Multi-isolate GWAS identifies broad-spectrum and isolate-specific Septoria tritici blotch resistance loci in synthetic hexaploid wheat

Multi-isolate GWAS identifies broad-spectrum and isolate-specific Septoria tritici blotch resistance loci in synthetic hexaploid wheat | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi-isolate GWAS identifies broad-spectrum and isolate-specific Septoria tritici blotch resistance loci in synthetic hexaploid wheat Anisa Blower, Richard Horsnell, Phil M. Howell, Tally I. C. Wright, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9451480/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Septoria tritici blotch (STB), caused by Zymoseptoria tritici , remains a major constraint on wheat production worldwide. Erosion of host resistance and declining fungicide efficacy highlight the need to exploit the new sources of resistance, including synthetic hexaploid wheat (SHW). Here, we used a three-family nested association mapping (NAM) population derived from Niab SHW donors (SHW.035, SHW.054, and SHW.075) crossed with the elite UK variety Robigus to dissect the genetic basis of STB resistance under controlled conditions. Seedlings were challenged with five Z. tritici isolates differing in virulence, and disease progress was quantified separately as necrosis (AUDPC_N) and pycnidia coverage (AUDPC_P). Many SHW-derived lines showed strong suppression of pycnidia despite visible necrosis, indicating partial genetic decoupling of pathogen reproduction and host tissue damage. Genome-wide association analysis identified a major broad-spectrum resistance locus on chromosome 3D ( qSTB-3D.1 ), contributed by SHW.035, that co-localised with the Stb16q region. Additional isolate-specific loci were detected on chromosomes 1B, 2D, 3A, and 6D. In an F 2 population derived from a resistant SHW.035-derived NAM line, suppression of pycnidia segregated as a single dominant factor, whereas necrosis showed quantitative inheritance. These findings show that Niab SHWs are a valuable source of STB resistance and highlight the potential to breed for reduced pathogen reproduction independently of visible leaf damage. Wheat Disease resistance Septoria tritici blotch Synthetic hexaploid wheat Nested association mapping Genome-wide association study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Key message Synthetic hexaploid wheat harbours broad-spectrum and isolate-specific Septoria tritici blotch resistance loci, with partly independent genetic control of pycnidia suppression and necrosis. Introduction Zymoseptoria tritici , previously known as Septoria tritici and Mycosphaerella graminicola , is a filamentous ascomycete fungus and the causal agent of Septoria tritici blotch (STB) in wheat. The disease is particularly prevalent in temperate regions with cool, humid climates, including Northern Europe and North America, where severe epidemics can reduce grain yield and quality by up to 50% in susceptible varieties under favourable conditions (Eyal 1987; Fones et al. 2015; Castro et al. 2017). Disease management is further complicated by the large and genetically diverse populations of Z. tritici , which promote the rapid evolution of fungicide resistance and the breakdown of host resistance genes (McDonald et al. 2022 ). In Europe, STB management accounts for approximately 70% of the total fungicide market in wheat, at an estimated annual cost of €1.0 billion (Torriani et al. 2015 ). However, the effectiveness of fungicide-based control is declining due because of the widespread emergence of fungicide resistance, including reduced sensitivity to azoles (Heick et al. 2020 ; Jørgensen et al. 2021 ), strobilurins (Torriani et al. 2009 ), and succinate dehydrogenase inhibitors (Dooley et al. 2016 ; Hagerty et al. 2017 ; Hagerty et al. 2021 ). Consequently, STB remains a major challenge to sustainable wheat production. The development of new wheat varieties with durable genetic resistance, combined with integrated disease management, is therefore widely regarded as the most effective long-term strategy for STB control. Twenty-three major STB resistance ( Stb ) genes have been identified in wheat (Brown et al. 2015 ; Yang et al. 2018 ; Langlands-Perry et al. 2021 ), in addition to more than 160 resistance-associated quantitative trait loci (QTL) (Brown et al. 2015 ). Several qualitative resistance genes have been cloned, including Stb6 , encoding a wall-associated kinase-like protein (WAK) (Saintenac et al. 2018 ), Stb16q , encoding a cysteine-rich receptor-like kinase (CRK) (Saintenac et al. 2021 ), and Stb15 , encoding a lectin receptor-like kinase (LecRK) (Hafeez et al. 2025 ). While many Stb genes confer isolate-specific resistance, some, including Stb10 , Stb11 , Stb12 , Stb16q , Stb17 , and Stb19 , provide resistance to diverse field isolates, with synthetic hexaploid wheat (SHW) emerging as a particularly promising source of such resistances (Tidd et al. 2023). SHW, generated by hybridising tetraploid durum wheat with the wild D-genome progenitor Aegilops tauschii (Wright et al. 2024 ), has enabled the transfer of drought tolerance (Mokhtari et al. 2022 ) and disease resistance (Ogbonnaya et al. 2008 ; Jighly et al. 2016 ; Blower et al. 2025 ) from wild relatives into bread wheat. Several STB resistance loci, including Stb5 (Arraiano et al. 2001 ), Stb8 (Adhikari et al. 2003 ), Stb16q (Saintenac et al. 2021 ), and Stb17 (Ghaffary et al. 2012 ), were identified in SHW backgrounds, underlining the value of SHW as a reservoir of useful alleles for STB resistance improvement. To dissect complex resistance traits derived from exotic germplasm, structured multi-parent populations such as nested association mapping (NAM) populations provide high mapping resolution while reducing confounding effects of population structure. NAM populations combine multiple donor genomes within a common elite background, enabling the detection of both major and minor loci (Yu et al. 2008 ). Using an SHW-derived NAM population developed from crosses with the elite UK variety Robigus, Wright et al. ( 2024 ) identified novel SHW-derived variation for flowering time, plant height, and yellow rust resistance. In the present study, a subset of this SHW-derived NAM population (Wright et al. 2024 ) was used to investigate the genetic basis of STB resistance by combining artificial seedling inoculation assays with genome-wide association analysis. The Z. tritici isolate panel was selected on the basis of prior virulence profiling against differential wheat lines carrying major Stb genes and their combinations and was collectively virulent on 19 of 23 characterised resistance genes (Tidd et al. 2023), enabling evaluation of resistance breadth against diverse pathogen genotypes. Although resistance expressed at the adult plant stage can involve additional loci and environmental interactions (Thauvin et al. 2024 ), controlled seedling assays provide a robust and reproducible framework for resolving isolate-specific resistance loci under defined conditions. These findings provide a foundation for the targeted deployment of SHW-derived diversity to improve STB resistance Materials and Methods Plant material and population development A three-family nested association mapping (NAM) population was used to investigate genetic resistance to Zymoseptoria tritici in hexaploid wheat. The population was developed from crosses between the elite UK winter wheat variety Robigus and three primary synthetic hexaploid wheat (SHW) lines: SHW.035, SHW.054, and SHW.075. Population development followed a backcrossing and single-seed descent (SSD) strategy, as described in detail by Wright et al. ( 2024 ). The final population consisted of three nested families (Table 1 ) comprising 201 backcross-derived lines. All 201 NAM lines were genotyped at the BC 1 F 5 generation, together with the four parents: the recurrent parent Robigus and the three SHW donor parents. Phenotypic evaluations were conducted at the BC 1 F 7 generation on 180 NAM lines, together with five reference lines: Robigus, the three SHW donor parents, and the highly susceptible UK winter wheat variety KWS Cashel. Genetic analyses were conducted on 171 NAM lines, together with the four parents, all of which had genotype and phenotype data of sufficient quality for genetic analysis, as described below. Fungal material Five Z. tritici isolates were selected from a panel of 93 previously characterised UK field isolates (Tidd et al. 2023). These isolates were originally cultured from STB lesions collected from hexaploid wheat grown in the UK between 2015 and 2017. The isolates used in this study were HT-18, HT-44, HT-53, HT-74, and HT-96. All isolates were maintained as pure blastospore stocks in 50% (v/v) glycerol and stored at − 80°C. Inoculum preparation and inoculation Susceptibility of wheat lines was assessed using a method adapted from Tidd et al. (2023). Z. tritici isolates were streaked from glycerol stocks onto antibiotic-free yeast peptone dextrose (YPD) agar (Formedium) and incubated in the dark for 5–7 days at 15°C before inoculation. Fungal blastospores were harvested from plates using sterile loops and transferred to tubes containing 5 mL sterile distilled water. The spore suspension was filtered through autoclaved Miracloth (Merck). Spore concentration was determined using a haemocytometer from the mean of four replicated counts, and suspensions were adjusted to 5 x 10 6 spores per mL before inoculation. Tween 20 was added to a final concentration of 0.05% v/v to improve leaf wetting, reduce spore aggregation, and facilitate stomatal entry. Wheat seedlings were grown for approximately 3 weeks under controlled conditions before inoculation. Leaf 2 was fixed to an aluminium platform with the adaxial surface facing upwards and secured using rubber bands and double-sided sticky tape. Cotton buds soaked in the spore suspension were used to distribute inoculum evenly across the leaf surface. A sterile solution of 0.05% v/v Tween 20 in distilled water was used to mock inoculation as a negative control. KWS Cashel, a wheat variety lacking established Stb resistance genes, was