Multi-locus GWAS uncovers favorable alleles and candidate genes underlying water stress response in chrysanthemum

Multi-locus GWAS uncovers favorable alleles and candidate genes underlying water stress response in chrysanthemum | 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 Multi-locus GWAS uncovers favorable alleles and candidate genes underlying water stress response in chrysanthemum Yang Yang, Xiaoli Ou, Xiaoxue Wang, Yuhan Qiao, Yu Xiang, Siyue Wang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9267894/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Drought is one of the most serious abiotic stresses limiting plant productivity and becomes increasingly extreme worldwide due to the ongoing deterioration of the global climate. Chrysanthemum ( Chrysanthemum morifolium Ramat.), one of the four most popular cut flowers in the world, is sensitive to water-limited environment. However, the genetic basis and causal genes underlying drought tolerance (DT) remain largely unknown. In this research, multi-locus GWAS was employed to detect the genetic loci and candidate genes for DT in a diverse panel of 200 cut chrysanthemum accessions that were genotyped with 330,710 high-quality SNPs. As a result, 43 stable QTNs in single-environment analysis, 18 stable QTNs and 115 QEIs in multiple-environments analysis were identified via the 3VmrMLM method. Among the genes around stable QTNs and QEIs, eleven were homologous to known DT regulatory genes in other plants such as WRKY57 , MYB121 , GH3.6 . In addition, seven candidate genes were predicted to be associated with DT related traits by combing the functional annotation, transcriptomics data and quantitative real-time PCR. More importantly, four drought-tolerant cultivars harboring favorable alleles were identified as pre-bred material to improve tolerance of cultivated chrysanthemum. These findings provide robust insights into the genetic architecture of DT and offer valuable prospects for the molecular breeding of chrysanthemum. chrysanthemum drought tolerance GWAS genetic characteristics favorable alleles candidate genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Key Message Multi-locus GWAS identified 43 stable QTNs and 7 candidate genes for drought tolerance in chrysanthemum, providing valuable genetic resources for future genome-based molecular breeding. Introduction Drought is one of the most important abiotic stresses affecting plant growth, development and production [ 1 , 2 ]. As the global warming and climate change, the drought episodes will become frequency and severe in the future [ 3 , 4 ]. Therefore, developing water-deficit tolerant varieties represents an urgent priority for crop breeders to address increasing climatic instability. Chrysanthemum ( Chrysanthemum morifolium Ramat.), a globally important ornamental plant with great economic, culture, and symbolic value, which is sensitive to drought stress [ 5 ]. Drought stress not only limits the plant growth, but also drastically affects the quality of chrysanthemum. Clarifying the genetic architecture of drought tolerance (DT) in chrysanthemum will be of great value for its tolerance improvement. However, few studies have reported the genetic mechanisms underlying DT in chrysanthemum compared to other abiotic stresses, such as waterlogging [ 6 ] and salt [ 7 ]. Bi-parental mapping has been a widely used method for dissecting the genetic architecture of quantitative traits in chrysanthemum, successfully uncovering massive quantitative trait loci (QTL) underlying flowering time [ 8 ] plant architecture-related traits [ 9 , 10 ], flower shape [ 11 , 12 ]. Nevertheless, most of these studies depend on low-resolution linkage maps constructed by a limited number of gel-based markers with low genome coverage, resulting in the poor efficiency and accuracy of identifying markers linkage with the target traits. This greatly restricts the application of marker assisted selection (MAS) in chrysanthemum breeding programs. In addition, chrysanthemum is a complex polyploid with high heterozygosity, self-incompatibility, large size genome characteristics, and linkage phase is ambiguous. Therefore, traditional linkage mapping is still a great challenge to explore the genetic architecture of complex quantitative traits in chrysanthemum, especially for the traits controlled by small-effect genes including DT [ 13 , 14 ]. Based on the linkage disequilibrium (LD), genome wide association studies (GWAS) have emerged as an efficient tool for dissecting the genetic basis of complex agronomic traits [ 15 , 16 ]. Compared to traditional bi-parental mapping, GWAS eliminates the costs and time involved in population construction and effectively utilizes the numerous historical crossover event with a high mapping resolution [ 17 ]. To date, GWAS has been extensively used to explore the genetic basis of drought tolerance in rice [ 18 , 19 ], wheat [ 20 ], maize [ 14 , 21 ] and cotton [ 22 ]. Most of these studies used single-locus GWAS methods, such as mixed linear model (MLM) [ 23 ], efficient mixed-model association expedited (EMMAX) [ 24 ], and genome-wide efficient mixed model association (GEMMA) [ 25 ]. However, the effectiveness of single-locus GWAS methods in identifying quantitative trait nucleotides (QTNs) with marginal effects is constrained by the polygenic background and the stringent Bonferroni correction [ 26 ]. To solve the questions in single-locus GWAS, a series of multi-locus GWAS approaches were proposed to improve the power of QTNs detection. Among theme, a new methodological framework named 3VmrMLM enables to detect all types of loci including QTN-by-environment interactions (QEIs) and QTN-by-QTN interactions (QQIs) and estimates their effects with high powers [ 27 ]. Currently, the 3VmrMLM method has been successfully used for the study of epicotyl length in soybean [ 28 ], grain size traits in ratoon rice [ 29 ] and rice seed germination under drought stress [ 30 ]. DT is a complex quantitative trait governed by multiple genes and modulated by environmental factors [ 31 ]. The identification of QEIs can be used to discover elite genes suitable for different environments and provide valuable genetic sources for genetic improvement of chrysanthemum. In this research, we utilized a genetic panel of 200 diverse cut chrysanthemum accessions to revel the genetic mechanisms of DT in chrysanthemum through multi-locus GWAS. Based on 330,710 high-quality SNPs, the stable QTNs and QEIs significantly associated with DT were identified via 3VmrMLM method. Around these QTNs and QEIs, several candidate genes were mined by combing the functional annotation and experimental data. Findings from this study will provide novel perspectives for future gene cloning and studies of molecular mechanisms underlying DT in chrysanthemum. Materials and methods Plant materials and treatment As described by Lou et al. [ 32 ], a total of 200 representative cut chrysanthemum accessions were selected to explore their DT in this study (Table S1 ). All the materials were preserved at Nanjing Agricultural University Chrysanthemum Germplasm Resource Preserving Centre (Nanjing, China). The experiment was carried out flowing a randomized complete block design, with three replicates subjected to drought stress as treatment groups and two replicates under well-watered condition as control groups. Each replicate contained four plants per entry. In early July 2019, healthy and uniform cuttings of each chrysanthemum entry were planted in 105 holes sockets filled with a 1:1 mixture of perlite and peat. After about two weeks, each of the rooted cutting was transplanted into a black plastic flowerpot (5 cm up-diameter, 4 cm down-diameter, 9 cm deep) and placed in blue boxes (65 cm × 43 cm × 16 cm) in a greenhouse. Each blue box was irrigated 5 L water until soil water content reached full soil moisture capacity. Control plants were watered normally, while treatment plants were stopped watering until the panel showed visible difference in drought damage symptoms for 15 d and then recovered by replenishing water. Phenotypic trait measurements After 15 d exposure to drought stress and recovery for 2 d, the wilting index (WI) was respectively recorded for each plant, based on a symptom severity scale from 1 (slight wilting) to 5 (severe wilting) according to Li et al. [ 33 ]. The plant height (PH), fresh shoot weight (SFW), and dry shoot weight (SDW) were measured as the DT-related traits using the method described by Su et al. [ 34 ]. Half of the plants in each plot were used for the destructive measurement of biomass traits (PH, SFW and SDW) after drought treatment (named with a prefix ‘F’), and the remaining plants were used for the phenotypic trait measurements after re-watering (named with a prefix ‘A’). The derivative stress susceptibility index for each biomass trait (named with a suffix ‘I’) was calculated as the ratio of the performance of a genotype under stress or re-watering and its performance under well-watered condition. Detailed information of DT traits is listed in Table S2 . Evaluation for drought tolerance A membership function value of drought (MFVD, 0–1) integrating the WI and three stress susceptibility index traits investigated after drought stress (F_MFVD) and re-watering (A_MFVD) was respectively calculated for each entry following the formulae: if the trait was positively correlated with DT, U ij = ( X ij − X j min )/( X j max − X j min ); otherwise, U ij = 1 − ( X ij − X j min )/( X j max − X j min ), where U ij represented the membership function value of the j th indicator for the i th entry, X ij the observed value of the j th indicator for the i th variety, X j min and X j max , respectively, the minimum and maximum value of the j th indicator. The average of F_MFVD and A_MFVD was used as a comprehensive evaluation index for DT, with higher values indicating greater drought tolerance. Statistical analysis of phenotypic data To visualize the DT-related traits, descriptive statistical analysis for each phenotypic trait was performed, including the mean, minimum, maximum, skewness, kurtosis, and coefficient of variation ( CV ) values. In addition, we employed the ggplot2 package in R version 4.0.3 to draw violin diagrams depicting the extensive variation in phenotypic data. Pearson correlation analysis for 23 DT-related traits was performed in R version 4.0.3. The box plots were generated by R packages ggplot2 and ggsignif to show the significant difference among the 200 chrysanthemum accessions with different genotypes. Multi-locus genome-wide association studies for drought tolerance The genotyping-by-sequencing (GBS) technology was used for the genotyping of the 200 cut chrysanthemum accessions, which had been described in our previous report of Lou et al. [ 32 ]. Briefly, the genome sequence of C. morifolium cv. ‘Zhongshan Zigui’ [ 5 ] was served as a reference for SNP calling. The raw SNPs with genotyping rate < 85% and minor allele frequency (MAF) < 0.05 were discarded. As a result, a total of 330,710 high-quality SNPs was used for the subsequent analysis. To eliminate the effect of population structure on GWAS, the kinship matrix was computed using the EMMAX software [ 35 ], and the Q matrix was constructed using the top ten principal components derived from a principal component analysis (PCA) conducted with the GCTA software [ 36 ]. Based on 330,710 high-quality SNPs, the single environment module in R software IIIVmrMLM [ 37 ] was used to detect QTNs for DT-related traits in each environment, while its multiple environment module was used to identify QTNs and QEIs for the above traits using the phenotypic values collected under drought and well-watered conditions. A Bonferroni-corrected significance threshold was set at 0.05/m (where m represents the number of markers), corresponding to 2.48e-08, for identifying significant QTNs and QEIs. In order to mitigate the exclusion of potentially vital candidate genes, the insignificant QTNs and QEIs with a LOD score of ≥ 3.0 were considered as suggested QTNs and QEIs. Moreover, the QTNs associated with more than two traits were defined as stable QTNs. The associated network of stable QTNs in single environment analysis was visualized by software Cytoscape version 3.10.0 ( https://cytoscape.org ). UpSet plots were used to display QTNs and QEIs shared across multiple traits, generated via the free online bioinformatics platform ( http://www.bioinformatics.com.cn ). Mining the candidate genes and qRT-PCR verification Candidate genes were initially screened within 200 kb flacking regions (100 kb upstream/downstream) around the significant or suggested QTNs and QEIs. Subsequently, potential candidates were further filtered by integrating functional annotations of Arabidopsis orthologs ( https://www.arabidopsis.org ) with expression profiles across 14 tissues in the cut chrysanthemum cultivar ‘Jinba’ [ 5 ]. the potential candidate genes were further filtered. A heatmap visualizing the expression profiles of candidate genes was constructed using TBtools software version 1.132 [ 38 ]. Here, we focused on genes with high expression in roots and leaves. Furtherly, several selected candidate genes were validated through quantitative real-time PCR (qRT-PCR) in accordance with the specifications of the manufacturer of the SYBR Green Pro Taq HS qPCR Kit (AG Bio). The experiment contained three biological replicates, and the primers used for qRT-PCR amplification are listed in Table S3 . Total RNA of each sample was extracted from the root of chrysanthemum cultivar ‘Jinba’ that subjected to drought stress for 0, 1, 6, 12, 24, 48 h and 48 + 2 h (recovery for 2 h after 48 h drought treatment) at 8-10-leaf stage with 20% w/v PEG6000 for simulated drought treatment [ 39 ]. The whole RNA was extracted with the plant Quick RNA Isolation Kit GK (Huayueyang Bio) and cDNA was synthesized using Evo M - MLV RT Mix Kit with gDNA Clean (AG Bio). To normalize the expression levels among samples, CmEF1 α was chosen as the reference gene to calculate the relative gene expression levels via the 2 −ΔΔCT method [ 40 ]. Results Phenotypic variation of drought tolerance-related traits Descriptive statistics data and violin plots showed an extensive variation on the DT-related traits among the 200 cut chrysanthemum accessions (Table 1 ; Fig. 1 ). The average coefficient of variation ( CV ) for these DT traits was 24.98%, ranging from 11.22% (F_PHI) to 46.12% (A_WI). The absolute values of skewness and kurtosis for 23 DT-related traits and the normal test revealed most of these DT traits showed an abnormal distribution (Table 2 ; Fig. 1 ), suggesting their typical quantitative characteristics. Table 1 Summary statistics for drought tolerance-related traits in 200 chrysanthemum accessions Traits Min Max Mean CV (%) Skew Kurt normtest. W normtest. P F_MFVD 0.12 0.87 0.43 36.13 0.35 -0.58 0.98 0.01 F_WI 1.00 5.00 2.94 36.59 0.19 -0.96 0.97 0.00 F_PH_T 7.53 25.70 15.11 19.30 0.44 0.58 0.99 0.05 F_SFW_T 0.69 2.18 1.23 24.13 0.45 -0.17 0.98 0.01 F_SDW_T 0.27 0.94 0.62 18.55 0.14 0.48 0.99 0.11 F_PH_CK 7.40 25.95 15.74 21.37 0.35 0.09 0.99 0.11 F_SFW_CK 1.57 6.67 3.10 23.58 0.92 2.50 0.96 0.00 F_SDW_CK 0.27 1.27 0.67 24.33 0.58 0.67 0.98 0.00 F_PHI 0.62 1.35 0.97 11.25 0.32 1.12 0.98 0.02 F_SFWI 0.20 0.94 0.42 35.15 1.05 1.03 0.92 0.00 F_SDWI 0.66 1.35 0.95 15.76 0.27 -0.44 0.99 0.10 A_MFVD 0.05 0.94 0.47 36.19 -0.11 -0.41 0.99 0.55 A_WI 