Genome-wide association mapping for resistance against Septoria nodorum blotch and tan spot in a diverse wheat panel

Genome-wide association mapping for resistance against Septoria nodorum blotch and tan spot in a diverse wheat panel | 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 Genome-wide association mapping for resistance against Septoria nodorum blotch and tan spot in a diverse wheat panel Dehua Wang, Xinyao He, Zhiying Deng, Matthew Reynolds, Susanne Dreisigacker, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6370929/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Oct, 2025 Read the published version in Molecular Breeding → Version 1 posted 5 You are reading this latest preprint version Abstract Septoria nodorum blotch (SNB) and tan spot (TS) are two globally significant foliar diseases affecting wheat, causing substantial reductions in both yield and quality. In this study, genome-wide association study (GWAS) was conducted using an elite diversity panel comprising 150 lines to identify genetic loci associated with resistance to SNB and TS. Resistance was evaluated in greenhouse experiments at the seedling stage, with two replicates for each disease. For SNB, the majority of lines demonstrated good level of resistance, with 53% rated as resistant or moderately resistant (R/MR). Similarly, for TS, 60% of the lines exhibited R/MR resistance. Some lines exhibited high resistance to both SNB and TS. The panel was genotyped with the Illumina Infinium 25K BeadChip. GWAS revealed several significant marker-trait associations on chromosome 5B associated with SNB resistance, all of which were located in the vicinity of the Tsn1 gene, suggesting its important role in conferring SNB resistance within this population. In addition, two quantitative trait loci (QTL) on chromosomes 2AL and 7AS were identified. For TS, significant markers were primarily found within a 20 Mb region on the long arm of chromosome 7B, with phenotypic variation explained ranging from 8.34% to 12.31%. Additional TS QTL with minor effects were identified on chromosomes 3A, 5A, 7A, and 7D. These resistant lines and identification of markers for SNB and TS resistance hold potential for use in wheat breeding programs aimed at improving resistance to the two diseases. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Bread wheat ( Triticum aestivum L.) is one of the most extensively cultivated and economically significant crops globally, contributing approximately 20% of the total human calory intake worldwide. The unique viscoelastic properties of wheat flour dough make it particularly suitable for the production of a wide range of breads and other baked products, contributing to its central role in global food security (Shewry, 2009; Shiferaw et al., 2013). However, grown on around 220 million hectares worldwide, wheat production faces constant threats from various diseases, including tan spot (TS) and Septoria nodorum blotch (SNB), which can severely reduce both yield and grain quality under disease-conducive conditions. Tan spot, caused by the fungal pathogen Pyrenophora tritici-repentis ( Ptr ), is a major foliar disease in wheat, resulting in necrotic lesions and/or chlorosis that reduce the photosynthetic capacity of the plant (Lamari & Bernier, 1991). It occurs in most wheat-growing regions around the world, including Europe, the Americas, Australia, and Asia. In severe cases, it can result in yield losses of up to about 50% and negatively affects grain quality (Schilder & Bergstrom, 1994; Shabeer & Bockus, 1988; Reynoso et al., 2023). The fungus persists on infected stubble remaining on the soil surface. Consequently, the widespread adoption of conservation tillage practices, which retain crop residues, has contributed to a rise in the incidence of TS in recent decades (Lamari & Strelkov, 2010). As for now, five host-selective toxins (HSTs) produced by Ptr have been identified: Ptr ToxA, Ptr ToxB, Ptr ToxC, and two newly discovered toxins, ToxE1 and ToxE2 (Rawlinson et al., 2024). These toxins interact directly or indirectly with the products of the dominant host genes: Ptr ToxA with Tsn1 (Faris et al., 1996), Ptr ToxB with Tsc2 (Friesen & Faris, 2004), and Ptr ToxC with Tsc1 (Effertz et al., 2002). The newly identified toxins, ToxE1 and ToxE2, have shown to induce chlorosis in a cultivar-specific manner, although their corresponding wheat sensitivity genes remain unknown (Rawlinson et al., 2024). A few qualitative genes were identified through the inoculation of conidial spores and were named Tsr1 to Tsr5 (Faris et al., 2013). Eight races of Ptr have been identified to date, producing different combinations of toxins (Faris et al., 2013). The resistance to TS is genetically complex, controlled by multiple quantitative trait loci (QTL), which has posed challenges in traditional breeding programs (Faris et al., 2013). Recent research has revealed multiple QTL linked to TS resistance, offering valuable insights into candidate genes and molecular markers for marker-assisted selection (MAS). For example, a genome-wide association study (GWAS) identified 14 markers associated with TS resistance at the seedling stage on chromosomes 1AS, 2AL, 2BL, 3AS, 3AL, 3B, 6AS, and 6AL (Juliana et al., 2018). Based on 104 QTL reported in 15 previous mapping studies, Liu et al. (2020a) identified a total of 19 meta-QTL conferring resistance to TS, of which the ones on 2A, 3B, and 5A exhibited large effects and broad resistance spectrum. Septoria nodorum blotch (SNB) is caused by the necrotrophic fungal pathogen Parastagonospora nodorum (formerly Stagonospora nodorum ), and poses a similar threat to global wheat yields, particularly in North America, Europe, and Australia. It leads to significant reductions in both yield and quality, with severe cases resulting in yield losses of up to 50% (Eyal, 1981; Bhathal et al., 2003). Like TS, SNB affects wheat leaves, causing necrosis, chlorosis, and premature senescence, thereby reducing the photosynthetic capacity and overall productivity (Solomon et al., 2006; Eyal, 1987). Nine HSTs have been identified in SNB, namely SnToxA, SnTox1, SnTox2, SnTox2A, SnTox3, SnTox4, SnTox5, SnTox6 and SnTox7, of which SnToxA shares high homology with Ptr ToxA and both interact with the same host gene, Tsn1 (Friesen et al., 2006). Except for SnTox2A, which is associated with the QTL Qsnb.cur–2AS1 (Pan et al., 2016), all other HSTs correspond to specific host genes: SnTox1 corresponds to the host gene Snn1 (Liu et al., 2004), SnTox2 to Snn2 (Friesen et al., 2007), SnTox3 to Snn3 (Friesen et al., 2008), SnTox4 to Snn4 (Paillard et al., 2003), SnTox5 to Snn5 (Friesen et al., 2012), SnTox6 to Snn6 (Gao et al., 2015), SnTox7 to Snn7 (Shi et al., 2015).These eight HSTs are critical components of the P. nodorum pathosystem, directly interacting with their corresponding host susceptibility genes mentioned above to trigger infection. Although SnTox2, SnTox6, and SnTox7 were initially thought to be distinct effectors targeting different sensitivity genes, Richards et al. (2022) recently revealed that they are the same protein, named SnTox267. It is important to mention that both TS and SNB follow the inverse gene-for-gene hypothesis and its implications (resistance is due to absence of susceptibility factors in the host). In recent years, numerous QTL associated with SNB resistance have been identified using GWAS. For instance, a GWAS on winter durum wheat identified seven QTL associated with SNB resistance, being located on chromosomes 1B, 2AL, 2DS, 4AL, 5BL, 6BS, and 7AL (AlTameemi et al., 2021). Another study conducted in Nordic wheat identified 11 QTL associated with SNB, including a robust QTL, QSnb.nmbu-2AS , located on the short arm of chromosome 2A. However, only 15.7% of the lines (n = 296) carried this resistance QTL, suggesting its significant potential for enhancing SNB resistance through breeding efforts (Lin et al., 2022). Both studies demonstrated a clear trend of significantly enhanced SNB resistance levels with an increasing number of QTL present in the materials. Furthermore, a study using a more diverse germplasm source identified 20 QTL associated with SNB resistance, distributed across chromosomes 1A, 1B, 4B, 5A, 5B, 6A, 7A, 7B, and 7D. Most of these QTL were detected in only a single environment, further highlighting the genetic complexity of SNB resistance. (Francki et al., 2020). The objectives of the current study were to screen a diverse panel of bread wheat for seedling resistance against the two foliar diseases, and to identify the underlying genetic loci and molecular markers through GWAS for their utilization in wheat breeding. 2. Materials and methods 2.1 Plant material and genotyping A panel of 150 CIMMYT bread wheat lines were used in the present study. These lines were pre-selected for heat and/or drought tolerance with additional considerations for phenology (height and days to heading). Genomic DNA was extracted from young leaves according to the CTAB method described in the CIMMYT laboratory protocols (Dreisigacker et al., 2016). The panel was genotyped with Illumina Infinium 25K BeadChip at Trait Genetics GmbH, Germany. 2.2 Disease screening for TS and SNB The experiments were conducted at the seedling stage in a greenhouse, located in the El Batan experimental station of CIMMYT-Mexico, at 22 °C (day) and 18 °C (night) temperatures with a 16-h photoperiod. Two experiments were performed for each disease, with two replications for each experiment. For disease reaction screening, the Mexican Ptr isolate MexPtr1 and P. nodorum isolate MexSn4 were used to inoculate plants for TS and SNB, respectively. Both isolates are known ToxA producers. The isolates were cultured on V8-PDA media (Lamari & Bernier, 1989), and spore concentrations were adjusted to 4 × 10 3 spores mL −1 ( MexPtr1 ) and 1 × 10 7 spores mL −1 ( MexSn4 ) for inoculation (Singh et al., 2007; Singh et al., 2016). Four seedlings were grown as experimental unit in plastic containers, and the mean disease scores were used for further analysis. Erik and Glenlea were included as resistant and susceptible controls, respectively. Inoculation was performed at approximately 14 days after sowing, when the second leaf was fully expanded. Inoculum was applied to the seedlings using a hand sprayer until runoff (approximately 0.5 mL per plant). After inoculation, trays were placed in a humid chamber (100% relative humidity, 20°C) to promote infection and returned to the greenhouse after 24 hours. Disease severity for both SNB and TS was scored on a linear scale of 1–5 at seven days post-inoculation (Feng et al., 2004; Hu et al., 2019), with resistance categories defined as follows: Resistant (R, 1.0–1.5), Moderately Resistant (MR, 1.6–2.5), Moderately Susceptible (MS, 2.6–3.5), and Susceptible (S, 3.6–5.0). 