLD1 ’s regulation of GTPase activity in the G-protein α-subunit. The cold sensor complexes of COLD6 and OSM1 trigger the production of 2′,3′-cyclic adenosine monophosphate (2′,3′-cAMP), enhancing cold tolerance. Variation in the number of leucine codons (CTC) in COLD6 between indica and japonica leads to reduced plasma membrane accumulation of japonica-derived COLD6 under low temperatures, thereby enhancing tolerance. Finally, OsWRKY53 negatively regulates cold tolerance at the booting stage by directly suppressing gibberellin (GA) biosynthesis genes OsGA20ox1 , OsGA20ox3 , and OsGA3ox1 . We identified four key cold tolerance candidate genes distributed across different growth stages. At the seedling stage, the superior haplotypes of OsCTD2 and OsLTPL159 each reached a frequency of 80% in KSR. At the germination stage, the superior haplotype of OsGRS7 occurred in 72% of KSR accessions—substantially higher than in other subpopulations (CC: 13%, EC: 0%, SC: 19%, SW: 23%, GZ: 38%) (Fig S7F). At the bud stage, the superior haplotype of OsBSR6 was also highly enriched in KSR (66%), compared to much lower frequencies in CC (9%), EC (9%), SC (31%), SW (10%), and GZ (23%) (Fig S8E). Additionally, HAN1 and CTB4a , two cold-tolerance gene detected in our selection signature analysis, exhibited its highest superior haplotype frequency in KSR, which were 83% and 63%, respectively. Liu et al. (Liu et al. 2023) previously reported two other domestication targets for cold tolerance in KSR— LTG1 and MYBS3 —with superior haplotypes present in 56% and 77% of accessions, respectively. This consistent enrichment of favorable haplotypes in KSR provides strong genetic evidence for its adaptation to montane cold environments and further validates its value as a resource for mining cold tolerance genes and alleles. Notably, we identified 14 KSR accessions, such as Baishanuo, Gouhuanggang, and Tianhe, harboring superior alleles across different cold tolerance genes, all of which displayed strong cold tolerance in at least two growth stages (Table S8). These germplasm accessions can be strategically utilized as parental lines in cold tolerance breeding programs for rice. Collectively, our findings successfully identified four cold tolerance candidate genes and their superior haplotypes across various growth stages, highlight the genetic potential and practical breeding value of KSR for developing cold-tolerant rice cultivars. This work thus establishes a strong foundation for precision cloning of cold tolerance genes and support the development of molecular breeding strategies to produce cold-tolerant rice varieties. Declarations Author contributions Conceived and designed the experiments: Soon-Wook Kwon, Longzhi Han, Di Cui. Performed the experiments: Kunchi Yu, Chunhui Liu, Joohyun Lee. Analyzed and interpreted the data: Kunchi Yu, Chunhui Liu, Joohyun Lee. Contributed reagents, materials, analysis tools, or data: Longzhi Han, Di Cui, Zhengwu Zhao, Xiaoding Ma, Bing Han. Wrote the paper: Kunchi Yu. Funding This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. RS-2024-00391988), the National Natural Science Foundation of China (32201765), the Key R&D Program of Ningxia Hui Autonomous Region (2023BCF01010), Strategic Cooperation Project of Agricultural Science and Technology Innovation between Chongqing Municipal People's Government and Chinese Academy of Agricultural Sciences, the National Key Research and Development Program of China (2021YFD1200500), and the CAAS Science and Technology Innovation Program. Data availability The datasets supporting the conclusions of this article are provided within the article and its additional files. Declaration of competing interest On behalf of all authors, the corresponding author states that there is no conflict of interest. References Bedigian D (2003) Specialty rices of the world. breeding, production and marketing. 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Plant Biotechnol J 18:756–769 Supplementary Files Supplementaryfigures.docx TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx TableS5.xlsx TableS6.xlsx TableS7.xlsx TableS8.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revisions 17 Jan, 2026 Reviewers agreed at journal 27 Sep, 2025 Reviewers invited by journal 11 Sep, 2025 Editor assigned by journal 30 Aug, 2025 First submitted to journal 30 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7495419","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":513732671,"identity":"89a670c5-b743-4569-bcef-f3debe2f963a","order_by":0,"name":"Kunchi Yu","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences Institute of Crop Sciences","correspondingAuthor":false,"prefix":"","firstName":"Kunchi","middleName":"","lastName":"Yu","suffix":""},{"id":513732672,"identity":"2c309e2f-5179-4c7c-a13a-51e636d4aa2a","order_by":1,"name":"Chunhui Liu","email":"","orcid":"","institution":"China Agricultural University College of Agronomy and Biotechnology","correspondingAuthor":false,"prefix":"","firstName":"Chunhui","middleName":"","lastName":"Liu","suffix":""},{"id":513732673,"identity":"7015fd24-7736-4d30-8f93-7d4e34ebad41","order_by":2,"name":"Joohyun Lee","email":"","orcid":"","institution":"Konkuk University Department of Crop Science","correspondingAuthor":false,"prefix":"","firstName":"Joohyun","middleName":"","lastName":"Lee","suffix":""},{"id":513732674,"identity":"d45f10a1-7409-4fb8-9e5a-afb39607ca4a","order_by":3,"name":"Xiaoding Ma","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences Institute of Crop Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xiaoding","middleName":"","lastName":"Ma","suffix":""},{"id":513732675,"identity":"ff70d684-9c7a-47d5-b025-2e00e7266d89","order_by":4,"name":"Bing Han","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences Institute of Crop Sciences","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Han","suffix":""},{"id":513732676,"identity":"f25b2de5-fba0-4d4f-8714-d253aabfeabe","order_by":5,"name":"Zhengwu Zhao","email":"","orcid":"","institution":"Chongqing Normal University College of Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhengwu","middleName":"","lastName":"Zhao","suffix":""},{"id":513732677,"identity":"f460047e-a484-4b13-b8c4-453d42bcac8d","order_by":6,"name":"Soon-Wook Kwon","email":"","orcid":"","institution":"Pusan National University Department of Plant Biosciences","correspondingAuthor":false,"prefix":"","firstName":"Soon-Wook","middleName":"","lastName":"Kwon","suffix":""},{"id":513732678,"identity":"7bdbb381-b585-4cc3-9717-6efd103babbe","order_by":7,"name":"Longzhi Han","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences Institute of Crop Sciences","correspondingAuthor":false,"prefix":"","firstName":"Longzhi","middleName":"","lastName":"Han","suffix":""},{"id":513732679,"identity":"594196ed-bcb1-439b-9399-3a2a08db74b4","order_by":8,"name":"Di Cui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAmElEQVRIiWNgGAWjYFACxgaJhArStZwh1R4JxjZSlPPPSG688XCenTx//wHGzwVE2XAjsdkicVuy4YwbCczSM4jRYiCR2CaRuO0AY8MNBjZmHuK1zDlgP//8AZK0NBxI3HAggUgtEmceNlskHEtO3gj0lDRRWvjb0x/e/FFjZzvv/OGDn4nSggQYG0jUMApGwSgYBaMAJwAAp/swa72f1ZcAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0001-7990-914X","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":true,"prefix":"","firstName":"Di","middleName":"","lastName":"Cui","suffix":""}],"badges":[],"createdAt":"2025-08-30 12:58:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7495419/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7495419/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91615673,"identity":"c755adb8-86be-46b0-bfd9-0db6b943baff","added_by":"auto","created_at":"2025-09-18 10:32:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":447105,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhenotypic variation observed within subpopulations\u003c/strong\u003e. (A) Germination energy following cold stress at the germination stage. (B) Germination rate following cold stress at the germination stage. (C) Survival rate following cold stress at the bud stage. (D) Cold tolerance grade following cold stress at the seedling stage.