1.00 5.00 2.59 46.12 0.45 -0.94 0.93 0.00 A_PH_T 7.83 28.57 15.44 19.55 0.63 1.42 0.98 0.00 A_SFW_T 0.63 3.34 2.03 29.58 -0.29 -0.57 0.98 0.03 A_SDW_T 0.28 1.01 0.61 19.57 0.25 0.39 0.99 0.50 A_PH_CK 7.25 30.80 16.61 21.40 0.57 1.29 0.98 0.00 A_SFW_CK 1.71 5.49 3.18 19.59 0.53 0.64 0.98 0.01 A_SDW_CK 0.29 1.22 0.71 22.41 0.42 0.38 0.99 0.08 A_PHI 0.67 1.32 0.94 11.23 0.41 0.64 0.99 0.10 A_SFWI 0.18 1.22 0.66 32.79 -0.16 -0.43 0.99 0.13 A_SDWI 0.54 1.35 0.88 16.39 0.27 0.19 0.99 0.32 MFVD 0.14 0.82 0.45 33.56 0.03 -0.73 0.99 0.11 Person correlation coefficients (PCCs) were calculated among the 23 traits, and significant positive or negative correlations ( r = -0.18 ~ 0.93) were observed between MFVD and all other 22 traits (Fig. 2 ). We found the F_MFVD and A_MFVD had a significant positive correlation ( r = 0.72). As expected, both F_WI and A_WI were strongly negatively correlated with MFVD ( r = -0.79 and r = -0.83, respectively). These results implied the existence of common QTNs or similar genetic mechanisms underlying drought responses in chrysanthemum among these DT-related traits. Based on the MFVD and the resistance classification described by Li et al. (2018b), 200 cut chrysanthemum accessions were classified into four scales: susceptible (MFVD < 0.4); slightly tolerant (0.4 ≤ MFVD < 0.6); moderately tolerant (0.6 ≤ MFVD < 0.8); highly tolerant (MFVD ≥ 0.8). As a result (Table S3 ), only one accession, ‘Nannong Chengpingpang’, was identified as highly tolerant, exhibiting the highest MFVD value (0.82). Moreover, over 30 varieties were classified as moderately tolerant. In contrast, the lowest MFVD (0.14) was observed in ‘Lvcui’ and ‘Jingdian’, and low values (0.16–0.17) were also recorded in ‘Anastasia Sunny’, ‘Huangyu’, and ‘Qinhuai Bailu’, suggesting heightened sensitivity to water deficit in these accessions. Identification of QTNs for drought tolerance-related traits in single environment In the single-environment analysis, the phenotypic data collected from different growth conditions and the derivative stress susceptibility index of the 200 cut chrysanthemum accessions were used for GWAS analysis. As a result, a total of 527 QTNs associated with DT-related traits were identified containing the duplicated QTNs (Table S4 ). Due to the complex correlation among the traits, we focused on the stable QTNs that were simultaneously detected for more than two traits. Forty-three out of the 527 QTNs were regarded as the stable QTNs, and their interaction network is visualized in Fig. 3 . Among the stable QTNs, Chr16__125764264 was uniquely associated with five traits (F_SDW_T, F_SDW_CK, A_SDW_T, A_PH_CK, and A_SDW_CK), with LOD and R 2 ranging from 7.74 to 38.28, and 0.82% to 4.76%, respectively. Chr24__115918888 was simultaneously identified for F_WI, A_WI, A_SFW_T and A_SFWI, with LOD and R 2 ranging from 7.55 to 26.76, and 1.20% to 4.36%, respectively. Three QTNs, Chr27__191718985, Chr25__211042485, and Chr10__302208185, were commonly detected for A_WI and F_WI, with LOD ranging from 8.83 to 15.63, 10.56 to 16.02, and 12.25 to 13.89, respectively. Among them, Chr10__302208185 showed a positive additive effect (0.07 ~ 0.10), while Chr27__191718985 (0.16 ~ 0.24) and Chr25__211042485 (0.33 ~ 0.42) exhibited negative additive effects. For A_PH_T and F_PH_T, two significant QTNs, Chr13__242555416 and Chr16__165273239, were detected with a high LOD value (Table S3 ). Interestingly, Chr2__68509602 and Chr27__161373885 were simultaneously detected for F_MFVD and F_SFWI, and Chr2__68509602 exhibited the maximum R 2 (7.10%) for F_MFVD among the stable QTNs. In addition, 8 stable QTNs were detected for MFVD, among which Chr8__136219302 showed the maximum LOD of 31.73, while Chr1__98055371 exhibited the maximum positive additive effect of 0.05 with a high LOD value (31.70) for MFVD. More importantly, we identified a complicated association network among A_WI, F_WI, A_MFVD, F_MFVD, and MFVD (Fig. 3 ), which are important indicators for evaluating DT in chrysanthemum. Identification of QTNs for drought tolerance-related traits in multiple environments To further explore the genetic loci of DT-related traits, the phenotypic data of WI, PH, SFW and SDW collected from drought stress and well-watered, re-watering and well-watered conditions, were respectively used to conduct the multiple environments joint analysis module in IIIVmrMLM software. As a result, 181 QTNs were identified to be associated with the eight traits, including 29, 26, 18, 21, 33, 28, 25, and 20 for F_PH, F_SDW, F-SFW, F_WI, A_PH, A_SDW, A_SFW, and A_WI, respectively (Table S5 ; Fig. S1 A). All the QTNs showed a small-effect with R 2 ranging from 0.22% to 5.44%, with Chr17__252381770 exhibiting the maximum value for F_SDW. The significant QTN Chr22__83920183 detected for F_PH exhibited a maximum LOD value of 80.75 with a negative addictive effect of -1.41 and dominance effect of -0.59. In total, 18 QTNs detected for two or more traits were considered as stable QTNs. Among them, Chr2__93719052 was the only QTN associated with three traits (F_SDW, F_SFW, and A_SDW) with LOD values ranging from 6.99 to 25.70 and positive additive effects ranging from 0.04 to 0.09. The remaining 17 QTNs were each linked to two traits. We found that there were 7 QTNs were simultaneously detected for A_PH and F_PH, namely Chr12__203004016, Chr14__6165751, Chr17__339244693, Chr2__115624958, Chr25__35158285, Chr25__38860133, and Chr9__80117750. For A_WI and F_WI, 4 common QTNs were detected, i.e., Chr2__106053210, Chr24__115918888, Chr27__191718985, and Chr9__95363777, with LOD and additive effects ranging from 9.81 to 30.94 and from − 0.15 to 0.07, respectively. Chr14__60967743, commonly detected for A_PH and A_SDW, exhibited a negative additive effect and nonzero-dominance effect. The mining of highly favorable QTNs Based on the phenotypic performance and the stability of QTNs detected via 3VmrMLM method, 6 favorable QTNs were furtherly selected, i.e., Chr18__327209663, Chr26__113725691, Chr22__201784939, Chr24__115918888, Chr9__95363777, and Chr6__268784715. The QTNs Chr18__327209663 and Chr26__113725691 were detected for MFVD and A_SFWI, whereas the other four QTNs were all linked to F_WI. Both the 6 QTNs showed significant phenotypic difference in 200 accessions with different genotypes (Fig. 4 ). For example, the GG genotype at QTN Chr26__113725691 was associated with a higher MFVD than the AG genotype, suggesting that this locus may play a significant regulatory role in drought tolerance. Furthermore, we found that four cut chrysanthemum cultivars (‘Nannong Chengpingpang’, ‘Nannong Bingqilin’, ‘Nannong Xuefeng’ and ‘QD3-107’) harboring 6 favorable alleles also exhibited relative higher MFVD values (> 0.70). These findings highlighted the breeding value of the aforementioned six QTNs as well as the four drought tolerant cultivars in future genetic improvement of chrysanthemum. Identification of QEIs for drought tolerance-related traits in multiple environments To research the interaction between genotype and environment, QEIs for eight DT-related traits were detected via the multiple-environment module of IIIVmrMLM software. Among the 115 QEIs, 6, 10, 19, 23, 8, 10, 24, and 22 were associated with F_PH, F_SDW, F_SFW, F_WI, A_PH, A_SDW, A_SFW, and A_WI, respectively (Table S6; Fig. S1 B). The ranges of LOD and R 2 values were 4.28 ~ 47.58 and 0.23%~4.71%, respectively (Table S5 ). The QEI Chr18__300164469 detected for A_SFW exhibited the maximum LOD value of 47.58 with a small positive additive and dominance effect of 0.02 and 0.23, respectively. Chr17__18678198 showed the largest R 2 value (4.71%) with a high LOD value of 46.64 for A_WI. We found that more than 50 QEIs exhibited a nonzero dominant-by-environment interaction effects, among which Chr1__174486633 detected for F_PH had the maximum additive-by-environment interaction effect of 0.34. Seven out of the 115 QEIs were identified to be associated with two traits, and six of the seven stable QEIs were related with A_WI. Specifically, two QEIs (Chr14__5605892 and Chr20__53310555) were commonly detected for A_SFW and A_WI, while the other four QEIs (Chr10__240678284, Chr15__224740673, Chr25__60694702, and Chr5__169353215) were shared by A_WI and F_WI. Notably, the QEI Chr22__201784939 detected for A_SFW (LOD = 31.02) and F_WI (LOD = 11.49) was also a significant QTN. And Chr9__95363777 was also simultaneously identified in QTN and QEI analysis. Known genes around the stable QTNs and QEIs In the single- and multiple- environments analysis, we obtained 43 and 18 stable QTNs for DT-related traits, respectively. There were six genes around the 51 stable QTNs had been verified to play a significant role in regulating drought tolerance in Arabidopsis or other species (Figs. S2, S3). evm.model.scaffold_879.7 , a homologous gene of Arabidopsis NLP7 ( NIN-LIKE PROTEIN 7 ) underlying a stable QTN Chr13__242555416 that was repeatedly identified 2 times in single environment analysis, is a member of the NLP subfamily of RWP-RK transcription factors that regulates NRT1.1/NPF6.3 expression to modulate plant responses to drought [ 41 , 42 ]. Interestingly, evm.model.scaffold_10190.57 and evm.model.scaffold_10190.58 , located 12.2 kb and 15.8 kb downstream of stable QTN Chr2__93719052, are homologous to AHK2 (Fig. 5 ), one of the six nonethylene receptor histidine kinases in Arabidopsis, has been proven function in drought stress [ 43 ]. A known gene, evm.model.scaffold_190.143 , located 28.1 kb downstream of Chr14__179983947 and Chr14__179983930, is homologous to Arabidopsis SnRK2.4 ( SNF1-RELATED PROTEIN KINASE 2.4 ), which has been reported to play a positive role in drought tolerance by facilitating putrescine synthesis [ 44 ]. Another reported gene, evm.model.scaffold_130.410 , was consistently detected in both single- and multiple-environments analyses. It located within the region of stable QTN Chr24__115918888 and is a homology of Arabidopsis proline transporter protein PROT2 , a known regulator of multiple abiotic stress responses [ 45 ]. Besides, evm.model.scaffold_1097.67 was detected two times in single environment analysis, located 47.2 kb downstream of QTN Chr26__113725691, encoding a homolog of R2R3 MYB transcription factor MYB121, which could remarkably enhance the tolerance to drought stress in apple [ 46 ]. A total of 506 genes were initially identified within 115 QEIs in multiple environment analysis. Among these, five genes were screened out that have been previously reported (Fig. S4 ). evm.model.scaffold_1509.80 , located 54.1 kb upstream of QEI Chr16__214738761, is homologous to WRKY57 in Arabidopsis, has already been proven to enhance the DT of Arabidopsis by elevating ABA levels [ 47 ]. evm.model.scaffold_108.97 , detected in QEI Chr17__260900946, is a homology of Arabidopsis COI1 ( CORONATINE INSENSITIVE 1 ), which has been found closely related with various plant abiotic and biotic stresses [ 48 ]. One known gene named bZIP42 ( evm.model.scaffold_915.202 ), located on chrysanthemum chromosome 4, has been reported to enhance the drought stress tolerance by mediating ABA signaling pathway in rice [ 49 ]. evm.model.scaffold_798.411 , located 68.2 kb downstream of QEI Chr24__300974094 for A_WI, is homologous to GH3.6 ( GRETCHEN HAGEN3.6 ) in apple, has been demonstrated to play a negative role in regulating water-deficit stress tolerance [ 50 ]. Another reported gene, evm.model.scaffold_6275.107 , is a member of plasma membrane intrinsic protein (PIP) subfamily, homologous to PIP1;4 in Arabidopsis, has been reported to response drought stress [ 51 ]. These results suggested the reliability of our GWAS results. The candidate genes around the stable QTNs and QEIs were selected by combing the gene function annotation and transcriptome data of cut chrysanthemum cultivar ‘Jinba’. As a result, a total of 25 genes were preliminarily considered as potential candidates with relatively high expression levels in root or leaf tissues (Table 2 ; Fig. 6 ). NLP6 and LOX2 were simultaneously identified within the regions of QTN Chr18__327209663 for MFVD and A_SFWI, which also had a relatively higher expression in shoot (Table S4 ; Fig. 6 ). Notably, bHLH112 exhibited a higher expression level in leaf. Auxin is a critical hormone to plant growth, development, and stress responses [ 52 ]. SAUR51 ( evm.model.scaffold_1433.17 ), as a member of auxin responsive protein in Arabidopsis, located on chromosome 16, was considered as a candidate gene to response drought stress. More importantly, the members of NAC transcription factors, NAC090 ( evm.model.scaffold_732.153 ) and ANAC087 ( evm.model.scaffold_136.26 ), were identified as candidates. In addition, SnRK1.1 , PAP2 , DGS1 , and SPHKI were also regarded as potential candidate genes to response drought stress in chrysanthemum (Fig. 6 ). Table 2 Candidate genes located within stable QTNs and QEIs for drought tolerance-related traits Taxa Traits QTNs/QEIs Gene ID Homologous gene in Arabidopsis Gene Symbol Annotation QTN A_MFVD, F_WI Chr22__201784939 evm.model.scaffold_732.153 AT5G22380.1 NAC090 NAC domain containing protein 90 A_SFWI, MFVD Chr18__327209663 evm.model.scaffold_468.349 AT3G45140.1 LOX2 Lipoxygenase 2 A_SFWI, A_SFW_T, A_WI, F_WI Chr24__115918888 evm.model.scaffold_130.407 AT5G12290.3 DGS1 DGD1 suppressor 1 A_SFWI, MFVD Chr26__113725691 evm.model.scaffold_1097.68 AT3G30180.1 BR6OX2 Brassinosteroid-6-oxidase 2 A_SFW_T, F_SFW_CK Chr4__258242409 evm.model.scaffold_1477.59 AT1G67580.2 CDKG2 Protein kinase superfamily protein F_MFVD, F_SFWI Chr27__161373885 evm.model.scaffold_1687.78 AT3G14690.2 CYP72A15 Cytochrome P450, family 72, subfamily A, polypeptide 15 F_PHI, F_SDW_CK Chr12__129903214 evm.model.scaffold_2433.75 AT4G21540.1 SPHK1 Sphingosine kinase 1 A_PH_T,F_PH_T Chr16__165273239 evm.model.scaffold_1433.17 AT1G75580.1 SAUR51 SAUR-like auxin-responsive protein family A_SDW_CK,A_SFW_CK,F_SDW_CK Chr27__148805349 evm.model.scaffold_11305.16 AT1G75580.1 SAUR22 SAUR-like auxin-responsive protein family A_SDW_CK,A_SFW_CK,F_SDW_CK Chr27__148805349 evm.model.scaffold_11305.18 AT4G38840.1 SAUR14 SAUR-like auxin-responsive protein family A_SDW_CK,A_SFW_CK,F_SDW_CK Chr27__148805349 evm.model.scaffold_11305.23 AT4G34770.1 SAUR1 SAUR-like auxin-responsive protein family A_SFWI,MFVD Chr18__327209663 evm.model.scaffold_468.346 AT1G64530.1 NLP6 Plant regulator RWP-RK family protein A_WI, F_WI Chr24__115918888 evm.model.scaffold_130.407 AT5G12290.3 DGS1 DGD1 suppressor 1 A_WI, F_WI Chr24__115918888 evm.model.scaffold_130.406 AT1G09530.6 PIF3 Phytochrome interacting factor 3 A_WI, F_WI Chr9__95363777 evm.model.scaffold_628.81 AT3G01090.3 SNRK1.1 SNF1 kinase homolog 10 F_WI Chr6__268784715 evm.model.scaffold_1537.72 AT4G29080.1 PAP2 Phytochrome-associated protein 2 QEI A_SFW, F_WI Chr22__201784939 evm.model.scaffold_732.153 AT5G22380.1 NAC090 NAC domain containing protein 90 A_SFW Chr23__279219985 evm.model.scaffold_10498.181.1 AT1G17440.2 TAF12B Transcription initiation factor TFIID subunit A A_WI Chr14__283776194 evm.model.scaffold_5125.46 AT1G30460.1 CPSF30 Cleavage and polyadenylation specificity factor 30 A_WI Chr15__239636786 evm.model.scaffold_622.301 AT4G03090.5 NDX Sequence-specific DNA binding transcription factor ATNDX F_WI Chr27__58232617 evm.model.scaffold_3677.89 AT2G17800.2 ARAC1 Rac-like GTP-binding protein F_WI Chr6__268784715 evm.model.scaffold_1537.72 AT4G29080.1 PAP2 Phytochrome-associated protein 2 A_WI