2.3 Genome-wide association analysis The genotype data was filtered using the R package dplyr (Wickham et al., 2023) with a minor allele frequency (MAF) cutoff of 0.05 and a missing rate of 0.1. Marker-trait associations (MTAs) were evaluated using the mixed linear model (MLM) in TASSEL 5.0 software (https://tassel.bitbucket.io/). Manhattan and Q-Q plots were generated with the R package CMplot (Yin et al., 2021), using R version 4.4.1 (https://www.r-project.org/). Visualization of 3D PCA plots was carried out with the scatterplot3d package (Ligges & Mächler, 2002) in R. Linkage disequilibrium (LD) heatmaps were produced using the R package LDheatmap (Shin et al., 2006). Population structure was analyzed using Admixture version 1.3.0 (Alexander et al., 2009) and visualized with the ggplot2 package in R (Wickham, 2011). An MTA was declared significant with a P-value threshold of ≤ 0.001. Markers identified in two or more experiments were considered stable loci associated with the target trait. 3. Results 3.1 Disease resistance The population exhibited significant variation in resistance to SNB and TS, with the majority lines showing good resistance (Figure 1). For SNB, 48 (32%) genotypes in Exp1 and 69 (46%) in Exp2 exhibited a resistant (R) response, whereas only 39 (26%) and 32 (21%), respectively, were susceptible (S). The remaining lines fell into the moderately resistant (MR) or moderately susceptible (MS) categories. For TS, 52 (35%) of the genotypes in Exp1 and 66 (44%) in Exp2 exhibited a resistant (R) response, whereas only 12 (8%) and 18 (12%), respectively, were susceptible (S). Some genotypes demonstrated high resistance to both TS and SNB, such as entries 9640 (RL84) and 9589 (ATTILA/BAV92//PASTOR) (Table 1). The two experiments demonstrated high repeatability for SNB, with a Pearson correlation coefficient of 0.84. For TS, the repeatability was slightly lower, with a Pearson correlation coefficient of 0.70. Given the relatively high repeatability observed in both experiments, the average phenotypic values across Exp1 and Exp2 were used in subsequent analyses. Table 1 Genotypes with high levels of resistance to both Septoria nodorum blotch (SNB) and tan spot (TS). Entry Name or pedigree SNB TS 9640 RL84 1.0 1.1 9589 ATTILA/BAV92//PASTOR 1.0 1.0 9644 ATTILA/3*BCN/3/CROC_1/AE.SQUARROSA (224)//OPATA 1.1 1.1 9525 SUPER 152 1.1 1.1 9548 MUNAL #1 1.0 1.1 9628 SLVS/3/CROC_1/AE.SQUARROSA (224) //OPATA/5/VEE/LIRA//BOW/3/BCN/4/KAUZ 1.0 1.2 9542 PARA2//JUP/BJY/3/VEE/JUN/4/2*KAUZ/5/BOW/PRL//BUC 1.1 1.1 9562 CIRO NL F2016 1.1 1.0 9584 BOW/VEE/5/ND/VG9144//KAL/BB/3/YACO/4/CHIL/6/CASKOR/3/CROC_1/AE.SQUARROSA (224) //OPATA/7/PASTOR//MILAN/KAUZ/3/BAV92 1.1 1.1 9598 PASTOR//HXL7573/2*BAU/3/ATTILA/3*BCN 1.1 1.1 9541 W15.92 1.0 1.2 9641 PASTOR//HXL7573/2*BAU/3/MEX94.2.19//ATTILA/3*BCN 1.1 1.1 9551 CMH79A.955/4/AGA/3/4*SN64/CNO67//INIA66/5/NAC 1.0 1.1 9521 CROC_1/AE.SQUARROSA (224)//OPATA 1.0 1.0 9588 PASTOR//HXL7573/2*BAU/3/ATTILA/3*BCN/5/CROC_1/AE.SQUARROSA (205)//BORL95/3/PRL/SARA//TSI/VEE#5/4/FRET2 1.1 1.0 9539 SOKOLL/3/PASTOR//HXL7573/2*BAU/5/CROC_1/AE.SQUARROSA (205)//BORL95/3/PRL/SARA//TSI/VEE#5/4/FRET2 1.0 1.2 R check Erik 1 1 S check Glenlea 4.9 4.8 3.2 Genotyping and SNP Density Analysis The 25K SNP array successfully scored 21,643 SNPs across the 21 pairs of chromosomes of wheat, of which 15,290 SNPs were retained for subsequent analysis according to the filtering criteria. The SNP density plot (Figure 2) reveals variation in marker density across chromosomes; overall, the SNP distribution was relatively even, providing comprehensive genomic coverage. On average, the A and B genomes carried ~1.33 SNPs/Mb, whereas the D genome carried only ~0.48 SNPs/Mb, indicating its substantially lower marker density. The distribution observed in this study aligns with findings from previous research, showing lower marker density in the centromeric regions and higher marker density in the distal regions of the chromosomes. 3.3 Population structure analysis The population structure is shown in an admixture plot and a 3D PCA plot (Figure 3). The admixture plot revealed three distinct populations (V1, V2, and V3), where each individual’s genetic ancestry was represented as proportions across these populations. While some individuals showed pure ancestry, most exhibited mixed ancestry, suggesting gene flow between populations. The 3D PCA plot showed that individuals were generally dispersed across the principal component space, indicating high genetic diversity within the population. However, subtle clustering was still evident, supporting the population structure identified in the admixture analysis. 3.4 Genome-wide association study For SNB, the Q–Q plots showed well-calibrated test statistics with no evidence of inflation, supporting the reliability of the GWAS results (Figure S2). A total of 25 MTAs were detected in Exp1 and 27 MTAs in Exp2, resulting in the identification of 46 unique MTAs (Table S1) that are distributed across 11 chromosomes, specifically 1A, 2A, 2B, 3A, 3B, 3D, 4A, 5A, 5B, 6A and 7A. Notably, a significant peak was observed on chromosome 5B (Figure 4), represented by markers AX-158525572 and IACX9261 , demonstrating their strong associations with the SNB resistance across experiments and accounting for 6.99-16.79% of the phenotypic variance. This peak on chromosome 5B corresponds to the Tsn1 gene region, known for its significant role in SNB resistance. Several other MTAs with notable contributions were also identified on different chromosomes, including AX-94604036 on chromosome 1A and RAC875_c346_1226 on chromosome 7A, explaining 11.79% and 10.44% of the phenotypic variance, respectively; however, these associations were significant in only one experiment. For TS, the Q–Q plots also indicated well-controlled population structure and no overall inflation, supporting the robustness of the GWAS results (Figure S2). 11 MTAs were detected in TS Exp1 and 24 MTAs in TS Exp2, resulting in a total of 33 unique MTAs (Table S2), being located on chromosomes 2D, 3A, 4A, 5A, 5B, 5D, 6B, 7A, 7B, and 7D (Figure 4). Notably, markers Excalibur_c81824_411 and RAC875_c8137_128 on the long arm of chromosome 7B were consistently detected in both experiments, explaining phenotypic variations between 7.05-12.31% in the experiments. Several MTAs on other chromosomes also showed relatively high phenotypic effects, though they were detected in only a single experiment. For instance, Kukri_rep_c106477_365 on chromosome 7D and GENE-3572_70 on chromosome 5D explained 11.79% and 11.19% of the phenotypic variance, respectively. Several markers were detected in both experiments based on nominal P values, suggesting their potential importance in the genetic control of SNB and TS (Table 2). Nevertheless, permutation-based empirical thresholds showed that most loci reached experiment-wise significance in only one environment. Additional significant markers are listed in supplementary Tables S1 and S2 for comprehensive reference. Table 2 Significant MTAs detected in both experiments for Septoria nodorum blotch (SNB) and tan spot (TS) Trait Marker Chr Position P-value Perm_P PVE (%) Exp1 Exp2 Exp1 Exp2 Exp1 Exp2 SNB AX-158525572 5B 546970656 5.67E-07 3.54E-05 0.002 0.250 16.34 12.76 SNB IACX9261 5B 546704069 1.07E-06 3.89E-05 0.015 0.261 16.79 13.25 SNB AX-158525569 5B 546672582 1.68E-06 6.67E-05 0.005 0.360 15.34 12.11 SNB AX-111509567 5B 546826552 1.68E-06 6.67E-05 0.005 0.360 15.34 12.11 SNB tplb0027f13_1493 5B 546827810 1.68E-06 6.67E-05 0.005 0.360 15.34 12.11 SNB Tdurum_contig25513_123 5B 565753529 7.73E-06 3.87E-04 0.047 0.625 15.84 11.57 TS Excalibur_c81824_411 7B 739931859 9.00E-04 1.51E-05 0.479 0.027 7.07 11.9 TS RAC875_c8137_128 7B 745585191 9.71E-04 3.52E-05 0.432 0.052 7.05 10.99 3.5 Grouping MTAs into QTL The MTAs for SNB on chromosome 5B were located within a 0.3 Mb chromosome region (near Tsn1 ), and LD analysis indicated that they are tightly linked (Figure 5). Thus, we designated this LD region as one QTL. Haplotype analysis revealed that allele combinations CGTATA and CGTATG at the markers IACX9261 , AX-158525572 , AX-158525569 , AX-111509567 , tplb0027f13_1493 and AX-108829232 were associated with SNB resistance. Therefore, these two haplotypes were classified as the resistant group (R), while all other haplotypes were grouped as susceptible (S). A significant difference was observed between the two groups (p = 1.23e-06, Figure 6). Furthermore, linear regression analysis estimated that this QTL accounts for approximately 20% (R 2 =19.46%) of the phenotypic variance for SNB. To further clarify the relationship between the identified QTL and Tsn1 , we genotyped all accessions using fcp623 , the gene-specific marker for Tsn1 . Among the 112 lines carrying the resistant haplotype, 108 (96.4%) carried the insensitive allele ( tsn1 , T:T), three carried the sensitive allele ( Tsn1 , C:C). Conversely, among the 38 lines carrying the susceptible haplotype, 27 (71.1%) carried the sensitive allele ( Tsn1 , C:C), while 11 carried the insensitive allele ( tsn1 ). These results demonstrate a strong correspondence between the resistant haplotype and the insensitive allele ( tsn1 ), indicating that the QTL near Tsn1 is most likely explained by this locus. Overall, the resistant haplotype showed a very strong correspondence with the insensitive allele ( tsn1 ), while the few inconsistencies were mainly observed