Identification of QTLs for adult-plant stripe rust resistance in Chinese wheat landrace Yizhanghongkemai and assessment of their utility for decreasing yield loss

Identification of QTLs for adult-plant stripe rust resistance in Chinese wheat landrace Yizhanghongkemai and assessment of their utility for decreasing yield loss | 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 Identification of QTLs for adult-plant stripe rust resistance in Chinese wheat landrace Yizhanghongkemai and assessment of their utility for decreasing yield loss Yumei Li, Jiaru Yang, Jing Zhang, Shuanglin Du, Hongli Ji, Zehou Liu, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5643654/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jul, 2025 Read the published version in Molecular Breeding → Version 1 posted 4 You are reading this latest preprint version Abstract Stripe rust is prevalent in the wheat-growing region of southwestern China. Frequent changes in stripe rust pathogen virulence in this region lead to a rapid loss of disease resistance among wheat varieties. However, Chinese wheat landrace Yizhanghongkemai (YZHK) has exhibited adult-plant stripe rust resistance for more than one decade in a disease nursery in southwestern China. To elucidate the underlying genetic basis, quantitative trait loci (QTLs) for adult-plant stripe rust resistance in YZHK were analyzed using an inclusive composite interval mapping method. Six QTLs for adult-plant stripe rust resistance were detected on chromosomes 1BL, 2BL, 3DS, 5BL, 5DL, and 7DS in multiple environments. Notably, QYrYZHK.saas-1B, QYrYZHK.saas-2B and QYrCY.saas-5D were likely new disease resistance loci. By comparing the effects of QTL alleles on yield and its components in field trials in which stripe rust was severe and effectively controlled, we determined that three QTLs significantly decreased yield losses due to stripe rust, among which the QTLs on chromosomes 1BL and 7DS were from YZHK, whereas the QTL on chromosome 5DL was from the other parent Chuanyu 12. These QTLs represent elite genetic resources for developing wheat varieties with adult-plant stripe rust resistance in the wheat-growing region of southwestern China. Wheat Landrace Stripe rust resistance QTL mapping Yield effects Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Wheat is one of the three major cereal crops cultivated worldwide, with a planting area and yield second only to corn and rice in China. The annual planting area of wheat in China is approximately 24 million hectares, with a total output of about 130 million tons, accounting for nearly 18% of the total area and output of grain crops. Therefore, sustainable and stable wheat production is crucial for national food security, social stability, and international economic development (He et al., 2018). Wheat production is constrained by numerous biotic and abiotic stresses, especially diseases caused by pathogenic microbes, among which stripe rust is the most severe (Duchenne-Moutien et al., 2021; Hasegawa et al., 2021). Wheat stripe rust disease caused by Puccinia striiformis f. sp. tritici ( Pst ) is widespread in major wheat-growing regions worldwide. According to reports from over 64 countries, the disease can significantly decrease yields in all wheat-growing regions (Carmona et al., 2020). The frequent occurrence of new wheat stripe rust pathogen races and virulence changes has led to the rapid loss of stripe rust resistance in selected and promoted wheat varieties, which has become a major challenge for wheat breeders and producers. To date, 87 wheat stripe rust resistance genes ( Yr1 – Yr87 ), over 100 temporarily named genes, and more than 300 quantitative trait loci (QTLs) controlling wheat stripe rust resistance have been identified on wheat chromosomes (McIntosh, 2024; Klymiuk et al., 2022; Feng et al., 2023; Zhu et al., 2023). Twelve stripe rust resistance genes have been cloned ( Yr5 , YrSP , Yr7 , Yr10 , Yr15 , Yr18 , Yr28 , Yr36 , Yr46 , Yr 87 , YrU1 and YrNAM ). Among these genes, Yr5 , Yr7 , and YrSP , which are located on chromosome 2BL, encode BED-NLR proteins (Marchal et al., 2018), Yr10 , Yr28 ( YrAS2388 ) and Yr87 encode nucleotide-binding site-leucine-rich repeat (NBS-LRR) proteins containing N-terminal coiled-coil domains (Liu et al., 2014; Zhang et al., 2019; Sharma et al., 2024), Yr15 encodes a tandem kinase domain protein (wheat tandem kinase, WTK1) (Klymiuk et al., 2018), Yr18 encodes an ATP-binding cassette transporter (ABC transporter) (Krattinger et al., 2009), Yr36 encodes WHEAT KINASE START1 (WKS1) (Fu et al., 2009), Yr46 encodes a hexose transporter (Moore et al., 2015), YrU1 encodes an NBS-LRR protein with an N-terminal ankyrin-repeat and C-terminal WRKY domains (Wang et al., 2020c), and YrNAM encodes a protein with a NAM domain and a ZnF-BED domain (Ni et al., 2023). Although many stripe rust resistance genes have been identified, less than half have been used by breeders. To avoid the excessive use of individual resistance genes, new genes conferring adult-plant stripe rust resistance in wheat must be identified and characterized to effectively control stripe rust. Wheat landraces are often considered to be the elite genetic resources that have been preserved through long-term natural selection and human intervention. They are highly adaptable to local natural environmental conditions. Moreover, they have potentially useful production-related traits (e.g., multiple flowers and abundant grains) and are applicable for distant hybridizations. They also have a relatively rich genetic background and are important sources of genes for improving modern wheat cultivars (Jiang et al., 2022; Wang et al., 2022). Identifying new stripe rust resistance genes can diversify the available wheat disease resistance genetic resources and mitigate the disadvantages of stripe rust resistance breeding involving a single gene. Yizhanghongkemai (YZHK), which is a wheat landrace, has been highly resistant to stripe rust during the adult stage for 15 consecutive years the wheat-growing region of southwestern China, despite many cultivars lost their resistance to stripe rust in recent years. To identify YZHK genes mediating adult-plant stripe rust resistance, Wheat 15K SNP array technology and an inclusive composite interval mapping (ICIM) method were used to map QTLs in the F 8 generation recombinant inbred lines (RILs) derived from the YZHK × Chuanyu 12 (CY) hybridization. The effects of stripe rust resistance QTL alleles on stabilizing yield were also analyzed. Furthermore, kompetitive allele-specific PCR (KASP) markers were developed for molecular marker-assisted selection of adult-plant stripe rust resistance genes. Materials and methods Plant materials For the hybridization in this study, Chinese wheat landrace YZHK, collected from Yizhang County in Hunan Province under sub-tropical monsoonal climate of central China, was used as the female parent, whereas wheat variety CY (pedigree: 80-9418 × 83-4516) susceptible to stripe rust in the wheat-growing region of southwestern China was used as the male parent. A total of 200 F 8 generation RILs were obtained via a single seed descent method from F 2 lines. Phenotypic and genotypic analyses of adult-plant stripe rust disease and agronomic traits were conducted using RILs and their parents under field conditions. Investigation of disease resistance and yield traits In this study conducted from 2016 to 2023, 200 F 8 generation RILs and their parents were grown in the disease nursery of Pidu Experimental Base under sub-tropical monsoonal climate of southwestern China, Sichuan Academy of Agricultural Sciences (SAAS), China from late October to early November (2016–2023PD). Specifically, a randomized block design with two replicates was applied, with 1–2 rows (1.5 m long and separated by 0.3 m) in each block. Individual rows consisted of 10 plants, with CY planted at one end of each block as an inoculum source. In the disease nursery of Pidu Experimental Base in the wheat-growing region of southwestern China, stripe rust is generally a typical natural disease which often sufficiently occurs without any specialized artificial inoculation with stripe rust pathogen. However, in a few years when the natural incidence of stripe rust was insufficient or uneven across different field regions, wheat plants were artificially inoculated during the 3- to 4-leaf stage. The seedlings were inoculated by the staff from Institute of Plant Protection in SAAS (Yang et al., 2024), using the stripe rust pathogen strains sourced from a severely infected field regions of Pidu Experimental Base. The stripe rust infection type (IT) of adult wheat plants was determined using the following 0–9 scale (Singh et al., 2000; Wan et al., 2023): 0–3, highly resistant (HR); 4–5, moderately resistant (MR); 6–7, moderately susceptible (MS); and 8–9, highly susceptible (HS). Stripe rust IT values were determined at the booting stage, flowering stage, and milk ripening stage, with plants examined approximately every 7 days (five times in total). To investigate agronomic traits, in addition to the 2023PD experiment in the disease nursery, field experiments were conducted in 2023 in Guanghan County (2023GH) of Sichuan Province and Dongtai County (2023DT) of Jiangsu Province, where stripe rust disease was effectively controlled. The experimental design was basically the same as that used to analyze disease resistance, with conventional wheat field production management practices. During the harvest season, eight plants in the middle of each plot in the disease nursery (2023PD) and stripe rust control area (2023GH and 2023DT) were examined in terms of effective spike number per plant (SN), grain number per spike (GNP), and thousand-grain weight (TGW) after harvest. Eight plants were sampled to calculate the average of SN, and the average of eight main stem panicles per plant was calculated for GNP. For TGW, the average of three samples per plot was determined. Statistical analysis WPS Spreadsheet 2024 (Kingsoft Inc., Beijing, China) was used to organize data for phenotypic traits and yield-related traits of adult plants infected with stripe rust disease, whereas SPSS Statistics v27.0 (IBM Co., New York, USA) was used to analyze phenotypic variations. SPSS was also used for an analysis of variance (ANOVA) and correlation analysis. Broad-sense heritability ( h 2 ) was calculated using the following equations (Jia et al., 2013): h 2 = σ 2 g /(σ 2 g + σ 2 e ) in a single environment or h 2 = σ 2 g /(σ 2 g + σ 2 i + σ 2 e ) in multiple environments. Phenotypic variances in a single environment and in multiple environments were calculated as (rσ 2 g + σ 2 e ) and (nrσ 2 g + rσ 2 i + σ 2 e ), where r is the number of repetitions, n is the number of experiments, σ 2 g is the genetic variance, σ 2 i is the genotype × environment interaction variance, and σ 2 e is the experimental error. Construction of a genetic linkage map and QTL localization A plant DNA extraction kit (Tiangen Biotech Co., Ltd., Beijing, China) was used to extract genomic DNA from YZHK, CY, and F 8 generation RIL plants. Samples were sent to China Golden Marker (Beijing) Biotechnology Co., Ltd. for genotyping using a Wheat 15K SNP array. Detected SNPs were filtered according to Wan et al. (2022). The “MAP” function of the QTL IciMapping v4.2 program (Meng et al., 2015) along with stripe rust resistance phenotypic data were used to construct a genetic linkage map. QTLs were detected using the ICIM method, with a threshold LOD ≥2.5. KASP genotyping assays The online program PolyMarker (http://www.polymarker.info/) was used to develop KASP markers for single nucleotide polymorphisms (SNPs) closely linked to QTLs for adult-plant stripe rust resistance. Primers were synthesized by Sangon Biotech (Shanghai) Co., Ltd. KASP markers that clearly identified polymorphisms between parents were designed for population genotyping. Specifically, KASP genotyping was performed using a CFX96 fluorescence quantitative PCR instrument (Bio-Rad, Hercules, CA, USA), with a 10 μL reaction volume comprising 5 μL 2×KASP Master Mix, 0.04 μL upstream FAM primer (F1), 0.04 μL upstream HEX primer (F2), 0.08 μL downstream common primer (C), 4 μL DNA template, and double-distilled water. The PCR program was as follows: 94 °C for 15 min; 10 cycles of 94 °C for 20 s and 61–55 °C for 60 s (the temperature decreased by 0.6 °C with each cycle); 30 cycles of 94 °C for 20 s and 55 °C for 60 s; fluorescence was detected at 30 °C for 30 s. After obtaining population genotypic data for KASP markers, the disease resistance phenotypic effects of closely linked QTLs and their consistency were analyzed using the original chip marker molecular data as the control. Results Phenotypic analysis For wheat landrace YZHK grown over several years in the disease nursery at the Pidu Experimental Base, the average stripe rust IT value for adult plants was 2.35 (ranging from 1 to 3; i.e., plants were mostly highly resistant). In contrast, the average stripe rust IT value for CY was 8.10 (ranging from 8 to 9; i.e., highly susceptible) (Fig. 1, Fig. 2). In eight field experiments conducted from 2016 to 2023, the average stripe rust IT value of F 8 generation RILs was 4.78 (ranging from 0.92 to 8.56). The average IT values of the two parents were within the population range, indicative of transgressive segregation in the RIL population (Fig. 2, Table 1), implying both parents have disease resistance-related genes. The coefficient of variation for phenotypic data in eight environments was less than 0.5. Additionally, broad-sense heritability in a single environment was 0.40–0.92, with an average of 0.74. Broad-sense heritability in different years was 0.45, suggesting stripe rust resistance was mostly controlled by genes, but it was also influenced by environmental factors in different years (e.g., diversity in stripe rust pathogen physiological races in different years) and disease severity affected by climate. Genetic map construction The Wheat 15K SNP array was used for the genotyping of 200 RILs and their parents (with two duplicate samples for each parent to evaluate the consistency of chip-based genotyping). This array contains 13,947 relatively evenly distributed SNP markers in the wheat genome. In this study, markers absent in the parents or on unknown chromosomes were removed. Homozygous SNP markers that were repeatedly revealed as polymorphic between the parents were identified. SNP markers with heterozygosity exceeding 10% in the population were also removed. Finally, a genetic linkage map was constructed (3,004.98 cM, including 25 linkage groups and 2,139 SNP markers). The average distance between adjacent markers was 1.40 cM. Notably, markers were distributed on all 21 wheat chromosomes. QTL mapping On the basis of the genetic map and stripe rust IT values of RILs, six stable QTLs for stripe rust resistance were detected on chromosomes 1BL, 2BL, 3DS, 5BL, 5DL, and 7DS in multiple trials (at least 4 trials) using the ICIM mapping method (Fig. 3, Table 2). The major QTL QYrYZHK.saas-1B on chromosome 1BL (YZHK in the QTL name indicates that the resistance-related QTL allele was from YZHK) was detected in the 2018PD, 2019PD, 2020PD, 2022PD and 2023PD trials. This QTL was detected between AX-109882817 and AX-109824050 and explained 3.51%–18.69% of the phenotypic variation (i.e., PVE). QYrYZHK.saas-2B , which was detected in the 2016PD, 2018PD, 2019PD, 2020PD, 2021PD and 2022PD trials, was located on chromosome 2BL (between AX-109849173 and AX-111071533 ) and regarded as a major QTL with PVE ranging from 5.10% to 23.09%. The minor QTL QYrCY.saas-3D (CY in the QTL name indicates that the resistance-related QTL allele was from CY), which was detected in the 2017-2020PD trials, was located on chromosome 3DS (between AX-89574305 and AX-108852641 ), with PVE ranging from 4.38% to 6.39%. QYrYZHK.saas-5B , which was detected in five trials, was located on chromosome 5BL (between AX-110506915 and AX-111762700 ), with