used as a susceptible control. After inoculation, each half-tray containing inoculated plants was placed in a high-humidity box for three days. For each wheat genotype × Z. tritici isolate interaction, leaves from a minimum of four individual plants were inoculated. Plants were maintained for 29 days post inoculation (dpi) to allow full assessment of disease development. Phenotyping was conducted in an average of twelve independent experimental batches per isolate, with approximately 5–7 plants per genotype × isolate combination in each batch. Plants were screened without randomisation. Phenotypic analysis Visual phenotypic trait evaluation Disease symptom development on inoculated leaves was scored visually at 2–3-day intervals from 10- to 29-dpi. Percentage leaf coverage by necrosis, chlorosis, and pycnidia was scored in 20% increments from 0 to 100%. Representative examples illustrating necrosis and pycnidia are shown in Fig. 1 . The area under the disease progress curve (AUDPC) was calculated for each plant–pathogen interaction according to Simko et al. (2012): $$\:AUDPC=\:\frac{\sum\:({t}_{i+1}-{t}_{i})({y}_{i}+{y}_{i+1})}{2}$$ where y i and y i+1 are the percentages of disease severity at observations i and i + 1 and ( \(\:{t}_{i+1}-{t}_{i}\) ) is the number of days between observations. AUDPC values were calculated separately for necrosis (AUDPC_N), and pycnidia (AUDPC_P). The same scoring schedule was applied across all experimental batches. Non-parametric analysis of AUDPC data AUDPC values for pycnidia (AUDPC_P) and necrosis (AUDPC_N) were analysed using non-parametric statistical methods because the data were non-normally distributed and showed heterogeneity of variance. All analyses were conducted in R v.4.3.2 (R Core Team 2021 ). To assess isolate-specific virulence across contrasting host backgrounds, AUDPC values were analysed separately for three host groups: the susceptible control variety KWS Cashel, the susceptible parent Robigus, and the SHW founders together with their derived NAM lines. For each host group and trait, overall differences among the five Z. tritici isolates were tested using Kruskal-Wallis rank-sum tests. Where significant global effects were detected ( p < 0.05), pairwise isolate comparisons were performed using Dunn’s post hoc test with Benjamini-Hochberg correction. Family-level differences in disease severity were analysed using the same approach, comparing AUDPC values across eight genetic groups comprising the susceptible controls, the SHW founders, and the three NAM families. Estimation of genotypic effects (BLUEs) Best linear unbiased estimates (BLUEs) were calculated for necrosis (AUDPC_N) and pycnidia development (AUDPC_P) using linear mixed models implemented in lme4 (Bates et al. 2015 ), with inference supported by lmerTest (Kuznetsova et al. 2017 ) and marginal means obtained using emmeans (Lenth 2023 ). For BLUE estimation, genotype was fitted as a fixed effect to obtain adjusted genotype means for each isolate. BLUEs were estimated separately for each Z. tritici isolate (HT-18, HT-44, HT-53, HT-74, and HT-96) using the following mixed-effects model: $$\:{y}_{ij}=\mu\:+{g}_{i}+{B}_{j}+(g\times\:B{)}_{ij}+{e}_{ij}$$ where \(\:{y}_{ij\:}\) is the AUDPC value of the i- th genotype in the j- th batch, \(\:\mu\:\:\) is the overall mean, \(\:{g}_{i}\:\) is the fixed effect of genotype, \(\:{B}_{j}\) is the random effect of batch, \(\:(g\times\:B{)}_{ij}\) is the random genotype-by-batch interaction, and \(\:{e}_{ij}\) is the residual error term. Models were fitted using restricted maximum likelihood (REML), and BLUEs were extracted using emmeans. For each isolate, BLUEs were calculated from untransformed AUDPC values and used in downstream analyses. Relationships among BLUEs were evaluated using Spearman’s rank correlation coefficients. Correlation analysis and significance testing ( p < 0.05) were performed using Hmisc (Harrell et al. 2025 ) and visualised with corrplot (Wei et al. 2024). For heritability estimation, the same model structure was used, but with genotype fitted as a random effect, enabling extraction of best linear unbiased predictors (BLUPs) and the corresponding variance components. Broad-sense heritability estimation Broad-sense heritability ( \(\:{H}^{2}\) ) was estimated separately for each isolate and trait using variance components from the mixed model, with genotype treated as a random effect to obtain BLUPs and \(\:{\sigma\:}_{G}^{2}\) . Heritability was calculated using the generalised heritability approach of Cullis et al. ( 2006 ): $$\:{H}_{\text{Cullis}}^{2}=1-\frac{\stackrel{\text{⃐}}{\text{PEV}}}{2{\sigma\:}_{G}^{2}}$$ where \(\:\stackrel{\text{⃐}}{\text{PEV}}\:\) is the mean prediction error variance of pairwise differences between genotype BLUPs, and \(\:{\sigma\:}_{G}^{2}\:\) is the estimated genotypic variance. This approach provides robust heritability estimates under unbalanced experimental design by accounting for unequal replication and missing data. All heritability analyses were conducted in R using sommer (Covarrubias-Pazaran 2016 ). Genotypic analysis DNA extraction and genotyping Genomic DNA was previously extracted from seedling leaf tissue of the NAM population and parental lines using a modified CTAB protocol (Fulton et al. 1995 ) as described in Wright et al. ( 2024 ). Genotyping was conducted at the University of Bristol using the Axiom 35K Wheat Breeders’ SNP array (Thermo Fisher Scientific), which assays 35,143 SNP markers (Allen et al. 2017 ). All 201 NAM lines were genotyped at the BC 1 F 5 generation, together with their recurrent parent Robigus and the three SHW donor parents, SHW.035, SHW.054, and SHW.075. Initial genotype calling and marker filtering Genotype calling and initial quality control were performed in Axiom Analysis Suite v.5.4 (Thermo Fisher Scientific) following procedures adapted from Wright et al. ( 2024 ). An inbred penalty of 4 was applied to account for the highly inbred nature of the material. Samples failing standard quality thresholds (DishQC ≥ 0.80, call rate ≥ 95%) were excluded. Markers were filtered on the basis of Axiom cluster classification and genotype class representation. Monomorphic markers, markers with poor call rates, and off-target variants were excluded. Segregating markers were retained only when clear genotype clustering was observed, and the minor genotype class was represented by at least eight individuals. All retained markers were visually inspected to confirm clustering quality, and markers showing ambiguous clustering were discarded. Following these filtering steps, the dataset comprised 8,868 SNP markers across 202 genotypes that passed the quality control thresholds. These 202 genotypes included NAM lines with genotype files available, Robigus, and the three SHW donor parents, and were counted before the removal of additional genotypes during downstream quality control steps outside Axiom Analysis Suite. Downstream quality control and imputation Further genotype quality control was conducted in R v.4.3.2 (R Core Team 2021 ). SNP markers were removed if they exhibited > 7% missing data, > 7% heterozygosity, or fewer than 12 individuals homozygous for the minor allele ( Supplementary Fig. S1 ). Genotypes exceeding the same thresholds for missing data or heterozygosity were also excluded. Principal coordinate analysis (PCoA) was used to identify potential outlier genotypes, both across the full NAM population and within each nested family ( Supplementary Fig. S2 ). The Pearson correlation coefficients among genotype pairs were used to detect highly similar or potentially duplicated samples, including selfed individuals or lines excessively similar to parental controls. Erroneous genotypes were identified and removed. For the remaining lines, missing genotype data were imputed using the random forest algorithm implemented in missForest (Stekhoven et al. 2012), using 200 trees per forest. After imputation, the final dataset comprised 8,040 SNP markers across 187 NAM genotypes, together with four parental controls. Physical marker positioning and LD-based reordering Physical positions for 5,327 SNP markers were obtained from the IWGSC RefSeq v.1.0 wheat genome assembly (IWGSC 2018) using coordinates from the dataset described by Wright et al. ( 2024 ). For the remaining 2,714 unmapped markers, SNP probe sequences from the Axiom 35K Wheat Breeders’ array were retrieved from CerealsDB (Wilkinson et al. 2016 ) and aligned to the reference genome using BLAST+ (Camacho et al. 2009 ). For markers with a single high-confidence BLAST hit, physical positions were assigned as the midpoint between alignment coordinates, enabling placement of 496 additional markers. Remaining unmapped markers were anchored using linkage disequilibrium (LD) with mapped markers, assigning positions when strong LD ( R ² > 0.7) and consistent BLAST hits supported a chromosomal location. Marker placement was further refined using LD-based binning ( R ² > 0.5) to improve collinearity. Markers lacking reliable placement were excluded from downstream analyses. Marker ordering and map consistency were evaluated using LD heatmaps ( Supplementary Fig. S3, S4 ) were generated using LDheatmap (Shin et al. 2006 ). After marker positioning and filtering, 7,032 SNP markers were retained for analysis. Population structure analysis Population structure within the NAM population, including the recurrent parent Robigus and the SHW founders, was assessed using genome-wide SNP data. To reduce marker redundancy, markers were thinned by removing one marker from each pair with strong correlation (| r | ≥ 0.9). Population structure was visualised using principal component analysis (PCA) based on a genetic distance matrix calculated from the filtered marker set. Analyses were conducted in R and plots were generated using ggplot2 (Wickham 2016 ). Principal components and pedigree information were used to define covariates included in the genome-wide association study (GWAS). Genome-wide association study (GWAS) GWAS was performed on 171 phenotyped NAM lines, for which high-quality genotype data were available, together with