in the susceptible haplotype group, likely reflecting variation in other loci that interact with Tsn1 . The MTAs identified on chromosome 7B for TS were also tightly linked, with all markers except for RAC875_c31791_559 located within the physical range of 730.9 Mb to 750.0 Mb (Figure 6). Therefore, we designated this LD region as a QTL and named it Qts.cim-7BL . Haplotype analysis identified an allele combination CGGCTTGACAG at the markers Excalibur_c81824_411 , RAC875_c8137_128 , RAC875_c34939_963 , AX-94748974 , AX-94393446 , RAC875_c14064_308 , AX-94467581 , RAC875_rep_c108382_824 , TG0119, RAC875_c34939_467 and BobWhite_c43557_103 to be the resistant group (R), while all other haplotypes were categorized as the susceptible group (S). A significant difference was observed between the two groups (p = 7.07e-06, Figure 6). Furthermore, linear regression analysis estimated that this QTL accounted for 12.84% of the phenotypic variance for TS. In addition to the two QTL mentioned above, we identified two additional QTL for SNB on chromosomes 2A and 7A, and four additional QTL for TS on chromosomes 3A, 5A, 7A and 7D. (Table S3, Figure S2). However, these QTL were only detected in a single experiment. However, there is a clear trend of increased resistance to SNB and TS with a higher number of QTL, indicating their positive correlation (Figure 7). However, among all tested lines, only one line (9511) carries all eight QTL, yet it exhibited relatively poor resistance to SNB, with a disease score of 2.9. In comparison, lines 9521, 9539, 9551, and 9598 each carry seven of these eight QTL and demonstrated good level of resistance. Specifically, their resistance scores were 1.0, 1.0, 1.0, and 1.1 for SNB, and 1.0, 1.2, 1.1, and 1.1 for TS, respectively. These lines could be considered valuable parental materials for improving SNB and TS resistance in future breeding programs. Candidate genes associated with disease resistance have been identified within a 2-Mb window harboring the identified QTL, except for the one corresponding to Tsn1 on chromosome 5BL (Table S4). These genes encode a variety of proteins, including NB-ARC domain-containing protein, disease resistance N-terminal domain-containing protein, Rx N-terminal domain-containing protein, disease resistance protein RPM1, among others. 4. Discussion The identification of genetic loci associated with resistance to SNB and TS through GWAS provides valuable insights into the genetic architecture of disease resistance in wheat. In this study, we identified multiple significant loci across chromosomes, with a number showing strong effects, especially the QTL on chromosomes 5B for SNB and on 7B for TS resistance. Notably, the former is located in close proximity to the well-characterized Tsn1 locus, which has been extensively linked to susceptibility to ToxA (Faris et al., 2010). It is noteworthy that all SNB resistant lines in this study contained this QTL, suggesting that Tsn1 may play a significant role in conferring SNB susceptibility in our population. Unexpectedly, this region was only significant for SNB but not for TS, which could be due to the presence of an epistatic gene that masked the interaction between Tsn1 and ToxA. Such a case was observed in Kariyawasam et al. (2016), where a QTL on chromosome 3B exhibited epistasis over the Ptr ToxA- Tsn1 interaction. Previous studies have identified a few significant loci for TS on chromosome 7B. Laribi et al. (2023) identified an MTA for TS at 453.4 Mb in durum wheat, while Kokhmetova et al. (2021) mapped significant loci at approximately 538.1 Mb in Kazakh hexaploid wheat. Additionally, a QTL has been reported on the short arm of chromosome 7B (Faris et al., 2012; Singh et al., 2019). In our study, Qts.cim-7BL was located at approximately 740 Mb, suggesting that this could be a novel QTL conferring resistance to TS. Moreover, all R lines identified in our study contained this QTL, demonstrating its significant role in conferring TS resistance in the studied germplasm. In addition to the two QTL mentioned above, the remaining QTL we identified were detected in only a single experiment. This suggests that the effectiveness of QTL may be influenced by various factors. Among these QTL, Qts.cim.3AS is likely the same as the QTL RP696_86-124_3A reported by Liu et al. (2020b) and QTs.fcu-3A reported by Chu et al. (2010), based on the overlapped physical regions. The remaining QTL identified in this study have not been previously reported in association with SNB or TS resistance, suggesting they may represent novel loci specific to this population. Our results demonstrate a clear positive correlation between the number of QTL and resistance levels for both SNB and TS, consistent with previous studies (Lin et al., 2021; Navathe et al., 2023). However, one line (9511) carried all eight identified QTL (three for SNB and five for TS) but exhibited only moderate resistance to SNB (score 2.9), despite showing strong resistance to TS (score 1.2). This exception suggests that the presence of multiple QTL does not always translate into high resistance, likely due to gene–environment interactions, disease-specific effects, or the presence of specific yet unknown gene interactions. Overall, the positive correlation observed in most cases indicates that cumulative effects of multiple resistance loci can significantly enhance disease resistance. Future research should prioritize validating these QTL across diverse environments to evaluate their stability and practical utility. The positive correlation observed between QTL count and resistance levels suggests that stacking multiple resistance loci could be a promising strategy to enhance disease resistance in wheat breeding. Additionally, as some QTL identified in this study appear to be novel, further functional analyses are needed to elucidate their roles and assess their potential in resistance breeding programs. Expanding this research through multi-location trials and employing advanced genomic tools will be essential for translating these findings into practical solutions for sustainable wheat production. Declarations Conflict of interest The authors declare that the research was conducted without commercial or financial relationships that could be construed as potential conflicts of interest. Ethics No human/animal studies are presented. No potentially identifiable human images or data are presented in this study. Data Availability Statement Data supporting the findings of this study are available within the article and its supplementary materials. Additional datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Author Contributions DW collected and analyzed the data and drafted the initial version of the manuscript. XH contributed to disease evaluation, data analysis, and revised the manuscript. ZD revised the manuscript. MR developed the plant materials. SD performed genotyping and revised the manuscript. PKS conceptualized the study and administered the project. All authors reviewed and approved the final version of the manuscript. Acknowledgements Financial support from the China Scholarship Council, the Bill and Melinda Gates Foundation, USAID, and One CGIAR Initiatives-ABI and PHI for conducting this research is gratefully acknowledged. References Alexander, D. H., Novembre, J., & Lange, K. (2009). Fast model-based estimation of ancestry in unrelated individuals. Genome Research, 19(9), 1655-1664. AlTameemi, R., Gill, H. S., Ali, S., Ayana, G., Halder, J., Sidhu, J. S., Gill, U. S., Turnipseed, B., Hernandez, J. L., & Sehgal, S. K. (2021). Genome-wide association analysis permits characterization of Stagonospora nodorum blotch (SNB) resistance in hard winter wheat. Scientific Reports, 11(1), 12570. Bhathal, J. S., Loughman, R., & Speijers, J. (2003). 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SnTox5– Snn5 : a novel Stagonospora nodorum effector–wheat gene interaction and its relationship with the SnToxA– Tsn1 and SnTox3– Snn3–B1 interactions. Molecular Plant Pathology, 13(9), 1101-1109. Friesen, T. L., Meinhardt, S. W., & Faris, J. D. (2007). The Stagonospora nodorum ‐wheat pathosystem involves multiple proteinaceous host‐selective toxins and corresponding host sensitivity genes that interact in an inverse gene-for-gene manner. The Plant Journal, 51(4), 681-692. Friesen, T. L., Stukenbrock, E. H., Liu, Z., Meinhardt, S., Ling, H., Faris, J. D., Rasmussen, J. B., Solomon, P. S., McDonald, B. A., & Oliver, R. P. (2006). Emergence of a new disease as a result of interspecific virulence gene transfer. Nature Genetics, 38(8), 953-956. Friesen, T. L., Zhang, Z., Solomon, P. S., Oliver, R. P., & Faris, J. D. (2008). Characterization of the interaction of a novel Stagonospora nodorum host-selective toxin with a wheat susceptibility gene. Plant Physiology, 146(2), 682. Gao, Y., Faris, J. D., Liu, Z., Kim, Y. M., Syme, R. A., Oliver, R. P., Xu, S.S., & Friesen, T. L. (2015). Identification and characterization of the SnTox6- Snn6 interaction in the Parastagonospora nodorum –wheat pathosystem. Molecular Plant-Microbe Interactions, 28(5), 615-625. Hu, W., He, X., Dreisigacker, S., Sansaloni, C. P., Juliana, P., & Singh, P. K. (2019). A wheat chromosome 5AL region confers seedling resistance to both tan spot and Septoria nodorum blotch in two mapping populations. The Crop Journal, 7(6), 809-818. Juliana, P., Singh, R. P., Singh, P. K., Poland, J. A., Bergstrom, G. C., Huerta-Espino, J., Bhavani, S., Crossa, J., & Sorrells, M. E. (2018). Genome-wide association mapping for resistance to leaf rust, stripe rust and tan spot in wheat reveals potential candidate genes. Theoretical and Applied Genetics, 131, 1405-1422. Kariyawasam, G. K., Carter, A. H., Rasmussen, J. B., Faris, J., Xu, S. S., Mergoum, M., & Liu, Z. (2016). 