Chr16__279042622 evm.model.scaffold_136.26 AT5G18270.2 ANAC087 NAC domain containing protein 87 A_WI Chr19__215484175 evm.model.scaffold_8721.52 AT5G47010.1 UPF1 RNA helicase A_WI Chr23__255122841 evm.model.scaffold_1763.277 AT4G02570.4 CUL1 Cullin 1 F_SFW Chr14__302132429 evm.model.scaffold_5000.11 AT1G61660.7 bHLH112 Basic helix-loop-helix (bHLH) DNA-binding superfamily protein F_WI Chr18__333348032 evm.model.scaffold_9289.20 AT1G18660.4 IAP1 Zinc finger (C3HC4-type RING finger) family protein F_WI Chr5__110219202 evm.model.scaffold_1519.189 AT1G22920.2 CSN5A COP9 signalosome 5A F_WI Chr9__95363777 evm.model.scaffold_628.81 AT3G01090.3 SnRK1.1 SNF1-related protein kinase 1.1 Note: the bold character is repeatedly detected. Candidate genes prediction and qRT-PCR validation Using transcriptome data from drought-tolerant variety ‘Seiko Eizan’ and drought-sensitive variety ‘Escama’(unpublished), we analyzed the expression of 25 candidate genes under drought stress. To further investigate their roles, a subset of 7 genes with distinct expression patterns was selected for qRT-PCR validation of their drought-responsive expression characteristics. As shown in Fig. 7 , all candidate genes exhibited different degrees of response to drought stress and recovery. Overall, the relative expression levels of bHLH112 ( evm.model.scaffold_5000.11 ), NLP6 ( evm.model.scaffold_468.346 ), PIF3 ( evm.model.scaffold_130.406 ), BR6OX2 ( evm.model.scaffold_1097.68 ), CYP72A15 ( evm.model.scaffold_1687.78 ), PAP2 ( evm.model.scaffold_1537.72 ), reached the highest after 48 h of drought stress, while subsequently down-regulated after 2 h of recovery. In contrast, SAUR51 ( evm.model.scaffold_1433.17 ) was first induced after exposure to drought stress and then downregulated, showing the highest and lowest expression levels at 12 h and 48 h, respectively. In conclusion, the 7 above-mentioned key candidate genes might participate in regulating of drought tolerance in chrysanthemum. Discussion Drought tolerance is a complex quantitative trait influenced by both growth and developmental factors, as well as environmental factors. It is reported to be regulated by multiple genes and manifested as a series of morphological and physiological changes [ 53 – 55 ]. With the global climate change, the drought stress has a dramatically influence on crop production [ 56 ]. And there is urgent need to enhance the drought tolerance of plants for agricultural researchers and plant breeders. Chrysanthemum, with high economical value, is also facing serious drought threats to its production worldwide. But the genetic mechanisms of drought tolerance in chrysanthemum still remains largely unknown, which hinders its breeding process. Here, we characterized the drought and re‑watering responses of 200 chrysanthemum accessions and applied the MFVD as a comprehensive indicator to evaluate their drought tolerance. Regrettably, only one accession was identified as highly tolerant, specifically ‘Nannong Chengpingpang’. Nevertheless, the moderate drought tolerance exhibited by over 30 cultivars represents a valuable resource for breeding programs aimed at enhancing drought resilience in chrysanthemum. Recently, remarkable progress has been made in understanding the molecular mechanisms of drought tolerance in chrysanthemum. BBX transcription factors play a vital role in plant growth, development, and stress responses [ 57 ]. Xu et al. [ 58 ] found CmBBX19 was a negative regulator of DT in chrysanthemum through modulating the accumulation of protective proteins, maintaining cellular redox balance, and promoting cell wall biogenesis in the ABA-dependent pathway. CmBBX22 was demonstrated to function as a negative regulator of drought tolerance in chrysanthemum by modulating ABA signaling, stomatal conductance, and antioxidant responses [ 39 ]. Wang et al. [ 59 ] reported that CmNF-YB8 , a nuclear factor from chrysanthemum, changed the stomatal status and cuticle thickness of the leaf epidermis by regulating the expression of the serine/threonine protein kinase gene CmCIPK6 and the cuticle biosynthesis regulatory factor CmSHN3 to affect drought resistance. In addition, DgNAC1 , CmRH56 , and CdICE1 have been implicated in the drought stress response of chrysanthemum in previous studies [ 60 – 62 ]. However, most of these genes are transcription factors identified through homologous cloning, a strategy that inherently limits the discovery of novel genes. GWAS is a powerful analytical approach to identify genetic variants and novel genes in genetics and genomics studies [ 63 , 64 ]. Chrysanthemum is a highly heterozygous polyploid plant with an extremely complex genetic background [ 65 , 66 ], which resulted in a lag in research progress compared to other plants. Li et al. [ 33 ] used 159 chrysanthemum varieties to conduct association mapping based on 707 informative SRAP, SCoT and EST-SSR markers, and 4 favorable alleles from 16 associations were identified related to drought tolerance. However, due to the small size of population, the low efficiency of traditional markers, and previous lack of reference genome information, it was difficult to discover potential genes for a better understand underlying the genetic mechanisms of DT in chrysanthemum. Thanks to the development of SNP genotyping technologies, the reduction of high-throughput sequencing costs as well as the release of chrysanthemum reference genome data [ 5 ], GWAS has been successfully applied to explore the genetic basis of several traits of interest, such as plant height [ 67 ], flowering time [ 68 ], root system [ 69 ] and black spot disease [ 70 ], which brings down to dissect candidate genes of DT in chrysanthemum via GWAS. In our research, GWAS was performed based on 330,710 high quality GBS-based SNPs and 200 chrysanthemum accessions via 3VmrMLM method [ 27 ], a new multi-locus method that further expanded to cover QTNs and QEIs. Among the 54 stable QTNs detected for DT-related traits, Chr12__203004016, Chr14__60967743, Chr16__125764264 and the other four QTNs were simultaneously detected in both single- and multi-environment analysis. Moreover, we also detected 115 QEIs by 3VmrMLM method, Chr22__201784939 and Chr9__95363777 were repeatedly detected in stable QTNs and QEIs. This finding suggested the interactions between genotype and environment play a significant role in regulating the drought tolerance of chrysanthemum. Notably, the stable QTNs (Chr18__327209663 and Chr26__11372569) detected for MFVD and A_SFWI showed a significant difference between two genotypes (Fig. 4 ). Additionally, Chr6__268784715, Chr9__95363777, Chr22__201784939, and Chr24__115918888 were consistently detected across multiple environments and traits, indicating their potential utility in future marker-assisted selection (MAS) breeding. By integrating functional annotations from Arabidopsis and previous reports, we identified 11 known genes near the QTNs/QEIs, including WRKY57 , SnRK2.4 , and MYB121 . These genes have been reported to regulate drought tolerance in plants [ 47 , 44 , 46 ] These findings confirm the reliability of our study and demonstrate the applicability of the 3VmrMLM method for dissecting genes underlying drought tolerance and other complex traits. We identified seven candidate genes showing distinct expression patterns under drought stress. Previous studies have demonstrated that key phytohormones, including auxin and ABA, play significant roles in regulating plant stress tolerance [ 71 ]. In our study, SAUR51 ( evm.model.scaffold_1433.17 ), a member of the SAUR-like auxin-responsive protein family, has been reported show a significant repression of gene expression in Arabidopsis leaves by the drought stress. In addition, the other three genes, SAUR22 , SAUR14 and SAUR1 around the QTN Chr27__148805349 (Table 2 ) were also showed the similar gene expression patterns under the drought condition in Arabidopsis leaves [ 72 ].Moreover, PAP2 , an AUX/IAA transcriptionnal regulator, has been reported to repress lignin biosynthesis‑related and LBD genes, thereby affecting plant growth and lateral root formation in tomato [ 73 ]. This suggests it may also play a role in the drought stress response of chrysanthemum. The candidate gene bHLH112 ( evm.model.scaffold_5000.11 ), located 87.8 kb downstream of QEI Chr14__302132429 (LOD = 19.60) and highly expressed in leaf and stem (Fig. 6 ), has been shown to enhance ROS scavenging and proline accumulation in Arabidopsis, thereby regulating stomatal aperture in Arabidopsis [ 74 ]. Another novel candidate gene evm.model.scaffold_1097.68 ( BR6OX2 ), a member of CYP450 family, has been reported to confer salt stress tolerance and elevate endogenous brassinosteroid levels when overexpressed in apple callus [ 75 ]. In addition, other candidate genes related to salicylic acid response and abscisic acid-activated signaling pathway were also identified. These genes, expressed in leaf, root, shoot, and stem (Fig. 6 ), have not been directly linked to drought tolerance. Further investigation and validation of these novel candidate genes are crucial for gaining a deeper understanding of the molecular mechanisms underlying chrysanthemum’s adaptation to drought stress. Conclusions This study conducted a multi-locus GWAS for DT related traits using a panel of 200 chrysanthemum cultivars, yielding 51 stable QTNs and 115 QEIs. Eleven genes homologous to drought‑responsive genes in model species were located near these loci, supporting the reliability of our findings. Through integrated analysis of functional annotation, transcriptomics, and qRT‑PCR data, seven putative DT key candidate genes were further identified. In addition, four highly or moderately drought-tolerant cultivars (‘Nannong Chengpingpang’, ‘Nannong Bingqilin’, ‘Nannong Xuefeng’ and ‘QD3-107’) harboring five of the six favorable alleles were prioritized as potential breeding donors. These discoveries present here deep our insights into the genetic mechanisms underlying chrysanthemum’s response to water scarcity and provide promising targets for future molecular breeding endeavors. Nevertheless, further research is necessary to functionally validate the candidate genes and elucidate the gene regulatory mechanisms underlying drought stress. Abbreviations DT drought tolerance GWAS Genome-wide association study QTN the quantitative trait nucleotides QEI QTN-by-environment interaction QTL quantitative trait loci PCC Person correlation coefficients Declarations Ethics approval and consent to participate The experiments were performed in accordance with the relevant legislation and adhered to ethical standards. This study did not involve human participants, animals, or endangered/protected species. The collection of plant materials and all experimental procedures complied with institutional, national, and international guidelines. Consent for publication Not applicable. Competing Interests The authors declare no competing interests Funding This work was supported by the “JBGS” Project of Seed Industry Revitalization in Jiangsu Province (JBGS[2021]020), the National Natural Science Foundation of China (32271938), China Agriculture Research System (CARS-23-A18), Hainan Province Science and Technology Special Fund (ZDYF2025XDNY085), and the Priority Academic Program Development of Jiangsu Higher Education Institutions. Author Contribution YY and XLO have contributed equally to this work. FZ, FDC, JSS, and WMF conceived and designed the experiments; YY wrote the draft manuscript; XLO, XXW, YHQ, YX, SYW, SYW and WYW analyzed the data; JSS performed the experiments and modified the manuscript. All the authors have read and approved the final manuscript. Acknowledgements We thank the high-performance computing platform of Bioinformatic Center, Nanjing Agricultural University, for providing data analysis platform services. Data Availability The GBS sequencing data have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA1004079. All other data supporting the findings of this study are included in this published article and its supplementary information files. Plant materials are available from the corresponding author upon reasonable request. References Tavakol E, Elbadry N, Tondelli A, Cattivelli L, Rossini L. Genetic dissection of heading date and yield under mediterranean dry climate in barley ( Hordeum vulgare L). Euphytica. 2016;212:343–53. https://doi.org/10.1007/s10681-016-1785-0 . Reddy SS, Saini DK, Singh GM, Sharma S, Mishra VK, Joshi AK. Genome-wide association mapping of genomic regions associated with drought stress tolerance at seedling and reproductive stages in bread wheat. Front Plant Sci. 2023;14. https://doi.org/10.3389/fpls.2023.1166439 . Sallam A, Alqudah AM, Dawood MFA, Baenziger PS, Boerner A. 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Funct Integr Genomics. 2018;18:341–53. https://doi.org/10.1007/s10142-018-0590-3 . Liu YJ, Ji XY, Nie XG, Qu M, Zheng L, Tan ZL, et al. Arabidopsis AtbHLH112 regulates the expression of genes involved in abiotic stress tolerance by binding to their E-box and GCG-box motifs. New Phytol. 2015;207:692–709. https://doi.org/10.1111/nph.13387 . Zhang HY, Wang X, Wang XN, Liu HF, Zhang TT, Wang DR, et al. Brassinosteroids biosynthetic gene MdBR6OX2 regulates salt stress tolerance in both apple and Arabidopsis . Plant Physiol Biochem. 2024;212:108767. https://doi.org/10.1016/j.plaphy.2024.108767 . Additional Declarations No competing interests reported. Supplementary Files Fig.S1.tiff Fig. S1 The UpSet plots showing the detected common QTNs and QEIs via multiple-environment analysis. A, shows the number of unique and shared QTNs; B, shows the number of unique and shared QEIs. Fig.S2.tiff Fig. S2 Manhattan plots showing the known and candidate genes around the stable QTNs in single-environment analysis. A-F indicate the stable QTNs and their associated genes. Genes in black denote known genes. Those in green and red indicate candidate genes associated with one or with more than two traits, respectively. Fig.S3.tiff Fig. S3 Manhattan plots showing the known and candidate genes around the stable QTNs in multiple-environment analysis. A-D indicate the stable QTNs and their associated genes. Genes in black denote known genes. Those in green and red indicate candidate genes associated with one or with more than two traits, respectively. Fig.S4.tiff Fig. S4 Manhattan plots showing the known and candidate genes around the QEIs in multiple-environment analysis. A-F indicate the detected QEIs and their associated genes. Genes in black denote known genes. Those in green and red indicate candidate genes associated with one or with more than two traits, respectively. SupplementaryTables.xlsx Table S1 the MFVD values of 200 chrythesanmum accessions Table S2 The information of measured traits Table S3 Names and sequences of primers used in this study Table S4Summary of significant and suggested QTNs for drought tolerance-related traits in single environment using 3VmrMLM model Table S5Summary of significant and suggested QTNs for drought tolerance-related traits in multiple-environment using 3VmrMLM model Table S6 The detected QTN-by-environment interactions for drought tolerance- related traits under multiple environments analysis Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 Apr, 2026 Reviews received at journal 28 Apr, 2026 Reviews received at journal 17 Apr, 2026 Reviewers agreed at journal 11 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 06 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Editor invited by journal 06 Apr, 2026 Submission checks completed at journal 03 Apr, 2026 First submitted to journal 03 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-9267894","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621552533,"identity":"4e0cb123-2edf-4e91-90eb-7bf266fda418","order_by":0,"name":"Yang Yang","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Yang","suffix":""},{"id":621552534,"identity":"6e7d168b-14ae-494e-b842-e2aa0c153851","order_by":1,"name":"Xiaoli Ou","email":"","orcid":"","institution":"Nanjing Agricultural 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13:54:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9267894/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9267894/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106780284,"identity":"e550f91e-4af2-466c-a948-6faf515d0752","added_by":"auto","created_at":"2026-04-13 11:34:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2187277,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhenotypic variation of drought tolerance related-traits in 200 chrysanthemum accessions.