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7495419/v1/06e67635515ff29a85d8d6de.png"},{"id":91615700,"identity":"956795f9-8ee6-4d86-9732-e713eb562655","added_by":"auto","created_at":"2025-09-18 10:32:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1078120,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenome-wide selective signals (CLR score) across six subpopulations.\u003c/strong\u003e(A) Comparison of identified selective sweeps among the different subpopulations. (B) number of genes associated with the selective sweeps in each subpopulation. (C) CLR score for KSR, with cold tolerance genes highlighted in red font. (D) CLR score and π-value (genetic diversity) plot for \u003cem\u003eHan1\u003c/em\u003e. (E) Box plot showing cold tolerance grade at the seedling stage for different haplotypes of \u003cem\u003eHan1\u003c/em\u003e. (F) Box plot showing survival rate at the bud stage for different haplotypes of \u003cem\u003eHan1\u003c/em\u003e. (G) Frequency changes of \u003cem\u003eHan1\u003c/em\u003e haplotypes in KSR, CC, EC, SC, SW, and GZ subpopulations.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7495419/v1/1318afb1fb71d9b59afbe9e1.png"},{"id":91615674,"identity":"43f6081d-3d9f-4c3d-9e27-304cb9d1936f","added_by":"auto","created_at":"2025-09-18 10:32:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1119760,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eManhattan plots of GWAS for four cold tolerance traits across various growth stages (-log10\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e = 5).\u003c/strong\u003e (A) Germination energy at the germination stage. (B) Germination rate at the germination stage. (C) Survival rate f at the bud stage. (D) Cold tolerance grade at the seedling stage. Known genes are highlighted in black, and significant loci are indicated in red.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7495419/v1/ad76c619d3a2911911164cfd.png"},{"id":91616806,"identity":"a6b3bf09-e141-4ce4-afe8-8cacba76b535","added_by":"auto","created_at":"2025-09-18 10:40:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1834984,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocalization of the candidate gene \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eOsCTD2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eon chromosome 2.\u003c/strong\u003e (A) Partial Manhattan plot (upper panel) and linkage disequilibrium heatmap (lower panel) surrounding the peak on chromosome 2. (B) Gene structure and polymorphisms identified in \u003cem\u003eOsCTD2\u003c/em\u003e. (C‒D) Violin plots showing cold tolerance grade at the seedling stage and survival rate at the bud stage for different haplotypes of \u003cem\u003eOsCTD2\u003c/em\u003e. *\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05 and **\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01. (E-F) Violin plots showing germination energy and germination rate at the germination stage for different haplotypes of\u003cem\u003e OsCTD2\u003c/em\u003e. (G) Expression analysis of \u003cem\u003eOsCTD2\u003c/em\u003e by qRT-PCR. CK, control; 8 h, cold stress for 8 hours. B204, Longhuamaohu; B205, Cunsanli; B246, Laozaogu; B091, Xiaohonggu. *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05 and **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01. Error bars indicate ± SD; n = 3 independent biological replicates. (H) Frequency distribution of haplotypes Hap1 and Hap2 across different subpopulations.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7495419/v1/906bceedb90b43a97b7ec7d0.png"},{"id":91616808,"identity":"3218b41d-1471-4bc8-949c-305e5877a29b","added_by":"auto","created_at":"2025-09-18 10:40:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1659634,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocalization of the candidate gene \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eOsLTPL125\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eon chromosome 4.\u003c/strong\u003e (A) Partial Manhattan plot (upper panel) and linkage disequilibrium heatmap (lower panel) surrounding the peak on chromosome 4. (B) Gene structure and polymorphisms identified in \u003cem\u003eOsLTPL125\u003c/em\u003e. (C‒D) Violin plots showing cold tolerance grade at the seedling stage and survival rate f at the bud stage for different haplotypes of \u003cem\u003eOsLTPL125\u003c/em\u003e. *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05 and **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01. (E-F) Violin plots showing germination energy and germination rate at the germination stage for different haplotypes of \u003cem\u003eOsLTPL125\u003c/em\u003e. (G) Expression analysis of \u003cem\u003eOsLTPL125 \u003c/em\u003eby qRT-PCR. CK, control; 8 h, cold stress for 8 hours. B258, Xingguo; B164, Qingke; B246, Laozaogu; B091, Xiaohonggu. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01. Error bars indicate ± SD; n = 3 independent biological replicates. (H) Frequency distribution of haplotypes Hap1, Hap2, and Hap3 across different subpopulations.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-7495419/v1/ae45034af662a6048b280dd4.png"},{"id":91617025,"identity":"e315a605-024d-4775-a92f-7e2de79fdaf0","added_by":"auto","created_at":"2025-09-18 10:48:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5911950,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7495419/v1/b8d72c03-499f-4aa3-b370-ff919cf9d80c.pdf"},{"id":91615698,"identity":"71ed7466-f528-424b-92a8-10eccf810f50","added_by":"auto","created_at":"2025-09-18 10:32:49","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":18742082,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7495419/v1/6ca1d06d6c91092bd1a4dda9.docx"},{"id":91615683,"identity":"2ac339ac-c885-4ec1-b24c-4d8d188570d2","added_by":"auto","created_at":"2025-09-18 10:32:48","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":25127,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7495419/v1/7c80d01650de033786aa934b.xlsx"},{"id":91615685,"identity":"0d0b4dec-1548-4c27-a823-714f3f892898","added_by":"auto","created_at":"2025-09-18 10:32:48","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":10388,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7495419/v1/e13e0141afa3cca31bd5b5f7.xlsx"},{"id":91616810,"identity":"d00569dd-be42-4f11-a394-15350292575c","added_by":"auto","created_at":"2025-09-18 10:40:48","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":10338,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7495419/v1/703a58cc0bae34faffae1c11.xlsx"},{"id":91616811,"identity":"f0f8ba36-0be2-42c4-a7a8-eadffb199a98","added_by":"auto","created_at":"2025-09-18 10:40:49","extension":"xlsx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":600042,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7495419/v1/c94b1d2fdb1ef2c2c819244b.xlsx"},{"id":91616812,"identity":"6031a4d1-090d-45d1-aa42-3edfc98a888d","added_by":"auto","created_at":"2025-09-18 10:40:49","extension":"xlsx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":2092777,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7495419/v1/b3c2169cb5ed8d75274cd0e1.xlsx"},{"id":91615693,"identity":"74577fab-4a45-40da-941c-28dd9dc02b98","added_by":"auto","created_at":"2025-09-18 10:32:49","extension":"xlsx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":14936,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7495419/v1/4acc621e9ec6bd87fe9dd389.xlsx"},{"id":91615694,"identity":"efe8c6be-2716-49eb-a21c-4b517c3deea9","added_by":"auto","created_at":"2025-09-18 10:32:49","extension":"xlsx","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":27452,"visible":true,"origin":"","legend":"","description":"","filename":"TableS7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7495419/v1/71b8b9ab22f0fd79e4a83fd3.xlsx"},{"id":91615691,"identity":"1eca6604-94d1-415a-9ba0-e9705c241789","added_by":"auto","created_at":"2025-09-18 10:32:49","extension":"xlsx","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":10359,"visible":true,"origin":"","legend":"","description":"","filename":"TableS8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7495419/v1/a343120701c9a929d1631d91.xlsx"}],"financialInterests":"","formattedTitle":"Discovery of Cold tolerance genes and Favorable Alleles in Kam Sweet Rice Across Various Growth Stages","fulltext":[{"header":"Key message ","content":"\u003cp\u003eCombined association and selection sweep analyses identified \u003cem\u003eOsCTD2\u003c/em\u003e and \u003cem\u003eOsLTPL159\u003c/em\u003e as key candidate genes associated with cold tolerance at both germination and seedling stages.