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Theoretical and Applied Genetics, 135(12), 4169-4182. Lin, M., Stadlmeier, M., Mohler, V., Tan, K. C., Ficke, A., Cockram, J., & Lillemo, M. (2021). Identification and cross-validation of genetic loci conferring resistance to Septoria nodorum blotch using a German multi-founder winter wheat population. Theoretical and Applied Genetics, 134, 125-142. Liu, Y., Salsman, E., Wang, R., Galagedara, N., Zhang, Q., Fiedler, J. D., Liu, Z., Xu, S., Faris, J. D., & Li, X. (2020a). Meta-QTL analysis of tan spot resistance in wheat. Theoretical and Applied Genetics, 133, 2363-2375. Liu, Y., Zhang, Q., Salsman, E., Fiedler, J. D., Hegstad, J. B., Liu, Z., Faris, J. D., Xu, S. S., & Li, X. (2020b). QTL mapping of resistance to tan spot induced by race 2 of Pyrenophora tritici-repentis in tetraploid wheat. Theoretical and Applied Genetics, 133, 433-442. Liu, Z. H., Faris, J. D., Meinhardt, S. W., Ali, S., Rasmussen, J. B., & Friesen, T. L. (2004). Genetic and physical mapping of a gene conditioning sensitivity in wheat to a partially purified host-selective toxin produced by Stagonospora nodorum . Phytopathology, 94(10), 1056-1060. Navathe, S., He, X., Kamble, U., Kumar, M., Patial, M., Singh, G., Singh, G. P., Joshi, A. K., & Singh, P. K. (2023). Assessment of Indian wheat germplasm for Septoria nodorum blotch and tan spot reveals new QTLs conferring resistance along with recessive alleles of Tsn1 and Snn3 . Frontiers in Plant Science, 14, 1223959. Paillard, S., Schnurbusch, T., Winzeler, M., Messmer, M., Sourdille, P., Abderhalden, O., Keller, B., & Schachermayr, G. (2003). An integrative genetic linkage map of winter wheat ( Triticum aestivum L.). Theoretical and Applied Genetics, 107(7), 1235-1242. Phan, H. T., Rybak, K., Furuki, E., Breen, S., Solomon, P. S., Oliver, R. P., & Tan, K. C. (2016). Differential effector gene expression underpins epistasis in a plant fungal disease. The Plant Journal, 87(4), 343-354. Rawlinson, C., Nealon, G., Chooi, Y. H., Oliver, R. P., Moffat, C. S., & See, P. T. (2024). Discovery of two novel phthalide phytotoxins from the wheat tan spot fungal pathogen Pyrenophora tritici-repentis . Journal of Agricultural and Food Chemistry, 72(36), 19594-19603. Reynoso, A., Sautua, F., Carmona, M., Chulze, S., & Palazzini, J. (2023). Tan spot of wheat: can biological control interact with actual management practices to counteract this global disease. European Journal of Plant Pathology, 166(1), 27-38. Richards, J. K., Kariyawasam, G. K., Seneviratne, S., Wyatt, N. A., Xu, S. S., Liu, Z., Faris, J. D., & Friesen, T. L. (2022). A triple threat: the Parastagonospora nodorum SnTox267 effector exploits three distinct host genetic factors to cause disease in wheat. New Phytologist, 233(1), 427-442. Schilder, A. M. C., & Bergstrom, G. C. (1994). Infection of wheat seed by Pyrenophora tritici-repentis . Canadian Journal of Botany, 72(4), 510-519. Shabeer, A., & Bockus, W. W. (1988). Tan spot effects on yield and yield components relative to growth stage in winter wheat. Plant Disease, 72: 599-602. Shewry, P. R. (2009). Wheat. Journal of Experimental Botany, 60(6), 1537-1553. Shi, G., Friesen, T. L., Saini, J., Xu, S. S., Rasmussen, J. B., & Faris, J. D. (2015). The wheat Snn7 gene confers susceptibility on recognition of the Parastagonospora nodorum necrotrophic effector SnTox7. The Plant Genome, 8(2), plantgenome2015-02. Shiferaw, B., Smale, M., Braun, H. J., Duveiller, E., Reynolds, M., & Muricho, G. (2013). Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security. Food security, 5, 291-317. Shin, J. H., Blay, S., McNeney, B., & Graham, J. (2006). LDheatmap: an R function for graphical display of pairwise linkage disequilibria between single nucleotide polymorphisms. Journal of statistical software, 16, 1-9. Singh, D. P. (2007). First report of tan spot of wheat caused by Pyrenophora tritici-repentis in the Northern Hills and Northwestern Plains Zones of India. Plant Disease, 91(4), 460-460. Singh, P. K., Crossa, J., Duveiller, E., Singh, R. P., & Djurle, A. (2016). Association mapping for resistance to tan spot induced by Pyrenophora tritici-repentis race 1 in CIMMYTs historical bread wheat set. Euphytica, 207, 515-525. Singh, P. K., Singh, S., Deng, Z., He, X., Kehel, Z., & Singh, R. P. (2019). Characterization of QTLs for seedling resistance to tan spot and Septoria nodorum blotch in the PBW343/Kenya Nyangumi wheat recombinant inbred lines population. International Journal of Molecular Sciences, 20(21), 5432. Solomon, P. S., Lowe, R. G., Tan, K. C., Waters, O. D., & Oliver, R. P. (2006). Stagonospora nodorum : cause of Stagonospora nodorum blotch of wheat. Molecular Plant Pathology, 7(3), 147-156. https://doi.org/10.1111/j.1364-3703.2006.00326.x Wickham, H. (2011). ggplot2. Wiley interdisciplinary reviews: computational statistics, 3(2), 180-185. Wickham, H., François, R., Henry, L., Müller, K., & Vaughan, D. (2023). dplyr: a grammar of data manipulation. R package version 1.1. 2. Computer software. Yin, L., Zhang, H., Tang, Z., Xu, J., Yin, D., Zhang, Z., Yuan, X., Zhu, M., Zhao, S., Li, X., & Liu, X. (2021). rMVP: a memory-efficient, visualization-enhanced, and parallel-accelerated tool for genome-wide association study. Genomics, Proteomics and Bioinformatics, 19(4), 619-628. Supplementary Files Supp.docx Cite Share Download PDF Status: Published Journal Publication published 23 Oct, 2025 Read the published version in Molecular Breeding → Version 1 posted Editorial decision: Accept 08 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers invited by journal 02 Sep, 2025 Editor assigned by journal 02 Sep, 2025 First submitted to journal 29 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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-6370929","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":509140242,"identity":"a907bbc0-df43-4a23-a38f-6d21645467fd","order_by":0,"name":"Dehua Wang","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Dehua","middleName":"","lastName":"Wang","suffix":""},{"id":509140243,"identity":"c1145bc1-2940-4256-a11b-598cefc7046d","order_by":1,"name":"Xinyao He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBACgwM8BgwfGBh4SNPCOIMkLZINPAbMJKgHAn4GHsPHNjW1MvyzDzA+rvhFhBY2Bv7PxjnHjvNInEtgNjzbR5QWHjPp3IZjPAxnGNgkG3uIc5j5b0ugFnmitQC9b8bM2FDDYwDS0vCDCC0Gh3mMJXuOHeAxPMPYbNjYQIyW4z2GH37U1NnLnWE++LDhDxFaGJjB5GEgZmxgYGwjRgsE1EFpomwZBaNgFIyCkQYAJWww+qlQi+YAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-0217-9510","institution":"CIMMYT: Centro Internacional de Mejoramiento de Maiz y Trigo","correspondingAuthor":true,"prefix":"","firstName":"Xinyao","middleName":"","lastName":"He","suffix":""},{"id":509140244,"identity":"56cdb485-a348-4ba2-be67-c9a3688cd3d9","order_by":2,"name":"Zhiying Deng","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Zhiying","middleName":"","lastName":"Deng","suffix":""},{"id":509140245,"identity":"d293e1a3-fa06-47e4-8ff0-09f3bf7f0a32","order_by":3,"name":"Matthew Reynolds","email":"","orcid":"","institution":"CIMMYT: Centro Internacional de Mejoramiento de Maiz y Trigo","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Reynolds","suffix":""},{"id":509140246,"identity":"c154309e-b02f-420b-a9b8-c9a5e74bdac3","order_by":4,"name":"Susanne Dreisigacker","email":"","orcid":"","institution":"CIMMYT: Centro Internacional de Mejoramiento de Maiz y Trigo","correspondingAuthor":false,"prefix":"","firstName":"Susanne","middleName":"","lastName":"Dreisigacker","suffix":""},{"id":509140247,"identity":"ba557e86-1ec9-4334-98dd-1ba393601676","order_by":5,"name":"Pawan K. Singh","email":"","orcid":"","institution":"CIMMYT: Centro Internacional de Mejoramiento de Maiz y Trigo","correspondingAuthor":false,"prefix":"","firstName":"Pawan","middleName":"K.","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2025-04-03 16:21:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6370929/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6370929/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11032-025-01602-z","type":"published","date":"2025-10-23T16:16:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91362535,"identity":"f764bddc-bce0-4fdb-8105-183acd86b761","added_by":"auto","created_at":"2025-09-15 16:43:11","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48803,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the population’s resistance levels.\u003c/p\u003e\n\u003cp\u003e(a) Septoria nodorum blotch (SNB) disease scores from two experiments (Exp1, Exp2).\u003c/p\u003e\n\u003cp\u003e(b) Tan spot (TS) disease scores from two experiments (Exp1, Exp2).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6370929/v1/24103cbb414a9fa8f84b1a7c.jpg"},{"id":91362870,"identity":"eb839233-3e40-488f-9a95-d940f92b8bcc","added_by":"auto","created_at":"2025-09-15 16:51:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":410548,"visible":true,"origin":"","legend":"\u003cp\u003eSNP marker distribution on different wheat chromosomes\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6370929/v1/7697c47200dac553bc00146b.jpg"},{"id":91363627,"identity":"461c4846-065c-4054-a9b6-9c9c274f5ed5","added_by":"auto","created_at":"2025-09-15 16:59:11","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54217,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic structure of the population based on ancestry proportions and 3D Principal Component Analysis (PCA) analysis.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6370929/v1/91b1d9ddc399cf882c75f3e9.jpg"},{"id":91362873,"identity":"93d85e2d-b40a-4f65-8885-aba384e96b7b","added_by":"auto","created_at":"2025-09-15 16:51:11","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":555268,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plots for Septoria nodorum blotch (SNB) and tan spot (TS) resistance across two experiments.