PVE ranging from 3.17% to 6.99%. QYrCY.saas-5D , which was detected in 2020-2023 trials, was located on chromosome 5DL (between AX-110985437 and AX-110570148 ), with PVE ranging from 6.31% to 10.30%. The major QTL QYrYZHK.saas-7D , which was detected in the 2017-2023PD trials, was located on chromosome 7DS (between AX-110888456 and AX-111468131 ), with PVE ranging from 9.01% to 23.17% (Fig. 3, Table 2). KASP marker development and validation In this study, several designed KASP markers were developed from their SNP tags closely linked to detected QTLs, which could divide the RILs into two main groups by fluorescence signal clustering. However, in order to guarantee that at least one accurate KASP marker was successfully used as PCR markers for genotyping, among these designed KASP markers, six KASP markers obtained the genotypes of the RIL population that were mostly consistent with their SNP-chip genotypes (Table 3, Fig. 4). And the genetic effects of QTLs calculated according to KASP genotypes were also consistent with those calculated on the basis of SNP chip genotypes; the differences in mean IT values between genotypes from different parental sources were extremely significant using both KASP and Chip genotype) (Fig. 5). Considering the major QTL QYrYZHK.saas-7D is located in a chromosome region corresponding to the 41.41–51.96 Mb interval of chromosome 7DS consistent with the interval of Yr18 in the Chinese Spring genome (IWGSC RefSeq v1.0). One reported diagnostic KASP marker for Yr18 (Table 3) was used for the validation of the relationship between QYrYZHK.saas-7D and Yr18 . In this study, the wMAS000003F1 sequence with FAM was used for identifying the susceptible allele at the site of Yr18 , whereas wMAS000003F2 with HEX was for the resistant allele. Screening for candidate gene Yr18 in two parents revealed only YZHK has the resistance allele, which is same to the control Chinese Spring carrying Yr18 (Fig. S1). In general, there were relatively small differences in the mean IT values of any one parental QTL allele between the designed KASP and SNP chip genotypes in the RIL population (Fig. 5). The selected six KASP markers designed from tags of SNP chip were available as the PCR marker used for molecular assisted selection for adult-plant stripe rust resistance. Effects of QTLs for stripe rust resistance on yield In environments in which stripe rust disease was under control (2023GH and 2023DT), the effects of six adult-plant stripe rust resistance QTLs on yield were not significant. Specifically, there were no significant differences in the yield-related traits between the resistance and susceptibility alleles (Table 4), indicating that there were no yield-related QTLs closely linked to the adult-plant stripe rust resistance QTLs identified in this study in this population. In the nursery where stripe rust disease was relatively severe (2023PD), four QTLs were significantly correlated with yield and its related traits (Table 4). The average GNP (48.40) of lines with the disease resistance QYrYZHK.saas-1B allele from YZHK was significantly greater than the average GNP (43.38) of lines with the susceptibility allele (i.e., 11.57% increase) (Table 4). Ultimately, the average yield of plants with the disease resistance allele (7.29 g) was 28.12% higher than that of plants with the disease susceptibility allele (5.69 g) (Table 4). For QYrYZHK.saas-7D , the average TGW, GNP, and YLD (28.60 g, 48.73, and 7.28 g, respectively) were 14.49%, 16.91%, and 22.35% higher, respectively, for the lines with the disease resistance QTL allele than for the lines without the disease resistance QTL allele (Table 4). The average SN, GNP, and YLD (5.24, 48.26, and 7.57 g, respectively) for the lines with the disease resistance QYrCY.saas-5D allele from the parent CY were 10.55%, 13.39%, and 36.64% higher than the lines with QTL allele from YZHK, respectively. In addition, QYrYZHK.saas-2B significantly affected SN; lines with the disease resistance allele had a significantly higher average SN than lines lacking the disease resistance allele (Table 4). Overall, the above-mentioned adult-plant stripe rust resistance QTLs, especially those located on chromosomes 1B, 7D, and 5D, significantly decreased the adverse effects of stripe rust on grain yield, thereby sign the adverse effects ificantly stabilizing wheat grain production (Table 4). QTL pyramiding The effect of pyramiding four QTLs significantly associated with yield-related traits was analyzed by comparing the average phenotypic values between haplotypes with disease resistance QTL alleles and haplotypes without disease resistance QTL alleles. The average stripe rust IT value was significantly lower for RILs in which disease resistance QTL alleles were pyramided than for RILs without disease resistance QTL alleles. Generally, the stripe rust IT value decreased as the number of pyramided disease resistance QTL alleles increased (Fig. 6a). The difference between the average stripe rust IT value of plants containing disease resistance QYrYZHK.saas-1B and QYrCY.saas-5D alleles and the average stripe rust IT value of plants without these two disease resistance QTL alleles revealed the smallest pyramiding effect (Fig. 6a: phenotypic effect (PE) <1.5, i.e., difference between the average stripe rust IT values of the two haplotypes). The largest pyramiding effect was observed for disease resistance QYrYZHK.saas-1B , QYrYZHK.saas-2B , QYrCY.saas-5D , and QYrYZHK.saas-7D alleles (Fig. 6a: PE ≥2). Combining the major QTL QYrYZHK.saas-7D , which had the greatest PVE, with other QTLs significantly increased the associated stripe rust resistance (Fig. 6a: PE ≥1.5). In fact, the effect of pyramiding QYrYZHK.saas-7D with one other disease resistance QTL (e.g., QYrYZHK.saas-2B ) was greater than that of pyramiding the other three disease resistance QTLs (e.g., QYrYZHK.saas-1B , QYrYZHK.saas-2B , and QYrCY.saas-5D ) (Fig. 6a). In terms of yield and its related traits, increases in the number of pyramided disease resistance QTLs decreased yield losses in a severely infected field (Fig. 6b). The same trend was observed for three yield-related traits (Fig. 6c, 6d, 6e), but the pyramiding of QYrYZHK.saas-1B and QYrYZHK.saas-2B did not significantly decrease yield losses (Fig. 6b). Notably, the effect of disease resistance QTLs on stabilizing yield was greatest when QYrYZHK.saas-7D , the major QTL for adult-plant stripe rust resistance, was pyramided with other disease resistance QTLs (Fig. 6b). This finding was supported by the results for three yield-related traits (Fig. 6c, 6d, 6e). In the population, the effect of pyramiding three disease resistance QTL alleles ( QYrYZHK.saas-1B , QYrCY.saas-5D , and QYrYZHK.saas-7D ) on yield was similar to the effect of pyramiding four disease resistance QTL alleles (Fig. 6c, 6d, 6e). However, both pyramiding haplotypes had the most significant effect on stabilizing yield (PE ≥4) and decreased yield losses by 58.07% on average (Fig. 6b). Overall, when conducting molecular marker-assisted selection, the pyramiding of adult-plant stripe rust resistance QTL alleles at three loci ( QYrYZHK.saas-1B , QYrCY.saas-5D , and QYrYZHK.saas-7D ) can significantly improve disease resistance and minimize potential yield losses due to stripe rust. Discussion The wheat-growing region of southwestern China is one of the main areas in which stripe rust is prevalent. Frequent physiological virulence changes in this region may help to explain the relatively rapid loss of stripe rust resistance among wheat varieties (Liu et al., 2024). YZHK is a wheat landrace that originated in Yizhang County of Hunan province that also has a sub-tropical monsoonal climate. In the disease nursery in southwestern China, YZHK exhibited adult-plant stripe rust resistance for 15 years (2009–2024). During this same period, many approved Sichuan wheat cultivars lost their adult-plant stripe rust resistance (Zhang et al., 2023; Li et al., 2023). In consideration of its performance on adult-plant stripe rust resistance over the past 15 years, YZHK is potentially useful for breeding, successfully withstanding the unpredictable biotic stress via the frequent physiological virulence changes. Accordingly, in this study, a QTL analysis of YZHK adult-plant stripe rust resistance was conducted and the effects of the identified QTLs on stabilizing yield were also examined. QYrYZHK.saas-1B was detected on chromosome 1BL in YZHK, with a physical location of 394.00–497.47 Mb in the Chinese Spring reference genome (IWGSC RefSeq v1.0) (IWGSC, 2018). On the basis of comparisons in different environments, the physical location of this QTL may be narrowed to 476.08–497.47 Mb. The Chinese Spring genome includes three annotated disease resistance-related genes, including two genes encoding nucleotide-binding adaptor shared by Apaf-1, R proteins, and CED-4 (NB-ARC) domain-containing proteins (belonging to the NBS-LRR family) and one disease resistance response protein, as well as 22 transporter and kinase genes (Table S1). There are three officially named stripe rust resistance genes, Yr21 (Chen and Line, 1995), Yr26 (Ma et al., 2001), and Yr29 (William et al., 2006), on chromosome 1BL, of which Yr21 and Yr26 are all-stage resistance genes, whereas Yr29 is an adult-plant resistance gene. Yr26 is located at 307.62–321.70 Mb ( Xbarc181 – Xbarc187 ), whereas Yr29 is located at 662.20–675.69 Mb ( Xwmc44 – Xwmc367 ). Moreover, among the QTLs on chromosome 1BL, QYrZM9023.swust-1BL is located at 670.43–681.69 Mb (Yan et al., 2023). QYr.sicau-1B.1 (Ma et al., 2019) for all-stage resistance is located at a physical position corresponding to 461.68–487.42 Mb in the Chinese Spring genome ( Xwmc216–Xwmc156 ), which was partially overlapped with the physical interval of QYrYZHK.saas-1B in current study. However, comparing to QYr.sicau-1B.1 for all-stage resistance (Ma et al., 2019), QYrYZHK.saas-1B for adult-plant stripe rust resistance did not exhibit significant genetic effect on IT value on seedling stage (data not shown), indicating a new adult-plant stripe rust resistance QTL. The major QTL QYrYZHK.saas-2B was detected near 779.85 Mb ( AX-109849173 ) (IWGSC RefSeq v1.0) on chromosome 2BL. A probe sequence ( AX-111071533 ) to the right of the QTL interval had a physical position of 786.72 Mb in the homologous sequence on chromosome 2BL. There are six annotated disease resistance-related genes in the 779.85–786.72 Mb interval of the Chinese Spring genome (Table S1), including four genes encoding proteins with NB-ARC domains belonging to the NBS-LRR family. This interval also contains 13 transporter and kinase genes (Table S1). There are seven officially named stripe rust resistance genes on chromosome 2BL, most of which are all-stage resistance genes, including Yr5 and Yr7 (Macer, 1963; Marchal et al., 2018), Yr43 (Cheng and Chen, 2010), Yr44 (Sui et al., 2009), Yr53 (Xu et al., 2013), Yr72 (McIntosh et al., 2016), and YrSP (Feng et al., 2015). In the Chinese Spring genome, the cloned genes Yr5 , Yr7 , and YrSP are located at 685.27 Mb, whereas Yr43 has been localized to 672.08–673.41 Mb ( Xwgp110 – Xwgp103 ) and Yr44 is close to 732.35 Mb ( XpWB5/N1R1 – Xwgp100 ). Yr53 is located near 598.06 Mb ( Xwmc441 – XLRRrev/NLRRrev350 ), whereas Yr72 is between 767.17 and 771.78 Mb ( Xsun481 – IWB12294 ). The physical locations of QTLs for adult-plant stripe rust resistance in the Chinese Spring genome are as follows: QYr.hbaas-2BL at 453.3 Mb (Jia et al., 2020: IWA586 ), QYr.nafu-2BL at 553.73–615.79 Mb (Hu et al., 2020: Xwgp5770 – Xcfd73 ), QYrww.wgp.2B-4 at 524.00 Mb (Mu et al., 2020: IWB34793 – IWW34793 ), QYr.inla-2BL at 615.79–621.47 Mb (Mallard et al., 2005: Xbarc101 – Xgwm120 ), and QYr.caas-2BL at 693.74–733.16 Mb (Ren et al., 2012b: XwPt-8460 – XwPt-3755 ). By comparison, rare reported QTL/genes for stripe rust resistance were detected in the QTL interval of QYrYZHK.saas-2B identified in this study, indicating it may be a new disease resistance-related locus. QYrCY.saas-3D corresponds to the 27.88–43.38 Mb region on chromosome 3DS in the Chinese Spring genome (IWGSC RefSeq v1.0). The reported stripe rust resistance genes nearby include Yr49 (McIntosh et al., 2014: gpw7321 – gwm161 ) and Yr66 (Bariana et al., 2022: KASP_18087 – KASP_48179 ), which are physically located in the 2.19–7.09 Mb region. This part of the Chinese Spring genome has 10 annotated disease resistance-related genes, including six genes encoding NB-ARC domain-containing proteins, as well as 34 transporter and kinase genes (Table S1). The stripe rust resistance genes and QTLs overlapping the QYrYZHK.saas-5B interval (473.35–535.39 Mb) include Yr74 (Dracatos et al., 2016), YrAYH , and QYr.YBZR-5BL , with physical locations of 531.00 Mb, 530.60–534.30 Mb (Long et al., 2024: KP5B_530.6 – KP5B_534.3 ), and 519.00–542.70 Mb (Deng et al., 2022: AX-111002705 – AX-108929069 ), respectively. The corresponding physical interval in the Chinese Spring genome contains seven annotated disease resistance-related genes and 50 transporter and kinase genes (Table S1). The major QTL QYrYZHK.saas-7D is located in a region corresponding to the 41.41–51.96 Mb interval of chromosome 7DS in the Chinese Spring genome (IWGSC RefSeq v1.0). This interval includes the known adult-plant stripe rust resistance gene Yr18 (Krattinger et al., 2009; Lagudah et al., 2009: Xgwm1220 – Xgwm29 , physical position 47.41–47.42 Mb). Although this physical interval in the Chinese Spring genome does not contain annotated disease resistance-related genes, it has 16 transporter and kinase genes (Table S1), among which Yr18 is considered to be a pleiotropic drug resistance ABC transporter gene (Krattinger et al., 2009). The genotypes by its diagnostic KASP marker (Fig. S1) revealed only YZHK has the resistance allele as same to Chinese Spring, indicating the candidate gene for QYrYZHK.saas-7D is Yr18 . The disease resistance-related locus QYrCY.saas-5D was recently detected only in recent years from 2020 to 2023, and it has potential value for wheat stripe rust resistance breeding in future in the southwestern wheat-growing region of China. This QTL was localized to the 404.83–407.76 Mb interval on chromosome 5DL in the Chinese Spring genome (IWGSC RefSeq v1.0). This physical interval comprises four annotated disease resistance-related genes, including two genes encoding proteins with NBS-LRR domains and two genes encoding disease resistance response proteins (Table S1). The reported QTLs for adult-plant stripe rust resistance on chromosome 5DL include the major QTL QYr.GTM-5DL in the interval of AX-109855976 (449.29 Mb) - AX- 109453419 (451.17 Mb) (Wu et al., 2021), which is the reported nearest QTL to our interval from 404.83 Mb to 407.76 Mb. Consideringits inconsistency with QYr.GTM-5DL and significant association with grain yield, QYrCY.saas-5D may be a new effective locus associated with stripe rust disease resistance in wheat. In this study, YZHK was stably resistant to adult-plant stripe rust, but in the RIL population derived from the cross between YZHK and CY, some plants exhibited unstable resistance between years in the disease nursery. This unstable resistance may be associated with the fact relatively few QTLs for adult-plant stripe rust resistance were pyramided in these plants. Li et al. (2024) conducted a 9-year study on the stripe rust resistance of near-isogenic wheat lines with the Avocet S genetic background in Pidu and Xindu (in Xindu County of Sichuan Province) stripe rust disease nurseries. They observed that many materials containing a single disease resistance gene exhibited unstable resistance between years, which was related to the diversity in the predominant stripe rust pathogen physiological races among years. This was supported by the findings of a study by Yang et al. (2024), which identified stripe rust pathogen physiological races in multiple fields in Sichuan, China from 2020 to 2021. In the present study, natural infections were supplemented by artificial inoculations