\u003c/p\u003e\n"},{"header":"Introduction","content":"\u003cp\u003eRice (\u003cem\u003eOryza sativa\u003c/em\u003e), a thermophilic crop originating from tropical and subtropical regions, is highly sensitive to low temperatures. Cold stress\u0026mdash;one of the major global abiotic constraints\u0026mdash;causes stage-specific damage during germination, seedling growth, and booting, leading to substantial regional yield losses, particularly in temperate japonica and high-altitude indica cultivation areas. In China, chilling damage alone results in estimated annual grain losses of 5.0\u0026ndash;10.0 million metric tons, equivalent to 6\u0026ndash;12% of total national production (National Agricultural Statistics, 2023). These losses underscore the urgency of breeding cold-tolerant rice varieties to safeguard food security and promote sustainable agriculture.\u003c/p\u003e\n\u003cp\u003eKam Sweet Rice (KSR), designated as a Specialty Rice by the FAO International Rice Commission (IRC), is a distinctive germplasm domesticated and preserved for millennia by the Dong ethnic community in Congjiang County, Qiandongnan Prefecture, Guizhou Province (Bedigian et al. 2003). Congjiang County lies on the southeastern Yunnan\u0026ndash;Guizhou Plateau, transitioning from the Miaoling Mountains to the Guangxi hills. Mountainous and hilly terrain covers 98% of its area, with elevations from 137 m to 1,676 m, producing a topographic relief of 1,539 m. This altitudinal range creates diverse microclimates: higher elevations are foggy and cool with limited thermal accumulation, whereas lower areas are warm, rainy, and receive abundant precipitation. These terraced ecosystems provide ideal conditions for selecting indigenous KSR varieties. KSR combines high grain quality with resilience to low soil fertility, drought, waterlogging, and shade, and is particularly notable for strong cold tolerance. Our previous population structure analysis showed that KSR accessions from Qiandongnan (QDN) form a distinct genetic cluster, with significantly lower genetic diversity (\u0026pi;) and higher genetic differentiation (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e) compared to accessions from Central China (CC), East China (EC), South China (SC), Southwest China (SW), and non-QDN Guizhou cultivars (GZ) (Liu et al. 2023). This distinct genomic profile makes KSR an exceptional resource for identifying cold tolerance genes.\u003c/p\u003e\n\u003cp\u003eCold tolerance in rice is a quantitative trait regulated by multiple genetic factors (Lv et al. 2019; Mishra et al. 1996). Mapping quantitative trait loci (QTLs) and identifying functional genes associated with cold tolerance provide a targeted strategy for developing resilient cultivars through molecular breeding. Recent advances in QTL mapping and genome-wide association studies (GWAS) have uncovered numerous cold tolerance\u0026ndash;related QTLs across key developmental stages, including germination, seedling, and booting, with several now cloned. At the germination stage,\u003cem\u003e\u0026nbsp;OsNAC5\u003c/em\u003e (Li et al. 2024) and \u003cem\u003eOsbZIP23\u003c/em\u003e (Sun et al. 2024) have been identified. Seedling-stage genes include \u003cem\u003eHAN1\u003c/em\u003e (Mao et al. 2019), \u003cem\u003eCOLD1\u003c/em\u003e (Luo et al. 2021), \u003cem\u003eCOLD11\u003c/em\u003e (Li et al. 2023), \u003cem\u003eCOG1\u003c/em\u003e (Xia et al. 2023), \u003cem\u003eCOG2\u003c/em\u003e (Feng et al. 2023), \u003cem\u003eOsSEH1\u003c/em\u003e (Gu et al. 2023), \u003cem\u003eCOG4\u003c/em\u003e (Sun et al. 2024), \u003cem\u003eCOLD6\u003c/em\u003e (Luo et al. 2024) and \u003cem\u003eCTK1\u003c/em\u003e (Wu et al. 2024) and \u003cem\u003eOsbZIP72\u003c/em\u003e (Gu et al. 2024). At booting, notable genes include \u003cem\u003eCtb1\u003c/em\u003e (Saito et al. 2010; Saito et al. 2004; Saito et al. 2001), \u003cem\u003eOsLea9\u003c/em\u003e-\u003cem\u003eOsMAPK3\u003c/em\u003e (Lou et al. 2022), \u003cem\u003eCTB3\u003c/em\u003e (Li et al. 2025), \u003cem\u003eCTB6\u0026nbsp;\u003c/em\u003e(Gao et al. 2025), and \u003cem\u003eCTF1\u003c/em\u003e (Dong et al. 2025). Several genes, such as \u003cem\u003eCTB4a\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Li et al. 2021; Zhang et al. 2017) , \u003cem\u003ebZIP73\u003c/em\u003e (Liu et al. 2018; Liu et al. 2019), \u003cem\u003eCTB2\u003c/em\u003e (Li et al. 2021), \u003cem\u003eCOG3\u003c/em\u003e (Liu et al. 2024), \u003cem\u003eOsSRO1c\u003c/em\u003e (Hu et al. 2024a; You et al. 2014), and \u003cem\u003eCTB5\u003c/em\u003e (Guo et al. 2025), contribute to cold tolerance at both the seedling and booting stages.\u003c/p\u003e\n\u003cp\u003ePrevious GWAS analyses have identified several cold tolerance\u0026ndash;associated genes, including \u003cem\u003eOsLea9\u0026ndash;OsMAPK3\u003c/em\u003e, \u003cem\u003eCTK1\u003c/em\u003e, \u003cem\u003eOsSRO1c\u003c/em\u003e, \u003cem\u003eOsbZIP72\u003c/em\u003e, and \u003cem\u003eCTB5\u003c/em\u003e. For example, Lou et al. (Lou et al. 2022) selected four rice varieties with contrasting cold response phenotypes and identified two key reproductive-stage regulators, \u003cem\u003eOsLea9\u0026nbsp;\u003c/em\u003eand \u003cem\u003eOsMAPK3\u003c/em\u003e, by integrating RNA-seq data with GWAS results. The superior haplotype combination \u003cem\u003eOsMAPK3\u003c/em\u003e^Geng/\u003cem\u003eOsLEA9\u003c/em\u003e^KL enhances low-temperature adaptation in temperate japonica cultivated in high-altitude environments. Similarly, Dong et al. (Dong et al. 2025) identified \u003cem\u003eOsCTK1\u003c/em\u003e as a seedling-stage cold tolerance regulator\u003cem\u003e\u0026nbsp;\u003c/em\u003ethrough a GWAS scan of 413 diverse accessions. Three putative substrates\u0026mdash;acidic ribosomal protein \u003cem\u003eOsP3B\u003c/em\u003e, cyclic nucleotide-gated ion channel \u003cem\u003eOsCNGC9\u003c/em\u003e, and dual-specific mitogen-activated protein kinase phosphatase \u003cem\u003eOsMKP1\u003c/em\u003e\u0026mdash;are each involved in chilling tolerance.\u003c/p\u003e\n\u003cp\u003eHu et al. (Hu et al. 2024a; You et al. 2014) identified the pleiotropic gene \u003cem\u003eOsSRO1c\u003c/em\u003e via a GWAS of seedling cold tolerance in a core set of 529 accessions. The \u003cem\u003eOsSRO1c\u003c/em\u003e protein forms a heteromeric complex with \u003cem\u003eOsDREB2B\u003c/em\u003e, co-regulating transcription of the pivotal cold tolerance gene \u003cem\u003eCOLD1\u003c/em\u003e, thereby enhancing adaptation at both seedling and heading stages. Gu et al., (Gu et al. 2024) working with 540 accessions, detected \u003cem\u003eOsbZIP72\u003c/em\u003e within the significant locus \u003cem\u003eqCTS9.1\u003c/em\u003e; functional analysis showed it enhances cold tolerance by modulating reactive oxygen species (ROS) accumulation. Guo et al. (Guo et al. 2025) identified the transcription factor \u003cem\u003eCTB5\u003c/em\u003ethrough QTL mapping in biparental populations and GWAS of 155 accessions, revealing its dual regulatory role at the booting and seedling stages. \u003cem\u003eCTB5\u003c/em\u003e directly regulates \u003cem\u003ePYL9\u003c/em\u003e and improves seedling cold tolerance by reducing ROS accumulation.\u003c/p\u003e\n\u003cp\u003eDespite these advances, the number of identified cold tolerance genes remains limited, and novel genes applicable to molecular breeding are still lacking. In this study, we conducted a comprehensive evaluation of cold tolerance in 104 KSR accessions and 268 other rice landraces across the germination, bud, and seedling stages. GWAS identified cold tolerance genes and their superior haplotypes across various growth stages. These findings provide a strong foundation for precision cloning of cold tolerance genes and support the development of molecular breeding strategies to produce cold-tolerant rice varieties.