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6370929/v1/32e74fc8b3276ede668ceedc.jpg"},{"id":91362542,"identity":"f6dc66a7-dc14-4971-8a55-feb1e8f9bf99","added_by":"auto","created_at":"2025-09-15 16:43:11","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":33858,"visible":true,"origin":"","legend":"\u003cp\u003eLinkage disequilibrium heatmap of significant MTAs on chromosome 5B (left) and 7B (right)\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6370929/v1/7ddde326ee6fb8fdd2ea94e6.jpg"},{"id":91362874,"identity":"9c75c680-4276-41aa-befd-e4a4a614cb14","added_by":"auto","created_at":"2025-09-15 16:51:11","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":54425,"visible":true,"origin":"","legend":"\u003cp\u003eHaplotype analysis of the quantitative trait loci (QTL) on chromosomes 5B for Septoria nodorum blotch (SNB) and 7B for tan spot (TS)\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6370929/v1/71cef6ccd04067c55199b52d.jpg"},{"id":91362539,"identity":"57feeaaf-4148-4803-85fa-f17beefd5650","added_by":"auto","created_at":"2025-09-15 16:43:11","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":46716,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of quantitative trait loci (QTL) count on resistance levels for Septoria nodorum blotch (SNB) and tan spot (TS)\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6370929/v1/2994c161777bd22c3e78ac32.jpg"},{"id":94490281,"identity":"593a134e-8806-462b-a48e-8ba71ab9a4d3","added_by":"auto","created_at":"2025-10-27 17:08:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1772444,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6370929/v1/800a26e2-1488-439b-a9c4-003b94539133.pdf"},{"id":91362536,"identity":"15cd540e-66e2-4523-aae5-ef360bf7cc7d","added_by":"auto","created_at":"2025-09-15 16:43:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":382732,"visible":true,"origin":"","legend":"","description":"","filename":"Supp.docx","url":"https://assets-eu.researchsquare.com/files/rs-6370929/v1/1e668ade72563ba8e677b3cb.docx"}],"financialInterests":"","formattedTitle":"Genome-wide association mapping for resistance against Septoria nodorum blotch and tan spot in a diverse wheat panel","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBread wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) is one of the most extensively cultivated and economically significant crops globally, contributing approximately 20% of the total human calory intake worldwide. The unique viscoelastic properties of wheat flour dough make it particularly suitable for the production of a wide range of breads and other baked products, contributing to its central role in global food security (Shewry, 2009; Shiferaw et al., 2013). However, grown on around 220 million hectares worldwide, wheat production faces constant threats from various diseases, including tan spot (TS) and Septoria nodorum blotch (SNB), which can severely reduce both yield and grain quality under disease-conducive conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTan spot, caused by the fungal pathogen \u003cem\u003ePyrenophora tritici-repentis\u0026nbsp;\u003c/em\u003e(\u003cem\u003ePtr\u003c/em\u003e), is a major foliar disease in wheat, resulting in necrotic lesions and/or chlorosis that reduce the photosynthetic capacity of the plant (Lamari \u0026amp; Bernier, 1991). It occurs in most wheat-growing regions around the world, including Europe, the Americas, Australia, and Asia. In severe cases, it can result in yield losses of up to about 50% and negatively affects grain quality (Schilder \u0026amp; Bergstrom, 1994; Shabeer \u0026amp; Bockus, 1988; Reynoso et al., 2023). The fungus persists on infected stubble remaining on the soil surface. Consequently, the widespread adoption of conservation tillage practices, which retain crop residues, has contributed to a rise in the incidence of TS in recent decades (Lamari \u0026amp; Strelkov, 2010).\u003c/p\u003e\n\u003cp\u003eAs for now, five host-selective toxins (HSTs) produced by Ptr have been identified: Ptr ToxA, Ptr ToxB, Ptr ToxC, and two newly discovered toxins, ToxE1 and ToxE2 (Rawlinson et al., 2024).\u0026nbsp;These toxins interact directly or indirectly with the products of the dominant host genes:\u0026nbsp;Ptr ToxA with \u003cem\u003eTsn1\u003c/em\u003e (Faris et al., 1996), Ptr ToxB with \u003cem\u003eTsc2\u003c/em\u003e (Friesen \u0026amp; Faris, 2004), and Ptr ToxC with \u003cem\u003eTsc1\u003c/em\u003e (Effertz et al., 2002).\u0026nbsp;The newly identified toxins, ToxE1 and ToxE2, have shown to induce chlorosis in a cultivar-specific manner, although their corresponding wheat sensitivity genes remain unknown (Rawlinson et al., 2024). A few qualitative genes were identified through the inoculation of conidial spores and were named \u003cem\u003eTsr1\u003c/em\u003e to \u003cem\u003eTsr5\u003c/em\u003e (Faris et al., 2013). Eight races of Ptr have been identified to date, producing different combinations of toxins (Faris et al., 2013).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe resistance to TS is genetically complex, controlled by multiple quantitative trait loci (QTL), which has posed challenges in traditional breeding programs (Faris et al., 2013). Recent research has revealed multiple QTL linked to TS resistance, offering valuable insights into candidate genes and molecular markers for marker-assisted selection (MAS). For example, a genome-wide association study (GWAS) identified 14 markers associated with TS resistance at the seedling stage on chromosomes 1AS, 2AL, 2BL, 3AS, 3AL, 3B, 6AS, and 6AL (Juliana et al., 2018). Based on 104 QTL reported in 15 previous mapping studies, Liu et al. (2020a) identified a total of 19 meta-QTL conferring resistance to TS, of which the ones on 2A, 3B, and 5A exhibited large effects and broad resistance spectrum.\u003c/p\u003e\n\u003cp\u003eSeptoria nodorum blotch (SNB) is caused by the necrotrophic fungal pathogen \u003cem\u003eParastagonospora nodorum\u003c/em\u003e (formerly \u003cem\u003eStagonospora nodorum\u003c/em\u003e), and poses a similar threat to global wheat yields, particularly in North America, Europe, and Australia. It leads to significant reductions in both yield and quality, with severe cases resulting in yield losses of up to 50% (Eyal, 1981; Bhathal et al., 2003). Like TS, SNB affects wheat leaves, causing necrosis, chlorosis, and premature senescence, thereby reducing the photosynthetic capacity and overall productivity (Solomon et al., 2006; Eyal, 1987).\u003c/p\u003e\n\u003cp\u003eNine HSTs have been identified in SNB, namely SnToxA, SnTox1, SnTox2, SnTox2A, SnTox3, SnTox4, SnTox5, SnTox6 and SnTox7, of which SnToxA shares high homology with Ptr ToxA and both interact with the same host gene, \u003cem\u003eTsn1\u003c/em\u003e (Friesen et al., 2006). Except for SnTox2A, which is associated with the QTL\u003cem\u003e\u0026nbsp;Qsnb.cur–2AS1\u003c/em\u003e (Pan et al., 2016), all other HSTs correspond to specific host genes: SnTox1 corresponds to the host gene \u003cem\u003eSnn1\u003c/em\u003e (Liu et al., 2004), SnTox2 to \u003cem\u003eSnn2\u0026nbsp;\u003c/em\u003e(Friesen et al., 2007), SnTox3 to \u003cem\u003eSnn3\u003c/em\u003e (Friesen et al., 2008), SnTox4 to \u003cem\u003eSnn4\u003c/em\u003e (Paillard et al., 2003), SnTox5 to \u003cem\u003eSnn5\u003c/em\u003e (Friesen et al., 2012), SnTox6 to \u003cem\u003eSnn6\u003c/em\u003e (Gao et al., 2015), SnTox7 to \u003cem\u003eSnn7\u003c/em\u003e (Shi et al., 2015).These eight HSTs are critical components of the \u003cem\u003eP. nodorum\u003c/em\u003e pathosystem, directly interacting with their corresponding host susceptibility genes mentioned above to trigger infection. Although SnTox2, SnTox6, and SnTox7 were initially thought to be distinct effectors targeting different sensitivity genes, Richards et al. (2022) recently revealed that they are the same protein, named SnTox267. It is important to mention that both TS and SNB follow the inverse gene-for-gene hypothesis and its implications (resistance is due to absence of susceptibility factors in the host).\u003c/p\u003e\n\u003cp\u003eIn recent years, numerous QTL associated with SNB resistance have been identified using GWAS. For instance, a GWAS on winter durum wheat identified seven QTL associated with SNB resistance, being located on chromosomes 1B, 2AL, 2DS, 4AL, 5BL, 6BS, and 7AL (AlTameemi et al., 2021). Another study conducted in Nordic wheat identified 11 QTL associated with SNB, including a robust QTL, \u003cem\u003eQSnb.nmbu-2AS\u003c/em\u003e, located on the short arm of chromosome 2A. However, only 15.7% of the lines (n = 296) carried this resistance QTL, suggesting its significant potential for enhancing SNB resistance through breeding efforts (Lin et al., 2022). Both studies demonstrated a clear trend of significantly enhanced SNB resistance levels with an increasing number of QTL present in the materials. Furthermore, a study using a more diverse germplasm source identified 20 QTL associated with SNB resistance, distributed across chromosomes 1A, 1B, 4B, 5A, 5B, 6A, 7A, 7B, and 7D. Most of these QTL were detected in only a single environment, further highlighting the genetic complexity of SNB resistance. (Francki et al., 2020).\u003c/p\u003e\n\u003cp\u003eThe objectives of the current study were to screen a diverse panel of bread wheat for seedling resistance against the two foliar diseases, and to identify the underlying genetic loci and molecular markers through GWAS for their utilization in wheat breeding.\u0026nbsp;\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e2.1 Plant material and genotyping\u003c/p\u003e\n\u003cp\u003eA panel of 150 CIMMYT bread wheat lines were used in the present study. These lines were pre-selected for heat and/or drought tolerance with additional considerations for phenology (height and days to heading).\u003c/p\u003e\n\u003cp\u003eGenomic DNA was extracted from young leaves according to the CTAB method described in the CIMMYT laboratory protocols (Dreisigacker et al., 2016). The panel was genotyped with Illumina Infinium 25K BeadChip at Trait Genetics GmbH, Germany.