using strains collected in the field. Therefore, in the eight field experiments conducted in this study, some QTLs were not detected every year or their effects differed significantly between years. In fact, similar findings were reported in earlier studies (Yang et al., 2019; Ma et al., 2019; Yan et al., 2023). However, pyramiding more identified QTLs may enhance the disease resistance stability of wheat plants. Among the six QTLs identified in this study, three ( QYrYZHK.saas-1B , QYrCY.saas-5D , and QYrYZHK.saas-7D ) were significantly correlated with grain yield, but they differed in terms of their relationships with three yield components. QYrYZHK.saas-1B was significantly correlated with GNP, whereas QYrCY.saas-5D and QYrYZHK.saas-7D were correlated with all three yield components (TGW, SN, and GNP). Considering the lack of significant differences in yield-related traits between the two parental genotypes in the two field trials in which stripe rust was effectively controlled, we believe that the significant correlation between these three stripe rust resistance QTLs and yield is associated with stripe rust resistance. The significant correlation also indicates that these three QTLs can significantly decrease yield losses due to stripe rust. Earlier research showed adult-stage stripe rust infections can significantly decrease TGW and YLD of susceptible wheat plants (Sharma et al., 2016; Srinivas et al., 2023; Chen et al., 2024) and can affect quality-related wheat traits, including kernel hardness, flour yield, and flour whiteness (Zhou et al., 2022). Yang et al. (2021) obtained 87 introgression lines from germplasm resource PI610750 carrying Yr48 (adult-plant resistance) and three excellent varieties. Comparative analyses of different alleles indicated that Yr48 confers adult-plant stripe rust resistance, while also significantly increasing yield and its related traits (e.g., SN, GNP, and TGW). In the current study, we artificially inoculated wheat seedlings during the 3- to 4-leaf stage. Severe stripe rust infections during the seedling stage reportedly decrease the number of tillers (Allan and Pritchett, 1972; Wellings, 2011). However, pyramiding multiple QTLs for adult-plant stripe rust resistance can significantly increase seedling resistance to stripe rust (Wang et al., 2023), thereby mitigating the detrimental effects of a severe stripe rust infection during the seedling stage on tiller formation. In fact, several RILs in this study are resistant to stripe rust throughout the growth period (data not shown). We also analyzed the yield effects of the adult-plant stripe rust resistance QTLs detected in this study, enabling us to select effective/suitable QTLs for pyramiding to significantly decrease yield losses, with potential implications for optimizing wheat production in southwestern China. In the field trial in which stripe rust disease was severe, there was no significant difference in the average yield between the alleles of the major QTL QYrYZHK.saas-2B from the two parents. This differed from the yield performance of the other two major QTLs ( QYrYZHK.saas-1B and QYrYZHK.saas-7D ). This indicates that QYrYZHK.saas-2B may protect wheat plants from disease through the induction of defense responses, thereby preventing severe yield losses (i.e., disease tolerance) (Pagán and García-Arenal, 2020). On the basis of the grain yield in field trials in which stripe rust was severe or effectively controlled, we mapped the epistatic QTLs for grain yield. The results showed that QYrYZHK.saas-2B (detected as epQYld.saas-2B ) interacted with the AX-108807126 – AX-111060229 interval (detected as epQYld.saas-4A ) on chromosome 4A only in the field trial in which stripe rust was severe (Table S2 and Fig. S2). Specifically, the interaction between a QYrYZHK.saas-2B genotype from one parent and a chromosome 4A QTL epQYld.saas-2B (also contains multiple disease resistance genes according to Chinese Spring RefSeq v1.0) genotype from the other parent restricted the yield loss caused by stripe rust (i.e., disease tolerance). Plant disease resistance mechanisms involve complex signaling pathways and interactions between disease resistance-related genes (Ding et al., 2022). Hence, analyzing gene interactions may provide relevant insights into plant disease tolerance. Conclusion In this study, we identified six QTLs for adult-plant stripe rust resistance using RILs and SNP genetic maps. Among these QTLs, QYrYZHK.saas-1B , QYrYZHK.saas-2B , QYrYZHK.saas-5B , and QYrYZHK.saas-7D were from Chinese wheat landrace YZHK, which has been stably resistant to stripe rust under field conditions for many years. In contrast, QYrCY.saas-3D and QYrCY.saas-5D were derived from the susceptible parent CY. On the basis of genetic and physical location analyses, QYrYZHK.saas-1B , QYrYZHK.saas-2B and QYrCY.saas-5D are newly identified QTLs for adult-plant stripe rust resistance. According to analyses of the effects of different QTL alleles on wheat grain yield and its components in field trials in which stripe rust was severe or effectively controlled, combining the two newly identified QTLs ( QYrYZHK.saas-1B and QYrCY.saas-5D ) with QYrYZHK.saas-7D ( Yr18 ) may maximize the stable yield in fields severely affected by stripe rust. Abbreviations GNP: grain number per spike; ICIM: inclusive composite interval mapping; IT: infection type; KASP: kompetitive allele-specific PCR; PE: phenotypic effect; QTL: quantitative trait locus; RIL: recombinant inbred line; SN: effective spike number per plant; SNP: single nucleotide polymorphism; TGW: thousand-grain weight; YLD: grain yield per plant. Declarations Acknowledgments We thank the Key Laboratory of Wheat Biology and Genetic Improvement in Southwestern China (Ministry of Agriculture and Rural Affairs of the P.R.C.) and the Environment-Friendly Crop Germplasm Innovation and Genetic Improvement Key Laboratory of Sichuan Province, Crop Research Institute of Sichuan Academy of Agricultural Sciences for their support. We also thank Liwen Bianji (Edanz) (www.liwenbianji.cn) for editing the English text of a draft of this manuscript. Author contributions Y. Li and J. Yang performed most of the experiments; J. Li and W. Yang helped create and genotype study materials; S. Du, H. Ji, Z. Liu, H. Tang, P. Liu, and Q. Wang helped conduct field experiments and analyses. Y. Li and J. Zhang wrote the manuscript, whereas H. Zhang revised the manuscript. H. Wan and W. Yang designed and supervised this study. All authors read and approved the final manuscript. Funding This study was partially supported by the Sichuan Provincial Finance Department (1+9KJGG001 and YSCX2035-001) and the Sichuan Province Science and Technology Department (2023NSFSC1925 and 2022ZDZX0014-1). Data availability All data are provided in the main text or as supplementary material. Data can be requested from the corresponding author. Code availability declaration Not applicable Ethics approval/compliance with ethical standards Not applicable Consent to participate Not applicable Consent for publication Not applicable Conflict of interest The authors have no competing interests to declare. References Allan RE and Pritchett JA (1972) Relationships of stripe rust spike infection to morphologic and agronomic traits of wheat1. Crop Sci 12(4):412-414 Bariana H, Kant L, Qureshi N, Forrest K, Miah H, Bansal U (2022) Identification and characterisation of stripe rust resistance genes Yr66 and Yr67 in wheat cultivar VL gehun 892. 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Theor Appl Genet 136(6):142 Tables Table 1 Statistical analysis of IT in eight field trials from 2016 to 2023 Trait Trials Min Max Mean SD CV h 1 2 h 2 2 IT 2016PD 0.25 8.50 4.53 1.52 0.33 0.59 0.45 2017PD 0.50 8.50 4.74 1.66 0.35 0.40 2018PD 0.33 9.00 5.21 2.10 0.40 0.88 2019PD 0.83 9.00 4.19 1.63 0.39 0.85 2020PD 1.17 8.67 4.89 1.77 0.36 0.71 2021PD 1.00 8.33 4.83 1.39 0.29 0.82 2022PD 1.35 8.08 4.65 1.48 0.32 0.78 2023PD 1.90 8.35 5.17 1.80 0.35 0.92 SD, standard deviation; CV, coefficient of variation; h 1 2 and h 2 2 , broad-sense heritability in a single environment and multiple environments, respectively. Table 2 QTLs for IT in field trials using the YZHK × CY F 8 generation RIL population QTL Trial Left marker of QTL peak a Right marker of QTL peak LOD b PVE (%) Add c QTL Left Position (cM) QTL Right Position (cM) QYrYZHK.saas-1B 2018PD AX-109882817 : 476.08 Mb AX-109824050 : 497.47 Mb 11.15 10.00 -1.00 53.50 56.50 2019PD AX-108855359 : 497.98 Mb AX-110670988 : 489.97 Mb 2.65 5.65 -0.45 54.50 60.50 2020PD AX-109882817 : 476.08 Mb AX-109824050 : 497.47 Mb 3.69 6.21 -0.51 53.50 55.50 2022PD AX-109882817 : 476.08 Mb AX-109824050 : 497.47 Mb 5.73 3.51 -0.49 54.50 55.50 2023PD AX-109882817 : 476.08 Mb AX-109824050 : 497.47 Mb 10.25 18.69 -0.83 54.50 55.50 QYrYZHK.saas-2B 2016PD AX-109849173 : 779.85 Mb AX-111071533 : 786.72Mb 11.5 23.09 -0.69 162.50 167.00 2018PD AX-109849173 : 779.85Mb AX-111071533 : 786.72Mb 3.12 5.10 -0.61 148.50 167.00 2019PD AX-109849173 : 779.85 Mb AX-111071533 : 786.72Mb 6.13 11.09 -0.57 155.50 167.00 2020PD AX-109849173 : 779.85 Mb AX-111071533 : 786.72Mb 6.20 11.64 -0.66 154.50 167.00 2021PD AX-109849173 : 779.85 Mb AX-111071533 : 786.72Mb 9.87 19.22 -0.65 159.50 167.00 2022PD AX-109849173 : 779.85 Mb AX-111071533 : 786.72Mb 3.33 5.33 -0.59 150.50 167.00 QYrCY.saas-3D 2017PD AX-89574305 : 27.88 Mb AX-108852641 : 43.38 Mb 4.63 5.90 0.63 1.50 13.50 2018PD AX-89574305 : 27.88 Mb AX-108852641 : 43.38 Mb 4.87 6.39 0.68 1.50 15.50 2019PD AX-89574305 : 27.88 Mb AX-108852641 : 43.38 Mb 3.41 5.29 0.39 0.00 16.50 2020PD AX-89574305 : 27.88 Mb AX-108852641 : 43.38 Mb 2.60 4.38 0.35 1.50 16.50 QYrYZHK.saas-5B 2016PD AX-109526372 : 498.26 Mb AX-111762700 : 535.39 Mb 2.71 5.32 -0.33 63.00 65.00 2018PD AX-110031479 : 531.64 Mb AX-110912471 : 520.56 Mb 2.94 3.31 -0.39 61.50 62.50 2020PD AX-109526372 : 498.26 Mb AX-111762700 : 535.39 Mb 3.19 4.26 -0.40 62.50 64.50 2021PD AX-110506915 : 473.35 Mb AX-111212757 : 477.98 Mb 3.07 6.99 -0.40 54.50 58.50 2022PD AX-109526372 : 498.26 Mb AX-111762700 : 535.39 Mb 3.55 3.17 -0.41 62.50 64.50 QYrCY.saas-5D 2020PD AX-110985437 : 404.83 Mb AX-110570148 : 407.76 Mb 4.58 9.84 0.61 99.50 102.50 2021PD AX-111756142 : 398.10 Mb AX-110570148 : 407.76 Mb 5.65 10.30 0.46 98.50 101.50 2022PD AX-110985437 : 404.83 Mb AX-110570148 : 407.76 Mb 7.69 6.41 0.65 99.50 101.50 2023PD AX-110985437 : 404.83 Mb AX-110570148 : 407.76 Mb 3.38 6.31 0.47 99.50 103.50 QYrYZHK.saas-7D 2017PD AX-109857040 : 47.71 Mb AX-111468131 : 51.96 Mb 16.59 21.85 -1.22 74.50 75.50 2018PD AX-110888456 : 41.41 Mb AX-89378255 : 47.38 Mb 7.31 9.01 -0.63 66.50 74.50 2019PD AX-110888456 : 41.41 Mb AX-89378255 : 47.38 Mb 14.14 23.17 -1.01 68.50 74.50 2020PD AX-109857040 : 47.71 Mb AX-111468131 : 51.96 Mb 8.04 13.89 -0.76 74.50 75.50 2021PD AX-110888456 : 41.41 Mb AX-89378255 : 47.38 Mb 3.92 9.72 -0.45 56.50 70.50 2022PD AX-110888456 : 41.41 Mb AX-89378255 : 47.38 Mb 18.18 21.29 -1.05 67.50 74.50 2023PD AX-110888456 : 41.41 Mb AX-89378255 : 47.38 Mb 7.10 15.24 -0.83 65.50 74.50 a Physical position of the marker was determined according to the Chinese Spring reference genome (RefSeq v1.0) (The IWGSC, 2018). b LOD, logarithm of odds score. c Add, additive effect of the disease resistance allele; positive and negative values, YZHK and CY alleles were associated with larger values, respectively. Table 3 Primer sequences of KASP markers tightly linked to the detected QTLs QTL/gene KASP marker Primer sequence (5'-3') QYrYZHK.saas-1B KASP_AX-109272373F1 GAAGGTGACCAAGTTCATGCTcctcgctcataactaactaaagcaT KASP_AX-109272373F2 GAAGGTCGGAGTCAACGGATTcctcgctcataactaactaaagcaA KASP_AX-109272373C gcacctgctaattaactggga QYrYZHK.saas-2B KASP_AX-111071533F1 GAAGGTGACCAAGTTCATGCTacagaagatcatggccctgtG KASP_AX-111071533F2 GAAGGTCGGAGTCAACGGATTacagaagatcatggccctgtC KASP_AX-111071533C cgtggtcgcagctacatact QYrCY.saas-3D KASP_AX-89574305F1 GAAGGTGACCAAGTTCATGCTaagaacgtatcaccatagtcagT KASP_AX-89574305F2 GAAGGTCGGAGTCAACGGATTaagaacgtatcaccatagtcagC KASP_AX-89574305F1 gctacagagacgaaccgctt QYrYZHK.saas-5B KASP_AX-109526372F1 GAAGGTGACCAAGTTCATGCTcgatctaggctactcttcaacgG KASP_AX-109526372F2 GAAGGTCGGAGTCAACGGATTcgatctaggctactcttcaacgA KASP_AX-109526372C cccataggaccagtgcactag QYrCY.saas-5D KASP_AX-110985437F1 GAAGGTGACCAAGTTCATGCTaccggcaccaatcttccatC KASP_AX-110985437F2 GAAGGTCGGAGTCAACGGATTaccggcaccaatcttccatT KASP_AX-110985437C aattgtttcgccatttatcaaacaa QYrYZHK.saas-7D KASP_AX-111468131F1 GAAGGTGACCAAGTTCATGCTgttcccacgacacagtacaA KASP_AX-111468131F2 GAAGGTCGGAGTCAACGGATTgttcccacgacacagtacaG KASP_AX-111468131C ccccgccggatgtatcattt Yr18 (Brown-Guedira and Dreisigacker, 2013) wMAS000003F1 GAAGGTGACCAAGTTCATGCTctggtatgccatttaacataatcatgaA wMAS000003F2 GAAGGTCGGAGTCAACGGATTctggtatgccatttaacataatcatgaT wMAS000003C cgcatgacaataagtttcactcatgcaaa Table 4 Phenotypic effects of different parental QTL alleles on grain yield and related components QTL Genotype YLD TGW SN GNP 2023PD 2023GH 2023DT 2023PD 2023GH 2023DT 2023PD 2023GH 2023DT 2023PD 2023GH 2023DT QYrYZHK.saas-1B H YZHK 7.29 ** 14.02 20.48 27.48 34.88 36.87 4.98 7.33 8.12 48.40 ** 55.23 67.22 H CY 5.69 14.30 20.75 25.95 35.75 37.57 5.03 7.22 8.43 43.38 54.76 65.80 QYrYZHK.saas-2B H YZHK 6.96 14.70 21.05 27.10 35.61 37.13 5.18 ** 7.31 8.37 46.25 55.38 66.33 H CY 6.52 13.56 21.80 27.28 35.27 38.21 4.71 6.93 8.21 47.51 54.54 68.24 QYrCY.saas-3D H YZHK 6.34 13.91 20.70 25.46 35.09 36.79 4.95 7.17 8.20 45.78 55.25 68.04 H CY 6.95 14.26 21.35 27.73 * 35.61 37.84 5.02 7.26 8.39 46.35 55.17 66.94 QYrYZHK.saas-5B H YZHK 6.64 14.09 20.00 27.92 34.95 36.73 4.97 7.39 8.06 45.62 54.05 66.06 H CY 6.74 13.83 21.27 26.24 35.31 37.61 5.04 7.16 8.52 46.45 55.12 66.45 QYrCY.saas-5D H YZHK 5.54 14.01 20.58 24.68 35.10 37.09 4.74 7.24 8.23 42.56 55.08 67.59 H CY 7.57 ** 14.19 21.68 28.69 * 35.58 37.54 5.24 ** 7.32 8.49 48.26 ** 54.32 66.86 QYrYZHK.saas-7D H YZHK 7.28 ** 13.86 21.10 28.60 ** 35.55 37.34 5.14 * 7.15 8.28 48.73 ** 55.25 66.81 H CY 5.95 14.42 21.00 24.98 35.43 37.42 4.81 7.37 8.31 41.68 53.64 66.42 YLD, grain yield per plant; TGW, thousand-grain weight; SN, effective spike number per plant; GNP, grain number per spike. * and ** indicate significant correlations at P = 0.05 and 0.01, respectively. Supplementary Files FigureS1R1.docx FigureS2.docx TableS1.xlsx TableS2.xlsx Cite Share Download PDF Status: Published Journal Publication published 14 Jul, 2025 Read the published version in Molecular Breeding → Version 1 posted Reviewers agreed at journal 21 Mar, 2025 Reviewers invited by journal 20 Mar, 2025 Editor assigned by journal 20 Mar, 2025 First submitted to journal 19 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5643654","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":431458049,"identity":"4fd30dfd-f984-490f-83d6-1b0c17de046c","order_by":0,"name":"Yumei Li","email":"","orcid":"","institution":"Sichuan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yumei","middleName":"","lastName":"Li","suffix":""},{"id":431458050,"identity":"f07ef04d-ae43-488b-9f70-d322bcbc85ab","order_by":1,"name":"Jiaru Yang","email":"","orcid":"","institution":"Sichuan Agricultural 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The arrow indicates the parental value. YZHK, Yizhanghongkemai; CY, Chuanyu 12.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5643654/v1/6113f65b962bcb726ca61b9f.png"},{"id":78940930,"identity":"c6da2d68-cbf1-4c15-a12e-0e045f3258b4","added_by":"auto","created_at":"2025-03-21 06:45:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38468,"visible":true,"origin":"","legend":"\u003cp\u003eInfection type (IT) distribution of the parents and their RILs in the Pidu disease nursery (PD) in Sichuan from 2016 to 2023.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5643654/v1/ec382e5effa524c1e5cf1fb1.png"},{"id":78940126,"identity":"7421fe96-0ca4-456d-b9a7-49facdc6dd33","added_by":"auto","created_at":"2025-03-21 06:29:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":312367,"visible":true,"origin":"","legend":"\u003cp\u003eLinkage map of QTLs for adult-plant stripe rust resistance and their physical locations in Chinese Spring RefSeq v1.0. The vertical solid line represents the QTL peak interval. The triangle or rhombus marks the genetic position of QTL peak in each field trial.