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eMaterials and genomic data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed a core panel of 372 rice accessions from previous studies, comprising 104 KSR accessions and 268 other landraces collected from 12 provinces. The curated dataset contained 3,566,872 quality single-nucleotide polymorphisms (SNPs) with an average sequencing depth of ~12.43\u0026times; per accession (Liu et al. 2023, Table S1).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eIdentification of cold tolerance in rice at the germination stage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing the Description Specification and Data Standard of Rice Germplasm Resources (Han, 2006) and the method of Han et al. (Han and Zhang, 2004) cold tolerance was assessed at the germination, bud, and seedling stages.\u003c/p\u003e\n\u003cp\u003eThe experiment followed a completely randomized design with three biological replicates per treatment. Each replicate contained 50 surface-sterilized seeds, treated with 0.5% sodium hypochlorite (NaClO) for 15 minutes and rinsed three times with sterile distilled water. Seeds were aseptically placed on moistened filter paper in 90 mm Petri dishes. After 24 hours of imbibition in darkness at 25 \u0026plusmn; 1 \u0026deg;C, the dishes were transferred to an illuminated growth chamber maintained at 14 \u0026plusmn; 0.5 \u0026deg;C for a 20-day germination period. Germination was considered successful when both the radicle (\u0026gt;1 mm) and coleoptile had emerged. Germination energy and germination rate were calculated as:\u003c/p\u003e\n\u003cp\u003eGermination energy (%) = (Total seeds tested/Number of seeds germinated within specified days) \u0026times;100%\u003c/p\u003e\n\u003cp\u003eGermination rate (%) = (Total seeds tested/Total germinated seeds) \u0026times;100%\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eIdentification of cold tolerance in rice at the bud stage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree biological replicates were established, each containing 50 surface-sterilized seeds (treated with 0.5% NaClO for 15 minutes, followed by triple rinsing with sterile distilled water). After standard germination treatment, 30 seeds with uniform coleoptile length (approximately 5.0 mm) were selected per replicate. The Seeds were then subjected to low-temperature stress at 5 \u0026deg;C for 10 days in darkness, followed by transfer to a controlled environment (20\u0026ndash;30 \u0026deg;C) for 7 days. After 7 days, the number of dead seedlings was recorded, and the survival rate of seedlings was calculated as follows:\u003c/p\u003e\n\u003cp\u003eSurvival rate\u0026nbsp;(%) = [1 - (Number of dead seedlings/Total seedlings)] \u0026times; 100%\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eIdentification of cold tolerance in rice at the seedling stage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe seedlings were surface-sterilized following standard protocols, soaked, and germinated. After germination, the seedlings were grown in a greenhouse or outdoor environment, with temperatures maintained between 20\u0026ndash;30 \u0026deg;C. Upon reaching the three- to four-leaf stage, seedlings were subjected to a 7-day low-temperature treatment at 10\u0026deg;C. Following the cold treatment, seedlings were transferred to outdoor recovery conditions (20\u0026ndash;30 \u0026deg;C) for a 7 days. Cold tolerance was evaluated based on a leaf withering grade scale:\u003c/p\u003e\n\u003cp\u003eGrade 1: Strong cold tolerance, all leaves remain green or nearly green.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGrade 3: Strong cold tolerance, leaves show slight decolorization or yellowing.\u003c/p\u003e\n\u003cp\u003eGrade 5: moderate cold tolerance, most leaves are yellowed.\u003c/p\u003e\n\u003cp\u003eGrade 7: weak cold tolerance, leaves dry out, and some seedlings die.\u003c/p\u003e\n\u003cp\u003eGrade 9: Very weak\u0026nbsp;cold tolerance, most or all seedlings are dead.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eThe statistical analysis of phenotypic data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experiments followed a completely randomized design with triple replication. Mean values, standard deviations, ranges, and coefficients of variation (CV) were analyzed using ANOVA with Duncan\u0026apos;s multiple range test (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) in IBM SPSS v26. The diversity index (H\u0026rsquo;) was calculated as described by Xue et al. (Xue et al. 2020)\u0026nbsp;using Excel 2019. Student\u0026apos;s \u003cem\u003et\u003c/em\u003e-tests, with Bonferroni correction,\u0026nbsp;were employed to analyze variations in cold tolerance values across various growth stages among the subpopulations, using the \u0026lsquo;ggstatsplot\u0026rsquo; and \u0026lsquo;ggplot2\u0026rsquo; packages in R. Additionally, the variances in cold tolerance-related trait values between haplotypes of each gene were assessed using the same statistical methods. Scatter plots, violin plots, and stacked column graphs were generated using the \u0026lsquo;ggpubr\u0026rsquo; and \u0026lsquo;ggplot2\u0026rsquo; packages in R.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eDetection of selective sweeps\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify regions of selective scanning in each subpopulation, Likelihood ratio (CLR) values for each window were calculated using the command \u0026apos;-CLR -grid 4000\u0026apos; (where \u0026apos;grid 4000\u0026apos; partitions each chromosome into 4000 windows). The top 5% of windows with the highest CLR values were identified as regions of selective scanning. Peak maps of CLR were generated using the \u0026apos;CMplot\u0026apos; package in R.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eGWAS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCold tolerance across various growth stages (germination, bud, and seedling) was evaluated using the mixed linear model (MLM) available in the Tassel (v5.0) software (PJ et al., 2007). Based on the results of previous principal component analysis (PCA), the top three PCs were selected as covariates for inclusion in downstream analyses. The significance threshold for GWAS was set at \u003cem\u003ep\u003c/em\u003e \u0026lt; 1 \u0026times; 10⁻⁴ through Bonferroni correction, based on the effective number of independent SNPs (Li et al. 2012). To functionally annotate significant SNP markers, SNPeff software was used, following established protocols (Cingolani et al. 2012; Cui et al. 2022). Candidate gene identification involved scanning genomic regions 100 kb upstream and downstream of significant SNPs. Data visualization was performed using two approaches: 1) Manhattan plots generated with the \u0026lsquo;CMplot\u0026rsquo; package in the R environment, and 2) linkage disequilibrium patterns illustrated through heatmaps rendered by LDBlockShow (v1.40) (Dong et al. 2020).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eRNA-seq data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo varieties, Gouhuanggang (cold-tolerant) and Heigu (cold-sensitive), were subjected to a 7 \u0026deg;C low-temperature treatment at the seedling stage. Leaf samples were collected before the low-temperature treatment and 8 hours after treatment. Three biological replicates were performed. Total RNA was extracted using a RNeasy Plant Mini Kit (Qiagen). Sequencing was conducted on the Illumina platform (Illumina, San Diego, CA, USA), generating Approximately 6 Gb of 150-bp paired-end reads per sample. Sequence alignment was performed using TopHat2 (v2.1.1) to map quality-filtered RNA-seq reads against the japonica rice reference genome assembly IRGSP-1.0 (Trapnell et al. 2012). Differential gene expression analysis was performed with DESeq2 (Love et al. 2014), based on the read count data from the gene expression analysis. Weighted gene co-expression network analysis (WGCNA) was conducted using Cytoscape (v3.9.1) (Shannon et al. 2003). The transcriptomic sequencing data of this study was deposited in the NCBI Sequence Read Archive under the accession number: PRJNA1181572.