\u003c/p\u003e\n\u003cp\u003e2.2 Disease screening for TS and SNB\u003c/p\u003e\n\u003cp\u003eThe experiments were conducted at the seedling stage in a greenhouse, located in the El Batan experimental station of CIMMYT-Mexico, at 22 °C (day) and 18 °C (night) temperatures with a 16-h photoperiod. Two experiments were performed for each disease, with two replications for each experiment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor disease reaction screening, the Mexican Ptr isolate \u003cem\u003eMexPtr1\u003c/em\u003e and \u003cem\u003eP. nodorum\u003c/em\u003e isolate \u003cem\u003eMexSn4\u003c/em\u003e were used to inoculate plants for TS and SNB, respectively. Both isolates are known ToxA producers. The isolates were cultured on V8-PDA media (Lamari \u0026amp; Bernier, 1989), and spore concentrations were adjusted to 4 × 10\u003csup\u003e3\u003c/sup\u003e spores mL\u003csup\u003e−1\u003c/sup\u003e (\u003cem\u003eMexPtr1\u003c/em\u003e) and 1 × 10\u003csup\u003e7\u003c/sup\u003e spores mL\u003csup\u003e−1\u003c/sup\u003e (\u003cem\u003eMexSn4\u003c/em\u003e) for inoculation (Singh et al., 2007; Singh et al., 2016). Four seedlings were grown as experimental unit in plastic containers, and the mean disease scores were used for further analysis. Erik and Glenlea were included as resistant and susceptible controls, respectively. Inoculation was performed at approximately 14 days after sowing, when the second leaf was fully expanded. Inoculum was applied to the seedlings using a hand sprayer until runoff (approximately 0.5 mL per plant). After inoculation, trays were placed in a humid chamber (100% relative humidity, 20°C) to promote infection and returned to the greenhouse after 24 hours. Disease severity for both SNB and TS was scored on a linear scale of 1–5 at seven days post-inoculation (Feng et al., 2004; Hu et al., 2019), with resistance categories defined as follows: Resistant (R, 1.0–1.5), Moderately Resistant (MR, 1.6–2.5), Moderately Susceptible (MS, 2.6–3.5), and Susceptible (S, 3.6–5.0).\u003c/p\u003e\n\u003cp\u003e2.3 Genome-wide association analysis\u003c/p\u003e\n\u003cp\u003eThe genotype data was filtered using the R package dplyr (Wickham et al., 2023) with a minor allele frequency (MAF) cutoff of 0.05 and a missing rate of 0.1. Marker-trait associations (MTAs) were evaluated using the mixed linear model (MLM) in TASSEL 5.0 software (https://tassel.bitbucket.io/). Manhattan and Q-Q plots were generated with the R package CMplot (Yin et al., 2021), using R version 4.4.1 (https://www.r-project.org/). Visualization of 3D PCA plots was carried out with the scatterplot3d package (Ligges \u0026amp; Mächler, 2002) in R. Linkage disequilibrium (LD) heatmaps were produced using the R package LDheatmap (Shin et al., 2006). Population structure was analyzed using Admixture version 1.3.0 (Alexander et al., 2009) and visualized with the ggplot2 package in R (Wickham, 2011). An MTA was declared significant with a P-value threshold of\u0026nbsp;≤\u0026nbsp;0.001. Markers identified in two or more experiments were considered stable loci associated with the target trait.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1 Disease resistance\u003c/p\u003e\n\u003cp\u003eThe population exhibited significant variation in resistance to SNB and TS, with the majority lines showing good resistance (Figure 1). For SNB, 48 (32%) genotypes in Exp1 and 69 (46%) in Exp2 exhibited a resistant (R) response, whereas only 39 (26%) and 32 (21%), respectively, were susceptible (S). The remaining lines fell into the moderately resistant (MR) or moderately susceptible (MS) categories. For TS, 52 (35%) of the genotypes in Exp1 and 66 (44%) in Exp2 exhibited a resistant (R) response, whereas only 12 (8%) and 18 (12%), respectively, were susceptible (S). Some genotypes demonstrated high resistance to both TS and SNB, such as entries 9640 (RL84) and 9589 (ATTILA/BAV92//PASTOR) (Table 1). The two experiments demonstrated high repeatability for SNB, with a Pearson correlation coefficient of 0.84. For TS, the repeatability was slightly lower, with a Pearson correlation coefficient of 0.70. Given the relatively high repeatability observed in both experiments, the average phenotypic values across Exp1 and Exp2 were used in subsequent analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Genotypes with high levels of resistance to both Septoria nodorum blotch (SNB) and tan spot (TS).\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eEntry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 384px;\"\u003e\n \u003cp\u003eName or pedigree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eSNB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eTS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e9640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 384px;\"\u003e\n \u003cp\u003eRL84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e9589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 384px;\"\u003e\n \u003cp\u003eATTILA/BAV92//PASTOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e9644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 384px;\"\u003e\n \u003cp\u003eATTILA/3*BCN/3/CROC_1/AE.SQUARROSA (224)//OPATA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e9525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 384px;\"\u003e\n \u003cp\u003eSUPER 152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e9548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 384px;\"\u003e\n \u003cp\u003eMUNAL #1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e9628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 384px;\"\u003e\n \u003cp\u003eSLVS/3/CROC_1/AE.SQUARROSA (224) //OPATA/5/VEE/LIRA//BOW/3/BCN/4/KAUZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e9542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 384px;\"\u003e\n \u003cp\u003ePARA2//JUP/BJY/3/VEE/JUN/4/2*KAUZ/5/BOW/PRL//BUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e9562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 384px;\"\u003e\n \u003cp\u003eCIRO NL F2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e9584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 384px;\"\u003e\n \u003cp\u003eBOW/VEE/5/ND/VG9144//KAL/BB/3/YACO/4/CHIL/6/CASKOR/3/CROC_1/AE.SQUARROSA (224) //OPATA/7/PASTOR//MILAN/KAUZ/3/BAV92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e9598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 384px;\"\u003e\n \u003cp\u003ePASTOR//HXL7573/2*BAU/3/ATTILA/3*BCN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e9541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 384px;\"\u003e\n \u003cp\u003eW15.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e9641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 384px;\"\u003e\n \u003cp\u003ePASTOR//HXL7573/2*BAU/3/MEX94.2.19//ATTILA/3*BCN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e9551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 384px;\"\u003e\n \u003cp\u003eCMH79A.955/4/AGA/3/4*SN64/CNO67//INIA66/5/NAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e9521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 384px;\"\u003e\n \u003cp\u003eCROC_1/AE.SQUARROSA (224)//OPATA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e9588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 384px;\"\u003e\n \u003cp\u003ePASTOR//HXL7573/2*BAU/3/ATTILA/3*BCN/5/CROC_1/AE.SQUARROSA (205)//BORL95/3/PRL/SARA//TSI/VEE#5/4/FRET2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e9539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 384px;\"\u003e\n \u003cp\u003eSOKOLL/3/PASTOR//HXL7573/2*BAU/5/CROC_1/AE.SQUARROSA (205)//BORL95/3/PRL/SARA//TSI/VEE#5/4/FRET2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;R check\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 384px;\"\u003e\n \u003cp\u003eErik\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;S check\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 384px;\"\u003e\n \u003cp\u003eGlenlea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e3.2 Genotyping and SNP Density Analysis\u003c/p\u003e\n\u003cp\u003eThe 25K SNP array successfully scored 21,643 SNPs across the 21 pairs of chromosomes of wheat, of which 15,290 SNPs were retained for subsequent analysis according to the filtering criteria. The SNP density plot (Figure 2) reveals variation in marker density across chromosomes; overall, the SNP distribution was relatively even, providing comprehensive genomic coverage. On average, the A and B genomes carried ~1.33 SNPs/Mb, whereas the D genome carried only ~0.48 SNPs/Mb, indicating its substantially lower marker density. The distribution observed in this study aligns with findings from previous research, showing lower marker density in the centromeric regions and higher marker density in the distal regions of the chromosomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.3 Population structure analysis\u003c/p\u003e\n\u003cp\u003eThe population structure is shown in an admixture plot and a 3D PCA plot (Figure 3). The admixture plot revealed three distinct populations (V1, V2, and V3), where each individual\u0026rsquo;s genetic ancestry was represented as proportions across these populations. While some individuals showed pure ancestry, most exhibited mixed ancestry, suggesting gene flow between populations. \u0026nbsp;The 3D PCA plot showed that individuals were generally dispersed across the principal component space, indicating high genetic diversity within the population. However, subtle clustering was still evident, supporting the population structure identified in the admixture analysis.\u003c/p\u003e\n\u003cp\u003e3.4 Genome-wide association study\u003c/p\u003e\n\u003cp\u003eFor SNB, the Q\u0026ndash;Q plots showed well-calibrated test statistics with no evidence of inflation, supporting the reliability of the GWAS results (Figure S2). A total of 25 MTAs were detected in Exp1 and 27 MTAs in Exp2, resulting in the identification of 46 unique MTAs (Table S1) that are distributed across 11 chromosomes, specifically 1A, 2A, 2B, 3A, 3B, 3D, 4A, 5A, 5B, 6A and 7A. Notably, a significant peak was observed on chromosome 5B (Figure 4), represented by markers \u003cem\u003eAX-158525572\u003c/em\u003e and \u003cem\u003eIACX9261\u003c/em\u003e, demonstrating their strong associations with the SNB resistance across experiments and accounting for 6.99-16.79% of the phenotypic variance. This peak on chromosome 5B corresponds to the \u003cem\u003eTsn1\u003c/em\u003e gene region, known for its significant role in SNB resistance. Several other MTAs with notable contributions were also identified on different chromosomes, including \u003cem\u003eAX-94604036\u003c/em\u003e on chromosome 1A and \u003cem\u003eRAC875_c346_1226\u003c/em\u003e on chromosome 7A, explaining 11.79% and 10.44% of the phenotypic variance, respectively; however, these associations were significant in only one experiment.