the four parental controls ( Supplementary Table S1 ). GWAS was performed using an additive Q + K mixed model implemented in GWASpoly (Rosyara et al. 2016 ). Population structure (Q) was accounted for using fixed-effect covariates corresponding to family and tetraploid donor, together with the first ten principal components ( Supplementary Fig. S5 ), while genetic relatedness (K) was controlled using a marker-derived kinship matrix estimated with the GWASpoly function set.K . Leave-one-chromosome-out (LOCO) correction was not applied. Prior to GWAS, markers were skimmed to remove non-unique markers (| r | = 1) to reduce redundancy and avoid inflation of kinship estimates ( Supplementary Table S2 ). Model suitability was assessed by inspection of quantile–quantile (QQ) plots and estimation of genomic inflation factors ( λ ; Devlin et al. (1999)), and the final model settings were applied consistently across all traits. Significance thresholds were determined using the GWASpoly set.threshold function, applying both false discovery rate (FDR; q = 0.05) (Benjamini et al. 1995) and permutation-derived thresholds based on 1,000 permutations ( α = 0.05) (Churchill et al. 1994). Candidate QTLs were defined based on −log10( p ) values, allele effect direction (using Robigus as the reference allele), and physical marker position. LD between markers was calculated as r² using the GWASpoly LD.plot function. LD decay was assessed by plotting r² against physical distance, and the LD decay threshold was defined as the physical distance at which r² declined to 0.2 ( Supplementary Fig. S6 ). Final candidate QTLs were defined as the most significant SNP within ± LD-decay distance (Mb) of each association peak. Identification of candidate genes Candidate genes underlying significant QTLs were identified by analysing gene content within QTL intervals defined by the outermost significant flanking SNPs and extended to include one additional marker on each side. Physical coordinates were based on the IWGSC RefSeq v.1.1 wheat genome assembly. Gene models were retrieved from the Ensembl Plants BioMart database using biomaRt (Durinck 2009 ), extracting gene identifiers, genomic coordinates, functional descriptions, and InterPro domain annotations. Candidate genes were prioritised on the basis of resistance-associated domains, including nucleotide-binding leucine-rich repeat proteins (NLRs), wall-associated kinase-like proteins (WAKs), cysteine-rich receptor-like kinases (CRKs), lectin receptor-like kinases (LecRKs), leucine-rich repeat receptor-like kinases (LRR-RLKs), receptor-like proteins (RLPs), and other kinase-containing proteins, whereas genes lacking diagnostic domains were classified as other or unknown. Development and screening of the F 2 mapping population To investigate the genetic architecture of resistance identified in the NAM.144 family, genotype SHW.BC.144.12.1.4.1 (BC₁F₇; SHW.035 × Robigus) was selected as the resistant parent on the basis of its consistent resistance to multiple Z. tritici isolates. SHW.BC.144.12.1.4.1 was crossed with the susceptible parent Robigus (female) under glasshouse conditions, and F₁ plants were selfed to produce an F₂ population. A total of 256 F 2 seedlings, derived from a single crossing event, were evaluated for resistance alongside the parental controls SHW.BC.144.12.1.4.1 (resistant parent) and Robigus (susceptible parent). The population was inoculated with Z. tritici isolate HT-74 and disease progression was assessed at 29-dpi. Phenotyping was conducted in a single experimental batch, with twelve parental control plants included. Quantitative phenotypic data recorded at 29-dpi were converted to binary classes based on parental phenotype distributions. Plants with < 20% leaf coverage were classified as resistant for pycnidia, and plants with ≤ 20% leaf coverage were classified as resistant for necrosis. Segregation ratios for each trait were tested against expected Mendelian ratios using a chi-squared ( χ² ) goodness-of-fit test. Results Phenotypic analysis of resistance to Z. tritici Resistance to STB was assessed in a three-family NAM population under controlled growth-room conditions following separate inoculations with five Z. tritici isolates (HT-18, HT-44, HT-53, HT-74, and HT-96). Disease progression was assessed at several timepoints between 10- and 29-dpi by scoring the percentage leaf area covered by pycnidia and necrosis. These scores were used to calculate area under the disease progress curve values for pycnidia (AUDPC_P) and necrosis (AUDPC_N). Isolate-specific virulence across host backgrounds Clear isolate-specific differences in virulence were observed across host genetic backgrounds ( Fig. 2 ), with a broadly consistent virulence ranking across genotypes. Successful inoculation was confirmed for all five isolates, each of which induced substantial infection on the susceptible control variety KWS Cashel ( Fig. 2 ). In the susceptible parent Robigus, significant differences in virulence among isolates were observed for both pycnidia and necrosis ( Fig. 2b,e ). HT-74 was the most virulent isolate for pycnidia, whereas HT-96 consistently induced the lowest levels. Differences in necrosis were less pronounced, with only HT-74 inducing greater necrosis than HT-96. Across the combined SHW founders and NAM families, significant isolate-specific differences were also observed for both pycnidia and necrosis ( Fig. 2c,f ). Pycnidia development followed a consistent virulence hierarchy across these genetic backgrounds (HT-74 > HT-53 > HT-44 > HT-18 > HT-96), with HT-74 inducing significantly greater AUDPC_P than all other isolates ( Fig. 2c ). Necrosis severity also differed significantly among isolates, although separation was less marked than for pycnidia; HT-44 and HT-74 generally induced the highest AUDPC_N values, whereas HT-18- and HT-96-induced necrosis were among the least severe ( Figure 2f ). NAM family-level variation in pycnidia and necrosis severity To evaluate the contribution of genetic wheat background to STB resistance, AUDPC_P and AUDPC_N were compared across eight genetic groups: KWS Cashel, Robigus, three SHW founders (SHW.035, SHW.054, and SHW.075), and three corresponding SHW-derived NAM families (NAM.144, NAM.212, and NAM.233) ( Fig. 3 ). Highly significant differences among genetic groups were detected for both pycnidia development and necrosis ( Fig. 3 ). As expected, the susceptible control variety KWS Cashel exhibited the highest AUDPC_P values, significantly exceeding those of all other groups. Robigus developed fewer pycnidia than KWS Cashel but remained clearly susceptible, displaying significantly higher AUDPC_P values than the SHW founders or NAM lines ( Fig. 3a ). In contrast, the SHW founders showed near-complete suppression of pycnidia development ( Fig. 3a ). SHW.035 had virtually no visible pycnidia, whereas SHW.054 and SHW.075 exhibited only rare and low-severity sporulation. Despite strong restriction of pathogen reproduction, SHW backgrounds still developed some degree of necrosis, particularly SHW.075 ( Fig. 3b ). The SHW-derived NAM populations displayed intermediate phenotypes consistent with their mixed genetic origin. All three NAM populations showed strong suppression of pycnidia relative to their susceptible parent Robigus, with significantly lower AUDPC_P values than the susceptible controls ( Fig. 3a ). In contrast, necrosis severity differed markedly among the NAM populations: NAM.144 and NAM.212 exhibited moderate necrosis development despite low AUDPC_P, whereas NAM.233 displayed necrosis levels comparable to those of Robigus ( Fig. 3b ). Together, these patterns indicate that genetic factors limiting fungal reproduction may segregate independently from those modulating host tissue damage. In addition to these family-level trends, several individual SHW-derived NAM lines exhibited consistently low necrosis and near-complete suppression of pycnidia across all five isolates. This stable multi-isolate response suggests that resistance is mediated either by a major locus with broad-spectrum effects or by a combination of loci acting together to provide stable resistance across diverse pathogen genotypes. Heritability of isolate-specific disease responses Broad-sense heritability of disease responses was estimated separately for each Z. tritici isolate and disease component using the generalised heritability approach of Cullis et al. (2006), based on genotype BLUPs obtained from mixed-model analysis ( Table 2 ). Heritability for AUDPC_N was consistently moderate to high across isolates, ranging from 0.76 to 0.88. In contrast, heritability for AUDPC_P was lower overall, with estimates ranging from 0.65 to 0.79. For AUDPC_P, the highest heritability was observed for isolate HT-74 ( H ² = 0.79), whereas the lowest was observed for HT-96 ( H ² = 0.65). Across all isolates, heritability estimates were consistently higher for AUDPC_N than for AUDPC_P. Corresponding estimates of genotypic variance ( V g ) and mean prediction error variance (PEV) differed among isolates and traits and are presented in Table 2 . Correlation between AUDPC_N and AUDPC_P phenotypes across isolates Spearman rank correlation analysis using phenotype BLUEs calculated for each isolate revealed moderate positive correlations among necrosis traits across isolates ( ρ = 0.57–0.80, p < 0.05), indicating that genotypes exhibiting high necrosis to one isolate tended to respond similarly to others ( Fig. 4 ). Correlations among AUDPC_P BLUEs were weaker but consistently positive ( ρ ≈ 0.32–0.59) . Correlations between necrosis and pycnidia were generally weaker still ( ρ = 0.18–0.46) and were often non-significant. This pattern indicates that, although necrosis responses show moderate consistency across isolates, substantial isolate-specific variation remains, and cross-trait associations are limited. The decoupling of necrosis and pycnidia was particularly evident for HT-18 and HT-96, where necrosis severity showed little association with pycnidia development. Collectively, these findings suggest that individual isolates cannot fully predict symptom expression to