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eqRT-PCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted using TRIzol reagent (Invitrogen, Cat#. AM1912). \u0026nbsp;RNA (2 \u0026micro;g) was reverse transcribed to cDNA using a cDNA Synthesis SuperMix (TransGen, Cat#. AU311-02). qRT-PCR was performed using THUNDERBIRD SYBR qPCR Mix without Rox reagent (TOYOBO, Cat#. QPS-20(-)). The total reaction mix consisted of 10 \u0026mu;L of SYBR qPCR Mix, 7.2 \u0026micro;L of double-distilled water, 2 \u0026mu;L of complementary DNA (cDNA) template, and 0.4 \u0026mu;L of each upstream and downstream primer. The 2\u003csup\u003e\u0026minus;∆∆Ct\u003c/sup\u003e method was used to evaluate transcript levels of candidate genes from three biological replicates, analyzed using Prism v10. UBI was used as the internal control, and the primers for qRT-PCR are listed in the Supplementary Table (Table S2).\u003c/p\u003e\n"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePhenotypic diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed four cold tolerance\u0026ndash;related traits in 104 KSR accessions and 268 other rice landraces after low-temperature treatments at different growth stages (Table S3). At germination, germination energy ranged from 0% to 96.67% (mean: 24.37%), while germination rate ranged from 3.33% to 100% (mean: 82.13%). at the bud stage, survival rate varied from 0% to 97.78%, averaging 40.73%. At the seedling stage, cold tolerance grade ranged from 2 to 8, with a mean of 5.76.\u003c/p\u003e\n\u003cp\u003eThe coefficient of variation (CV) for these traits ranged from 24.02% to 92.74%, with the lowest CV observed for cold tolerance grade at the seedling stage (24.02%) and the highest for germination energy at the germination stage (92.74%). the diversity index (H\u0026prime;) also varied among traits, with the lowest value recorded for cold tolerance grade at the seedling stage (0.30) and the highest for survival rate at the bud stage (0.88).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhenotypic variation within subpopulations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrevious studies classified the 372 accessions into six subpopulations based on genetic structure and geographical origin: Kam Sweet Rice (KSR), Central China (CC), East China (EC), South China (SC), Southwest China (SW), and non-QDN Guizhou cultivars (GZ) (Liu et al. 2023). Our results show that KSR consistently outperformed at least two other subpopulations in cold tolerance across growth stages (Fig. 1A-D). After cold stress, KSR had a germination energy of 35.6% and a germination rate of 94.0%, both significantly higher than CC (13.0% and 68.7%; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), EC (20.9% and 74.6%; \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01), SC (19.3% and 73.6%; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), and GZ (15.9% and 77.7%; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01) (Fig. 1A-B).\u003c/p\u003e\n\u003cp\u003eAt the bud stage, KSR\u0026rsquo;s survival rate was 42.2%, significantly higher than SW (27.4%,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt; 0.01) and GZ (36.8%, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), and slightly higher than SC (41.0%) (Fig. 1C). At the seedling stage, KSR recorded a cold tolerance grade of 5.0, significantly better than EC (5.5, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), SC (5.6, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), SW (6.7, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), and GZ (6.3, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), and marginally better than CC (5.2) (Fig. 1D). These findings confirm KSR as an important genetic resource for cold tolerance improvement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of selective signals of six subpopulations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo better understand selective footprints in KSR and the 268 other accessions, we performed a genome scan using a composite likelihood ratio (CLR) approach to detect putative selection signatures across the six subpopulations: KSR, CC, EC, SC, SW, and GZ (Fig. 2, Fig.S1, Table S4).\u0026nbsp;In total, we identified 8,268 potential selective regions containing 5,954 genes, with an average size of 7.77 kb\u0026mdash;smaller than previously reported (Fig 2A-B; Table S4) (Liu et al. 2025). Among these, 684 common selective sweeps containing 566 genes were present in at least four subpopulations, representing strong candidates for selection during domestication (Figure 2A-B and Table S4). Notably, 43.3% of these sweeps overlapped with previously reported genomic regions associated with breeding improvements from different eras, indicating that they underwent selection during both domestication and later improvement (Cui et al. 2022). These regions represent priority targets for efficiently mining favorable alleles.\u003c/p\u003e\n\u003cp\u003eAs expected, several cloned genes directly involved in cold tolerance regulation were detected in KSR selective sweeps, including \u003cem\u003eLTG1\u0026nbsp;\u003c/em\u003e(Lu et al. 2014), \u003cem\u003eHAN1\u0026nbsp;\u003c/em\u003e(Mao et al. 2019), \u003cem\u003eCTB4a\u003c/em\u003e (Li et al. 2021; Zhang et al. 2017), \u003cem\u003eCOLD1\u0026nbsp;\u003c/em\u003e(Luo et al. 2021), and \u003cem\u003eCTB2\u0026nbsp;\u003c/em\u003e(Li et al. 2021) (Fig 2C). In addition, \u003cem\u003eHAN1\u003c/em\u003e and \u003cem\u003eCOLD1\u0026nbsp;\u003c/em\u003eappeared in the selective sweeps of SC and GZ, while \u003cem\u003eCTB2\u003c/em\u003e was found in CC. \u003cem\u003eHAN1\u003c/em\u003e encodes an oxidase that catalyzes the conversion of the bioactive jasmonoyl-L-isoleucine (JA-Ile) to its inactive form, 12-hydroxy-JA-Ile (12OH-JA-Ile), fine-tuning the jasmonic acid\u0026ndash;mediated chilling response. In this study, we observed that the three haplotypes of \u003cem\u003eHAN1\u003c/em\u003e differed significantly in cold tolerance grade at the seedling stage and in survival rate at the bud stage (Fig. 2D-F). the superior haplotype, Hap1, was most frequent in KSR (83%), with lower frequencies in CC (56%), EC (54%), SW (14%), SC (43%), and GZ (54%) (Fig. 2G). These findings suggest that \u003cem\u003eHAN1\u003c/em\u003e underwent selection during KSR domestication, consistent with previous reports showing that the superior haplotype is mainly distributed in rice from northern China, Japan, and the Yunnan\u0026ndash;Guizhou Plateau (Mao et al. 2019). Similarly, we found that the frequency of the superior haplotype (conferring cold tolerance at seedling and bud stages) of \u003cem\u003eCTB4a\u003c/em\u003e was highest in KSR (63%), compared with CC (45%), EC (50%), SC (53%), SW (8%), and GZ (35%) (Fig S2).\u003c/p\u003e\n\u003cp\u003eFurthermore, numerous cloned genes associated with yield-related traits were detected within selective sweeps. In KSR, these included \u003cem\u003eGIF1\u003c/em\u003e (Wang et al. 2008), \u003cem\u003eGAD1\u0026nbsp;\u003c/em\u003e(Jin et al. 2016), \u003cem\u003eGNP1\u0026nbsp;\u003c/em\u003e(Wu et al. 2016), \u003cem\u003eGW5\u003c/em\u003e (Liu et al. 2017), \u003cem\u003eqTGW3\u003c/em\u003e (Hu et al. 2018), \u003cem\u003eGW2\u003c/em\u003e (Hao et al. 2021),\u003cem\u003e\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;An-1\u003c/em\u003e (Song et al. 2022) (Fig. 2C). Several of these genes\u0026mdash;\u003cem\u003eGW2\u003c/em\u003e, \u003cem\u003eqTGW3\u003c/em\u003e, \u003cem\u003eAn-1\u003c/em\u003e,\u003cem\u003e\u0026nbsp;GIF1,\u0026nbsp;\u003c/em\u003eand \u003cem\u003eGNP1\u003c/em\u003e\u0026mdash;were also present in the selective sweeps of SC and GZ (Fig S1). In addition, \u003cem\u003ePROG1\u003c/em\u003e (Tan et al. 2008), \u003cem\u003eGS5\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Li et al. 2011), \u003cem\u003eGL3.1\u0026nbsp;\u003c/em\u003e(Qi et al. 2012), and \u003cem\u003eBG1\u0026nbsp;\u003c/em\u003e(Liu et al. 2015) were identified in the selective sweeps of CC and GZ (Fig S1). As expected, multiple disease resistance