\u003c/strong\u003e A, indicates the phenotypic variation of PH, SFW, SDW under different conditions. The prefixes ‘F_’ and ‘A_’ represent the drought stress and rehydration, respectively, while the suffixes ‘_CK’ and ‘_T’ denote the control and treatment groups, respectively. B, shows the phenotypic variation of WI, MFVD, PHI, SFWI, and SDWI under drought stress and rehydration conditions. The bold horizontal lines for each box plot indicated the median values.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-9267894/v1/69750e05944d53c8b9251acb.png"},{"id":106960141,"identity":"ec07bcfc-68c1-4220-8ac1-a9d11d72057a","added_by":"auto","created_at":"2026-04-15 09:19:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26654043,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation coefficients among the drought tolerance related traits.\u003c/strong\u003e The heatmap and significance markers are present above the diagonal, while the pairwise Pearson correlation coefficients are shown below the diagonal. The color bar from green to purple means the positive and negative correlation. *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, * \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-9267894/v1/ecebc735e05612b3a567a9ea.png"},{"id":106780287,"identity":"f0b3eea0-8fcb-4f47-97d0-f9bf16211f34","added_by":"auto","created_at":"2026-04-13 11:34:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":25634877,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe association networks across different drought tolerance related traits.\u003c/strong\u003e Each small turquoise node indicates a stable QTNs that detected in single-environment analysis. The big purple and red nodes mean the DT-related traits measured under drought stress and rehydration conditions.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-9267894/v1/627fc131b7bde45a790b6df5.png"},{"id":106780288,"identity":"37b0a6e7-5850-4262-89f0-05deb6a47c9b","added_by":"auto","created_at":"2026-04-13 11:34:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6488068,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBox plots indicating the variation of MFVD and F_WI for six QTNs with different genotypes. \u003c/strong\u003eThe mean value for each group is shown above the box, with the corresponding number of accessions in parenthesis. The significant differences were determined by Student’s \u003cem\u003et\u003c/em\u003e test. ***, **, * and NS differ at \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 and no significance, respectively.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-9267894/v1/ea775fd8bce01dbf6184837d.png"},{"id":106960051,"identity":"a00d8118-14ee-466f-be42-1801086f0682","added_by":"auto","created_at":"2026-04-15 09:18:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":23185929,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eManhattan plots showing the known and candidate genes around the detected QTNs or QEIs.\u003c/strong\u003e A, B indicate the detected QTNs and associated genes in single-environment analysis. C, D and E, F represent the significant QTNs and QEIs, respectively, identified through the multi‑environment analysis. The genes with black characters denote known genes identified herein. Green and red text correspond to candidate genes associated with a single trait and those associated with multiple (\u0026gt;2) traits, respectively.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-9267894/v1/069723ee4740688b738d9a7c.png"},{"id":106994175,"identity":"7f05ad12-2ef0-4b77-bc02-a0a47268f464","added_by":"auto","created_at":"2026-04-15 15:06:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":10207098,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expressional levels of 25 candidate genes around the stable QTNs and QEIs.\u003c/strong\u003e Bud_X2, Bud_2, Bud_4, Bud_6, Bud_8 indicate whole buds with diameter \u0026lt;2 mm, ~2 mm, ~4 mm, ~6 mm, ~8 mm, respectively; D_Pe, D_Pi, D_St indicate the petals, pistils, stamens of disc florets, respectively; R_Pe and R_Pi indicate the petals and pistils of ray florets, respectively. The ‘Row Scale’ pattern of TBtools was chosen to standardize the fragments per kilobase million (FPKM) values.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-9267894/v1/5f46e13e1d5714c420b24d43.png"},{"id":106780289,"identity":"690e8325-f7a6-4e15-8a58-184465797477","added_by":"auto","created_at":"2026-04-13 11:34:42","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":30532730,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relative expression levels of 7 key candidate genes in chrysanthemum cultivar of ‘Jinba’ under drought stress\u003c/strong\u003e. \u003cem\u003eCmEF1α\u003c/em\u003e was used as the internal control. Values are the mean ±\u003cem\u003e SE\u003c/em\u003e (n = 3). Significant differences were determined using Duncan’s multiple range test. Different uppercase letters above the bars indicate significant difference at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-9267894/v1/47e051bf5b92b64604fbb651.png"},{"id":106960862,"identity":"4e689588-26ac-41ac-a622-42798803f5af","added_by":"auto","created_at":"2026-04-15 09:23:27","extension":"tiff","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":130114,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S1 The UpSet plots showing the detected common QTNs and QEIs via multiple-environment analysis.\u003c/strong\u003e A, shows the number of unique and shared QTNs; B, shows the number of unique and shared QEIs.\u003c/p\u003e","description":"","filename":"Fig.S1.tiff","url":"https://assets-eu.researchsquare.com/files/rs-9267894/v1/415979018a2cb321dd12d5fb.tiff"},{"id":106780286,"identity":"99695491-65f8-4106-af0f-d9749453605c","added_by":"auto","created_at":"2026-04-13 11:34:42","extension":"tiff","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2603118,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S2 Manhattan plots showing the known and candidate genes around the stable QTNs in single-environment analysis. \u003c/strong\u003eA-F indicate the stable QTNs and their associated genes. Genes in black denote known genes. Those in green and red indicate candidate genes associated with one or with more than two traits, respectively.\u003c/p\u003e","description":"","filename":"Fig.S2.tiff","url":"https://assets-eu.researchsquare.com/files/rs-9267894/v1/1e5fafd09c2d45d2e83fbf3d.tiff"},{"id":106780291,"identity":"f8643d02-f332-45ac-b6eb-6a9297723723","added_by":"auto","created_at":"2026-04-13 11:34:43","extension":"tiff","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1687958,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S3 Manhattan plots showing the known and candidate genes around the stable QTNs in multiple-environment analysis.\u003c/strong\u003e A-D indicate the stable QTNs and their associated genes. Genes in black denote known genes. Those in green and red indicate candidate genes associated with one or with more than two traits, respectively.\u003c/p\u003e","description":"","filename":"Fig.S3.tiff","url":"https://assets-eu.researchsquare.com/files/rs-9267894/v1/d3357d512b8b916bf847c4ab.tiff"},{"id":106960122,"identity":"cd99dcc6-205f-4ef4-bebb-4147b63e1d83","added_by":"auto","created_at":"2026-04-15 09:19:00","extension":"tiff","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2438892,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S4 Manhattan plots showing the known and candidate genes around the QEIs in multiple-environment analysis.\u003c/strong\u003e A-F indicate the detected QEIs and their associated genes. Genes in black denote known genes. Those in green and red indicate candidate genes associated with one or with more than two traits, respectively.\u003c/p\u003e","description":"","filename":"Fig.S4.tiff","url":"https://assets-eu.researchsquare.com/files/rs-9267894/v1/7acb490283b057c2a2ad6662.tiff"},{"id":106959902,"identity":"45163b96-59b7-45a7-b976-c5dd55ab5d8e","added_by":"auto","created_at":"2026-04-15 09:16:40","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":108636,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1\u003c/strong\u003e the MFVD values of 200 chrythesanmum accessions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S2 \u003c/strong\u003eThe information of measured traits\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S3 \u003c/strong\u003eNames and sequences of primers used in this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S4\u003c/strong\u003eSummary of significant and suggested QTNs for drought tolerance-related traits in single environment using 3VmrMLM model\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S5\u003c/strong\u003eSummary of significant and suggested QTNs for drought tolerance-related traits in multiple-environment using 3VmrMLM model\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S6\u003c/strong\u003e The detected QTN-by-environment interactions for drought tolerance- related traits under multiple environments analysis\u003c/p\u003e","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9267894/v1/89a9ef2782f334bbdf556748.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-locus GWAS uncovers favorable alleles and candidate genes underlying water stress response in chrysanthemum","fulltext":[{"header":"Key Message","content":"\u003cp\u003eMulti-locus GWAS identified 43 stable QTNs and 7 candidate genes for drought tolerance in chrysanthemum, providing valuable genetic resources for future genome-based molecular breeding.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eDrought is one of the most important abiotic stresses affecting plant growth, development and production [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As the global warming and climate change, the drought episodes will become frequency and severe in the future [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, developing water-deficit tolerant varieties represents an urgent priority for crop breeders to address increasing climatic instability. Chrysanthemum (\u003cem\u003eChrysanthemum morifolium\u003c/em\u003e Ramat.), a globally important ornamental plant with great economic, culture, and symbolic value, which is sensitive to drought stress [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Drought stress not only limits the plant growth, but also drastically affects the quality of chrysanthemum. Clarifying the genetic architecture of drought tolerance (DT) in chrysanthemum will be of great value for its tolerance improvement. However, few studies have reported the genetic mechanisms underlying DT in chrysanthemum compared to other abiotic stresses, such as waterlogging [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and salt [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBi-parental mapping has been a widely used method for dissecting the genetic architecture of quantitative traits in chrysanthemum, successfully uncovering massive quantitative trait loci (QTL) underlying flowering time [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] plant architecture-related traits [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], flower shape [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Nevertheless, most of these studies depend on low-resolution linkage maps constructed by a limited number of gel-based markers with low genome coverage, resulting in the poor efficiency and accuracy of identifying markers linkage with the target traits. This greatly restricts the application of marker assisted selection (MAS) in chrysanthemum breeding programs. In addition, chrysanthemum is a complex polyploid with high heterozygosity, self-incompatibility, large size genome characteristics, and linkage phase is ambiguous. Therefore, traditional linkage mapping is still a great challenge to explore the genetic architecture of complex quantitative traits in chrysanthemum, especially for the traits controlled by small-effect genes including DT [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on the linkage disequilibrium (LD), genome wide association studies (GWAS) have emerged as an efficient tool for dissecting the genetic basis of complex agronomic traits [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Compared to traditional bi-parental mapping, GWAS eliminates the costs and time involved in population construction and effectively utilizes the numerous historical crossover event with a high mapping resolution [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. To date, GWAS has been extensively used to explore the genetic basis of drought tolerance in rice [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], wheat [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], maize [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and cotton [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Most of these studies used single-locus GWAS methods, such as mixed linear model (MLM) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], efficient mixed-model association expedited (EMMAX) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and genome-wide efficient mixed model association (GEMMA) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, the effectiveness of single-locus GWAS methods in identifying quantitative trait nucleotides (QTNs) with marginal effects is constrained by the polygenic background and the stringent Bonferroni correction [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. To solve the questions in single-locus GWAS, a series of multi-locus GWAS approaches were proposed to improve the power of QTNs detection. Among theme, a new methodological framework named 3VmrMLM enables to detect all types of loci including QTN-by-environment interactions (QEIs) and QTN-by-QTN interactions (QQIs) and estimates their effects with high powers [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Currently, the 3VmrMLM method has been successfully used for the study of epicotyl length in soybean [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], grain size traits in ratoon rice [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and rice seed germination under drought stress [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. DT is a complex quantitative trait governed by multiple genes and modulated by environmental factors [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The identification of QEIs can be used to discover elite genes suitable for different environments and provide valuable genetic sources for genetic improvement of chrysanthemum.