others and that necrosis and pycnidia represent partially independent components of STB resistance. Genotypic analysis Genomic variation, population structure, and linkage disequilibrium Analysis of SNP marker distribution across the three wheat subgenomes revealed a clear disparity in polymorphism ( Table 3 ). The B subgenome exhibited the highest marker density, averaging 486.1 SNPs per chromosome, whereas the D subgenome showed significantly lower variation, averaging 191.1 SNPs per chromosome. This pattern is consistent with the reduced genetic diversity of the D subgenome, resulting from the evolutionary bottleneck associated with hexaploid wheat formation. However, the lower proportion of retained D-subgenome markers relative to their representation on the 35K array suggests that ascertainment bias in marker design may also have contributed to this disparity ( Table 3 ). Marker distribution across the physical length of all 21 chromosomes indicated sufficient density for downstream LD analysis and GWAS. Prior to genetic analyses, markers were pruned to minimise redundancy by removing SNPs in perfect LD ( r² = 1). Because LD-based pruning filters markers on the basis of statistical correlation rather than physical position, the resulting pruned datasets does not accurately reflect physical genome coverage. Consequently, chromosome and subgenome coverage were evaluated using the unpruned dataset, whereas the LD-pruned dataset was used exclusively to reduce marker redundancy for downstream population structure and association analyses ( Fig. 5 ). PCA revealed clear population structure within the NAM panel, driven primarily by the synthetic founders ( Fig. 6 ). PC1 and PC2 together explained 13.69% of the total genetic variation (PC1 = 7.06%, PC2 = 6.63%). The four founders (SHW.035, SHW.054, SHW.075, and Robigus) formed distinct clusters. Progeny derived from SHW.035 and SHW.054 formed partially overlapping clusters, consistent with their shared tetraploid donor ( Table 1 ). Subgenome-specific comparison of founder SNPs showed that SHW.035 and SHW.054 exhibited minimal differentiation across the A and B subgenomes but extensive differentiation across the D subgenome ( Supplementary Fig. S6 ). Differentiating SNPs between these founders were distributed across all D-subgenome chromosomes rather than being confined to specific regions, consistent with their distinct Ae. tauschii lineage donors (L1 for SHW.035 and L2 for SHW.054). In contrast, SHW.075 displayed broader genome-wide differentiation, reflecting divergence in both tetraploid and D-subgenome ancestry. As expected for backcross-derived families, NAM lines clustered closer to Robigus than to their SHW parents. Overall, the PCA confirms strong founder-driven population structure shaped primarily by D-subgenome lineage differences among the SHW donors . Genome-wide association study (GWAS) GWAS was conducted to identify QTLs controlling resistance to Z. tritici in the NAM population. Analyses used BLUEs calculated for two disease metrics, AUDPC_N and AUDPC_P, across five isolates (HT-18, HT-44, HT-53, HT-74, and HT-96) ( Supplementary Table S3 ). Genome-wide LD decay analysis showed that the correlation coefficient ( r 2 ) declined to 0.2 at approximately 50 Mb ( Supplementary Fig. S7 ). This relatively slow LD decay is consistent with the backcross-derived nature of the population, limited historical recombination, and relatedness among lines. This distance was therefore used to delineate independent QTL regions by selecting the most significant SNP within each 50 Mb window following computation of marker significance as -log10 (p) . Across traits and isolates, several significant QTLs were identified ( Table 4 ). Genome-wide association mapping for necrosis (AUDPC_N) A major, highly reproducible QTL, designated qSTB-3D.1 , was detected on 3DL in response to four of the five tested isolates (HT-18, HT-53, HT-74, and HT-96) ( Table 4 , Fig. 7, Supplementary Fig. S8 ). No significant association at this locus was detected in response to isolate HT-44 ( Supplementary Fig. S8 ). In all cases, the SHW-derived allele significantly reduced AUDPC_N, with estimated effect sizes ranging from approximately −199 to −382. These consistently large negative effects indicate that qSTB-3D.1 represents a major-effect resistance QTL. Segregation of this QTL was highly family specific: a small subset of NAM.144 lines carried the resistance-associated allele, whereas the other two NAM families were fixed for the susceptible allele, consistent with SHW.035 being the sole donor of this resistance. Across all four responsive isolates, significant associations mapped to a consistent genomic interval spanning 587.09–610.79 Mb on chromosome 3D. The same peak SNP (AX-95090074; 609.21 Mb) was identified for all four isolates. All associations fell within a single extended LD block, indicating that these signals represent the same underlying QTL. To investigate the genetic basis of qSTB-3D.1 , the 23.7 Mb interval spanning 587.09–610.79 Mb on chromosome 3D was examined in detail. A total of 425 high-confidence genes were identified within this interval ( Supplementary Table S4 ). Consistent with known mechanisms of resistance to Z. tritici , the search prioritised genes encoding immune receptor-related proteins, including WAKs, CRKs, LecRKs, and NLRs. The most prominent feature of the interval was a dense cluster of seven CRKs located between 590.04 and 590.31 Mb. This cluster includes the cloned resistance gene Stb16q (TraesCS3D02G500800) (Ghaffary et al. 2012; Saintenac et al. 2021). Although the strongest statistical association across all isolates, SNP AX-95090074, occurred distally at approximately 609 Mb, this peak resides within the same extended LD block as the CRK cluster. The peak is located near a WAK gene (TraesCS3D02G533100) at 608.92 Mb. Although additional WAK, RLKs, NLRs, and RLPs are present within the broader interval, these represent lower-priority candidates than the Stb16q -containing CRK cluster. A summary of annotated candidate gene classes within the interval is provided in Table 5. Isolate-specific QTLs for necrosis (AUDPC_N) In addition to the major qSTB-3D.1 locus, several necrosis QTLs were detected in an isolate-specific manner ( Table 4 ). Two additional loci were identified in response to isolate HT-18 ( Fig. 7a ). The first, qSTB-1B.1 (28.56–32.77 Mb), was restricted to the SHW.075-derived family and conferred a substantial reduction in necrosis (ALT allele effect: −395.24), explaining 8.89% of the phenotypic variance. Candidate gene analysis within this 1BS interval identified a WAK gene (TraesCS1B02G050100) at 29.92 Mb, located proximal to the peak SNP ( Supplementary Table S5 ). The second locus, qSTB-6D.1 , was detected at approximately 27 Mb on chromosome 6D. Unlike the 1BS locus, the resistance allele for qSTB-6D.1 was present in all three SHW parents. This QTL explained 6.14% of the phenotypic variance (effect size: −282.01) and is located near the WAK candidate gene TraesCS6D02G056600 (27.06 Mb) ( Supplementary Table S6 ). A QTL on chromosome 3A, designated, qSTB-3A.1 , was detected exclusively in response to isolate HT-53 ( Fig. 7b , Supplementary Fig. S8 ). This locus mapped to an approximately 11.41 Mb region, had a substantial negative effect on AUDPC_N (effect size: −334.58), and explained 10.53% of the phenotypic variance (effect size: −374.94. The resistance-associated allele was restricted to the SHW.075-derived family. The interval contains ten WAK genes, including the cloned resistance gene Stb6 (TraesCS3A02G049500) ( Table 5 , Supplementary Table S7 ) (Saintenac et al. 2018). Finally, a single isolate-specific QTL was identified for HT-74 on chromosome 2D ( qSTB-2D.1 ) ( Fig. 7c ). Spanning a narrow 2.09 Mb interval (641.11–643.19 Mb), this locus accounted for 8.71% of the phenotypic variance, with an effect size of –329.67. The resistance allele was present in all three SHW parents. Within this region, an CRK gene, TraesCS2D02G579600, was identified as a primary candidate, together with two additional kinase domain-encoding genes ( Table 5 , Supplementary Table S8 ). GWAS for pycnidia development (AUDPC_P) In contrast to necrosis, GWAS identified no major or reproducible QTLs for pycnidia coverage (AUDPC_P) across the five tested isolates. Significant associations were detected only in response to isolate HT-96, which revealed a single modest-effect locus on the long arm of chromosome 1B, designated qSTB-1B.2 (~647.23 Mb) ( Table 4, Fig. 8 ). This QTL explained 7.41% of the phenotypic variance, and the resistance-associated allele was contributed by Robigus. The qSTB-1B.2 interval did not contain any annotated genes encoding obvious immune receptor-like proteins ( Supplementary Table S9 ). No significant associations were detected for the remaining four isolates ( Supplementary Fig. S9 ). Segregation of resistance phenotypes in a SHW.BC.144.12.1.4.1 × Robigus F 2 population An F 2 population derived from a cross between the highly resistant NAM line SHW.BC.144.12.1.4.1, a member of NAM.144 developed from SHW.035 × Robigus) and the susceptible variety Robigus was assessed for resistance to Z. tritici isolate HT-74. The principal disease components, percentage leaf coverage by pycnidia and by necrosis, were scored at 29-dpi ( Fig. 9 ). The distribution of percentage leaf coverage by pycnidia in the F 2 population was strongly skewed towards resistance ( Fig. 9a ). Most individuals showed no or very low visible pycnidia, resembling the resistant parent, whereas fewer individuals exhibited moderate or high levels. A small number of F 2 individuals displayed higher pycnidia coverage than Robigus, indicating transgressive segregation. When individuals were classified into resistant (0 %) and susceptible (>0 %) categories, the observed segregation (191 R: 65 S) did not deviate from a 3:1 expectation ( χ² = 0.021, p = 0.885), consistent with control by a single dominant locus. For percentage leaf coverage by necrosis, individuals showing 0–20% classed as resistant and those showing >20% as susceptible ( Fig. 9b ). T The observed