genes\u0026mdash;\u003cem\u003eXa26\u0026nbsp;\u003c/em\u003e(Wang et al. 2005), \u003cem\u003eEBR1\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e(You et al. 2016), \u003cem\u003eBph6\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Wu et al. 2022), and \u003cem\u003eBph14\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Guo et al. 2023)\u0026mdash;were located within selective sweeps spanning at least two subpopulations, including KSR (Fig. 2C, Fig S1). These genes likely played important roles in the domestication and adaptation of KSR and other rice populations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene-co expression network analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA-seq analysis was conducted on the cold-tolerant accession Gouhuanggang and the\u0026nbsp;cold-sensitive accession Heigu under both control and cold stress conditions. Compared with the control, Gouhuanggang exhibited 4,923 differentially expressed genes (DEGs), including 2,447 upregulated and 2,476 downregulated genes, while Heigu showed 6,090 DEGs, with 3,467 upregulated and 2,623 downregulated (Fig S3A-B; Table S5). Of these, 815 DEGs were commonly upregulated and 645 were commonly downregulated in both accessions (Fig S3C-D; Table S5).\u003c/p\u003e\n\u003cp\u003eOur weighted gene co-expression network analysis (WGCNA) identified 26 co-expressed gene modules (Fig S4A-B). The blue module (\u003cem\u003er\u003c/em\u003e = 0.98, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01) and the magenta module (\u003cem\u003er\u003c/em\u003e = 1, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01) were strongly correlated with cold treatment in Gouhuanggang and together contained 5,145 genes (Fig S4C). these modules included several known cold tolerance genes, such as\u003cem\u003e\u0026nbsp;COLD11\u003c/em\u003e (Luo et al. 2021), \u003cem\u003eOsMAPK3\u003c/em\u003e (Xia et al. 2021), and \u003cem\u003eOsRBCS3\u003c/em\u003e (Hu et al. 2024b),. Gene Ontology (GO) annotation showed that genes in these modules primarily regulate cellular activities, including membrane stabilization, signal perception, transcriptional regulation, and nitrogen metabolism homeostasis\u0026mdash;processes that collectively contribute to cold adaptation (Fig S5).\u003c/p\u003e\n\u003cp\u003eWithin the gene expression networks of these two modules, 37 hub genes were identified (Table S6). Integrating functional annotation with differential expression analysis, we pinpointed five high-confidence hub genes associated with cold tolerance: \u003cem\u003eLOC_Os01g42280\u003c/em\u003e, \u003cem\u003eLOC_Os02g12840\u003c/em\u003e, \u003cem\u003eLOC_Os04g55159\u003c/em\u003e, \u003cem\u003eLOC_Os06g06790\u003c/em\u003e, and \u003cem\u003eLOC_Os07g48730\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Fig S6, Table S6).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGWAS of cold tolerance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed a genome-wide association study (GWAS) to identify loci associated with cold tolerance across rice growth stages, detecting 89 significant loci (\u003cem\u003ep\u003c/em\u003e \u0026lt; 1 \u0026times; 10⁻⁵; Fig. 3; Table S7). Of these, 17 were associated with germination energy and 40 with germination rate at the germination stage; 9 loci were linked to seedling survival rate at the bud stage; and 28 were associated with cold tolerance grade at the seedling stage. Notably, four significant loci, \u003cem\u003eqCTS2.1\u003c/em\u003e (Chr2: 6,479,097\u0026ndash; 6,858,939), \u003cem\u003eqCTS4.2\u003c/em\u003e (Chr4: 32,044,990\u0026ndash;32,775,406), \u003cem\u003eqCTS6.5\u003c/em\u003e (Chr6: 16,541,073‒16,924,542), and \u003cem\u003eqCTS7.2\u003c/em\u003e (Chr7: 20,437,771\u0026ndash; 20,759,260), were identified across two various growth stages, suggesting their role in regulating cold tolerance at various growth stages in rice simultaneously (Table S7).\u003c/p\u003e\n\u003cp\u003eIn total, 28 loci (31%) overlapped with previously reported quantitative trait nucleotides (QTNs) or known cold tolerance genes, including \u003cem\u003eCOLD1\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Luo et al. 2021), \u003cem\u003eOsWRKY53\u003c/em\u003e (Tang et al. 2022), and \u003cem\u003eCOLD6\u0026nbsp;\u003c/em\u003e(Luo et al. 2024) (Fig. 3, Table S7). Notably, 56 loci (59.6%) overlapped with regions under selective sweep. Among these, two loci\u0026mdash;\u003cem\u003eqCTS2.1\u0026nbsp;\u003c/em\u003e(Chr2: 6,479,097\u0026ndash; 6,858,939) and \u003cem\u003eqCTS4.2\u003c/em\u003e (Chr4: 32,044,990\u0026ndash;32,775,406)\u0026mdash;located within selective sweep regions in at least five subpopulations, including KSR (Table S7). This overlap highlights their potential as key genomic targets for domestication and provides a solid foundation for further exploration and mining of cold tolerance candidate genes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCandidate genes for cold tolerance and their superior haplotypes across various growth stages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe significant locus \u003cem\u003eqCTS2.1\u003c/em\u003e, located at 6.47\u0026ndash;6.85 Mb on chromosome 2, was associated with cold tolerance at germination and seedling stages. This region falls within a linkage disequilibrium (LD) block (\u0026gt;200 kb) containing 11 genes with nonsynonymous mutations (Fig. 4A). Among these, \u003cem\u003eOsCTD2\u003c/em\u003e (\u003cem\u003eLOC_Os02g12840\u003c/em\u003e), a 7,071 bp gene encoding a DEAD-box ATP-dependent RNA helicase, emerged as a strong candidate. In the same protein family, TCD33 is known to regulate chloroplast development in rice seedlings under cold stress (Xiaomei et al. 2020). gene co-expression network analysis identified \u003cem\u003eOsCTD2\u003c/em\u003e as a high-confidence hub gene for cold tolerance (Table S6).\u003c/p\u003e\n\u003cp\u003eRNA-seq analysis showed that \u003cem\u003eOsCTD2\u003c/em\u003e was significantly upregulated in the cold-tolerant accession Gouhuanggang under cold stress, with no significant change observed in the cold-sensitive accession Heigu (Fig S6).\u0026nbsp;These findings support \u003cem\u003eOsCTD2\u003c/em\u003e as a robust candidate gene. Across the 372 rice accessions, 20 nonsynonymous mutations in the \u003cem\u003eOsCTD2\u003c/em\u003e coding region defined two haplotypes (Fig. 4B). Hap1 was identified as the superior haplotype, displaying significantly lower cold tolerance grades at the seedling stage than Hap2 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01) and higher survival rates at the bud stage (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01) (Fig. 4C-D). Furthermore, the Hap1 showed slightly higher average values for germination rate and germination energy at germination stage compared to Hap2 (Fig. 4E-F).\u003c/p\u003e\n\u003cp\u003eqRT-PCR confirmed that Hap1 accessions exhibited significantly increased \u003cem\u003eOsCTD2\u003c/em\u003e expression under cold stress compared with control conditions, whereas Hap2 accessions showed no significant expression changes\u0026mdash;consistent with RNA-seq results (Fig. 4G). Hap1 frequency varied markedly among subpopulations, being highest in KSR (80%), followed by CC (52%), EC (54%), SC (48%), SW (16%), and GZ (52%) (Fig. 4H). This distribution suggests that the superior Hap1 of \u003cem\u003eOsCTD2\u003c/em\u003e provides a selective advantage in KSR and represents a promising target for improving seedling-stage cold tolerance in rice breeding programs.\u003c/p\u003e\n\u003cp\u003eThe germination and seedling cold-tolerance locus \u003cem\u003eqCTS4.2\u003c/em\u003e (Chr4: 32.20\u0026ndash;32.77 Mb, \u003cem\u003ep\u003c/em\u003e \u0026lt; 1 \u0026times; 10⁻⁵) overlapped with a \u0026gt;200-kb LD block containing 21 genes with nonsynonymous SNPs (Fig. 5A). Among these, \u003cem\u003eOsLTPL125\u003c/em\u003e (\u003cem\u003eLOC_Os04g55159\u003c/em\u003e), a 1,963 bp gene encoding a lipid transfer protein (LTP) family protein, was identified as a strong candidate. Related LTP family members, including \u003cem\u003eOsLTPL159\u003c/em\u003e, \u003cem\u003ePsd1\u003c/em\u003e, and \u003cem\u003eOsHyPRP05\u003c/em\u003e, have been shown to mediate cold tolerance in rice (Deng et al. 2019; Fujino and Sekiguchi 2011; Zhao et al. 2020). gene co-expression network analysis confirmed \u003cem\u003eOsLTPL125\u003c/em\u003e as a high-confidence hub gene associated with cold tolerance (Table S6).