\u003c/p\u003e \u003cp\u003eIn this research, we utilized a genetic panel of 200 diverse cut chrysanthemum accessions to revel the genetic mechanisms of DT in chrysanthemum through multi-locus GWAS. Based on 330,710 high-quality SNPs, the stable QTNs and QEIs significantly associated with DT were identified via 3VmrMLM method. Around these QTNs and QEIs, several candidate genes were mined by combing the functional annotation and experimental data. Findings from this study will provide novel perspectives for future gene cloning and studies of molecular mechanisms underlying DT in chrysanthemum.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials and treatment\u003c/h2\u003e \u003cp\u003eAs described by Lou et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], a total of 200 representative cut chrysanthemum accessions were selected to explore their DT in this study (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). All the materials were preserved at Nanjing Agricultural University Chrysanthemum Germplasm Resource Preserving Centre (Nanjing, China). The experiment was carried out flowing a randomized complete block design, with three replicates subjected to drought stress as treatment groups and two replicates under well-watered condition as control groups. Each replicate contained four plants per entry. In early July 2019, healthy and uniform cuttings of each chrysanthemum entry were planted in 105 holes sockets filled with a 1:1 mixture of perlite and peat. After about two weeks, each of the rooted cutting was transplanted into a black plastic flowerpot (5 cm up-diameter, 4 cm down-diameter, 9 cm deep) and placed in blue boxes (65 cm \u0026times; 43 cm \u0026times; 16 cm) in a greenhouse. Each blue box was irrigated 5 L water until soil water content reached full soil moisture capacity. Control plants were watered normally, while treatment plants were stopped watering until the panel showed visible difference in drought damage symptoms for 15 d and then recovered by replenishing water.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePhenotypic trait measurements\u003c/h3\u003e\n\u003cp\u003e After 15 d exposure to drought stress and recovery for 2 d, the wilting index (WI) was respectively recorded for each plant, based on a symptom severity scale from 1 (slight wilting) to 5 (severe wilting) according to Li et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The plant height (PH), fresh shoot weight (SFW), and dry shoot weight (SDW) were measured as the DT-related traits using the method described by Su et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Half of the plants in each plot were used for the destructive measurement of biomass traits (PH, SFW and SDW) after drought treatment (named with a prefix \u0026lsquo;F\u0026rsquo;), and the remaining plants were used for the phenotypic trait measurements after re-watering (named with a prefix \u0026lsquo;A\u0026rsquo;). The derivative stress susceptibility index for each biomass trait (named with a suffix \u0026lsquo;I\u0026rsquo;) was calculated as the ratio of the performance of a genotype under stress or re-watering and its performance under well-watered condition. Detailed information of DT traits is listed in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eEvaluation for drought tolerance\u003c/h3\u003e\n\u003cp\u003eA membership function value of drought (MFVD, 0\u0026ndash;1) integrating the WI and three stress susceptibility index traits investigated after drought stress (F_MFVD) and re-watering (A_MFVD) was respectively calculated for each entry following the formulae: if the trait was positively correlated with DT, \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e = (\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e \u0026minus; \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003emin\u003c/sub\u003e)/(\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003emax\u003c/sub\u003e \u0026minus; \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003emin\u003c/sub\u003e); otherwise, \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e = 1 \u0026minus; (\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e \u0026minus; \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003emin\u003c/sub\u003e)/(\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003emax\u003c/sub\u003e \u0026minus; \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003emin\u003c/sub\u003e), where \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e represented the membership function value of the \u003cem\u003ej\u003c/em\u003eth indicator for the \u003cem\u003ei\u003c/em\u003eth entry, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e the observed value of the \u003cem\u003ej\u003c/em\u003eth indicator for the \u003cem\u003ei\u003c/em\u003eth variety, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003emin\u003c/sub\u003e and \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003emax\u003c/sub\u003e, respectively, the minimum and maximum value of the \u003cem\u003ej\u003c/em\u003eth indicator. The average of F_MFVD and A_MFVD was used as a comprehensive evaluation index for DT, with higher values indicating greater drought tolerance.\u003c/p\u003e\n\u003ch3\u003eStatistical analysis of phenotypic data\u003c/h3\u003e\n\u003cp\u003eTo visualize the DT-related traits, descriptive statistical analysis for each phenotypic trait was performed, including the mean, minimum, maximum, skewness, kurtosis, and coefficient of variation (\u003cem\u003eCV\u003c/em\u003e) values. In addition, we employed the ggplot2 package in R version 4.0.3 to draw violin diagrams depicting the extensive variation in phenotypic data. Pearson correlation analysis for 23 DT-related traits was performed in R version 4.0.3. The box plots were generated by R packages ggplot2 and ggsignif to show the significant difference among the 200 chrysanthemum accessions with different genotypes.\u003c/p\u003e\n\u003ch3\u003eMulti-locus genome-wide association studies for drought tolerance\u003c/h3\u003e\n\u003cp\u003eThe genotyping-by-sequencing (GBS) technology was used for the genotyping of the 200 cut chrysanthemum accessions, which had been described in our previous report of Lou et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Briefly, the genome sequence of \u003cem\u003eC. morifolium\u003c/em\u003e cv. \u0026lsquo;Zhongshan Zigui\u0026rsquo; [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] was served as a reference for SNP calling. The raw SNPs with genotyping rate\u0026thinsp;\u0026lt;\u0026thinsp;85% and minor allele frequency (MAF)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were discarded. As a result, a total of 330,710 high-quality SNPs was used for the subsequent analysis. To eliminate the effect of population structure on GWAS, the kinship matrix was computed using the EMMAX software [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], and the Q matrix was constructed using the top ten principal components derived from a principal component analysis (PCA) conducted with the GCTA software [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on 330,710 high-quality SNPs, the single environment module in R software IIIVmrMLM [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] was used to detect QTNs for DT-related traits in each environment, while its multiple environment module was used to identify QTNs and QEIs for the above traits using the phenotypic values collected under drought and well-watered conditions. A Bonferroni-corrected significance threshold was set at 0.05/m (where m represents the number of markers), corresponding to 2.48e-08, for identifying significant QTNs and QEIs. In order to mitigate the exclusion of potentially vital candidate genes, the insignificant QTNs and QEIs with a LOD score of \u0026ge;\u0026thinsp;3.0 were considered as suggested QTNs and QEIs. Moreover, the QTNs associated with more than two traits were defined as stable QTNs. The associated network of stable QTNs in single environment analysis was visualized by software Cytoscape version 3.10.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cytoscape.org\u003c/span\u003e\u003cspan address=\"https://cytoscape.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). UpSet plots were used to display QTNs and QEIs shared across multiple traits, generated via the free online bioinformatics platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioinformatics.com.cn\u003c/span\u003e\u003cspan address=\"http://www.bioinformatics.com.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMining the candidate genes and qRT-PCR verification\u003c/h2\u003e \u003cp\u003eCandidate genes were initially screened within 200 kb flacking regions (100 kb upstream/downstream) around the significant or suggested QTNs and QEIs. Subsequently, potential candidates were further filtered by integrating functional annotations of \u003cem\u003eArabidopsis orthologs\u003c/em\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.arabidopsis.org\u003c/span\u003e\u003cspan address=\"https://www.arabidopsis.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with expression profiles across 14 tissues in the cut chrysanthemum cultivar \u0026lsquo;Jinba\u0026rsquo; [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. the potential candidate genes were further filtered. A heatmap visualizing the expression profiles of candidate genes was constructed using TBtools software version 1.132 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Here, we focused on genes with high expression in roots and leaves.\u003c/p\u003e \u003cp\u003eFurtherly, several selected candidate genes were validated through quantitative real-time PCR (qRT-PCR) in accordance with the specifications of the manufacturer of the SYBR Green \u003cem\u003ePro Taq\u003c/em\u003e HS qPCR Kit (AG Bio). The experiment contained three biological replicates, and the primers used for qRT-PCR amplification are listed in Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e. Total RNA of each sample was extracted from the root of chrysanthemum cultivar \u0026lsquo;Jinba\u0026rsquo; that subjected to drought stress for 0, 1, 6, 12, 24, 48 h and 48\u0026thinsp;+\u0026thinsp;2 h (recovery for 2 h after 48 h drought treatment) at 8-10-leaf stage with 20% w/v PEG6000 for simulated drought treatment [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The whole RNA was extracted with the plant Quick RNA Isolation Kit GK (Huayueyang Bio) and cDNA was synthesized using \u003cem\u003eEvo M\u003c/em\u003e-\u003cem\u003eMLV\u003c/em\u003eRT Mix Kit with gDNA Clean (AG Bio). To normalize the expression levels among samples, \u003cem\u003eCmEF1\u003c/em\u003eα was chosen as the reference gene to calculate the relative gene expression levels via the 2\u003csup\u003e\u0026minus;ΔΔCT\u003c/sup\u003e method [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic variation of drought tolerance-related traits\u003c/h2\u003e \u003cp\u003eDescriptive statistics data and violin plots showed an extensive variation on the DT-related traits among the 200 cut chrysanthemum accessions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The average coefficient of variation (\u003cem\u003eCV\u003c/em\u003e) for these DT traits was 24.98%, ranging from 11.22% (F_PHI) to 46.12% (A_WI). The absolute values of skewness and kurtosis for 23 DT-related traits and the normal test revealed most of these DT traits showed an abnormal distribution (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting their typical quantitative characteristics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics for drought tolerance-related traits in 200 chrysanthemum accessions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCV\u003c/em\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSkew\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKurt\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003enormtest.\u003cem\u003eW\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003enormtest.\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF_MFVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF_WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF_PH_T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF_SFW_T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF_SDW_T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF_PH_CK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF_SFW_CK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF_SDW_CK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF_PHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF_SFWI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF_SDWI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA_MFVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA_WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA_PH_T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA_SFW_T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA_SDW_T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA_PH_CK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA_SFW_CK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA_SDW_CK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA_PHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA_SFWI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA_SDWI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMFVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePerson correlation coefficients (PCCs) were calculated among the 23 traits, and significant positive or negative correlations (\u003cem\u003er\u003c/em\u003e= -0.18\u0026thinsp;~\u0026thinsp;0.93) were observed between MFVD and all other 22 traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We found the F_MFVD and A_MFVD had a significant positive correlation (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.72). As expected, both F_WI and A_WI were strongly negatively correlated with MFVD (\u003cem\u003er\u003c/em\u003e = -0.79 and \u003cem\u003er\u003c/em\u003e = -0.83, respectively). These results implied the existence of common QTNs or similar genetic mechanisms underlying drought responses in chrysanthemum among these DT-related traits.