segregation (111 R : 145 S) among scored individuals was consistent with a 7:9 ratio ( χ² = 0.016, df = 1, p = 0.90), but inconsistent with simple single-gene models, including a 3:1 ratio ( χ ² = 136.69, df = 1, p < 2.2 × 10⁻¹⁶) and a 1:3 ratio ( χ ² = 46.02, df = 1, p = 1.17 × 10⁻¹¹). These results indicate more complex genetic control of this trait in this population. Discussion This study used a SHW-derived NAM population to dissect resistance to Z. tritici across multiple isolates differing in virulence. By integrating multi-isolate phenotyping with genetic mapping, the study identified both broad-spectrum and isolate-specific resistance loci derived from SHW and introgressed into an elite UK background. Importantly, the results show that genetic factors limiting pathogen reproduction are at least partly distinct from those controlling host tissue damage. Isolate-specific virulence and host response variation The five Z. tritici isolates used in this study exhibited a range of aggressiveness across host genetic backgrounds. HT-74 consistently induced the highest levels of disease, whereas HT-96 was markedly less aggressive, and this hierarchy was broadly maintained across both susceptible elite wheat controls and SHW-derived material ( Fig. 2 ). This range of virulence and aggressiveness reflects the extensive genetic and effector diversity characteristic of natural Z. tritici populations (McDonald et al. 2022). Whereas the susceptible controls KWS Cashel and Robigus developed substantial disease in response to all isolates, SHW-derived lines showed markedly reduced disease severity. A striking feature of these synthetic backgrounds was the frequent uncoupling of necrosis and pycnidia development ( Fig. 3 ). All three SHW-derived NAM families exhibited consistently low AUDPC_P, often approaching a complete absence of visible pycnidia, even when necrotic lesions (AUDPC_N) were present. This phenotype was most pronounced in the SHW.075-derived family, which displayed substantial necrosis despite strong suppression of pathogen reproduction. This response may reflect early activation of host defence mechanisms that limit fungal proliferation before pycnidia formation. Although principal component analysis showed NAM lines clustered genetically closer to the recurrent parent Robigus, their disease phenotypes, particularly for AUDPC_P, more closely resembled those of their respective SHW founders. This uncoupling suggests the presence of resistance mechanisms that restrict pathogen development rather than preventing initial infection, a recognised feature of the wheat– Z. tritici interaction (Fones et al. 2023). Similar phenotypes have been described in wheat lines carrying Stb3 , Stb6 , or Stb5 , where fungal ingress occurs but subsequent pathogen growth and reproduction are effectively arrested by host defence responses (Tidd et al. 2023). From an epidemiological perspective, suppression of pycnidia is especially important because it directly limits the production of secondary inoculum and subsequent spread within the crop canopy. Segregation analysis in the F 2 population derived from the cross between the highly resistant NAM line SHW.BC.144.12.1.4.1 and susceptible Robigus supported this interpretation. Resistance to pycnidia formation followed a clear 3:1 segregation ratio, consistent with control by a single dominant resistance factor. In contrast, necrosis severity did not conform to a simple Mendelian ratio and instead displayed a more complex distribution, suggesting quantitative control and/or a contribution from host physiological responses to infection. Together, these results suggest that resistance in this SHW-derived material is organised around a major factor that strongly restricts pathogen reproduction, with additional loci influencing the extent of host cell death. This resistance architecture is consistent with receptor-mediated recognition mechanisms, potentially involving WAKs or other RLKs that activate defence signalling upon pathogen detection. Such combination of a strong primary resistance factor with polygenic background effects may contribute to increased durability, as proposed in other plant–pathogen systems (Palloix et al. 2009). Broad-spectrum resistance on chromosome 3D The major QTL on chromosome 3D, qSTB-3D.1 , was detected in response to four of the five Z. tritici isolates tested and explained a substantial proportion of the variation in necrosis severity. The resistance-associated allele was contributed exclusively by the D-genome founder of SHW.035 ( Ae. tauschii accession WX224) and was absent from the other NAM families. The physical interval underlying qSTB-3D.1 overlaps a cluster of CRK genes, including Stb16q , a cloned STB resistance gene first identified in a synthetic hexaploid wheat M3 line (W-7976) developed by CIMMYT (Ghaffary et al. 2012; Saintenac et al. 2021). Stb16q confers broad-spectrum resistance by restricting stomatal penetration and inhibiting early fungal growth (Battache et al. 2022), which is consistent with the multi-isolate effect observed here. Although no significant effect was detected for isolate HT-44, this may reflect partial virulence, a smaller effect size, or experimental variation. Two local maxima were observed within the broader 3DL association interval: one near 588 Mb, close to the reported position of Stb16q , and a second near 609 Mb adjacent to a WAK gene. Resistance-associated regions spanning this interval have been reported in several independent studies, with uncertainty as to whether they correspond to Stb16q itself or to additional tightly linked loci. For example, Odilbekov et al. (2019) identified a resistance QTL spanning approximately 593.4–614.4 Mb and noted ambiguity regarding its relationship to Stb16q . More recently, Binalf et al. (2024) also highlighted the region around 609 Mb as being associated with STB resistance. Clarifying the genetic basis of this resistance will require targeted sequencing of the Stb16q -associated interval in SHW.035 to determine whether it corresponds to the previously characterised Stb16q allele now deployed in some European varieties or represents distinct allelic variation relative to the current European bread wheat gene pool. The emergence of Z. tritici isolates virulent towards Stb16q- containing wheat varieties (such as Cellule) in European populations further underlines the importance of evaluating the effectiveness of this locus against more recently sampled field isolates (Kildea et al. 2020; Orellana‐Torrejon et al. 2022). Isolate-specific resistance loci on chromosomes 1B, 2D, 3A and 6D Several isolate-specific resistance loci were detected, consistent with gene-for-gene interactions between NAM founders and individual Z. tritici isolates. The locus qSTB-1B.1 , detected in response to isolate HT-18, maps to 1BS within a region known to harbour multiple STB resistance genes, including Stb2 (Liu et al. 2013), Stb11 (Chartrain et al. 2005b), and several StbWW loci (Raman et al. 2009). However, the interval identified here lies outside the previously defined major resistance clusters and the QStb.wai.1B.1 region (Yang et al. 2022), suggesting that qSTB-1B.1 may represent either a distinct locus or allelic variation within this broader resistance gene-rich region. Candidate gene analysis identified a WAK (TraesCS1B02G050100) within the peak interval. A second HT-18-specific locus, qSTB-6D.1 , was identified on chromosome 6D. This region partially overlaps the reported position of Stb18 , an isolate-specific resistance gene (Tabib Ghaffary et al. 2011). However, the peak association detected here falls outside the currently defined Stb18 interval, leaving open the possibility that qSTB-6D.1 represents either Stb18 itself or a closely linked resistance factor. Notably, all other isolates tested were virulent on the variety Balance, which carries Stb6 and Stb18 (Tidd et al. 2023), supporting the isolate-specific nature of the HT-18 response. On chromosome 3A, qSTB-3A.1 was detected exclusively in response to isolate HT-53 and was contributed by SHW.075. This region overlaps the location of Stb6 , which encodes a WAK mediating gene-for-gene resistance (Brading et al. 2002; Saintenac et al. 2018). Although Stb6 is present in many European varieties, including Robigus (Chartrain et al. 2005a; Goudemand et al. 2013), the resistance-associated allele at qSTB-3A.1 was contributed by SHW.075 in this study. This suggests either allelic variation at the Stb6 locus or the presence of a tightly linked resistance gene within the same WAK cluster, producing a highly isolate-specific interaction with HT-53. The locus qSTB-2D.1 , detected in response to isolate HT-74, is of particular interest because no major Stb genes have been reported on chromosome 2D. This region overlaps previously reported STB resistance QTLs identified in synthetic-derived germplasm (Naz et al. 2015; Riaz et al. 2020), suggesting that chromosome 2D may represent a recurrent target for STB resistance. The peak interval contains a CRK-encoding gene (TraesCS2D02G579600), consistent with receptor-mediated defence mechanisms. The contribution of this allele by all three SHW founders further underlines the importance of synthetic wheat germplasm as a reservoir of novel STB resistance diversity. Genetic architecture of pycnidia resistance In contrast to necrosis severity, GWAS identified very few loci associated with AUDPC_P. Only one minor-effect QTL was detected in response to isolate HT-96, and no significant associations were identified for the more aggressive isolates ( Table 4 ). Although broad-sense heritability for resistance to pycnidia development was moderate to high, within-family variance was limited because sporulation was strongly suppressed in most SHW-derived NAM families. This restricted phenotypic variation likely reduced the power of association mapping to detect loci underlying pycnidia resistance ( Fig. 2,3 ). Consistent with this interpretation, candidate gene analysis provided little evidence for major resistance determinants underlying the detected AUDPC_P QTL. No obvious immune receptor-like genes were