\u003c/p\u003e\n\u003cp\u003eRNA-seq analysis revealed significant upregulation of \u003cem\u003eOs\u003c/em\u003e\u003cem\u003eLTPL125\u003c/em\u003e in the cold-tolerant accession Gouhuanggang under cold stress compared with control conditions, whereas no significant change was observed in the cold-sensitive accession Heigu (Fig S6). Haplotype analysis based on four SNPs identified three \u003cem\u003eOsLTPL159\u003c/em\u003e haplotypes (Fig. 5B). Hap1 conferred superior cold tolerance, showing significantly lower cold tolerance grades at the seedling stage than Hap2 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) and Hap3 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), as well as higher bud-stage survival rates (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01) (Fig. 5C-D).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, Hap1 exhibited significantly higher germination rate and germination energy than Hap2 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01) at the germination stage, with its mean values also marginally exceeding those of Hap3 (Fig. 5E-F). qRT-PCR analysis supported these results, showing that \u003cem\u003eOsLTPL159\u003c/em\u003e expression in Hap1 accessions increased significantly after cold stress, whereas Hap3 accessions exhibited no significant change\u0026mdash;consistent with RNA-seq findings (Fig. 5G). Hap1 frequency was highest in KSR (80%), followed by EC (56%), CC (47%), SC (42%), GZ (40%), and SW (16%) (Fig. 5H). This distribution pattern highlights Hap1 as a promising genetic resource for developing cold-tolerant rice varieties.\u003c/p\u003e\n\u003cp\u003eFurthermore, integrated haplotype analysis of genes with nonsynonymous mutations within QTL intervals, combined with functional annotation, identified two novel high-confidence candidates: \u003cem\u003eOsGRS7\u003c/em\u003e (\u003cem\u003eLOC_Os07g37210\u003c/em\u003e), associated with cold tolerance at the germination stage, and \u003cem\u003eOsBSR6\u003c/em\u003e (\u003cem\u003eLOC_Os06g34430\u003c/em\u003e), linked to bud-stage cold tolerance (Fig S7; Fig S8). Together, these findings expand the set of stage-specific cold tolerance genes and their superior haplotypes, providing a robust framework for targeted gene cloning and precision breeding to enhance cold resilience in rice.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eKSR is an invaluable genetic resource for improving cold tolerance in rice. Previous studies have confirmed its exceptional resilience, enabling adaptation to cold mountainous and valley environments. Unlike many varieties that fail to complete their life cycle in cold-water paddies, KSR maintains normal growth and produces viable yields under these challenging conditions. For example, Chen et al. (Chen et al et al. 1999) evaluated bud-stage cold tolerance in 286 KSR accessions and found that 179 (62.58%) exhibited moderate-to-high resistance.\u003c/p\u003e\n\u003cp\u003eIn the present study, we assessed cold tolerance in 104 KSR accessions and 268 other rice landraces across the germination, bud, and seedling stages.\u0026nbsp;comparative analysis among six subpopulations (CC, EC, SC, SW, GZ, and KSR) revealed marked phenotypic divergence, with KSR outperforming all other groups at every growth stage\u0026mdash;consistent with earlier findings.\u003c/p\u003e\n\u003cp\u003eAs expected, selective footprint analysis revealed several key cold tolerance genes in KSR, including \u003cem\u003eOsmyb4\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Vannini et al. 2004), \u003cem\u003eqLTG3-1\u0026nbsp;\u003c/em\u003e(Fujino and Sekiguchi 2011), \u003cem\u003eLTG1\u0026nbsp;\u003c/em\u003e(Lu et al. 2014), \u003cem\u003eHAN1\u0026nbsp;\u003c/em\u003e(Mao et al. 2019), \u003cem\u003eCTB4a\u003c/em\u003e (Li et al. 2021; Zhang et al. 2017), \u003cem\u003eCTB2\u0026nbsp;\u003c/em\u003e(Li et al. 2021), and \u003cem\u003eCOLD1\u0026nbsp;\u003c/em\u003e(Luo et al. 2021). Among these, \u003cem\u003eCOLD1\u003c/em\u003e and \u003cem\u003eCTB4a\u003c/em\u003e are major-effect genes. \u003cem\u003eCOLD1\u003c/em\u003e interacts with RGA1 to enhance GTPase activity, while \u003cem\u003eCTB4a\u0026nbsp;\u003c/em\u003ebinds to AtpB to stimulate ATP activity\u0026mdash;both contributing to improved cold tolerance. \u003cem\u003eCTB2\u003c/em\u003e has pleiotropic effects, enhancing resilience at the germination, seedling, and booting stages through dehydrin-mediated cellular protection.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eqLTG3-1\u003c/em\u003e is specifically expressed in the aleurone layer of the seed coat and the epiblast covering the coleoptile, where it may enhance germination vigor at low temperatures by promoting cellular vacuolization and tissue relaxation. \u003cem\u003eLTG1\u003c/em\u003e improves low-temperature germination by modulating ABA/GA homeostasis and has been identified in previous genomic studies as a domestication target under selection in rice landraces. Collectively, these genes have played pivotal roles in the domestication of KSR and reinforce its value as a rich source of cold tolerance alleles.\u003c/p\u003e\n\u003cp\u003eGWAS identified 89 significant loci associated with cold tolerance, of which 61 (69%) were novel, providing a valuable genetic basis for cloning new cold tolerance genes. The remaining 28 loci (31%) overlapped with previously reported QTLs or functional genes, supporting the reliability of our localization results. For example, Yang et al. (Thapa et al. 2020)\u0026nbsp;conducted a GWAS on low-temperature germination using 200 indica rice accessions and identified 159 loci, among which \u003cem\u003eqGI-5-1\u003c/em\u003e overlaps with our locus \u003cem\u003eqCTB5\u003c/em\u003e (Chr1: 651,903\u0026ndash;851,903). Using QTL-seq and linkage mapping, Yang et al.\u0026nbsp;(Yang et al. 2021) also identified \u003cem\u003eqCTS6\u003c/em\u003e on chromosome 6, associated with seedling-stage cold tolerance; In our study, \u003cem\u003eqCTS6.6\u003c/em\u003e (Chr6: 28,113,785\u0026ndash;28,439,348) overlapped with this QTL. In another study, Yang et al. (Yang et al. 2023)\u0026nbsp;mapped cold tolerance at the bud stage in an RIL population, identifying seven loci, including \u003cem\u003eqSR-6\u003c/em\u003e, which overlaps with our \u003cem\u003eqCTR6.4\u003c/em\u003e (Chr6: 10,059,539\u0026ndash;10,259,539).\u003c/p\u003e\n\u003cp\u003eSimilarly, Liu et al.\u0026nbsp;(Liu et al. 2025) performed a GWAS on cold tolerance across various growth stages in 166 Chinese rice mini-core accessions, identifying 63 loci. four of these\u0026mdash;\u003cem\u003eqCTSs2\u003c/em\u003e, \u003cem\u003eqCTSs6\u003c/em\u003e, \u003cem\u003eqCTBs8\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003eand \u003cem\u003eqCTBs12-1\u003c/em\u003e\u0026mdash;correspond to our loci \u003cem\u003eqCTS2.1\u003c/em\u003e (Chr2: 6,495,466\u0026ndash;6,733,833), \u003cem\u003eqCTS6.5\u0026nbsp;\u003c/em\u003e(Chr6: 16,724,542\u0026ndash;16,924,542), and \u003cem\u003eqCTR8.2\u0026nbsp;\u003c/em\u003e(Chr8: 21,603,782\u0026ndash;21,803,782), respectively. Rastogi et al.