\u003c/p\u003e \u003cp\u003eBased on the MFVD and the resistance classification described by Li et al. (2018b), 200 cut chrysanthemum accessions were classified into four scales: susceptible (MFVD\u0026thinsp;\u0026lt;\u0026thinsp;0.4); slightly tolerant (0.4\u0026thinsp;\u0026le;\u0026thinsp;MFVD\u0026thinsp;\u0026lt;\u0026thinsp;0.6); moderately tolerant (0.6\u0026thinsp;\u0026le;\u0026thinsp;MFVD\u0026thinsp;\u0026lt;\u0026thinsp;0.8); highly tolerant (MFVD\u0026thinsp;\u0026ge;\u0026thinsp;0.8). As a result (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e), only one accession, \u0026lsquo;Nannong Chengpingpang\u0026rsquo;, was identified as highly tolerant, exhibiting the highest MFVD value (0.82). Moreover, over 30 varieties were classified as moderately tolerant. In contrast, the lowest MFVD (0.14) was observed in \u0026lsquo;Lvcui\u0026rsquo; and \u0026lsquo;Jingdian\u0026rsquo;, and low values (0.16\u0026ndash;0.17) were also recorded in \u0026lsquo;Anastasia Sunny\u0026rsquo;, \u0026lsquo;Huangyu\u0026rsquo;, and \u0026lsquo;Qinhuai Bailu\u0026rsquo;, suggesting heightened sensitivity to water deficit in these accessions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of QTNs for drought tolerance-related traits in single environment\u003c/h2\u003e \u003cp\u003eIn the single-environment analysis, the phenotypic data collected from different growth conditions and the derivative stress susceptibility index of the 200 cut chrysanthemum accessions were used for GWAS analysis. As a result, a total of 527 QTNs associated with DT-related traits were identified containing the duplicated QTNs (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Due to the complex correlation among the traits, we focused on the stable QTNs that were simultaneously detected for more than two traits. Forty-three out of the 527 QTNs were regarded as the stable QTNs, and their interaction network is visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Among the stable QTNs, Chr16__125764264 was uniquely associated with five traits (F_SDW_T, F_SDW_CK, A_SDW_T, A_PH_CK, and A_SDW_CK), with LOD and \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e ranging from 7.74 to 38.28, and 0.82% to 4.76%, respectively. Chr24__115918888 was simultaneously identified for F_WI, A_WI, A_SFW_T and A_SFWI, with LOD and \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e ranging from 7.55 to 26.76, and 1.20% to 4.36%, respectively. Three QTNs, Chr27__191718985, Chr25__211042485, and Chr10__302208185, were commonly detected for A_WI and F_WI, with LOD ranging from 8.83 to 15.63, 10.56 to 16.02, and 12.25 to 13.89, respectively. Among them, Chr10__302208185 showed a positive additive effect (0.07\u0026thinsp;~\u0026thinsp;0.10), while Chr27__191718985 (0.16\u0026thinsp;~\u0026thinsp;0.24) and Chr25__211042485 (0.33\u0026thinsp;~\u0026thinsp;0.42) exhibited negative additive effects. For A_PH_T and F_PH_T, two significant QTNs, Chr13__242555416 and Chr16__165273239, were detected with a high LOD value (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Interestingly, Chr2__68509602 and Chr27__161373885 were simultaneously detected for F_MFVD and F_SFWI, and Chr2__68509602 exhibited the maximum \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (7.10%) for F_MFVD among the stable QTNs. In addition, 8 stable QTNs were detected for MFVD, among which Chr8__136219302 showed the maximum LOD of 31.73, while Chr1__98055371 exhibited the maximum positive additive effect of 0.05 with a high LOD value (31.70) for MFVD. More importantly, we identified a complicated association network among A_WI, F_WI, A_MFVD, F_MFVD, and MFVD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which are important indicators for evaluating DT in chrysanthemum.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of QTNs for drought tolerance-related traits in multiple environments\u003c/h2\u003e \u003cp\u003eTo further explore the genetic loci of DT-related traits, the phenotypic data of WI, PH, SFW and SDW collected from drought stress and well-watered, re-watering and well-watered conditions, were respectively used to conduct the multiple environments joint analysis module in IIIVmrMLM software. As a result, 181 QTNs were identified to be associated with the eight traits, including 29, 26, 18, 21, 33, 28, 25, and 20 for F_PH, F_SDW, F-SFW, F_WI, A_PH, A_SDW, A_SFW, and A_WI, respectively (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e; Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). All the QTNs showed a small-effect with \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e ranging from 0.22% to 5.44%, with Chr17__252381770 exhibiting the maximum value for F_SDW. The significant QTN Chr22__83920183 detected for F_PH exhibited a maximum LOD value of 80.75 with a negative addictive effect of -1.41 and dominance effect of -0.59. In total, 18 QTNs detected for two or more traits were considered as stable QTNs. Among them, Chr2__93719052 was the only QTN associated with three traits (F_SDW, F_SFW, and A_SDW) with LOD values ranging from 6.99 to 25.70 and positive additive effects ranging from 0.04 to 0.09. The remaining 17 QTNs were each linked to two traits. We found that there were 7 QTNs were simultaneously detected for A_PH and F_PH, namely Chr12__203004016, Chr14__6165751, Chr17__339244693, Chr2__115624958, Chr25__35158285, Chr25__38860133, and Chr9__80117750. For A_WI and F_WI, 4 common QTNs were detected, i.e., Chr2__106053210, Chr24__115918888, Chr27__191718985, and Chr9__95363777, with LOD and additive effects ranging from 9.81 to 30.94 and from \u0026minus;\u0026thinsp;0.15 to 0.07, respectively. Chr14__60967743, commonly detected for A_PH and A_SDW, exhibited a negative additive effect and nonzero-dominance effect.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe mining of highly favorable QTNs\u003c/h2\u003e \u003cp\u003eBased on the phenotypic performance and the stability of QTNs detected via 3VmrMLM method, 6 favorable QTNs were furtherly selected, i.e., Chr18__327209663, Chr26__113725691, Chr22__201784939, Chr24__115918888, Chr9__95363777, and Chr6__268784715. The QTNs Chr18__327209663 and Chr26__113725691 were detected for MFVD and A_SFWI, whereas the other four QTNs were all linked to F_WI. Both the 6 QTNs showed significant phenotypic difference in 200 accessions with different genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For example, the GG genotype at QTN Chr26__113725691 was associated with a higher MFVD than the AG genotype, suggesting that this locus may play a significant regulatory role in drought tolerance. Furthermore, we found that four cut chrysanthemum cultivars (\u0026lsquo;Nannong Chengpingpang\u0026rsquo;, \u0026lsquo;Nannong Bingqilin\u0026rsquo;, \u0026lsquo;Nannong Xuefeng\u0026rsquo; and \u0026lsquo;QD3-107\u0026rsquo;) harboring 6 favorable alleles also exhibited relative higher MFVD values (\u0026gt;\u0026thinsp;0.70). These findings highlighted the breeding value of the aforementioned six QTNs as well as the four drought tolerant cultivars in future genetic improvement of chrysanthemum.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of QEIs for drought tolerance-related traits in multiple environments\u003c/h2\u003e \u003cp\u003eTo research the interaction between genotype and environment, QEIs for eight DT-related traits were detected via the multiple-environment module of IIIVmrMLM software. Among the 115 QEIs, 6, 10, 19, 23, 8, 10, 24, and 22 were associated with F_PH, F_SDW, F_SFW, F_WI, A_PH, A_SDW, A_SFW, and A_WI, respectively (Table S6; Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB). The ranges of LOD and \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e values were 4.28\u0026thinsp;~\u0026thinsp;47.58 and 0.23%~4.71%, respectively (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). The QEI Chr18__300164469 detected for A_SFW exhibited the maximum LOD value of 47.58 with a small positive additive and dominance effect of 0.02 and 0.23, respectively. Chr17__18678198 showed the largest \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e value (4.71%) with a high LOD value of 46.64 for A_WI. We found that more than 50 QEIs exhibited a nonzero dominant-by-environment interaction effects, among which Chr1__174486633 detected for F_PH had the maximum additive-by-environment interaction effect of 0.34. Seven out of the 115 QEIs were identified to be associated with two traits, and six of the seven stable QEIs were related with A_WI. Specifically, two QEIs (Chr14__5605892 and Chr20__53310555) were commonly detected for A_SFW and A_WI, while the other four QEIs (Chr10__240678284, Chr15__224740673, Chr25__60694702, and Chr5__169353215) were shared by A_WI and F_WI. Notably, the QEI Chr22__201784939 detected for A_SFW (LOD\u0026thinsp;=\u0026thinsp;31.02) and F_WI (LOD\u0026thinsp;=\u0026thinsp;11.49) was also a significant QTN. And Chr9__95363777 was also simultaneously identified in QTN and QEI analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eKnown genes around the stable QTNs and QEIs\u003c/h2\u003e \u003cp\u003eIn the single- and multiple- environments analysis, we obtained 43 and 18 stable QTNs for DT-related traits, respectively. There were six genes around the 51 stable QTNs had been verified to play a significant role in regulating drought tolerance in Arabidopsis or other species (Figs. S2, S3). \u003cem\u003eevm.model.scaffold_879.7\u003c/em\u003e, a homologous gene of Arabidopsis \u003cem\u003eNLP7\u003c/em\u003e (\u003cem\u003eNIN-LIKE PROTEIN 7\u003c/em\u003e) underlying a stable QTN Chr13__242555416 that was repeatedly identified 2 times in single environment analysis, is a member of the \u003cem\u003eNLP\u003c/em\u003e subfamily of \u003cem\u003eRWP-RK\u003c/em\u003e transcription factors that regulates \u003cem\u003eNRT1.1/NPF6.3\u003c/em\u003e expression to modulate plant responses to drought [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Interestingly, \u003cem\u003eevm.model.scaffold_10190.57\u003c/em\u003e and \u003cem\u003eevm.model.scaffold_10190.58\u003c/em\u003e, located 12.2 kb and 15.8 kb downstream of stable QTN Chr2__93719052, are homologous to \u003cem\u003eAHK2\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003e), one of the six nonethylene receptor histidine kinases in Arabidopsis, has been proven function in drought stress [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. A known gene, \u003cem\u003eevm.model.scaffold_190.143\u003c/em\u003e, located 28.1 kb downstream of Chr14__179983947 and Chr14__179983930, is homologous to Arabidopsis \u003cem\u003eSnRK2.4\u003c/em\u003e (\u003cem\u003eSNF1-RELATED PROTEIN KINASE 2.4\u003c/em\u003e), which has been reported to play a positive role in drought tolerance by facilitating putrescine synthesis [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Another reported gene, \u003cem\u003eevm.model.scaffold_130.410\u003c/em\u003e, was consistently detected in both single- and multiple-environments analyses. It located within the region of stable QTN Chr24__115918888 and is a homology of Arabidopsis proline transporter protein \u003cem\u003ePROT2\u003c/em\u003e, a known regulator of multiple abiotic stress responses [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Besides, \u003cem\u003eevm.model.scaffold_1097.67\u003c/em\u003e was detected two times in single environment analysis, located 47.2 kb downstream of QTN Chr26__113725691, encoding a homolog of R2R3 MYB transcription factor MYB121, which could remarkably enhance the tolerance to drought stress in apple [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA total of 506 genes were initially identified within 115 QEIs in multiple environment analysis. Among these, five genes were screened out that have been previously reported (Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). \u003cem\u003eevm.model.scaffold_1509.80\u003c/em\u003e, located 54.1 kb upstream of QEI Chr16__214738761, is homologous to \u003cem\u003eWRKY57\u003c/em\u003e in Arabidopsis, has already been proven to enhance the DT of Arabidopsis by elevating ABA levels [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. \u003cem\u003eevm.model.scaffold_108.97\u003c/em\u003e, detected in QEI Chr17__260900946, is a homology of Arabidopsis \u003cem\u003eCOI1\u003c/em\u003e (\u003cem\u003eCORONATINE INSENSITIVE 1\u003c/em\u003e), which has been found closely related with various plant abiotic and biotic stresses [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. One known gene named \u003cem\u003ebZIP42\u003c/em\u003e (\u003cem\u003eevm.model.scaffold_915.202\u003c/em\u003e), located on chrysanthemum chromosome 4, has been reported to enhance the drought stress tolerance by mediating ABA signaling pathway in rice [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. \u003cem\u003eevm.model.scaffold_798.411\u003c/em\u003e, located 68.2 kb downstream of QEI Chr24__300974094 for A_WI, is homologous to \u003cem\u003eGH3.6\u003c/em\u003e (\u003cem\u003eGRETCHEN HAGEN3.6\u003c/em\u003e) in apple, has been demonstrated to play a negative role in regulating water-deficit stress tolerance [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Another reported gene, \u003cem\u003eevm.model.scaffold_6275.107\u003c/em\u003e, is a member of plasma membrane intrinsic protein (PIP) subfamily, homologous to \u003cem\u003ePIP1;4\u003c/em\u003e in Arabidopsis, has been reported to response drought stress [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. These results suggested the reliability of our GWAS results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe candidate genes around the stable QTNs and QEIs were selected by combing the gene function annotation and transcriptome data of cut chrysanthemum cultivar \u0026lsquo;Jinba\u0026rsquo;. As a result, a total of 25 genes were preliminarily considered as potential candidates with relatively high expression levels in root or leaf tissues (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003e). \u003cem\u003eNLP6\u003c/em\u003e and \u003cem\u003eLOX2\u003c/em\u003e were simultaneously identified within the regions of QTN Chr18__327209663 for MFVD and A_SFWI, which also had a relatively higher expression in shoot (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Notably, \u003cem\u003ebHLH112\u003c/em\u003e exhibited a higher expression level in leaf. Auxin is a critical hormone to plant growth, development, and stress responses [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. \u003cem\u003eSAUR51\u003c/em\u003e (\u003cem\u003eevm.model.scaffold_1433.17\u003c/em\u003e), as a member of auxin responsive protein in Arabidopsis, located on chromosome 16, was considered as a candidate gene to response drought stress. More importantly, the members of \u003cem\u003eNAC\u003c/em\u003e transcription factors, \u003cem\u003eNAC090\u003c/em\u003e (\u003cem\u003eevm.model.scaffold_732.153\u003c/em\u003e) and \u003cem\u003eANAC087\u003c/em\u003e (\u003cem\u003eevm.model.scaffold_136.26\u003c/em\u003e), were identified as candidates. In addition, \u003cem\u003eSnRK1.1\u003c/em\u003e, \u003cem\u003ePAP2\u003c/em\u003e, \u003cem\u003eDGS1\u003c/em\u003e, and \u003cem\u003eSPHKI\u003c/em\u003e were also regarded as potential candidate genes to response drought stress in chrysanthemum (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCandidate genes located within stable QTNs and QEIs for drought tolerance-related traits\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTaxa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQTNs/QEIs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGene ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eHomologous gene in Arabidopsis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSymbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAnnotation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA_MFVD, F_WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eChr22__201784939\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eevm.model.scaffold_732.153\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT5G22380.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eNAC090\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNAC domain containing protein 90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA_SFWI, MFVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eChr18__327209663\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eevm.model.scaffold_468.349\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT3G45140.