\u003c/p\u003e","description":"","filename":"Figure31.png","url":"https://assets-eu.researchsquare.com/files/rs-5643654/v1/54f910b2b77ff08b4fc4a4af.png"},{"id":78940132,"identity":"fc92f308-9549-4f76-8155-007da4341700","added_by":"auto","created_at":"2025-03-21 06:29:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":136123,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots for selected KASP assays showing clustering on the x-axis (FAM) and y-axis (HEX). (a) \u003cem\u003eKASP_AX-109272373\u003c/em\u003e; (b) \u003cem\u003eKASP_AX-111071533\u003c/em\u003e; (c) \u003cem\u003eKASP_AX-89574305\u003c/em\u003e; (d) \u003cem\u003eKASP_AX-109526372\u003c/em\u003e; (e) \u003cem\u003eKASP_AX-110985437\u003c/em\u003e; (f) \u003cem\u003eKASP_AX-111468131\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure41.png","url":"https://assets-eu.researchsquare.com/files/rs-5643654/v1/f99c0bb142ae4afe10c2cb8d.png"},{"id":78940135,"identity":"8770a7c6-c503-4b49-9087-7cd2e2b3bf15","added_by":"auto","created_at":"2025-03-21 06:29:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":23475,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of IT values between two parental QTL allele genotypes in the RIL population using KASP and SNP chip markers. * and **, significant difference at \u003cem\u003eP\u003c/em\u003e = 0.05 and 0.01, respectively.\u003c/p\u003e","description":"","filename":"Figure5R1.png","url":"https://assets-eu.researchsquare.com/files/rs-5643654/v1/5512eff1117259445394cfff.png"},{"id":78940125,"identity":"aa4b8a61-e3ac-4784-ac33-2e5a1e2cc01d","added_by":"auto","created_at":"2025-03-21 06:29:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":140146,"visible":true,"origin":"","legend":"\u003cp\u003eThe adult-plant stripe rust resistant QTL allele pyramidingeffects on IT (a) and YLD (b) as well as related components SN (c), GNP (d) and TGW (e). The number on the standard error bar indicates the sample size of resistant/ susceptible haplotype in the RIL population. * and **, significant difference at \u003cem\u003eP\u003c/em\u003e = 0.05 and 0.01, respectively. PE, phenotypic effect. IT, infection type; YLD, grain yield per plant; SN, effective spike number per plant; GNP, grain number per spike; TGW, thousand-grain weight.\u003c/p\u003e","description":"","filename":"Figure6R11.png","url":"https://assets-eu.researchsquare.com/files/rs-5643654/v1/46d6c91a9ab1808b08064949.png"},{"id":87220534,"identity":"f7774734-1d59-475c-84a1-77bf6baa1dcf","added_by":"auto","created_at":"2025-07-21 16:12:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1828704,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5643654/v1/655619b0-e110-4687-a9cd-c5575cd394b1.pdf"},{"id":78940546,"identity":"58b377c1-72cc-4eb4-a18d-bb93aaf6c7fd","added_by":"auto","created_at":"2025-03-21 06:37:05","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":102594,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1R1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5643654/v1/55d31b292e71680408a2b88b.docx"},{"id":78940549,"identity":"01a7da8a-61da-4d72-9bad-8eadc8f1a00c","added_by":"auto","created_at":"2025-03-21 06:37:05","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":15495,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5643654/v1/3ab948ec75431d1f5361769e.docx"},{"id":78940141,"identity":"e1eb4086-3254-4049-8646-cc88d8e62d79","added_by":"auto","created_at":"2025-03-21 06:29:06","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":24189,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5643654/v1/c7abd9329080e074d1d05f6c.xlsx"},{"id":78940548,"identity":"eaf18cd4-a10a-4b44-8d10-d28100e55a60","added_by":"auto","created_at":"2025-03-21 06:37:05","extension":"xlsx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":9487,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5643654/v1/8a3735bb1d85fb2407ea2822.xlsx"}],"financialInterests":"","formattedTitle":"Identification of QTLs for adult-plant stripe rust resistance in Chinese wheat landrace Yizhanghongkemai and assessment of their utility for decreasing yield loss","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWheat is one of the three major cereal crops cultivated worldwide, with a planting area and yield second only to corn and rice in China. The annual planting area of wheat in China is approximately 24 million hectares, with a total output of about 130 million tons, accounting for nearly 18% of the total area and output of grain crops. Therefore, sustainable and stable wheat production is crucial for national food security, social stability, and international economic development (He et al., 2018). Wheat production is constrained by numerous biotic and abiotic stresses, especially diseases caused by pathogenic microbes, among which stripe rust is the most severe (Duchenne-Moutien et al., 2021; Hasegawa et al., 2021). Wheat stripe rust disease caused by \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e (\u003cem\u003ePst\u003c/em\u003e) is widespread in major wheat-growing regions worldwide. According to reports from over 64 countries, the disease can significantly decrease yields in all wheat-growing regions (Carmona et al., 2020). The frequent occurrence of new wheat stripe rust pathogen races and virulence changes has led to the rapid loss of stripe rust resistance in selected and promoted wheat varieties, which has become a major challenge for wheat breeders and producers.\u003c/p\u003e\n\u003cp\u003eTo date, 87 wheat stripe rust resistance genes (\u003cem\u003eYr1\u003c/em\u003e–\u003cem\u003eYr87\u003c/em\u003e), over 100 temporarily named genes, and more than 300 quantitative trait loci (QTLs) controlling wheat stripe rust resistance have been identified on wheat chromosomes (McIntosh, 2024; Klymiuk et al., 2022; Feng et al., 2023; Zhu et al., 2023). Twelve stripe rust resistance genes have been cloned (\u003cem\u003eYr5\u003c/em\u003e, \u003cem\u003eYrSP\u003c/em\u003e, \u003cem\u003eYr7\u003c/em\u003e, \u003cem\u003eYr10\u003c/em\u003e, \u003cem\u003eYr15\u003c/em\u003e, \u003cem\u003eYr18\u003c/em\u003e, \u003cem\u003eYr28\u003c/em\u003e, \u003cem\u003eYr36\u003c/em\u003e, \u003cem\u003eYr46\u003c/em\u003e, Yr\u003cem\u003e87\u003c/em\u003e, \u003cem\u003eYrU1\u003c/em\u003e and\u0026nbsp;\u003cem\u003eYrNAM\u003c/em\u003e). Among these genes, \u003cem\u003eYr5\u003c/em\u003e, \u003cem\u003eYr7\u003c/em\u003e, and \u003cem\u003eYrSP\u003c/em\u003e, which are located on chromosome 2BL, encode BED-NLR proteins (Marchal et al., 2018), \u003cem\u003eYr10\u003c/em\u003e, \u003cem\u003eYr28\u003c/em\u003e (\u003cem\u003eYrAS2388\u003c/em\u003e) and \u003cem\u003eYr87\u003c/em\u003e encode nucleotide-binding site-leucine-rich repeat (NBS-LRR) proteins containing N-terminal coiled-coil domains (Liu et al., 2014; Zhang et al., 2019; Sharma et al., 2024), \u003cem\u003eYr15\u003c/em\u003e encodes a tandem kinase domain protein (wheat tandem kinase, WTK1) (Klymiuk et al., 2018), \u003cem\u003eYr18\u003c/em\u003e encodes an ATP-binding cassette transporter (ABC transporter) (Krattinger et al., 2009), \u003cem\u003eYr36\u003c/em\u003e encodes WHEAT KINASE START1 (WKS1) (Fu et al., 2009), \u003cem\u003eYr46\u003c/em\u003e encodes a hexose transporter (Moore et al., 2015), \u003cem\u003eYrU1\u003c/em\u003e encodes an NBS-LRR protein with an N-terminal ankyrin-repeat and C-terminal WRKY domains (Wang et al., 2020c), and\u0026nbsp;\u003cem\u003eYrNAM\u003c/em\u003e encodes a protein with a NAM domain and a ZnF-BED domain (Ni et al., 2023). Although many stripe rust resistance genes have been identified, less than half have been used by breeders. To avoid the excessive use of individual resistance genes, new genes conferring adult-plant stripe rust resistance in wheat must be identified and characterized to effectively control stripe rust.\u003c/p\u003e\n\u003cp\u003eWheat landraces are often considered to be the elite genetic resources that have been preserved through long-term natural selection and human intervention. They are highly adaptable to local natural environmental conditions. Moreover, they have potentially useful production-related traits (e.g., multiple flowers and abundant grains) and are applicable for distant hybridizations. They also have a relatively rich genetic background and are important sources of genes for improving modern wheat cultivars (Jiang et al., 2022; Wang et al., 2022). Identifying new stripe rust resistance genes can diversify the available wheat disease resistance genetic resources and mitigate the disadvantages of stripe rust resistance breeding involving a single gene.\u003c/p\u003e\n\u003cp\u003eYizhanghongkemai (YZHK), which is a wheat landrace, has been highly resistant to stripe rust during the adult stage for 15 consecutive years the wheat-growing region of southwestern China, despite many cultivars lost their resistance to stripe rust in recent years. To identify YZHK genes mediating adult-plant stripe rust resistance, Wheat 15K SNP array technology and an inclusive composite interval mapping (ICIM) method were used to map QTLs in the F\u003csub\u003e8\u003c/sub\u003e generation recombinant inbred lines (RILs) derived from the YZHK × Chuanyu 12 (CY) hybridization. The effects of stripe rust resistance QTL alleles on stabilizing yield were also analyzed. Furthermore, kompetitive allele-specific PCR (KASP) markers were developed for molecular marker-assisted selection of adult-plant stripe rust resistance genes.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003ePlant materials\u003c/p\u003e\n\u003cp\u003eFor the hybridization in this study, Chinese wheat landrace YZHK, collected from Yizhang County in Hunan Province under sub-tropical monsoonal climate of central China, was used as the female parent, whereas wheat variety CY (pedigree: 80-9418 × 83-4516) susceptible to stripe rust in the wheat-growing region of southwestern China was used as the male parent. A total of 200 F\u003csub\u003e8\u003c/sub\u003e generation RILs were obtained via a single seed descent method from F\u003csub\u003e2\u003c/sub\u003e lines. Phenotypic and genotypic analyses of adult-plant stripe rust disease and agronomic traits were conducted using RILs and their parents under field conditions.\u003c/p\u003e\n\u003cp\u003eInvestigation of disease resistance and yield traits\u003c/p\u003e\n\u003cp\u003eIn this study conducted from 2016 to 2023, 200 F\u003csub\u003e8\u003c/sub\u003e generation RILs and their parents were grown in the disease nursery of Pidu Experimental Base under sub-tropical monsoonal climate of southwestern China, Sichuan Academy of Agricultural Sciences (SAAS), China from late October to early November (2016–2023PD). Specifically, a randomized block design with two replicates was applied, with 1–2 rows (1.5 m long and separated by 0.3 m) in each block. Individual rows consisted of 10 plants, with CY planted at one end of each block as an inoculum source. In the disease nursery of Pidu Experimental Base in the wheat-growing region of southwestern China, stripe rust is generally a typical natural disease which often sufficiently occurs without any specialized artificial inoculation with stripe rust pathogen. However, in a few years when the natural incidence of stripe rust was insufficient or uneven across different field regions, wheat plants were artificially inoculated during the 3- to 4-leaf stage. The seedlings were inoculated by the staff from Institute of Plant Protection in SAAS (Yang et al., 2024), using the stripe rust pathogen strains sourced from a severely infected field regions of Pidu Experimental Base. The stripe rust infection type (IT) of adult wheat plants was determined using the following 0–9 scale (Singh et al., 2000; Wan et al., 2023): 0–3, highly resistant (HR); 4–5, moderately resistant (MR); 6–7, moderately susceptible (MS); and 8–9, highly susceptible (HS). Stripe rust IT values were determined at the booting stage, flowering stage, and milk ripening stage, with plants examined approximately every 7 days (five times in total).\u003c/p\u003e\n\u003cp\u003eTo investigate agronomic traits, in addition to the 2023PD experiment in the disease nursery, field experiments were conducted in 2023 in Guanghan County (2023GH) of Sichuan Province and Dongtai County (2023DT) of Jiangsu Province, where stripe rust disease was effectively controlled. The experimental design was basically the same as that used to analyze disease resistance, with conventional wheat field production management practices. During the harvest season, eight plants in the middle of each plot in the disease nursery (2023PD) and stripe rust control area (2023GH and 2023DT) were examined in terms of effective spike number per plant (SN), grain number per spike (GNP), and thousand-grain weight (TGW) after harvest. Eight plants were sampled to calculate the average of SN, and the average of eight main stem panicles per plant was calculated for GNP. For TGW, the average of three samples per plot was determined.\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eWPS Spreadsheet 2024 (Kingsoft Inc., Beijing, China) was used to organize data for phenotypic traits and yield-related traits of adult plants infected with stripe rust disease, whereas SPSS Statistics v27.0 (IBM Co., New York, USA) was used to analyze phenotypic variations. SPSS was also used for an analysis of variance (ANOVA) and correlation analysis. Broad-sense heritability (\u003cem\u003eh\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e) was calculated using the following equations (Jia et al., 2013):\u0026nbsp;\u003cem\u003eh\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eg\u003c/sub\u003e/(σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eg\u003c/sub\u003e + σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ee\u003c/sub\u003e) in a single environment or\u0026nbsp;\u003cem\u003eh\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eg\u003c/sub\u003e/(σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eg\u003c/sub\u003e + σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ei\u003c/sub\u003e + σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ee\u003c/sub\u003e)\u0026nbsp;in multiple environments. Phenotypic variances in a single environment and in multiple environments were calculated as (rσ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eg\u003c/sub\u003e + σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ee\u003c/sub\u003e)\u0026nbsp;and (nrσ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eg\u003c/sub\u003e + rσ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ei\u003c/sub\u003e + σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ee\u003c/sub\u003e), where r is the number of repetitions, n is the number of experiments,\u0026nbsp;σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eg\u003c/sub\u003e is the genetic variance,\u0026nbsp;σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ei\u003c/sub\u003e is the genotype × environment interaction variance, and\u0026nbsp;σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ee\u003c/sub\u003e is the experimental error.\u003c/p\u003e\n\u003cp\u003eConstruction of a genetic linkage map and QTL localization\u003c/p\u003e\n\u003cp\u003eA plant DNA extraction kit (Tiangen Biotech Co., Ltd., Beijing, China) was used to extract genomic DNA from YZHK, CY, and F\u003csub\u003e8\u003c/sub\u003e generation RIL plants. Samples were sent to China Golden Marker (Beijing) Biotechnology Co., Ltd. for genotyping using a Wheat 15K SNP array. Detected SNPs were filtered according to Wan et al. (2022). The “MAP” function of the QTL IciMapping v4.2 program (Meng et al., 2015) along with stripe rust resistance phenotypic data were used to construct a genetic linkage map. QTLs were detected using the ICIM method, with a threshold LOD ≥2.5.