\u003c/p\u003e\n\u003cp\u003eFor TS, the Q\u0026ndash;Q plots also indicated well-controlled population structure and no overall inflation, supporting the robustness of the GWAS results (Figure S2). 11 MTAs were detected in TS Exp1 and 24 MTAs in TS Exp2, resulting in a total of 33 unique MTAs (Table S2), being located on chromosomes 2D, 3A, 4A, 5A, 5B, 5D, 6B, 7A, 7B, and 7D (Figure 4). Notably, markers \u003cem\u003eExcalibur_c81824_411\u003c/em\u003e and \u003cem\u003eRAC875_c8137_128\u003c/em\u003e on the long arm of chromosome 7B were consistently detected in both experiments, explaining phenotypic variations between 7.05-12.31% in the experiments. Several MTAs on other chromosomes also showed relatively high phenotypic effects, though they were detected in only a single experiment. For instance, \u003cem\u003eKukri_rep_c106477_365\u003c/em\u003e on chromosome 7D and \u003cem\u003eGENE-3572_70\u003c/em\u003e on chromosome 5D explained 11.79% and 11.19% of the phenotypic variance, respectively.\u003c/p\u003e\n\u003cp\u003eSeveral markers were detected in both experiments based on nominal \u003cem\u003eP\u003c/em\u003e values, suggesting their potential importance in the genetic control of SNB and TS (Table 2). Nevertheless, permutation-based empirical thresholds showed that most loci reached experiment-wise significance in only one environment. Additional significant markers are listed in supplementary Tables S1 and S2 for comprehensive reference.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Significant MTAs detected in both experiments for Septoria nodorum blotch (SNB) and tan spot (TS)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 157px;\"\u003e\n \u003cp\u003eMarker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 35px;\"\u003e\n \u003cp\u003eChr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 75px;\"\u003e\n \u003cp\u003ePosition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 140px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 105px;\"\u003e\n \u003cp\u003ePerm_P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 94px;\"\u003e\n \u003cp\u003ePVE (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eExp1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eExp2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eExp1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eExp2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eExp1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eExp2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eSNB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-158525572\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e546970656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e5.67E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.54E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e16.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e12.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eSNB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cem\u003eIACX9261\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e546704069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.07E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.89E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e16.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e13.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eSNB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-158525569\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e546672582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.68E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e6.67E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e15.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e12.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eSNB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cem\u003eAX-111509567\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e546826552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.68E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e6.67E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e15.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e12.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eSNB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cem\u003etplb0027f13_1493\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e546827810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.68E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e6.67E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e15.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e12.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eSNB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cem\u003eTdurum_contig25513_123\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e565753529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e7.73E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.87E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e15.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e11.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eTS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cem\u003eExcalibur_c81824_411\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e739931859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e9.00E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.51E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e7.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eTS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cem\u003eRAC875_c8137_128\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e745585191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e9.71E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.52E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e7.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e10.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.5 Grouping MTAs into QTL\u003c/p\u003e\n\u003cp\u003eThe MTAs for SNB on chromosome 5B were located within a 0.3 Mb chromosome region (near \u003cem\u003eTsn1\u003c/em\u003e), and LD analysis indicated that they are tightly linked (Figure 5). Thus, we designated this LD region as one QTL. Haplotype analysis revealed that allele combinations CGTATA and CGTATG at the markers \u003cem\u003eIACX9261\u003c/em\u003e, \u003cem\u003eAX-158525572\u003c/em\u003e, \u003cem\u003eAX-158525569\u003c/em\u003e, \u003cem\u003eAX-111509567\u003c/em\u003e, \u003cem\u003etplb0027f13_1493\u003c/em\u003e and \u003cem\u003eAX-108829232\u003c/em\u003e were associated with SNB resistance. Therefore, these two haplotypes were classified as the resistant group (R), while all other haplotypes were grouped as susceptible (S). A significant difference was observed between the two groups (p = 1.23e-06, Figure 6). Furthermore, linear regression analysis estimated that this QTL accounts for approximately 20% (R\u003csup\u003e2\u003c/sup\u003e =19.46%) of the phenotypic variance for SNB.\u003c/p\u003e\n\u003cp\u003eTo further clarify the relationship between the identified QTL and \u003cem\u003eTsn1\u003c/em\u003e, we genotyped all accessions using \u003cem\u003efcp623\u003c/em\u003e, the gene-specific marker for \u003cem\u003eTsn1\u003c/em\u003e. Among the 112 lines carrying the resistant haplotype, 108 (96.4%) carried the insensitive allele (\u003cem\u003etsn1\u003c/em\u003e, T:T), three carried the sensitive allele (\u003cem\u003eTsn1\u003c/em\u003e, C:C). Conversely, among the 38 lines carrying the susceptible haplotype, 27 (71.1%) carried the sensitive allele (\u003cem\u003eTsn1\u003c/em\u003e, C:C), while 11 carried the insensitive allele (\u003cem\u003etsn1\u003c/em\u003e). These results demonstrate a strong correspondence between the resistant haplotype and the insensitive allele (\u003cem\u003etsn1\u003c/em\u003e), indicating that the QTL near \u003cem\u003eTsn1\u003c/em\u003e is most likely explained by this locus. Overall, the resistant haplotype showed a very strong correspondence with the insensitive allele (\u003cem\u003etsn1\u003c/em\u003e), while the few inconsistencies were mainly observed in the susceptible haplotype group, likely reflecting variation in other loci that interact with \u003cem\u003eTsn1\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThe MTAs identified on chromosome 7B for TS were also tightly linked, with all markers except for \u003cem\u003eRAC875_c31791_559\u003c/em\u003e located within the physical range of 730.9 Mb to 750.0 Mb (Figure 6). Therefore, we designated this LD region as a QTL and named it\u003cem\u003e\u0026nbsp;Qts.cim-7BL\u003c/em\u003e. Haplotype analysis identified an allele combination CGGCTTGACAG at the markers \u003cem\u003eExcalibur_c81824_411\u003c/em\u003e, \u003cem\u003eRAC875_c8137_128\u003c/em\u003e, \u003cem\u003eRAC875_c34939_963\u003c/em\u003e, \u003cem\u003eAX-94748974\u003c/em\u003e, \u003cem\u003eAX-94393446\u003c/em\u003e, \u003cem\u003eRAC875_c14064_308\u003c/em\u003e, \u003cem\u003eAX-94467581\u003c/em\u003e, \u003cem\u003eRAC875_rep_c108382_824\u003c/em\u003e, \u003cem\u003eTG0119, RAC875_c34939_467\u003c/em\u003e and \u003cem\u003eBobWhite_c43557_103\u003c/em\u003e to be the resistant group (R), while all other haplotypes were categorized as the susceptible group (S). A significant difference was observed between the two groups (p = 7.07e-06, Figure 6). Furthermore, linear regression analysis estimated that this QTL accounted for 12.84% of the phenotypic variance for TS.\u003c/p\u003e\n\u003cp\u003eIn addition to the two QTL mentioned above, we identified two additional QTL for SNB on chromosomes 2A and 7A, and four additional QTL for TS on chromosomes 3A, 5A, 7A and 7D. (Table S3, Figure S2). However, these QTL were only detected in a single experiment. However, there is a clear trend of increased resistance to SNB and TS with a higher number of QTL, indicating their positive correlation (Figure 7).