\u0026nbsp;(Rastogi et al. 2025) using a novel diversity panel of 238 accessions genotyped with the 7K SNP Cornell-IR LD Rice (C7AIR) array, detected 77 loci linked to 21 cold tolerance and related seedling-stage traits; among these, \u003cem\u003eqCTScold12\u003c/em\u003e overlaps with our \u003cem\u003eqCTE12.1\u0026nbsp;\u003c/em\u003e(Chr12: 1,626,962\u0026ndash;1,826,962). Notably, several known cold tolerance genes were also identified in our dataset, including \u003cem\u003eOsTB1\u0026nbsp;\u003c/em\u003e(Chen et al., 2018), \u003cem\u003eCOLD1\u003c/em\u003e (Luo et al. 2021), \u003cem\u003eOsWRKY53\u003c/em\u003e (Tang et al. 2022), \u003cem\u003eOsDREB1C\u0026nbsp;\u003c/em\u003e(Wang et al., 2022), and \u003cem\u003eCOLD6\u0026nbsp;\u003c/em\u003e(Luo et al. 2024). \u003cem\u003eCOLD1\u003c/em\u003e encodes a regulator of G-protein signaling localized to the plasma membrane and endoplasmic reticulum, with a key SNP distinguishing cold perception sensitivity between japonica and indica subspecies. This SNP confers cold tolerance in japonica by modulating \u003cem\u003eCOLD1\u003c/em\u003e\u0026rsquo;s regulation of GTPase activity in the G-protein \u0026alpha;-subunit. The cold sensor complexes of \u003cem\u003eCOLD6\u003c/em\u003e and \u003cem\u003eOSM1\u003c/em\u003e trigger the production of 2\u0026prime;,3\u0026prime;-cyclic adenosine monophosphate (2\u0026prime;,3\u0026prime;-cAMP), enhancing cold tolerance. Variation in the number of leucine codons (CTC) in \u003cem\u003eCOLD6\u003c/em\u003e between indica and japonica leads to reduced plasma membrane accumulation of japonica-derived \u003cem\u003eCOLD6\u003c/em\u003e under low temperatures, thereby enhancing tolerance. Finally, \u003cem\u003eOsWRKY53\u0026nbsp;\u003c/em\u003enegatively regulates cold tolerance at the booting stage by directly suppressing gibberellin (GA) biosynthesis genes \u003cem\u003eOsGA20ox1\u003c/em\u003e, \u003cem\u003eOsGA20ox3\u003c/em\u003e, and \u003cem\u003eOsGA3ox1\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eWe identified four key cold tolerance candidate genes distributed across different growth stages. At the seedling stage, the superior haplotypes of \u003cem\u003eOsCTD2\u003c/em\u003e and \u003cem\u003eOsLTPL159\u003c/em\u003e each reached a frequency of 80% in KSR. At the germination stage, the superior haplotype of \u003cem\u003eOsGRS7\u003c/em\u003e occurred in 72% of KSR accessions\u0026mdash;substantially higher than in other subpopulations (CC: 13%, EC: 0%, SC: 19%, SW: 23%, GZ: 38%) (Fig S7F). At the bud stage, the superior haplotype of \u003cem\u003eOsBSR6\u003c/em\u003e was also highly enriched in KSR (66%), compared to much lower frequencies in CC (9%), EC (9%), SC (31%), SW (10%), and GZ (23%) (Fig S8E).\u003c/p\u003e\n\u003cp\u003eAdditionally, \u003cem\u003eHAN1\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;CTB4a\u003c/em\u003e, two cold-tolerance gene detected in our selection signature analysis, exhibited its highest superior haplotype frequency in KSR, which were 83% and 63%, respectively. Liu et al. (Liu et al. 2023) previously reported two other domestication targets for cold tolerance in KSR\u0026mdash;\u003cem\u003eLTG1\u003c/em\u003e and \u003cem\u003eMYBS3\u003c/em\u003e\u0026mdash;with superior haplotypes present in 56% and 77% of accessions, respectively.\u0026nbsp;This consistent enrichment of favorable haplotypes in KSR provides strong genetic evidence for its adaptation to montane cold environments and further validates its value as a\u0026nbsp;resource for mining cold tolerance genes and alleles.\u003c/p\u003e\n\u003cp\u003eNotably, we identified 14 KSR accessions, such as Baishanuo, Gouhuanggang, and Tianhe, harboring superior alleles across different cold tolerance genes, all of which displayed strong cold tolerance in at least two growth stages (Table S8). These germplasm accessions can be strategically utilized as parental lines in cold tolerance breeding programs for rice. Collectively, our findings successfully identified four cold tolerance candidate genes and their superior haplotypes across various growth stages, highlight the genetic potential and practical breeding value of KSR for developing cold-tolerant rice cultivars. This work thus establishes a strong foundation for precision cloning of cold tolerance genes and support the development of molecular breeding strategies to produce cold-tolerant rice varieties.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceived and designed the experiments: Soon-Wook Kwon, Longzhi Han, Di Cui. Performed the experiments: Kunchi Yu, Chunhui Liu, Joohyun Lee. Analyzed and interpreted the data: Kunchi Yu, Chunhui Liu, Joohyun Lee. Contributed reagents, materials, analysis tools, or data: Longzhi Han, Di Cui, Zhengwu Zhao, Xiaoding Ma, Bing Han. Wrote the paper: Kunchi Yu.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. RS-2024-00391988), the National Natural Science Foundation of China (32201765), the Key R\u0026amp;D Program of Ningxia Hui Autonomous Region (2023BCF01010), Strategic Cooperation Project of Agricultural Science and Technology Innovation between Chongqing Municipal People's Government and Chinese Academy of Agricultural Sciences, the National Key Research and Development Program of China (2021YFD1200500), and the CAAS Science and Technology Innovation Program.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are provided within the article and its additional files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn behalf of all authors, the corresponding author states that there is no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBedigian D (2003) Specialty rices of the world. breeding, production and marketing. 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Plant Biotechnol J 18:756\u0026ndash;769\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Kam Sweet Rice, Cold-tolerance, Candidate gene, Superior haplotype, Various growth stages","lastPublishedDoi":"10.21203/rs.3.rs-7495419/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7495419/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Cold sensitivity in rice leads to significant yield losses. Identifying cold-tolerant germplasm and uncovering cold tolerance genes are essential for developing resilient rice varieties. Kam Sweet Rice (KSR) is notable for strong cold tolerance, which is an invaluable genetic resource for identifying such genes. In this study, we phenotyped cold tolerance across various growth stages using 104 KSR accessions and 268 other rice landraces. Genome-wide association studies (GWAS) identified 89 loci significantly associated with cold tolerance: 57 loci at the germination stage, 9 at the bud stage, and 28 at the seedling stage, with 61 loci (69%) being newly discovered. Through association and selection sweep analyses, we identified two high-confidence candidate genes, OsCTD2 and OsLTPL159, associated with cold tolerance at both germination and seedling stages. Haplotype analysis revealed significant differences in cold tolerance grade and survival rate among various haplotypes of these genes, with superior haplotypes predominantly present in KSR. RNA-seq and qRT-PCR results showed that the superior haplotypes of OsCTD2 and OsLTPL159 exhibited significantly higher expression in cold-tolerant accessions under cold stress, whereas no significant differences were observed in cold-sensitive accessions with the inferior haplotypes. These results indicated that OsCTD2 and OsLTPL159 are involved in cold stress response in rice. Additionally, we identified two other promising candidate genes and their superior haplotypes for cold tolerance: OsGRS7 at the germination stage and OsBSR6 at the bud stage. Our findings provide a solid foundation for cloning cold tolerance genes and offer insights for designing molecular breeding strategies.","manuscriptTitle":"Discovery of Cold tolerance genes and Favorable Alleles in Kam Sweet Rice Across Various Growth Stages","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-18 10:32:43","doi":"10.21203/rs.3.rs-7495419/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2026-01-17T12:54:30+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-09-27T04:28:24+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-11T14:51:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-30T13:27:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2025-08-30T08:58:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"334d4519-77a6-4647-88a3-9015cf774294","owner":[],"postedDate":"September 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T10:29:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-18 10:32:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7495419","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7495419","identity":"rs-7495419","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}