\u003c/p\u003e\n\u003cp\u003eKASP genotyping assays\u003c/p\u003e\n\u003cp\u003eThe online program PolyMarker (http://www.polymarker.info/) was used to develop KASP markers for single nucleotide polymorphisms (SNPs) closely linked to QTLs for adult-plant stripe rust resistance. Primers were synthesized by Sangon Biotech (Shanghai) Co., Ltd. KASP markers that clearly identified polymorphisms between parents were designed for population genotyping. Specifically, KASP genotyping was performed using a CFX96 fluorescence quantitative PCR instrument (Bio-Rad, Hercules, CA, USA), with a 10 μL reaction volume comprising 5 μL 2×KASP Master Mix, 0.04 μL upstream FAM primer (F1), 0.04 μL upstream HEX primer (F2), 0.08 μL downstream common primer (C), 4 μL DNA template, and double-distilled water. The PCR program was as follows: 94 °C for 15 min; 10 cycles of 94 °C for 20 s and 61–55 °C for 60 s (the temperature decreased by 0.6 °C with each cycle); 30 cycles of 94 °C for 20 s and 55 °C for 60 s; fluorescence was detected at 30 °C for 30 s. After obtaining population genotypic data for KASP markers, the disease resistance phenotypic effects of closely linked QTLs and their consistency were analyzed using the original chip marker molecular data as the control.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003ePhenotypic analysis\u003c/p\u003e\n\u003cp\u003eFor wheat landrace YZHK grown over several years in the disease nursery at the Pidu Experimental Base, the average stripe rust IT value for adult plants was 2.35 (ranging from 1 to 3; i.e., plants were mostly highly resistant). In contrast, the average stripe rust IT value for CY was 8.10 (ranging from 8 to 9; i.e., highly susceptible) (Fig. 1, Fig. 2). In eight field experiments conducted from 2016 to 2023, the average stripe rust IT value of F\u003csub\u003e8\u003c/sub\u003e generation RILs was 4.78 (ranging from 0.92 to 8.56). The average IT values of the two parents were within the population range, indicative of transgressive segregation in the RIL population (Fig. 2, Table 1), implying both parents have disease resistance-related genes. The coefficient of variation for phenotypic data in eight environments was less than 0.5. Additionally, broad-sense heritability in a single environment was 0.40–0.92, with an average of 0.74. Broad-sense heritability in different years was 0.45, suggesting stripe rust resistance was mostly controlled by genes, but it was also influenced by environmental factors in different years (e.g., diversity in stripe rust pathogen physiological races in different years) and disease severity affected by climate.\u003c/p\u003e\n\u003cp\u003eGenetic map construction\u003c/p\u003e\n\u003cp\u003eThe Wheat 15K SNP array was used for the genotyping of 200 RILs and their parents (with two duplicate samples for each parent to evaluate the consistency of chip-based genotyping). This array contains 13,947 relatively evenly distributed SNP markers in the wheat genome. In this study, markers absent in the parents or on unknown chromosomes were removed. Homozygous SNP markers that were repeatedly revealed as polymorphic between the parents were identified. SNP markers with heterozygosity exceeding 10% in the population were also removed. Finally, a genetic linkage map was constructed (3,004.98 cM, including 25 linkage groups and 2,139 SNP markers). The average distance between adjacent markers was 1.40 cM. Notably, markers were distributed on all 21 wheat chromosomes.\u003c/p\u003e\n\u003cp\u003eQTL mapping\u003c/p\u003e\n\u003cp\u003eOn the basis of the genetic map and stripe rust IT values of RILs, six stable QTLs for stripe rust resistance were detected on chromosomes 1BL, 2BL, 3DS, 5BL, 5DL, and 7DS in multiple trials (at least 4 trials) using the ICIM mapping method (Fig. 3, Table 2). The major QTL \u003cem\u003eQYrYZHK.saas-1B\u003c/em\u003e on chromosome 1BL (YZHK in the QTL name indicates that the resistance-related QTL allele was from YZHK) was detected in the 2018PD, 2019PD, 2020PD, 2022PD and 2023PD trials. This QTL was detected between \u003cem\u003eAX-109882817\u0026nbsp;\u003c/em\u003eand \u003cem\u003eAX-109824050\u003c/em\u003e and explained 3.51%–18.69% of the phenotypic variation (i.e., PVE). \u003cem\u003eQYrYZHK.saas-2B\u003c/em\u003e, which was detected in the 2016PD, 2018PD, 2019PD, 2020PD, 2021PD and 2022PD trials, was located on chromosome 2BL (between \u003cem\u003eAX-109849173\u003c/em\u003e and \u003cem\u003eAX-111071533\u003c/em\u003e) and regarded as a major QTL with PVE ranging from 5.10% to 23.09%. The minor QTL \u003cem\u003eQYrCY.saas-3D\u0026nbsp;\u003c/em\u003e(CY in the QTL name indicates that the resistance-related QTL allele was from CY), which was detected in the 2017-2020PD trials, was located on chromosome 3DS (between \u003cem\u003eAX-89574305\u003c/em\u003e and \u003cem\u003eAX-108852641\u003c/em\u003e), with PVE ranging from 4.38% to 6.39%. \u003cem\u003eQYrYZHK.saas-5B\u003c/em\u003e, which was detected in five trials, was located on chromosome 5BL (between \u003cem\u003eAX-110506915\u0026nbsp;\u003c/em\u003eand \u003cem\u003eAX-111762700\u003c/em\u003e), with PVE ranging from 3.17% to 6.99%. \u003cem\u003eQYrCY.saas-5D\u003c/em\u003e, which was detected in 2020-2023 trials, was located on chromosome 5DL (between \u003cem\u003eAX-110985437\u003c/em\u003e and \u003cem\u003eAX-110570148\u003c/em\u003e), with PVE ranging from 6.31% to 10.30%. The major QTL \u003cem\u003eQYrYZHK.saas-7D\u003c/em\u003e, which was detected in the 2017-2023PD trials, was located on chromosome 7DS (between \u003cem\u003eAX-110888456\u003c/em\u003e and \u003cem\u003eAX-111468131\u003c/em\u003e), with PVE ranging from 9.01% to 23.17% (Fig. 3, Table 2).\u003c/p\u003e\n\u003cp\u003eKASP marker development and validation\u003c/p\u003e\n\u003cp\u003eIn this study, several designed KASP markers were developed from their SNP tags closely linked to detected QTLs, which could divide the RILs into two main groups by fluorescence signal clustering. However, in order to guarantee that at least one accurate KASP marker was successfully used as PCR markers for genotyping, among these designed KASP markers, six KASP markers obtained the genotypes of the RIL population that were mostly consistent with their SNP-chip genotypes (Table 3, Fig. 4). And the genetic effects of QTLs calculated according to KASP genotypes were also consistent with those calculated on the basis of SNP chip genotypes; the differences in mean IT values between genotypes from different parental sources were extremely significant using both KASP and Chip genotype) (Fig. 5).\u003c/p\u003e\n\u003cp\u003eConsidering the major QTL \u003cem\u003eQYrYZHK.saas-7D\u003c/em\u003e is located in a chromosome region corresponding to the 41.41–51.96 Mb interval of chromosome 7DS consistent with the interval of \u003cem\u003eYr18\u003c/em\u003e in the Chinese Spring genome (IWGSC RefSeq v1.0). One reported diagnostic KASP marker for \u003cem\u003eYr18\u003c/em\u003e (Table 3) was used for the validation of the relationship between \u003cem\u003eQYrYZHK.saas-7D\u003c/em\u003e and \u003cem\u003eYr18\u003c/em\u003e. In this study, the wMAS000003F1 sequence with FAM was used for identifying the susceptible allele at the site of \u003cem\u003eYr18\u003c/em\u003e, whereas wMAS000003F2 with HEX was for the resistant allele. Screening for candidate gene \u003cem\u003eYr18\u003c/em\u003e in two parents revealed only YZHK has the resistance allele, which is same to the control Chinese Spring carrying \u003cem\u003eYr18\u003c/em\u003e (Fig. S1).\u003c/p\u003e\n\u003cp\u003eIn general, there were relatively small differences in the mean IT values of any one parental QTL allele between the designed KASP and SNP chip genotypes in the RIL population (Fig. 5). The selected six KASP markers designed from tags of SNP chip were available as the PCR marker used for molecular assisted selection for adult-plant stripe rust resistance.\u003c/p\u003e\n\u003cp\u003eEffects of QTLs for stripe rust resistance on yield\u003c/p\u003e\n\u003cp\u003eIn environments in which stripe rust disease was under control (2023GH and 2023DT), the effects of six adult-plant stripe rust resistance QTLs on yield were not significant. Specifically, there were no significant differences in the yield-related traits between the resistance and susceptibility alleles (Table 4), indicating that there were no yield-related QTLs closely linked to the adult-plant stripe rust resistance QTLs identified in this study in this population. In the nursery where stripe rust disease was relatively severe (2023PD), four QTLs were significantly correlated with yield and its related traits (Table 4).\u003c/p\u003e\n\u003cp\u003eThe average GNP (48.40) of lines with the disease resistance \u003cem\u003eQYrYZHK.saas-1B\u003c/em\u003e allele from YZHK was significantly greater than the average GNP (43.38) of lines with the susceptibility allele (i.e., 11.57% increase) (Table 4). Ultimately, the average yield of plants with the disease resistance allele (7.29 g) was 28.12% higher than that of plants with the disease susceptibility allele (5.69 g) (Table 4). For \u003cem\u003eQYrYZHK.saas-7D\u003c/em\u003e, the average TGW, GNP, and YLD (28.60 g, 48.73, and 7.28 g, respectively) were 14.49%, 16.91%, and 22.35% higher, respectively, for the lines with the disease resistance QTL allele than for the lines without the disease resistance QTL allele (Table 4). The average SN, GNP, and YLD (5.24, 48.26, and 7.57 g, respectively) for the lines with the disease resistance \u003cem\u003eQYrCY.saas-5D\u003c/em\u003e allele from the parent CY were 10.55%, 13.39%, and 36.64% higher than the lines with QTL allele from YZHK, respectively. In addition, \u003cem\u003eQYrYZHK.saas-2B\u003c/em\u003e significantly affected SN; lines with the disease resistance allele had a significantly higher average SN than lines lacking the disease resistance allele (Table 4). Overall, the above-mentioned adult-plant stripe rust resistance QTLs, especially those located on chromosomes 1B, 7D, and 5D, significantly decreased the adverse effects of stripe rust on grain yield, thereby sign the adverse effects ificantly stabilizing wheat grain production (Table 4).\u003c/p\u003e\n\u003cp\u003eQTL pyramiding\u003c/p\u003e\n\u003cp\u003eThe effect of pyramiding four QTLs significantly associated with yield-related traits was analyzed by comparing the average phenotypic values between haplotypes with disease resistance QTL alleles and haplotypes without disease resistance QTL alleles. The average stripe rust IT value was significantly lower for RILs in which disease resistance QTL alleles were pyramided than for RILs without disease resistance QTL alleles. Generally, the stripe rust IT value decreased as the number of pyramided disease resistance QTL alleles increased (Fig. 6a). The difference between the average stripe rust IT value of plants containing disease resistance \u003cem\u003eQYrYZHK.saas-1B\u003c/em\u003e and \u003cem\u003eQYrCY.saas-5D\u003c/em\u003e alleles and the average stripe rust IT value of plants without these two disease resistance QTL alleles revealed the smallest pyramiding effect (Fig. 6a: phenotypic effect (PE) \u0026lt;1.5, i.e., difference between the average stripe rust IT values of the two haplotypes). The largest pyramiding effect was observed for disease resistance \u003cem\u003eQYrYZHK.saas-1B\u003c/em\u003e, \u003cem\u003eQYrYZHK.saas-2B\u003c/em\u003e, \u003cem\u003eQYrCY.saas-5D\u003c/em\u003e, and \u003cem\u003eQYrYZHK.saas-7D\u003c/em\u003e alleles (Fig. 6a: PE ≥2). Combining the major QTL \u003cem\u003eQYrYZHK.saas-7D\u003c/em\u003e, which had the greatest PVE, with other QTLs significantly increased the associated stripe rust resistance (Fig. 6a: PE ≥1.5). In fact, the effect of pyramiding \u003cem\u003eQYrYZHK.saas-7D\u003c/em\u003e with one other disease resistance QTL (e.g., \u003cem\u003eQYrYZHK.saas-2B\u003c/em\u003e) was greater than that of pyramiding the other three disease resistance QTLs (e.g., \u003cem\u003eQYrYZHK.saas-1B\u003c/em\u003e,\u003cem\u003e\u0026nbsp;QYrYZHK.saas-2B\u003c/em\u003e, and \u003cem\u003eQYrCY.saas-5D\u003c/em\u003e) (Fig. 6a).\u003c/p\u003e\n\u003cp\u003eIn terms of yield and its related traits, increases in the number of pyramided disease resistance QTLs decreased yield losses in a severely infected field (Fig. 6b). The same trend was observed for three yield-related traits (Fig. 6c, 6d, 6e), but the pyramiding of \u003cem\u003eQYrYZHK.saas-1B\u003c/em\u003e and \u003cem\u003eQYrYZHK.saas-2B\u003c/em\u003e did not significantly decrease yield losses (Fig. 6b). Notably, the effect of disease resistance QTLs on stabilizing yield was greatest when \u003cem\u003eQYrYZHK.saas-7D\u003c/em\u003e, the major QTL for adult-plant stripe rust resistance, was pyramided with other disease resistance QTLs (Fig. 6b). This finding was supported by the results for three yield-related traits (Fig. 6c, 6d, 6e). In the population, the effect of pyramiding three disease resistance QTL alleles (\u003cem\u003eQYrYZHK.saas-1B\u003c/em\u003e,\u003cem\u003e\u0026nbsp;QYrCY.saas-5D\u003c/em\u003e, and \u003cem\u003eQYrYZHK.saas-7D\u003c/em\u003e) on yield was similar to the effect of pyramiding four disease resistance QTL alleles (Fig. 6c, 6d, 6e). However, both pyramiding haplotypes had the most significant effect on stabilizing yield (PE ≥4) and decreased yield losses by 58.07% on average (Fig. 6b).\u003c/p\u003e\n\u003cp\u003eOverall, when conducting molecular marker-assisted selection, the pyramiding of adult-plant stripe rust resistance QTL alleles at three loci (\u003cem\u003eQYrYZHK.saas-1B\u003c/em\u003e, \u003cem\u003eQYrCY.saas-5D\u003c/em\u003e, and \u003cem\u003eQYrYZHK.saas-7D\u003c/em\u003e) can significantly improve disease resistance and minimize potential yield losses due to stripe rust.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe wheat-growing region of southwestern China is one of the main areas in which stripe rust is prevalent. Frequent physiological virulence changes in this region may help to explain the relatively rapid loss of stripe rust resistance among wheat varieties (Liu et al., 2024). YZHK is a wheat landrace that originated in Yizhang County of Hunan province that also has a sub-tropical monsoonal climate. In the disease nursery in southwestern China, YZHK exhibited adult-plant stripe rust resistance for 15 years (2009–2024). During this same period, many approved Sichuan wheat cultivars lost their adult-plant stripe rust resistance (Zhang et al., 2023; Li et al., 2023). In consideration of its performance on adult-plant stripe rust resistance over the past 15 years, YZHK is potentially useful for breeding, successfully withstanding the unpredictable biotic stress via the frequent physiological virulence changes. Accordingly, in this study, a QTL analysis of YZHK adult-plant stripe rust resistance was conducted and the effects of the identified QTLs on stabilizing yield were also examined.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQYrYZHK.saas-1B\u003c/em\u003e was detected on chromosome 1BL in YZHK, with a physical location of 394.00–497.47 Mb in the Chinese Spring reference genome (IWGSC RefSeq v1.0) (IWGSC, 2018). On the basis of comparisons in different environments, the physical location of this QTL may be narrowed to 476.08–497.47 Mb. The Chinese Spring genome includes three annotated disease resistance-related genes, including two genes encoding nucleotide-binding adaptor shared by Apaf-1, R proteins, and CED-4 (NB-ARC) domain-containing proteins (belonging to the NBS-LRR family) and one disease resistance response protein, as well as 22 transporter and kinase genes (Table S1). There are three officially named stripe rust resistance genes, \u003cem\u003eYr21\u003c/em\u003e (Chen and Line, 1995), \u003cem\u003eYr26\u003c/em\u003e (Ma et al., 2001), and \u003cem\u003eYr29\u003c/em\u003e (William et al., 2006), on chromosome 1BL, of which \u003cem\u003eYr21\u003c/em\u003e and \u003cem\u003eYr26\u003c/em\u003e are all-stage resistance genes, whereas \u003cem\u003eYr29\u003c/em\u003e is an adult-plant resistance gene. \u003cem\u003eYr26\u003c/em\u003e is located at 307.62–321.70 Mb (\u003cem\u003eXbarc181\u003c/em\u003e–\u003cem\u003eXbarc187\u003c/em\u003e), whereas \u003cem\u003eYr29\u003c/em\u003e is located at 662.20–675.69 Mb (\u003cem\u003eXwmc44\u003c/em\u003e–\u003cem\u003eXwmc367\u003c/em\u003e). Moreover, among the QTLs on chromosome 1BL, \u003cem\u003eQYrZM9023.swust-1BL\u003c/em\u003e is located at 670.43–681.69 Mb (Yan et al., 2023).