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, among all tested lines, only one line (9511) carries all eight QTL, yet it exhibited relatively poor resistance to SNB, with a disease score of 2.9. In comparison, lines 9521, 9539, 9551, and 9598 each carry seven of these eight QTL and demonstrated good level of resistance. Specifically, their resistance scores were 1.0, 1.0, 1.0, and 1.1 for SNB, and 1.0, 1.2, 1.1, and 1.1 for TS, respectively. These lines could be considered valuable parental materials for improving SNB and TS resistance in future breeding programs.\u003c/p\u003e\n\u003cp\u003eCandidate genes associated with disease resistance have been identified within a 2-Mb window harboring the identified QTL, except for the one corresponding to \u003cem\u003eTsn1\u003c/em\u003e on chromosome 5BL (Table S4). These genes encode a variety of proteins, including NB-ARC domain-containing protein, disease resistance N-terminal domain-containing protein, Rx N-terminal domain-containing protein, disease resistance protein RPM1, among others.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe identification of genetic loci associated with resistance to SNB and TS through GWAS provides valuable insights into the genetic architecture of disease resistance in wheat. In this study, we identified multiple significant loci across chromosomes, with a number showing strong effects, especially the QTL on chromosomes 5B for SNB and on 7B for TS resistance. Notably, the former is located in close proximity to the well-characterized \u003cem\u003eTsn1\u003c/em\u003e locus, which has been extensively linked to susceptibility to ToxA (Faris et al., 2010). It is noteworthy that all SNB resistant lines in this study contained this QTL, suggesting that \u003cem\u003eTsn1\u003c/em\u003e may play a significant role in conferring SNB susceptibility in our population. Unexpectedly, this region was only significant for SNB but not for TS, which could be due to the presence of an epistatic gene that masked the interaction between \u003cem\u003eTsn1\u003c/em\u003e and ToxA. Such a case was observed in Kariyawasam et al. (2016), where a QTL on chromosome 3B exhibited epistasis over the Ptr ToxA-\u003cem\u003eTsn1\u003c/em\u003e interaction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevious studies have identified a few significant loci for TS on chromosome 7B. Laribi et al. (2023) identified an MTA for TS at 453.4 Mb in durum wheat, while Kokhmetova et al. (2021) mapped significant loci at approximately 538.1 Mb in Kazakh hexaploid wheat. Additionally, a QTL has been reported on the short arm of chromosome 7B (Faris et al., 2012; Singh et al., 2019). In our study, \u003cem\u003eQts.cim-7BL\u003c/em\u003e was located at approximately 740 Mb, suggesting that this could be a novel QTL conferring resistance to TS. Moreover, all R lines identified in our study contained this QTL, demonstrating its significant role in conferring TS resistance in the studied germplasm.\u003c/p\u003e\n\u003cp\u003eIn addition to the two QTL mentioned above, the remaining QTL we identified were detected in only a single experiment. This suggests that the effectiveness of QTL may be influenced by various factors. Among these QTL, \u003cem\u003eQts.cim.3AS\u003c/em\u003e is likely the same as the QTL \u003cem\u003eRP696_86-124_3A\u003c/em\u003e reported by Liu et al. (2020b) and \u003cem\u003eQTs.fcu-3A\u003c/em\u003e reported by Chu et al. (2010), based on the overlapped physical regions. The remaining QTL identified in this study have not been previously reported in association with SNB or TS resistance, suggesting they may represent novel loci specific to this population.\u003c/p\u003e\n\u003cp\u003eOur results demonstrate a clear positive correlation between the number of QTL and resistance levels for both SNB and TS, consistent with previous studies (Lin et al., 2021; Navathe et al., 2023). However, one line (9511) carried all eight identified QTL (three for SNB and five for TS) but exhibited only moderate resistance to SNB (score 2.9), despite showing strong resistance to TS (score 1.2). This exception suggests that the presence of multiple QTL does not always translate into high resistance, likely due to gene–environment interactions, disease-specific effects, or the presence of specific yet unknown gene interactions. Overall, the positive correlation observed in most cases indicates that cumulative effects of multiple resistance loci can significantly enhance disease resistance.\u003c/p\u003e\n\u003cp\u003eFuture research should prioritize validating these QTL across diverse environments to evaluate their stability and practical utility. The positive correlation observed between QTL count and resistance levels suggests that stacking multiple resistance loci could be a promising strategy to enhance disease resistance in wheat breeding. Additionally, as some QTL identified in this study appear to be novel, further functional analyses are needed to elucidate their roles and assess their potential in resistance breeding programs. Expanding this research through multi-location trials and employing advanced genomic tools will be essential for translating these findings into practical solutions for sustainable wheat production.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted without commercial or financial relationships that could be construed as potential conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo human/animal studies are presented. No potentially identifiable human images or data are presented in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting the findings of this study are available within the article and its supplementary materials. Additional datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDW collected and analyzed the data and drafted the initial version of the manuscript. XH contributed to disease evaluation, data analysis, and revised the manuscript. ZD revised the manuscript. MR developed the plant materials. SD performed genotyping and revised the manuscript. PKS conceptualized the study and administered the project. All authors reviewed and approved the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinancial support from the China Scholarship Council, the Bill and Melinda Gates Foundation, USAID, and One CGIAR Initiatives-ABI and PHI for conducting this research is gratefully acknowledged.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlexander, D. H., Novembre, J., \u0026amp; Lange, K. (2009). Fast model-based estimation of ancestry in unrelated individuals. Genome Research, 19(9), 1655-1664.\u003c/li\u003e\n \u003cli\u003eAlTameemi, R., Gill, H. S., Ali, S., Ayana, G., Halder, J., Sidhu, J. S., Gill, U. S., Turnipseed, B., Hernandez, J. L., \u0026amp; Sehgal, S. K. (2021). Genome-wide association analysis permits characterization of Stagonospora nodorum blotch (SNB) resistance in hard winter wheat. 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Genomics, Proteomics and Bioinformatics, 19(4), 619-628.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":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":"","lastPublishedDoi":"10.21203/rs.3.rs-6370929/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6370929/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Septoria nodorum blotch (SNB) and tan spot (TS) are two globally significant foliar diseases affecting wheat, causing substantial reductions in both yield and quality. In this study, genome-wide association study (GWAS) was conducted using an elite diversity panel comprising 150 lines to identify genetic loci associated with resistance to SNB and TS. Resistance was evaluated in greenhouse experiments at the seedling stage, with two replicates for each disease. For SNB, the majority of lines demonstrated good level of resistance, with 53% rated as resistant or moderately resistant (R/MR). Similarly, for TS, 60% of the lines exhibited R/MR resistance. Some lines exhibited high resistance to both SNB and TS. The panel was genotyped with the Illumina Infinium 25K BeadChip. GWAS revealed several significant marker-trait associations on chromosome 5B associated with SNB resistance, all of which were located in the vicinity of the Tsn1 gene, suggesting its important role in conferring SNB resistance within this population. In addition, two quantitative trait loci (QTL) on chromosomes 2AL and 7AS were identified. For TS, significant markers were primarily found within a 20 Mb region on the long arm of chromosome 7B, with phenotypic variation explained ranging from 8.34% to 12.31%. Additional TS QTL with minor effects were identified on chromosomes 3A, 5A, 7A, and 7D. These resistant lines and identification of markers for SNB and TS resistance hold potential for use in wheat breeding programs aimed at improving resistance to the two diseases.","manuscriptTitle":"Genome-wide association mapping for resistance against Septoria nodorum blotch and tan spot in a diverse wheat panel","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-15 16:43:06","doi":"10.21203/rs.3.rs-6370929/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accept","date":"2025-09-08T06:18:36+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-09-04T00:57:21+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-02T13:45:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-02T10:16:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Breeding","date":"2025-08-29T16:56:03+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":"6be5753f-b450-40e9-a32c-0f0f8e6d63b7","owner":[],"postedDate":"September 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T16:23:47+00:00","versionOfRecord":{"articleIdentity":"rs-6370929","link":"https://doi.org/10.1007/s11032-025-01602-z","journal":{"identity":"molecular-breeding","isVorOnly":false,"title":"Molecular Breeding"},"publishedOn":"2025-10-23 16:16:25","publishedOnDateReadable":"October 23rd, 2025"},"versionCreatedAt":"2025-09-15 16:43:06","video":"","vorDoi":"10.1007/s11032-025-01602-z","vorDoiUrl":"https://doi.org/10.1007/s11032-025-01602-z","workflowStages":[]},"version":"v1","identity":"rs-6370929","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6370929","identity":"rs-6370929","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}