identified within the qSTB-1B.2 interval. This suggests that qSTB-1B.2 represents a minor, background-dependent effect rather than a primary determinant of resistance to pycnidia formation in this NAM population. Further support for this interpretation comes from segregation analysis in the independent F 2 population derived from the SHW.BC.144.12.1.4.1 × Robigus cross, in which pycnidia presence or absence followed a clear 3:1 ratio, consistent with control by a single dominant resistance factor in that specific cross. However, this Mendelian segregation pattern cannot be assumed to extend across the wider NAM population, where the corresponding resistance-associated allele may be fixed within individual families or present at high frequency across multiple SHW-derived lineages. In a NAM framework, loci that are fixed within families or exhibit limited allelic contrast across the population are inherently difficult to detect by association-based approaches, even when they have strong phenotypic effects (Yu et al. 2008; McMullen et al. 2009). It is also plausible that strong pycnidia resistance alleles are shared across multiple SHW-derived families, resulting in limited segregation for this trait within the overall population. Under such circumstances, association mapping would be expected to have reduced power to detect the underlying loci because of insufficient allelic contrast among families (Korte et al. 2013). In addition, the resolution of the phenotyping scale may have further reduced sensitivity for detecting small genetic effects. Percentage leaf coverage by pycnidia was scored in 20% increments, producing a discrete ordinal phenotype that may compress variation, particularly when most genotypes cluster near the lower end of the scale. Limitations and future directions While this study provides a detailed analysis of the genetic basis of STB resistance in a SHW-derived NAM population, several limitations should be acknowledged. First, disease phenotyping relied on visual scoring using discrete 20% intervals. Although this approach was sufficient to capture major differences in disease severity, particularly for necrosis, it is inherently subjective and may reduce sensitivity for detecting subtle phenotypic variation associated with minor-effect loci. The development of image-based phenotyping pipelines offers a clear opportunity to address this limitation by enabling continuous, high-resolution quantification of necrotic and sporulating tissue on living leaves over time. Such approaches are likely to improve both the power and precision of future genome-wide association analyses, particularly for traits governed by small-effect loci. Second, all phenotyping was conducted at the seedling stage under controlled conditions. Validation of the identified QTLs under field conditions at the adult-plant stage, particularly under high disease pressure, will therefore be essential to confirm their breeding relevance and to assess their stability across environments. Finally, although the presence of major resistance factors within the SHW-derived material is well supported, the genetic basis of pycnidia suppression remains only partly resolved. The limited power of association mapping to detect loci controlling this trait suggests that targeted biparental populations may be better suited for further genetic dissection. In particular, the SHW.BC.144.12.1.4.1 × Robigus population provided an opportunity to characterise resistance factors that are difficult to resolve within the NAM framework and to distinguish between control by a single dominant gene and multiple tightly linked loci. Because this population was generated by crossing a BC 1 F 6 donor line to the recurrent parent and then advancing to the F 2 generation, it is more accurately described as a BC 2 F 2 population than as a true F 2 . This makes it especially attractive for integration into commercial breeding programmes, since much of the donor genome has already been eliminated through backcrossing. Such material will be valuable for fine mapping, validation of the resistance mechanisms inferred from the NAM analysis, and more rapid deployment of resistance in adapted germplasm. In addition, further characterisation of the Stb16q -associated interval in SHW.035 will be important to determine allelic novelty and assess resistance durability. Given reports of Z. tritici isolates virulent towards Stb16q in European populations, evaluating the effectiveness of this locus against contemporary field isolates will be critical for informing its potential deployment in breeding programmes. Overall, this study shows that resistance to STB in the investigated SHW-derived NAM population is governed by multiple genetic factors that differ in breadth and may involve distinct modes of action. With higher-resolution phenotyping, validation at the adult-plant stage, and targeted fine mapping, the resistance mechanisms identified here can be resolved more fully and translated into wheat improvement. Declarations Author contributions KK – conceptualisation; AB – experimental work, phenotyping, data analysis and writing; TICW – guidance on SNP genotyping and quality control; FL, RH, PH – research materials; AB, FL, KK, PH, RR, SR, and TICW – review and editing; FL, KK, PH, RR, and SR – supervision. Competing interests The authors declare that they have no relevant financial or non-financial interests to disclose. Acknowledgements AB was funded by the Biotechnology and Biological Sciences Research Council (BBSRC) through the Collaborative Training Program for Sustainable Agricultural Innovation (CTP-SAI) (BB/W009439/1), in partnership with The Morley Agricultural Foundation (TMAF) and the University of Nottingham. KK and PH acknowledge support from Defra through the Wheat Genetic Improvement Network (WGIN; contract C24770). PH was also supported by the BBSRC Institute Strategic Programme: Delivering Sustainable Wheat (DSW) – partner grant BB/Y000064/1. RR and SR provided formal support to AB on behalf of the University of Nottingham and TMAF, respectively. We thank the Research & Scientific Computing teams at the James Hutton Institute for providing access to the UK Crop Diversity High-Performance Computing platform, which supported the analyses presented in this study. Funding This work was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) through the Collaborative Training Program for Sustainable Agricultural Innovation (BB/W009439/1). Data Availability All the data supporting the findings of this study are available within the paper and within its Supporting Information. 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Genotypic variance ( V g ) and mean prediction error variance (PEV) are also shown. Isolate Trait H 2 cullis V g PEV HT-18 AUDPC_N 0.83 186910.20 64200.08 HT-44 AUDPC_N 0.76 203191.90 98541.13 HT-53 AUDPC_N 0.82 166334.30 59308.57 HT-74 AUDPC_N 0.88 281812.70 66853.40 HT-96 AUDPC_N 0.83 144762.50 47844.46 HT-18 AUDPC_P 0.67 18020.60 11743.40 HT-44 AUDPC_P 0.77 11047.91 5128.44 HT-53 AUDPC_P 0.73 30859.82 16778.84 HT-74 AUDPC_P 0.79 60558.65 25641.09 HT-96 AUDPC_P 0.65 4798.34 3326.06 Table 3. Physical coverage of SNP markers across wheat chromosomes. Coverage was calculated using 5 Mb windows and reflects the proportion of each chromosome represented by at least one SNP. Values are given before LD pruning. Chromosome SNPs on 35k array (%) SNPs retained ( n ) SNPs retained (%) Covered interval (Mb) Chromosome size (Mb) Genome coverage (%) 1A 1394 354 25 425 594 72 1B 2011 638 32 570 690 83 1D 1913 193 10 240 495 48 2A 1594 293 18 235 781 30 2B 1986 620 31 550 801 69 2D 2156 353 16 340 652 52 3A 1400 270 19 350 751 47 3B 1601 472 29 580 831 70 3D 1740 226 13 310 616 50 4A 1047 252 24 330 745 44 4B 1085 264 24 355 674 53 4D 828 74 9 220 510 43 5A 1478 425 29 410 710 58 5B 1652 612 37 565 713 79 5D 1533 243 16 355 566 63 6A 1096 276 25 285 618 46 6B 1603 506 32 545 721 76 6D 1159 100 9 180 474 38 7A 1546 421 27 540 737 73 7B 1559 291 19 440 751 59 7D 1545 149 10 305 639 48 Table 4. Significant QTLs detected for AUDPC necrosis (AUDPC_N) and pycnidia (AUDPC_P) across five Zymoseptoria tritici isolates in the three-family wheat NAM population. QTL Trait Isolate Significance threshold Marker Chr Peak SNP position (Mb) Peak SNP significance ( -log 10 (p) ) ALT allele effect Var. (%) qSTB-1B.1 AUDPC_N HT-18 4.09 AX-94632775 1B 28.63 4.3 -395.24 8.89 qSTB-3D.1 AUDPC_N HT-18 4.09 AX-95090074 3D 609.21 5.62 -198.90 8.01 qSTB-6D.1 AUDPC_N HT-18 4.09 AX-95175637 6D 27.42 4.94 -282.01 6.14 qSTB-3A.1 AUDPC_N HT-53 4.04 AX-94766803 3A 26.04 5.04 -374.94 10.53 qSTB-3D.1 AUDPC_N HT-53 4.04 AX-95090074 3D 609.21 5.01 -334.58 7.80 qSTB-2D.1 AUDPC_N HT-74 4.00 AX-94455930 2D 641.49 4.18 -329.67 8.71 qSTB-3D.1 AUDPC_N HT-74 4.00 AX-95090074 3D 609.21 5.73 -175.07 11.04 qSTB-3D.1 AUDPC_N HT-96 4.19 AX-95090074 3D 609.21 6.71 -382.16 8.29 qSTB-1B.2 AUDPC_P HT-96 4.88 AX-95193381 1B 647.23 4.99 101.60 7.41 The table lists the chromosome (Chr), physical position (Mb), and −log10 (p) value of the peak SNP for each QTL. Significance thresholds were determined separately for each isolate and trait using 1,000 permutations at α = 0.05. Effect sizes represent the estimated change in phenotype per dosage of the alternative allele, with the Robigus allele used as the reference. ‘Var. (%)’ indicates the percentage of total phenotypic variance explained by each QTL. Negative effect values indicate reduced necrosis or pycnidia associated with the alternative allele, whereas positive values indicate increased disease severity. Table 5. Candidate resistance genes identified within QTL intervals. Physical genomic coordinates and interval sizes (Mb) are based on the IWGSC RefSeq v.1.1 assembly. Candidate genes are categorised by functional class: CRK, cysteine-rich receptor-like kinase; WAK, wall-associated kinase; LecRLKs, lectin receptor-like kinase; LRR-RLK, leucine-rich repeat receptor-like kinase; NLR, nucleotide-binding leucine-rich repeat; Kinase, unspecified kinase; RLP, receptor-like protein. QTL Start position End position Window size CRK WAK LecRK LRR-RLK NLR Kinase RLP qSTB-1B.1 28560996 32769214 4.21 0 1 0 0 3 0 0 qSTB-1B.2 646172198 651275182 5.10 0 0 0 0 0 0 0 qSTB-2D.1 641106101 643193656 2.09 1 0 0 0 0 2 0 qSTB-3A.1 24057407 35463415 11.41 1 10 0 0 1 1 4 qSTB-3D.1 587096644 610795248 23.70 7 6 0 3 12 6 13 qSTB-6D.1 25615199 34974332 9.36 0 3 0 1 1 1 5 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTablesUpdated.xlsx SupplementaryFiguresupdated.pdf SupplementaryFigureandTablelegends.