\u003cem\u003e\u0026nbsp;QYr.sicau-1B.1\u003c/em\u003e (Ma et al., 2019) for all-stage resistance is located at a physical position corresponding to 461.68–487.42 Mb in the Chinese Spring genome (\u003cem\u003eXwmc216–Xwmc156\u003c/em\u003e), which was partially overlapped with the physical interval of \u003cem\u003eQYrYZHK.saas-1B\u003c/em\u003e in current study. However, comparing to \u003cem\u003eQYr.sicau-1B.1\u003c/em\u003e for all-stage resistance (Ma et al., 2019), \u003cem\u003eQYrYZHK.saas-1B\u003c/em\u003e for adult-plant stripe rust resistance did not exhibit significant genetic effect on IT value on seedling stage (data not shown), indicating a new adult-plant stripe rust resistance QTL.\u003c/p\u003e\n\u003cp\u003eThe major QTL\u003cem\u003e\u0026nbsp;QYrYZHK.saas-2B\u003c/em\u003e was detected near 779.85 Mb (\u003cem\u003eAX-109849173\u003c/em\u003e) (IWGSC RefSeq v1.0) on chromosome 2BL. A probe sequence (\u003cem\u003eAX-111071533\u003c/em\u003e) to the right of the QTL interval had a physical position of 786.72 Mb in the homologous sequence on chromosome 2BL. There are six annotated disease resistance-related genes in the 779.85–786.72 Mb interval of the Chinese Spring genome (Table S1), including four genes encoding proteins with NB-ARC domains belonging to the NBS-LRR family. This interval also contains 13 transporter and kinase genes (Table S1). There are seven officially named stripe rust resistance genes on chromosome 2BL, most of which are all-stage resistance genes, including \u003cem\u003eYr5\u003c/em\u003e and \u003cem\u003eYr7\u003c/em\u003e (Macer, 1963; Marchal et al., 2018), \u003cem\u003eYr43\u003c/em\u003e (Cheng and Chen, 2010), \u003cem\u003eYr44\u003c/em\u003e (Sui et al., 2009), \u003cem\u003eYr53\u003c/em\u003e (Xu et al., 2013), \u003cem\u003eYr72\u003c/em\u003e (McIntosh et al., 2016), and \u003cem\u003eYrSP\u003c/em\u003e (Feng et al., 2015). In the Chinese Spring genome, the cloned genes \u003cem\u003eYr5\u003c/em\u003e, \u003cem\u003eYr7\u003c/em\u003e, and \u003cem\u003eYrSP\u003c/em\u003e are located at 685.27 Mb, whereas \u003cem\u003eYr43\u003c/em\u003e has been localized to 672.08–673.41 Mb (\u003cem\u003eXwgp110\u003c/em\u003e–\u003cem\u003eXwgp103\u003c/em\u003e) and \u003cem\u003eYr44\u003c/em\u003e is close to 732.35 Mb (\u003cem\u003eXpWB5/N1R1\u003c/em\u003e–\u003cem\u003eXwgp100\u003c/em\u003e). \u003cem\u003eYr53\u003c/em\u003e is located near 598.06 Mb (\u003cem\u003eXwmc441\u003c/em\u003e–\u003cem\u003eXLRRrev/NLRRrev350\u003c/em\u003e), whereas \u003cem\u003eYr72\u003c/em\u003e is between 767.17 and 771.78 Mb (\u003cem\u003eXsun481\u003c/em\u003e–\u003cem\u003eIWB12294\u003c/em\u003e). The physical locations of QTLs for adult-plant stripe rust resistance in the Chinese Spring genome are as follows: \u003cem\u003eQYr.hbaas-2BL\u003c/em\u003e at 453.3 Mb (Jia et al., 2020: \u003cem\u003eIWA586\u003c/em\u003e), \u003cem\u003eQYr.nafu-2BL\u003c/em\u003e at 553.73–615.79 Mb (Hu et al., 2020: \u003cem\u003eXwgp5770\u003c/em\u003e–\u003cem\u003eXcfd73\u003c/em\u003e), \u003cem\u003eQYrww.wgp.2B-4\u003c/em\u003e at 524.00 Mb (Mu et al., 2020: \u003cem\u003eIWB34793\u003c/em\u003e–\u003cem\u003eIWW34793\u003c/em\u003e), \u003cem\u003eQYr.inla-2BL\u003c/em\u003e at 615.79–621.47 Mb (Mallard et al., 2005: \u003cem\u003eXbarc101\u003c/em\u003e–\u003cem\u003eXgwm120\u003c/em\u003e), and \u003cem\u003eQYr.caas-2BL\u003c/em\u003e at 693.74–733.16 Mb (Ren et al., 2012b: \u003cem\u003eXwPt-8460\u003c/em\u003e–\u003cem\u003eXwPt-3755\u003c/em\u003e). By comparison, rare reported QTL/genes for stripe rust resistance were detected in the QTL interval of \u003cem\u003eQYrYZHK.saas-2B\u003c/em\u003e identified in this study, indicating it may be a new disease resistance-related locus.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQYrCY.saas-3D\u003c/em\u003e corresponds to the 27.88–43.38 Mb region on chromosome 3DS in the Chinese Spring genome (IWGSC RefSeq v1.0). The reported stripe rust resistance genes nearby include \u003cem\u003eYr49\u003c/em\u003e (McIntosh et al., 2014: \u003cem\u003egpw7321\u003c/em\u003e–\u003cem\u003egwm161\u003c/em\u003e) and \u003cem\u003eYr66\u003c/em\u003e (Bariana et al., 2022: \u003cem\u003eKASP_18087\u003c/em\u003e–\u003cem\u003eKASP_48179\u003c/em\u003e), which are physically located in the 2.19–7.09 Mb region. This part of the Chinese Spring genome has 10 annotated disease resistance-related genes, including six genes encoding NB-ARC domain-containing proteins, as well as 34 transporter and kinase genes (Table S1). The stripe rust resistance genes and QTLs overlapping the \u003cem\u003eQYrYZHK.saas-5B\u003c/em\u003e interval (473.35–535.39 Mb) include \u003cem\u003eYr74\u003c/em\u003e (Dracatos et al., 2016), \u003cem\u003eYrAYH\u003c/em\u003e, and \u003cem\u003eQYr.YBZR-5BL\u003c/em\u003e, with physical locations of 531.00 Mb, 530.60–534.30 Mb (Long et al., 2024: \u003cem\u003eKP5B_530.6\u003c/em\u003e–\u003cem\u003eKP5B_534.3\u003c/em\u003e), and 519.00–542.70 Mb (Deng et al., 2022: \u003cem\u003eAX-111002705\u003c/em\u003e–\u003cem\u003eAX-108929069\u003c/em\u003e), respectively. The corresponding physical interval in the Chinese Spring genome contains seven annotated disease resistance-related genes and 50 transporter and kinase genes (Table S1). The major QTL \u003cem\u003eQYrYZHK.saas-7D\u003c/em\u003e is located in a region corresponding to the 41.41–51.96 Mb interval of chromosome 7DS in the Chinese Spring genome (IWGSC RefSeq v1.0). This interval includes the known adult-plant stripe rust resistance gene \u003cem\u003eYr18\u003c/em\u003e (Krattinger et al., 2009; Lagudah et al., 2009: \u003cem\u003eXgwm1220\u003c/em\u003e–\u003cem\u003eXgwm29\u003c/em\u003e, physical position 47.41–47.42 Mb). Although this physical interval in the Chinese Spring genome does not contain annotated disease resistance-related genes, it has 16 transporter and kinase genes (Table S1), among which \u003cem\u003eYr18\u0026nbsp;\u003c/em\u003eis considered to be a pleiotropic drug resistance ABC transporter gene (Krattinger et al., 2009). The genotypes by its diagnostic KASP marker (Fig. S1) revealed only YZHK has the resistance allele as same to Chinese Spring, indicating the candidate gene for \u003cem\u003eQYrYZHK.saas-7D\u003c/em\u003e is \u003cem\u003eYr18\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThe disease resistance-related locus \u003cem\u003eQYrCY.saas-5D\u003c/em\u003e was recently detected only in recent years from 2020 to 2023, and it has potential value for wheat stripe rust resistance breeding in future in the southwestern wheat-growing region of China. This QTL was localized to the 404.83–407.76 Mb interval on chromosome 5DL in the Chinese Spring genome (IWGSC RefSeq v1.0). This physical interval comprises four annotated disease resistance-related genes, including two genes encoding proteins with NBS-LRR domains and two genes encoding disease resistance response proteins (Table S1). The reported QTLs for adult-plant stripe rust resistance on chromosome 5DL include the major QTL \u003cem\u003eQYr.GTM-5DL\u003c/em\u003e in the interval of \u003cem\u003eAX-109855976\u003c/em\u003e (449.29 Mb) -\u003cem\u003eAX-\u003c/em\u003e109453419 (451.17 Mb) (Wu et al., 2021), which is the reported nearest QTL to our interval from 404.83 Mb to 407.76 Mb. Consideringits inconsistency with \u003cem\u003eQYr.GTM-5DL\u003c/em\u003e and significant association with grain yield, \u003cem\u003eQYrCY.saas-5D\u003c/em\u003e may be a new effective locus associated with stripe rust disease resistance in wheat.\u003c/p\u003e\n\u003cp\u003eIn this study, YZHK was stably resistant to adult-plant stripe rust, but in the RIL population derived from the cross between YZHK and CY, some plants exhibited unstable resistance between years in the disease nursery. This unstable resistance may be associated with the fact relatively few QTLs for adult-plant stripe rust resistance were pyramided in these plants. Li et al. (2024) conducted a 9-year study on the stripe rust resistance of near-isogenic wheat lines with the Avocet S genetic background in Pidu and Xindu (in Xindu County of Sichuan Province) stripe rust disease nurseries. They observed that many materials containing a single disease resistance gene exhibited unstable resistance between years, which was related to the diversity in the predominant stripe rust pathogen physiological races among years. This was supported by the findings of a study by Yang et al. (2024), which identified stripe rust pathogen physiological races in multiple fields in Sichuan, China from 2020 to 2021. In the present study, natural infections were supplemented by artificial inoculations using strains collected in the field. Therefore, in the eight field experiments conducted in this study, some QTLs were not detected every year or their effects differed significantly between years. In fact, similar findings were reported in earlier studies (Yang et al., 2019; Ma et al., 2019; Yan et al., 2023). However, pyramiding more identified QTLs may enhance the disease resistance stability of wheat plants.\u003c/p\u003e\n\u003cp\u003eAmong the six QTLs identified in this study, three (\u003cem\u003eQYrYZHK.saas-1B\u003c/em\u003e, \u003cem\u003eQYrCY.saas-5D\u003c/em\u003e, and \u003cem\u003eQYrYZHK.saas-7D\u003c/em\u003e) were significantly correlated with grain yield, but they differed in terms of their relationships with three yield components. \u003cem\u003eQYrYZHK.saas-1B\u003c/em\u003e was significantly correlated with GNP, whereas \u003cem\u003eQYrCY.saas-5D\u003c/em\u003e and \u003cem\u003eQYrYZHK.saas-7D\u003c/em\u003e were correlated with all three yield components (TGW, SN, and GNP). Considering the lack of significant differences in yield-related traits between the two parental genotypes in the two field trials in which stripe rust was effectively controlled, we believe that the significant correlation between these three stripe rust resistance QTLs and yield is associated with stripe rust resistance. The significant correlation also indicates that these three QTLs can significantly decrease yield losses due to stripe rust. Earlier research showed adult-stage stripe rust infections can significantly decrease TGW and YLD of susceptible wheat plants (Sharma et al., 2016; Srinivas et al., 2023; Chen et al., 2024) and can affect quality-related wheat traits, including kernel hardness, flour yield, and flour whiteness (Zhou et al., 2022). Yang et al. (2021) obtained 87 introgression lines from germplasm resource PI610750 carrying \u003cem\u003eYr48\u003c/em\u003e (adult-plant resistance) and three excellent varieties. Comparative analyses of different alleles indicated that \u003cem\u003eYr48\u003c/em\u003e confers adult-plant stripe rust resistance, while also significantly increasing yield and its related traits (e.g., SN, GNP, and TGW). In the current study, we artificially inoculated wheat seedlings during the 3- to 4-leaf stage. Severe stripe rust infections during the seedling stage reportedly decrease the number of tillers (Allan and Pritchett, 1972; Wellings, 2011). However, pyramiding multiple QTLs for adult-plant stripe rust resistance can significantly increase seedling resistance to stripe rust (Wang et al., 2023), thereby mitigating the detrimental effects of a severe stripe rust infection during the seedling stage on tiller formation. In fact, several RILs in this study are resistant to stripe rust throughout the growth period (data not shown). We also analyzed the yield effects of the adult-plant stripe rust resistance QTLs detected in this study, enabling us to select effective/suitable QTLs for pyramiding to significantly decrease yield losses, with potential implications for optimizing wheat production in southwestern China.\u003c/p\u003e\n\u003cp\u003eIn the field trial in which stripe rust disease was severe, there was no significant difference in the average yield between the alleles of the major QTL \u003cem\u003eQYrYZHK.saas-2B\u003c/em\u003e from the two parents. This differed from the yield performance of the other two major QTLs (\u003cem\u003eQYrYZHK.saas-1B\u003c/em\u003e and \u003cem\u003eQYrYZHK.saas-7D\u003c/em\u003e). This indicates that \u003cem\u003eQYrYZHK.saas-2B\u003c/em\u003e may protect wheat plants from disease through the induction of defense responses, thereby preventing severe yield losses (i.e., disease tolerance) (Pagán and García-Arenal, 2020). On the basis of the grain yield in field trials in which stripe rust was severe or effectively controlled, we mapped the epistatic QTLs for grain yield. The results showed that \u003cem\u003eQYrYZHK.saas-2B\u003c/em\u003e (detected as \u003cem\u003eepQYld.saas-2B\u003c/em\u003e) interacted with the \u003cem\u003eAX-108807126\u003c/em\u003e–\u003cem\u003eAX-111060229\u003c/em\u003e interval (detected as \u003cem\u003eepQYld.saas-4A\u003c/em\u003e) on chromosome 4A only in the field trial in which stripe rust was severe (Table S2 and Fig. S2). Specifically, the interaction between a \u003cem\u003eQYrYZHK.saas-2B\u003c/em\u003e genotype from one parent and a chromosome 4A QTL \u003cem\u003eepQYld.saas-2B\u003c/em\u003e (also contains multiple disease resistance genes according to Chinese Spring RefSeq v1.0) genotype from the other parent restricted the yield loss caused by stripe rust (i.e., disease tolerance). Plant disease resistance mechanisms involve complex signaling pathways and interactions between disease resistance-related genes (Ding et al., 2022). Hence, analyzing gene interactions may provide relevant insights into plant disease tolerance.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we identified six QTLs for adult-plant stripe rust resistance using RILs and SNP genetic maps. Among these QTLs, \u003cem\u003eQYrYZHK.saas-1B\u003c/em\u003e, \u003cem\u003eQYrYZHK.saas-2B\u003c/em\u003e, \u003cem\u003eQYrYZHK.saas-5B\u003c/em\u003e, and \u003cem\u003eQYrYZHK.saas-7D\u003c/em\u003e were from Chinese wheat landrace YZHK, which has been stably resistant to stripe rust under field conditions for many years. In contrast, \u003cem\u003eQYrCY.saas-3D\u003c/em\u003e and\u003cem\u003e\u0026nbsp;QYrCY.saas-5D\u003c/em\u003e were derived from the susceptible parent CY. On the basis of genetic and physical location analyses, \u003cem\u003eQYrYZHK.saas-1B\u003c/em\u003e, \u003cem\u003eQYrYZHK.saas-2B\u003c/em\u003e and \u003cem\u003eQYrCY.saas-5D\u003c/em\u003e are newly identified QTLs for adult-plant stripe rust resistance. According to analyses of the effects of different QTL alleles on wheat grain yield and its components in field trials in which stripe rust was severe or effectively controlled, combining the two newly identified QTLs (\u003cem\u003eQYrYZHK.saas-1B\u003c/em\u003e and \u003cem\u003eQYrCY.saas-5D\u003c/em\u003e) with \u003cem\u003eQYrYZHK.saas-7D\u003c/em\u003e (\u003cem\u003eYr18\u003c/em\u003e) may maximize the stable yield in fields severely affected by stripe rust.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGNP: grain number per spike; ICIM: inclusive composite interval mapping; IT: infection type; KASP: kompetitive allele-specific PCR; PE: phenotypic effect; QTL: quantitative trait locus; RIL: recombinant inbred line; SN: effective spike number per plant; SNP: single nucleotide polymorphism; TGW: thousand-grain weight; YLD: grain yield per plant.