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eLOX2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLipoxygenase 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA_SFWI, A_SFW_T, A_WI, F_WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eChr24__115918888\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eevm.model.scaffold_130.407\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT5G12290.3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eDGS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDGD1 suppressor 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA_SFWI, MFVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr26__113725691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eevm.model.scaffold_1097.68\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT3G30180.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBR6OX2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBrassinosteroid-6-oxidase 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA_SFW_T, F_SFW_CK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr4__258242409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eevm.model.scaffold_1477.59\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT1G67580.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCDKG2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProtein kinase superfamily protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF_MFVD, F_SFWI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr27__161373885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eevm.model.scaffold_1687.78\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT3G14690.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCYP72A15\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCytochrome P450, family 72, subfamily A, polypeptide 15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF_PHI, F_SDW_CK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr12__129903214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eevm.model.scaffold_2433.75\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT4G21540.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSPHK1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSphingosine kinase 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA_PH_T,F_PH_T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr16__165273239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eevm.model.scaffold_1433.17\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT1G75580.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSAUR51\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSAUR-like auxin-responsive protein family\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA_SDW_CK,A_SFW_CK,F_SDW_CK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eChr27__148805349\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eevm.model.scaffold_11305.16\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT1G75580.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSAUR22\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSAUR-like auxin-responsive protein family\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA_SDW_CK,A_SFW_CK,F_SDW_CK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eChr27__148805349\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eevm.model.scaffold_11305.18\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT4G38840.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSAUR14\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSAUR-like auxin-responsive protein family\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA_SDW_CK,A_SFW_CK,F_SDW_CK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eChr27__148805349\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eevm.model.scaffold_11305.23\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT4G34770.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSAUR1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSAUR-like auxin-responsive protein family\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA_SFWI,MFVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eChr18__327209663\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eevm.model.scaffold_468.346\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT1G64530.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eNLP6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePlant regulator RWP-RK family protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA_WI, F_WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eChr24__115918888\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eevm.model.scaffold_130.407\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT5G12290.3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eDGS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDGD1 suppressor 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA_WI, F_WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eChr24__115918888\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eevm.model.scaffold_130.406\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT1G09530.6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ePIF3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePhytochrome interacting factor 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA_WI, F_WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eChr9__95363777\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eevm.model.scaffold_628.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT3G01090.3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSNRK1.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSNF1 kinase homolog 10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF_WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eChr6__268784715\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eevm.model.scaffold_1537.72\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAT4G29080.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ePAP2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePhytochrome-associated protein 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQEI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA_SFW, F_WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eChr22__201784939\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eevm.model.scaffold_732.153\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT5G22380.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eNAC090\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNAC domain containing protein 90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA_SFW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr23__279219985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eevm.model.scaffold_10498.181.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT1G17440.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eTAF12B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTranscription initiation factor TFIID subunit A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA_WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr14__283776194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eevm.model.scaffold_5125.46\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT1G30460.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCPSF30\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCleavage and polyadenylation specificity factor 30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA_WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr15__239636786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eevm.model.scaffold_622.301\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT4G03090.5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eNDX\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSequence-specific DNA binding transcription factor ATNDX\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF_WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr27__58232617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eevm.model.scaffold_3677.89\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT2G17800.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eARAC1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRac-like GTP-binding protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF_WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eChr6__268784715\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eevm.model.scaffold_1537.72\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAT4G29080.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ePAP2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePhytochrome-associated protein 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA_WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr16__279042622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eevm.model.scaffold_136.26\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT5G18270.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eANAC087\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNAC domain containing protein 87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA_WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr19__215484175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eevm.model.scaffold_8721.52\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT5G47010.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eUPF1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRNA helicase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA_WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr23__255122841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eevm.model.scaffold_1763.277\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT4G02570.4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCUL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCullin 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF_SFW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr14__302132429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eevm.model.scaffold_5000.11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAT1G61660.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ebHLH112\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBasic helix-loop-helix (bHLH) DNA-binding superfamily protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF_WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr18__333348032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eevm.model.scaffold_9289.20\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAT1G18660.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eIAP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZinc finger (C3HC4-type RING finger) family protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF_WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr5__110219202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eevm.model.scaffold_1519.189\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAT1G22920.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCSN5A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCOP9 signalosome 5A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF_WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eChr9__95363777\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eevm.model.scaffold_628.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAT3G01090.3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSnRK1.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSNF1-related protein kinase 1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: the bold character is repeatedly detected.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCandidate genes prediction and qRT-PCR validation\u003c/h2\u003e \u003cp\u003eUsing transcriptome data from drought-tolerant variety \u0026lsquo;Seiko Eizan\u0026rsquo; and drought-sensitive variety \u0026lsquo;Escama\u0026rsquo;(unpublished), we analyzed the expression of 25 candidate genes under drought stress. To further investigate their roles, a subset of 7 genes with distinct expression patterns was selected for qRT-PCR validation of their drought-responsive expression characteristics. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e7\u003c/span\u003e, all candidate genes exhibited different degrees of response to drought stress and recovery. Overall, the relative expression levels of \u003cem\u003ebHLH112\u003c/em\u003e (\u003cem\u003eevm.model.scaffold_5000.11\u003c/em\u003e), \u003cem\u003eNLP6\u003c/em\u003e (\u003cem\u003eevm.model.scaffold_468.346\u003c/em\u003e), \u003cem\u003ePIF3\u003c/em\u003e (\u003cem\u003eevm.model.scaffold_130.406\u003c/em\u003e), \u003cem\u003eBR6OX2\u003c/em\u003e (\u003cem\u003eevm.model.scaffold_1097.68\u003c/em\u003e), \u003cem\u003eCYP72A15\u003c/em\u003e (\u003cem\u003eevm.model.scaffold_1687.78\u003c/em\u003e), \u003cem\u003ePAP2\u003c/em\u003e (\u003cem\u003eevm.model.scaffold_1537.72\u003c/em\u003e), reached the highest after 48 h of drought stress, while subsequently down-regulated after 2 h of recovery. In contrast, \u003cem\u003eSAUR51\u003c/em\u003e (\u003cem\u003eevm.model.scaffold_1433.17\u003c/em\u003e) was first induced after exposure to drought stress and then downregulated, showing the highest and lowest expression levels at 12 h and 48 h, respectively. In conclusion, the 7 above-mentioned key candidate genes might participate in regulating of drought tolerance in chrysanthemum.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDrought tolerance is a complex quantitative trait influenced by both growth and developmental factors, as well as environmental factors. It is reported to be regulated by multiple genes and manifested as a series of morphological and physiological changes [\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. With the global climate change, the drought stress has a dramatically influence on crop production [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. And there is urgent need to enhance the drought tolerance of plants for agricultural researchers and plant breeders. Chrysanthemum, with high economical value, is also facing serious drought threats to its production worldwide. But the genetic mechanisms of drought tolerance in chrysanthemum still remains largely unknown, which hinders its breeding process. Here, we characterized the drought and re‑watering responses of 200 chrysanthemum accessions and applied the MFVD as a comprehensive indicator to evaluate their drought tolerance. Regrettably, only one accession was identified as highly tolerant, specifically \u0026lsquo;Nannong Chengpingpang\u0026rsquo;. Nevertheless, the moderate drought tolerance exhibited by over 30 cultivars represents a valuable resource for breeding programs aimed at enhancing drought resilience in chrysanthemum.