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 27 Apr, 2026 Editor assigned by journal 20 Apr, 2026 Submission checks completed at journal 18 Apr, 2026 First submitted to journal 17 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9451480","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631506709,"identity":"6cefaf21-9fe3-44bf-ad39-672f9029e67f","order_by":0,"name":"Anisa Blower","email":"","orcid":"","institution":"University of Nottingham","correspondingAuthor":false,"prefix":"","firstName":"Anisa","middleName":"","lastName":"Blower","suffix":""},{"id":631506710,"identity":"2e8f9533-b6f7-404b-b331-67970db8f218","order_by":1,"name":"Richard Horsnell","email":"","orcid":"","institution":"Niab","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Horsnell","suffix":""},{"id":631506711,"identity":"be4e329f-e2ec-47f0-b0d9-b09b771cb487","order_by":2,"name":"Phil M. 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(B) Intermediate phenotype with visible necrosis but no pycnidia development. (C) Susceptible phenotype showing extensive necrosis and abundant pycnidia formation.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9451480/v1/8d76c961a4271a338869689a.jpeg"},{"id":108610565,"identity":"75fcbeb4-0e77-4de3-bdf0-c8afb2196589","added_by":"auto","created_at":"2026-05-06 12:57:40","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":235902,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIsolate-specific virulence of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eZymoseptoria tritici\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e Disease progression quantified as the area under the disease progress curve (AUDPC) for pycnidia formation (AUDPC_P; A–C) and necrosis development (AUDPC_N; D–F) following inoculation with five \u003cem\u003eZ. tritici\u003c/em\u003e isolates. Responses are shown for the susceptible control variety KWS Cashel (A, D), the susceptible parent Robigus (B, E), and combined SHW founders and SHW-derived NAM lines (C, F). Differences in response to individual isolates within each host background were tested using Kruskal-Wallis tests followed by Dunn’s post hoc comparisons with Benjamini-Hochberg correction (adjusted \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9451480/v1/5463bef0d41b2ffb7b6ba8d2.jpeg"},{"id":108610564,"identity":"e750caed-72a1-463f-9121-087179898d19","added_by":"auto","created_at":"2026-05-06 12:57:40","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":114326,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNAM family-level variation in response to \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eZymoseptoria tritici\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e Disease progression quantified as the area under the disease progress curve (AUDPC) for pycnidia formation (AUDPC_P; A) and necrosis development (AUDPC_N; B) across susceptible controls KWS Cashel and Robigus, SHW founders (SHW.035, SHW.054, SHW.075), and the corresponding SHW-derived NAM families (NAM.144, NAM.212, NAM.233). Differences among genetic groups were tested using Kruskal-Wallis tests followed by Dunn’s post hoc comparisons with Benjamini-Hochberg correction (adjusted \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9451480/v1/f4358bcd98bc2e4f63786c0a.jpeg"},{"id":108610523,"identity":"daac60fd-8b52-40fa-86eb-70828ee3910a","added_by":"auto","created_at":"2026-05-06 12:57:35","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":231893,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpearman correlation matrix for AUDPC_N and AUDPC_P across five \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eZymoseptoria tritici\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e isolates (HT-18, HT-44, HT-53, HT-74, HT-96). \u003c/strong\u003ePairwise trait associations were quantified using Spearman’s rank correlation coefficient (\u003cem\u003eρ\u003c/em\u003e). Black cross symbols denote non-significant correlations (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05), whereas circles without a cross represent statistically significant correlations (\u003cem\u003ep\u003c/em\u003e ≤ 0.05).\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9451480/v1/d56c9f080827ad44549fec81.jpeg"},{"id":108610529,"identity":"2f815e29-2502-4ec4-a943-8fa695518d5d","added_by":"auto","created_at":"2026-05-06 12:57:36","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":340318,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSNP distribution across the wheat genome before and after LD pruning.\u003c/strong\u003e SNP genomic positions are shown for the full marker set (left) and the LD-pruned marker set, where redundant SNPs in perfect linkage disequilibrium (\u003cem\u003er²\u003c/em\u003e= 1) were removed (right).\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9451480/v1/6482fcaeaa39e8148186159d.jpeg"},{"id":108610524,"identity":"e7fd814a-607a-427e-879d-89ad1adda13b","added_by":"auto","created_at":"2026-05-06 12:57:35","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":111041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrincipal component analysis (PCA) of genetic variation within the NAM population.\u003c/strong\u003e SHW.035 and its derived NAM.144 lines are shown in teal, SHW.054 and NAM.212 in orange, SHW.075 and NAM.233 in purple, and Robigus in grey.\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9451480/v1/9ebb1c7df7d0e66bad752236.jpeg"},{"id":108610558,"identity":"07b3fda5-1b48-4f07-a320-64b6b858d6a1","added_by":"auto","created_at":"2026-05-06 12:57:38","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":470120,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGWAS for AUDPC_N in the NAM population.\u003c/strong\u003e Manhattan plots (panels Ai–Ci, left) and corresponding Quantile-Quantile (QQ) plots (panels Aii–Cii, right) for GWAS of AUDPC_N in response to \u003cem\u003eZ. tritici\u003c/em\u003e isolates: (A) HT-18, (B) HT-53, and (C) HT-74. The dashed grey horizontal line indicates the genome-wide significance threshold determined by 1,000 permutations at \u003cem\u003eα\u003c/em\u003e = 0.05 for each isolate, while the dotted yellow line denotes the false discovery rate (FDR) threshold at \u003cem\u003eq\u003c/em\u003e = 0.05.\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9451480/v1/99176526d7922f15cd393d91.jpeg"},{"id":108610517,"identity":"dc9d66cf-c62d-4a96-82d7-3355a7e62c1f","added_by":"auto","created_at":"2026-05-06 12:57:35","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":152435,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGWAS for AUDPC_P in the NAM population in response to \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eZymoseptoria tritici\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e isolate HT-96.\u003c/strong\u003e Manhattan plot (A) and corresponding QQ plot (B). The dashed grey horizontal line indicates the genome-wide significance threshold determined by 1,000 permutations at \u003cem\u003eα\u003c/em\u003e= 0.05 for each isolate, while the dotted yellow line denotes the false discovery rate (FDR) threshold at \u003cem\u003eq\u003c/em\u003e = 0.05.\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9451480/v1/bf524a03feea0089efa9a15f.jpeg"},{"id":108610557,"identity":"eda4d5f7-e63e-41c8-a59b-b9d244b12dbc","added_by":"auto","created_at":"2026-05-06 12:57:38","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":81022,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhenotypic distributions of leaf coverage by pycnidia and necrosis in the F\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e population derived from the cross SHW.BC.144.12.1.4.1 × Robigus.\u003c/strong\u003e Histogram of percentage leaf coverage by pycnidia (A) and percentage leaf coverage by necrosis (B) scored at 29-dpi. Counts indicate the number of F\u003csub\u003e2\u003c/sub\u003e individuals in each phenotypic class. Parental phenotypes (SHW.BC.144.12.1.4.1, teal; Robigus, orange) are shown above each histogram, with points indicating the median parental score and vertical bars representing the range of values obtained across replicate measurements.\u003c/p\u003e","description":"","filename":"image9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9451480/v1/0fa94cf3a83072ffd1b7cfdb.jpeg"},{"id":108805479,"identity":"429d67ef-c34e-49ec-a930-c44c9353a031","added_by":"auto","created_at":"2026-05-08 15:26:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2587107,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9451480/v1/457076af-246f-496c-9022-d058f19258c3.pdf"},{"id":108610519,"identity":"dc5742a6-0b1e-44b1-9367-4f5d1e31837d","added_by":"auto","created_at":"2026-05-06 12:57:35","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":271039,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesUpdated.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9451480/v1/ccfb1d96af390531bfc0aab6.xlsx"},{"id":108610553,"identity":"90f9d171-2c1d-4277-85eb-fb2411f012f1","added_by":"auto","created_at":"2026-05-06 12:57:37","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1214111,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFiguresupdated.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9451480/v1/97eb89da21a53bd1c562272c.pdf"},{"id":108610566,"identity":"4b0881a0-4d5f-4ffc-a9ce-e62218212055","added_by":"auto","created_at":"2026-05-06 12:57:40","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15645,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureandTablelegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-9451480/v1/39d2c0f0860691240101bb9c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-isolate GWAS identifies broad-spectrum and isolate-specific Septoria tritici blotch resistance loci in synthetic hexaploid wheat","fulltext":[{"header":"Key message","content":"\u003cp\u003eSynthetic hexaploid wheat harbours broad-spectrum and isolate-specific Septoria tritici blotch resistance loci, with partly independent genetic control of pycnidia suppression and necrosis.