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Key Laboratory of Wheat Biology and Genetic Improvement in Southwestern China (Ministry of Agriculture and Rural Affairs of the P.R.C.) and the Environment-Friendly Crop Germplasm Innovation and Genetic Improvement Key Laboratory of Sichuan Province, Crop Research Institute of Sichuan Academy of Agricultural Sciences for their support. We also thank Liwen Bianji (Edanz) (www.liwenbianji.cn) for editing the English text of a draft of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e Y. Li and J. Yang performed most of the experiments; J. Li and W. Yang helped create and genotype study materials; S. Du, H. Ji, Z. Liu, H. Tang, P. Liu, and Q. Wang helped conduct field experiments and analyses. Y. Li and J. Zhang wrote the manuscript, whereas H. Zhang revised the manuscript. H. Wan and W. Yang designed and supervised this study. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This study was partially supported by the Sichuan Provincial Finance Department (1+9KJGG001 and YSCX2035-001) and the Sichuan Province Science and Technology Department (2023NSFSC1925 and 2022ZDZX0014-1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e All data are provided in the main text or as supplementary material. Data can be requested from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability declaration\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval/compliance with ethical standards\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e The authors have no competing interests to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllan RE and Pritchett JA (1972) Relationships of stripe rust spike infection to morphologic and agronomic traits of wheat1. 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Theor Appl Genet 136(6):142\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Statistical analysis of IT in eight field trials from 2016 to 2023\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"579\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eTrials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cem\u003eh\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cem\u003eh\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"8\" style=\"width: 54px;\"\u003e\n \u003cp\u003eIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e2016PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e8.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e4.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"8\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e2017PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e8.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e4.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e2018PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e9.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e5.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e2019PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e9.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e4.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e2020PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e8.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e4.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e2021PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e8.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e4.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e2022PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e8.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e4.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e2023PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e8.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSD, standard deviation; CV, coefficient of variation; \u003cem\u003eh\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e and \u003cem\u003eh\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003ebroad-sense heritability in a single environment and multiple environments, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 QTLs for IT in field trials using the YZHK\u0026nbsp;\u0026times; CY F\u003csub\u003e8\u003c/sub\u003e generation RIL population\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"911\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eQTL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eTrial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eLeft marker of QTL peak\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003eRight marker of QTL peak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eLOD\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003ePVE (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eAdd\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eQTL Left Position (cM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eQTL Right Position (cM)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003eQYrYZHK.saas-1B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2018PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-109882817\u003c/em\u003e: 476.08 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-109824050\u003c/em\u003e: 497.47 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e11.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e53.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e56.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2019PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-108855359\u003c/em\u003e: 497.98 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-110670988\u003c/em\u003e: 489.97 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e5.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e54.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e60.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2020PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-109882817\u003c/em\u003e: 476.08 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-109824050\u003c/em\u003e: 497.47 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e6.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e53.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e55.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2022PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-109882817\u003c/em\u003e: 476.08 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-109824050\u003c/em\u003e: 497.47 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e5.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e54.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e55.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2023PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-109882817\u003c/em\u003e: 476.08 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-109824050\u003c/em\u003e: 497.47 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e10.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e18.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e54.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e55.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003eQYrYZHK.saas-2B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2016PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-109849173\u003c/em\u003e: 779.85 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-111071533\u003c/em\u003e: 786.72Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e23.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e162.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e167.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2018PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-109849173\u003c/em\u003e: 779.85Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-111071533\u003c/em\u003e: 786.72Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e148.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e167.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2019PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-109849173\u003c/em\u003e: 779.85 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-111071533\u003c/em\u003e: 786.72Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e6.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e11.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e155.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e167.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2020PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-109849173\u003c/em\u003e: 779.85 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-111071533\u003c/em\u003e: 786.72Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e6.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e11.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e154.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e167.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2021PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-109849173\u003c/em\u003e: 779.85 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-111071533\u003c/em\u003e: 786.72Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e9.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e19.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e159.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e167.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2022PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-109849173\u003c/em\u003e: 779.85 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-111071533\u003c/em\u003e: 786.72Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e150.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e167.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003eQYrCY.saas-3D\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2017PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-89574305\u003c/em\u003e: 27.88 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-108852641\u003c/em\u003e: 43.38 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e4.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e5.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e13.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2018PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-89574305\u003c/em\u003e: 27.88 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-108852641\u003c/em\u003e: 43.38 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e4.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e6.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e15.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2019PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-89574305\u003c/em\u003e: 27.88 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-108852641\u003c/em\u003e: 43.38 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e5.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e16.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2020PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-89574305\u003c/em\u003e: 27.88 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-108852641\u003c/em\u003e: 43.38 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e16.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003eQYrYZHK.saas-5B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2016PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-109526372\u003c/em\u003e: 498.26 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-111762700\u003c/em\u003e: 535.39 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e5.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e63.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e65.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2018PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-110031479\u003c/em\u003e: 531.64 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-110912471\u003c/em\u003e: 520.56 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e61.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e62.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2020PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-109526372\u003c/em\u003e: 498.26 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-111762700\u003c/em\u003e: 535.39 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e4.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e62.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e64.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2021PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-110506915\u003c/em\u003e: 473.35 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-111212757\u003c/em\u003e: 477.98 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e6.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e54.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e58.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2022PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-109526372\u003c/em\u003e: 498.26 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-111762700\u003c/em\u003e: 535.39 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e62.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e64.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003eQYrCY.saas-5D\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2020PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-110985437\u003c/em\u003e: 404.83 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-110570148\u003c/em\u003e: 407.76 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e9.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e99.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e102.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2021PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-111756142\u003c/em\u003e: 398.10 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-110570148\u003c/em\u003e: 407.76 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e5.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e10.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e98.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e101.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2022PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-110985437\u003c/em\u003e: 404.83 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-110570148\u003c/em\u003e: 407.76 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e7.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e99.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e101.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2023PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-110985437\u003c/em\u003e: 404.83 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-110570148\u003c/em\u003e: 407.76 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e6.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e99.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e103.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003eQYrYZHK.saas-7D\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2017PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-109857040\u003c/em\u003e: 47.71 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-111468131\u003c/em\u003e: 51.96 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e16.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e21.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e74.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e75.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2018PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-110888456\u003c/em\u003e: 41.41 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-89378255\u003c/em\u003e: 47.38 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e7.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e9.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e66.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e74.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2019PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-110888456\u003c/em\u003e: 41.41 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-89378255\u003c/em\u003e: 47.38 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e14.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e23.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e68.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e74.