\u003c/p\u003e \u003cp\u003eRecently, remarkable progress has been made in understanding the molecular mechanisms of drought tolerance in chrysanthemum. BBX transcription factors play a vital role in plant growth, development, and stress responses [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Xu et al. [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] found \u003cem\u003eCmBBX19\u003c/em\u003e was a negative regulator of DT in chrysanthemum through modulating the accumulation of protective proteins, maintaining cellular redox balance, and promoting cell wall biogenesis in the ABA-dependent pathway. \u003cem\u003eCmBBX22\u003c/em\u003e was demonstrated to function as a negative regulator of drought tolerance in chrysanthemum by modulating ABA signaling, stomatal conductance, and antioxidant responses [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Wang et al. [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] reported that \u003cem\u003eCmNF-YB8\u003c/em\u003e, a nuclear factor from chrysanthemum, changed the stomatal status and cuticle thickness of the leaf epidermis by regulating the expression of the serine/threonine protein kinase gene \u003cem\u003eCmCIPK6\u003c/em\u003e and the cuticle biosynthesis regulatory factor \u003cem\u003eCmSHN3\u003c/em\u003e to affect drought resistance. In addition, \u003cem\u003eDgNAC1\u003c/em\u003e, \u003cem\u003eCmRH56\u003c/em\u003e, and \u003cem\u003eCdICE1\u003c/em\u003e have been implicated in the drought stress response of chrysanthemum in previous studies [\u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. However, most of these genes are transcription factors identified through homologous cloning, a strategy that inherently limits the discovery of novel genes.\u003c/p\u003e \u003cp\u003eGWAS is a powerful analytical approach to identify genetic variants and novel genes in genetics and genomics studies [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Chrysanthemum is a highly heterozygous polyploid plant with an extremely complex genetic background [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], which resulted in a lag in research progress compared to other plants. Li et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] used 159 chrysanthemum varieties to conduct association mapping based on 707 informative SRAP, SCoT and EST-SSR markers, and 4 favorable alleles from 16 associations were identified related to drought tolerance. However, due to the small size of population, the low efficiency of traditional markers, and previous lack of reference genome information, it was difficult to discover potential genes for a better understand underlying the genetic mechanisms of DT in chrysanthemum. Thanks to the development of SNP genotyping technologies, the reduction of high-throughput sequencing costs as well as the release of chrysanthemum reference genome data [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], GWAS has been successfully applied to explore the genetic basis of several traits of interest, such as plant height [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], flowering time [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], root system [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e] and black spot disease [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], which brings down to dissect candidate genes of DT in chrysanthemum via GWAS.\u003c/p\u003e \u003cp\u003eIn our research, GWAS was performed based on 330,710 high quality GBS-based SNPs and 200 chrysanthemum accessions via 3VmrMLM method [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], a new multi-locus method that further expanded to cover QTNs and QEIs. Among the 54 stable QTNs detected for DT-related traits, Chr12__203004016, Chr14__60967743, Chr16__125764264 and the other four QTNs were simultaneously detected in both single- and multi-environment analysis. Moreover, we also detected 115 QEIs by 3VmrMLM method, Chr22__201784939 and Chr9__95363777 were repeatedly detected in stable QTNs and QEIs. This finding suggested the interactions between genotype and environment play a significant role in regulating the drought tolerance of chrysanthemum. Notably, the stable QTNs (Chr18__327209663 and Chr26__11372569) detected for MFVD and A_SFWI showed a significant difference between two genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Additionally, Chr6__268784715, Chr9__95363777, Chr22__201784939, and Chr24__115918888 were consistently detected across multiple environments and traits, indicating their potential utility in future marker-assisted selection (MAS) breeding. By integrating functional annotations from Arabidopsis and previous reports, we identified 11 known genes near the QTNs/QEIs, including \u003cem\u003eWRKY57\u003c/em\u003e, \u003cem\u003eSnRK2.4\u003c/em\u003e, and \u003cem\u003eMYB121\u003c/em\u003e. These genes have been reported to regulate drought tolerance in plants [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] These findings confirm the reliability of our study and demonstrate the applicability of the 3VmrMLM method for dissecting genes underlying drought tolerance and other complex traits.\u003c/p\u003e \u003cp\u003eWe identified seven candidate genes showing distinct expression patterns under drought stress. Previous studies have demonstrated that key phytohormones, including auxin and ABA, play significant roles in regulating plant stress tolerance [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. In our study, \u003cem\u003eSAUR51\u003c/em\u003e (\u003cem\u003eevm.model.scaffold_1433.17\u003c/em\u003e), a member of the SAUR-like auxin-responsive protein family, has been reported show a significant repression of gene expression in Arabidopsis leaves by the drought stress. In addition, the other three genes, \u003cem\u003eSAUR22\u003c/em\u003e, \u003cem\u003eSAUR14\u003c/em\u003e and \u003cem\u003eSAUR1\u003c/em\u003e around the QTN Chr27__148805349 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were also showed the similar gene expression patterns under the drought condition in Arabidopsis leaves [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e].Moreover, \u003cem\u003ePAP2\u003c/em\u003e, an \u003cem\u003eAUX/IAA\u003c/em\u003e transcriptionnal regulator, has been reported to repress lignin biosynthesis‑related and LBD genes, thereby affecting plant growth and lateral root formation in tomato [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. This suggests it may also play a role in the drought stress response of chrysanthemum. The candidate gene \u003cem\u003ebHLH112\u003c/em\u003e (\u003cem\u003eevm.model.scaffold_5000.11\u003c/em\u003e), located 87.8 kb downstream of QEI Chr14__302132429 (LOD\u0026thinsp;=\u0026thinsp;19.60) and highly expressed in leaf and stem (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003e), has been shown to enhance ROS scavenging and proline accumulation in Arabidopsis, thereby regulating stomatal aperture in \u003cem\u003eArabidopsis\u003c/em\u003e [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Another novel candidate gene \u003cem\u003eevm.model.scaffold_1097.68\u003c/em\u003e (\u003cem\u003eBR6OX2\u003c/em\u003e), a member of CYP450 family, has been reported to confer salt stress tolerance and elevate endogenous brassinosteroid levels when overexpressed in apple callus [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. In addition, other candidate genes related to salicylic acid response and abscisic acid-activated signaling pathway were also identified. These genes, expressed in leaf, root, shoot, and stem (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003e), have not been directly linked to drought tolerance. Further investigation and validation of these novel candidate genes are crucial for gaining a deeper understanding of the molecular mechanisms underlying chrysanthemum\u0026rsquo;s adaptation to drought stress.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study conducted a multi-locus GWAS for DT related traits using a panel of 200 chrysanthemum cultivars, yielding 51 stable QTNs and 115 QEIs. Eleven genes homologous to drought‑responsive genes in model species were located near these loci, supporting the reliability of our findings. Through integrated analysis of functional annotation, transcriptomics, and qRT‑PCR data, seven putative DT key candidate genes were further identified. In addition, four highly or moderately drought-tolerant cultivars (\u0026lsquo;Nannong Chengpingpang\u0026rsquo;, \u0026lsquo;Nannong Bingqilin\u0026rsquo;, \u0026lsquo;Nannong Xuefeng\u0026rsquo; and \u0026lsquo;QD3-107\u0026rsquo;) harboring five of the six favorable alleles were prioritized as potential breeding donors. These discoveries present here deep our insights into the genetic mechanisms underlying chrysanthemum\u0026rsquo;s response to water scarcity and provide promising targets for future molecular breeding endeavors. Nevertheless, further research is necessary to functionally validate the candidate genes and elucidate the gene regulatory mechanisms underlying drought stress.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edrought tolerance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGWAS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenome-wide association study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eQTN\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe quantitative trait nucleotides\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eQEI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQTN-by-environment interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eQTL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003equantitative trait loci\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePCC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePerson correlation coefficients\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe experiments were performed in accordance with the relevant legislation and adhered to ethical standards. This study did not involve human participants, animals, or endangered/protected species. The collection of plant materials and all experimental procedures complied with institutional, national, and international guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003ch2\u003e\u0026nbsp;\u003c/h2\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the \u0026ldquo;JBGS\u0026rdquo; Project of Seed Industry Revitalization in Jiangsu Province (JBGS[2021]020), the National Natural Science Foundation of China (32271938), China Agriculture Research System (CARS-23-A18), Hainan Province Science and Technology Special Fund (ZDYF2025XDNY085), and the Priority Academic Program Development of Jiangsu Higher Education Institutions.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eYY and XLO have contributed equally to this work. FZ, FDC, JSS, and WMF conceived and designed the experiments; YY wrote the draft manuscript; XLO, XXW, YHQ, YX, SYW, SYW and WYW analyzed the data; JSS performed the experiments and modified the manuscript. All the authors have read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe thank the high-performance computing platform of Bioinformatic Center, Nanjing Agricultural University, for providing data analysis platform services.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe GBS sequencing data have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA1004079. All other data supporting the findings of this study are included in this published article and its supplementary information files. Plant materials are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTavakol E, Elbadry N, Tondelli A, Cattivelli L, Rossini L. Genetic dissection of heading date and yield under mediterranean dry climate in barley (\u003cem\u003eHordeum vulgare\u003c/em\u003e L). Euphytica. 2016;212:343\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10681-016-1785-0\u003c/span\u003e\u003cspan address=\"10.1007/s10681-016-1785-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReddy SS, Saini DK, Singh GM, Sharma S, Mishra VK, Joshi AK. Genome-wide association mapping of genomic regions associated with drought stress tolerance at seedling and reproductive stages in bread wheat. 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Plant Physiol Biochem. 2024;212:108767. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.plaphy.2024.108767\u003c/span\u003e\u003cspan address=\"10.1016/j.plaphy.2024.108767\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"chrysanthemum, drought tolerance, GWAS, genetic characteristics, favorable alleles, candidate genes","lastPublishedDoi":"10.21203/rs.3.rs-9267894/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9267894/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDrought is one of the most serious abiotic stresses limiting plant productivity and becomes increasingly extreme worldwide due to the ongoing deterioration of the global climate. Chrysanthemum (\u003cem\u003eChrysanthemum morifolium\u003c/em\u003eRamat.), one of the four most popular cut flowers in the world, is sensitive to water-limited environment. However, the genetic basis and causal genes underlying drought tolerance (DT) remain largely unknown. In this research, multi-locus GWAS was employed to detect the genetic loci and candidate genes for DT in a diverse panel of 200 cut chrysanthemum accessions that were genotyped with 330,710 high-quality SNPs. As a result, 43 stable QTNs in single-environment analysis, 18 stable QTNs and 115 QEIs in multiple-environments analysis were identified via the 3VmrMLM method. Among the genes around stable QTNs and QEIs, eleven were homologous to known DT regulatory genes in other plants such as \u003cem\u003eWRKY57\u003c/em\u003e, \u003cem\u003eMYB121\u003c/em\u003e, \u003cem\u003eGH3.6\u003c/em\u003e. In addition, seven candidate genes were predicted to be associated with DT related traits by combing the functional annotation, transcriptomics data and quantitative real-time PCR. More importantly, four drought-tolerant cultivars harboring favorable alleles were identified as pre-bred material to improve tolerance of cultivated chrysanthemum. These findings provide robust insights into the genetic architecture of DT and offer valuable prospects for the molecular breeding of chrysanthemum.\u003c/p\u003e","manuscriptTitle":"Multi-locus GWAS uncovers favorable alleles and candidate genes underlying water stress response in chrysanthemum","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-13 11:34:31","doi":"10.21203/rs.3.rs-9267894/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-29T07:16:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T13:14:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-17T11:49:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217934478511274887845882710077480812645","date":"2026-04-12T00:08:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9199534469401698529532321370184541002","date":"2026-04-08T07:47:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40278129410724395113485488617938862873","date":"2026-04-08T02:37:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338189840464011551753223581984049875233","date":"2026-04-07T06:00:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154324963309586978667068719387819593592","date":"2026-04-07T04:39:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-06T23:44:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T23:36:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-06T07:37:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-03T12:57:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2026-04-03T12:49:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7fb76750-c9e8-4780-8f31-8a80dee03b0e","owner":[],"postedDate":"April 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T10:57:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-13 11:34:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9267894","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9267894","identity":"rs-9267894","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}