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2020PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-109857040\u003c/em\u003e: 47.71 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-111468131\u003c/em\u003e: 51.96 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e8.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e13.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e74.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e75.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2021PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-110888456\u003c/em\u003e: 41.41 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-89378255\u003c/em\u003e: 47.38 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e9.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e56.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e70.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2022PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-110888456\u003c/em\u003e: 41.41 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-89378255\u003c/em\u003e: 47.38 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e18.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e21.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e67.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e74.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2023PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-110888456\u003c/em\u003e: 41.41 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-89378255\u003c/em\u003e: 47.38 Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e7.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e15.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e65.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e74.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003ePhysical position of the marker was determined according to the Chinese Spring reference genome (RefSeq v1.0) (The IWGSC, 2018).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eLOD, logarithm of odds score.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003eAdd, additive effect of the disease resistance allele; positive and negative values, YZHK and CY alleles were associated with larger values, respectively.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 Primer sequences of KASP markers tightly linked to the detected QTLs\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"811\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eQTL/gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eKASP marker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003ePrimer sequence (5\u0026apos;-3\u0026apos;)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eQYrYZHK.saas-1B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eKASP_AX-109272373F1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003eGAAGGTGACCAAGTTCATGCTcctcgctcataactaactaaagcaT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eKASP_AX-109272373F2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003eGAAGGTCGGAGTCAACGGATTcctcgctcataactaactaaagcaA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eKASP_AX-109272373C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003egcacctgctaattaactggga\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eQYrYZHK.saas-2B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eKASP_AX-111071533F1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003eGAAGGTGACCAAGTTCATGCTacagaagatcatggccctgtG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eKASP_AX-111071533F2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003eGAAGGTCGGAGTCAACGGATTacagaagatcatggccctgtC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eKASP_AX-111071533C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003ecgtggtcgcagctacatact\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eQYrCY.saas-3D\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eKASP_AX-89574305F1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003eGAAGGTGACCAAGTTCATGCTaagaacgtatcaccatagtcagT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eKASP_AX-89574305F2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003eGAAGGTCGGAGTCAACGGATTaagaacgtatcaccatagtcagC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eKASP_AX-89574305F1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003egctacagagacgaaccgctt\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eQYrYZHK.saas-5B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eKASP_AX-109526372F1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003eGAAGGTGACCAAGTTCATGCTcgatctaggctactcttcaacgG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eKASP_AX-109526372F2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003eGAAGGTCGGAGTCAACGGATTcgatctaggctactcttcaacgA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eKASP_AX-109526372C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003ecccataggaccagtgcactag\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eQYrCY.saas-5D\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eKASP_AX-110985437F1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003eGAAGGTGACCAAGTTCATGCTaccggcaccaatcttccatC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eKASP_AX-110985437F2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003eGAAGGTCGGAGTCAACGGATTaccggcaccaatcttccatT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eKASP_AX-110985437C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003eaattgtttcgccatttatcaaacaa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eQYrYZHK.saas-7D\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eKASP_AX-111468131F1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003eGAAGGTGACCAAGTTCATGCTgttcccacgacacagtacaA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eKASP_AX-111468131F2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003eGAAGGTCGGAGTCAACGGATTgttcccacgacacagtacaG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eKASP_AX-111468131C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003eccccgccggatgtatcattt\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eYr18\u003c/em\u003e (Brown-Guedira and Dreisigacker, 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003ewMAS000003F1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003eGAAGGTGACCAAGTTCATGCTctggtatgccatttaacataatcatgaA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003ewMAS000003F2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003eGAAGGTCGGAGTCAACGGATTctggtatgccatttaacataatcatgaT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003ewMAS000003C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 446px;\"\u003e\n \u003cp\u003ecgcatgacaataagtttcactcatgcaaa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4 Phenotypic effects of different parental QTL alleles on grain yield and related components\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"1029\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 136px;\"\u003e\n \u003cp\u003eQTL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 203px;\"\u003e\n \u003cp\u003eYLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 207px;\"\u003e\n \u003cp\u003eTGW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 207px;\"\u003e\n \u003cp\u003eSN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 207px;\"\u003e\n \u003cp\u003eGNP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e2023PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e2023GH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2023DT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e2023PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e2023GH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2023DT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e2023PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e2023GH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2023DT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e2023PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e2023GH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2023DT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003eQYrYZHK.saas-1B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cem\u003eH\u003csub\u003eYZHK\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e7.29\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e14.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e20.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e27.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n 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style=\"width: 71px;\"\u003e\n \u003cp\u003e8.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e45.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e55.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e68.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cem\u003eH\u003csub\u003eCY\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e14.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e21.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e27.73\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n 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style=\"width: 68px;\"\u003e\n \u003cp\u003e5.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e7.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e8.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e46.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e55.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e66.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003eQYrCY.saas-5D\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cem\u003eH\u003csub\u003eYZHK\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e5.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e14.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e20.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e24.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e35.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e37.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e4.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e7.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e8.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e42.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e55.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e67.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cem\u003eH\u003csub\u003eCY\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e7.57\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e14.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e21.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e28.69\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e35.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e37.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e5.24\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e7.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e8.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e48.26\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e54.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e66.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003eQYrYZHK.saas-7D\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cem\u003eH\u003csub\u003eYZHK\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e7.28\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e13.86\u003c/p\u003e\n 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\u003cp\u003e66.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cem\u003eH\u003csub\u003eCY\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e5.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e14.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e21.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e24.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e35.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e37.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e7.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e8.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e41.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e53.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e66.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eYLD, grain yield per plant; TGW, thousand-grain weight; SN, effective spike number per plant; GNP, grain number per spike.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e and \u003csup\u003e**\u003c/sup\u003e indicate significant correlations at \u003cem\u003eP\u003c/em\u003e = 0.05 and 0.01, respectively.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-breeding","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"molb","sideBox":"Learn more about [Molecular Breeding](https://www.springer.com/journal/11032)","snPcode":"11032","submissionUrl":"https://submission.nature.com/new-submission/11032/3","title":"Molecular Breeding","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Wheat, Landrace, Stripe rust resistance, QTL mapping, Yield effects","lastPublishedDoi":"10.21203/rs.3.rs-5643654/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5643654/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Stripe rust is prevalent in the wheat-growing region of southwestern China. Frequent changes in stripe rust pathogen virulence in this region lead to a rapid loss of disease resistance among wheat varieties. However, Chinese wheat landrace Yizhanghongkemai (YZHK) has exhibited adult-plant stripe rust resistance for more than one decade in a disease nursery in southwestern China. To elucidate the underlying genetic basis, quantitative trait loci (QTLs) for adult-plant stripe rust resistance in YZHK were analyzed using an inclusive composite interval mapping method. Six QTLs for adult-plant stripe rust resistance were detected on chromosomes 1BL, 2BL, 3DS, 5BL, 5DL, and 7DS in multiple environments. Notably, QYrYZHK.saas-1B, QYrYZHK.saas-2B and QYrCY.saas-5D were likely new disease resistance loci. By comparing the effects of QTL alleles on yield and its components in field trials in which stripe rust was severe and effectively controlled, we determined that three QTLs significantly decreased yield losses due to stripe rust, among which the QTLs on chromosomes 1BL and 7DS were from YZHK, whereas the QTL on chromosome 5DL was from the other parent Chuanyu 12. These QTLs represent elite genetic resources for developing wheat varieties with adult-plant stripe rust resistance in the wheat-growing region of southwestern China.","manuscriptTitle":"Identification of QTLs for adult-plant stripe rust resistance in Chinese wheat landrace Yizhanghongkemai and assessment of their utility for decreasing yield loss","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-21 06:29:00","doi":"10.21203/rs.3.rs-5643654/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-03-21T07:18:56+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-20T08:47:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-20T07:43:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Breeding","date":"2025-03-20T00:14:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-breeding","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"molb","sideBox":"Learn more about [Molecular Breeding](https://www.springer.com/journal/11032)","snPcode":"11032","submissionUrl":"https://submission.nature.com/new-submission/11032/3","title":"Molecular Breeding","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"16975b8f-3a78-4692-a689-f98987f9460f","owner":[],"postedDate":"March 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-21T16:10:34+00:00","versionOfRecord":{"articleIdentity":"rs-5643654","link":"https://doi.org/10.1007/s11032-025-01583-z","journal":{"identity":"molecular-breeding","isVorOnly":false,"title":"Molecular Breeding"},"publishedOn":"2025-07-14 16:05:35","publishedOnDateReadable":"July 14th, 2025"},"versionCreatedAt":"2025-03-21 06:29:00","video":"","vorDoi":"10.1007/s11032-025-01583-z","vorDoiUrl":"https://doi.org/10.1007/s11032-025-01583-z","workflowStages":[]},"version":"v1","identity":"rs-5643654","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5643654","identity":"rs-5643654","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}