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003eZymoseptoria tritici\u003c/em\u003e, previously known as \u003cem\u003eSeptoria tritici\u003c/em\u003e and \u003cem\u003eMycosphaerella graminicola\u003c/em\u003e, is a filamentous ascomycete fungus and the causal agent of Septoria tritici blotch (STB) in wheat. The disease is particularly prevalent in temperate regions with cool, humid climates, including Northern Europe and North America, where severe epidemics can reduce grain yield and quality by up to 50% in susceptible varieties under favourable conditions (Eyal 1987; Fones et al. 2015; Castro et al. 2017). Disease management is further complicated by the large and genetically diverse populations of \u003cem\u003eZ. tritici\u003c/em\u003e, which promote the rapid evolution of fungicide resistance and the breakdown of host resistance genes (McDonald et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Europe, STB management accounts for approximately 70% of the total fungicide market in wheat, at an estimated annual cost of \u0026euro;1.0\u0026nbsp;billion (Torriani et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, the effectiveness of fungicide-based control is declining due because of the widespread emergence of fungicide resistance, including reduced sensitivity to azoles (Heick et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; J\u0026oslash;rgensen et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), strobilurins (Torriani et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and succinate dehydrogenase inhibitors (Dooley et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hagerty et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hagerty et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consequently, STB remains a major challenge to sustainable wheat production. The development of new wheat varieties with durable genetic resistance, combined with integrated disease management, is therefore widely regarded as the most effective long-term strategy for STB control.\u003c/p\u003e \u003cp\u003eTwenty-three major STB resistance (\u003cem\u003eStb\u003c/em\u003e) genes have been identified in wheat (Brown et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Langlands-Perry et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), in addition to more than 160 resistance-associated quantitative trait loci (QTL) (Brown et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Several qualitative resistance genes have been cloned, including \u003cem\u003eStb6\u003c/em\u003e, encoding a wall-associated kinase-like protein (WAK) (Saintenac et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), \u003cem\u003eStb16q\u003c/em\u003e, encoding a cysteine-rich receptor-like kinase (CRK) (Saintenac et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and \u003cem\u003eStb15\u003c/em\u003e, encoding a lectin receptor-like kinase (LecRK) (Hafeez et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While many \u003cem\u003eStb\u003c/em\u003e genes confer isolate-specific resistance, some, including \u003cem\u003eStb10\u003c/em\u003e, \u003cem\u003eStb11\u003c/em\u003e, \u003cem\u003eStb12\u003c/em\u003e, \u003cem\u003eStb16q\u003c/em\u003e, \u003cem\u003eStb17\u003c/em\u003e, and \u003cem\u003eStb19\u003c/em\u003e, provide resistance to diverse field isolates, with synthetic hexaploid wheat (SHW) emerging as a particularly promising source of such resistances (Tidd et al. 2023).\u003c/p\u003e \u003cp\u003eSHW, generated by hybridising tetraploid durum wheat with the wild D-genome progenitor \u003cem\u003eAegilops tauschii\u003c/em\u003e (Wright et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), has enabled the transfer of drought tolerance (Mokhtari et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and disease resistance (Ogbonnaya et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Jighly et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Blower et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) from wild relatives into bread wheat. Several STB resistance loci, including \u003cem\u003eStb5\u003c/em\u003e (Arraiano et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), \u003cem\u003eStb8\u003c/em\u003e (Adhikari et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), \u003cem\u003eStb16q\u003c/em\u003e (Saintenac et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and \u003cem\u003eStb17\u003c/em\u003e (Ghaffary et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), were identified in SHW backgrounds, underlining the value of SHW as a reservoir of useful alleles for STB resistance improvement.\u003c/p\u003e \u003cp\u003eTo dissect complex resistance traits derived from exotic germplasm, structured multi-parent populations such as nested association mapping (NAM) populations provide high mapping resolution while reducing confounding effects of population structure. NAM populations combine multiple donor genomes within a common elite background, enabling the detection of both major and minor loci (Yu et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Using an SHW-derived NAM population developed from crosses with the elite UK variety Robigus, Wright et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) identified novel SHW-derived variation for flowering time, plant height, and yellow rust resistance.\u003c/p\u003e \u003cp\u003eIn the present study, a subset of this SHW-derived NAM population (Wright et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) was used to investigate the genetic basis of STB resistance by combining artificial seedling inoculation assays with genome-wide association analysis. The \u003cem\u003eZ. tritici\u003c/em\u003e isolate panel was selected on the basis of prior virulence profiling against differential wheat lines carrying major \u003cem\u003eStb\u003c/em\u003e genes and their combinations and was collectively virulent on 19 of 23 characterised resistance genes (Tidd et al. 2023), enabling evaluation of resistance breadth against diverse pathogen genotypes. Although resistance expressed at the adult plant stage can involve additional loci and environmental interactions (Thauvin et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), controlled seedling assays provide a robust and reproducible framework for resolving isolate-specific resistance loci under defined conditions. These findings provide a foundation for the targeted deployment of SHW-derived diversity to improve STB resistance\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003ePlant material and population development\u003c/h2\u003e\n \u003cp\u003eA three-family nested association mapping (NAM) population was used to investigate genetic resistance to \u003cem\u003eZymoseptoria tritici\u003c/em\u003e in hexaploid wheat. The population was developed from crosses between the elite UK winter wheat variety Robigus and three primary synthetic hexaploid wheat (SHW) lines: SHW.035, SHW.054, and SHW.075. Population development followed a backcrossing and single-seed descent (SSD) strategy, as described in detail by Wright et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The final population consisted of three nested families (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) comprising 201 backcross-derived lines. All 201 NAM lines were genotyped at the BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e5\u003c/sub\u003e generation, together with the four parents: the recurrent parent Robigus and the three SHW donor parents. Phenotypic evaluations were conducted at the BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e7\u003c/sub\u003e generation on 180 NAM lines, together with five reference lines: Robigus, the three SHW donor parents, and the highly susceptible UK winter wheat variety KWS Cashel. Genetic analyses were conducted on 171 NAM lines, together with the four parents, all of which had genotype and phenotype data of sufficient quality for genetic analysis, as described below.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eFungal material\u003c/h3\u003e\n\u003cp\u003eFive \u003cem\u003eZ. tritici\u003c/em\u003e isolates were selected from a panel of 93 previously characterised UK field isolates (Tidd et al. 2023). These isolates were originally cultured from STB lesions collected from hexaploid wheat grown in the UK between 2015 and 2017. The isolates used in this study were HT-18, HT-44, HT-53, HT-74, and HT-96. All isolates were maintained as pure blastospore stocks in 50% (v/v) glycerol and stored at \u0026minus;\u0026thinsp;80\u0026deg;C.\u003c/p\u003e\n\u003ch3\u003eInoculum preparation and inoculation\u003c/h3\u003e\n\u003cp\u003eSusceptibility of wheat lines was assessed using a method adapted from Tidd et al. (2023). \u003cem\u003eZ. tritici\u003c/em\u003e isolates were streaked from glycerol stocks onto antibiotic-free yeast peptone dextrose (YPD) agar (Formedium) and incubated in the dark for 5\u0026ndash;7 days at 15\u0026deg;C before inoculation. Fungal blastospores were harvested from plates using sterile loops and transferred to tubes containing 5 mL sterile distilled water. The spore suspension was filtered through autoclaved Miracloth (Merck). Spore concentration was determined using a haemocytometer from the mean of four replicated counts, and suspensions were adjusted to 5 x 10\u003csup\u003e6\u003c/sup\u003e spores per mL before inoculation. Tween 20 was added to a final concentration of 0.05% v/v to improve leaf wetting, reduce spore aggregation, and facilitate stomatal entry.\u003c/p\u003e\n\u003cp\u003eWheat seedlings were grown for approximately 3 weeks under controlled conditions before inoculation. Leaf 2 was fixed to an aluminium platform with the adaxial surface facing upwards and secured using rubber bands and double-sided sticky tape. Cotton buds soaked in the spore suspension were used to distribute inoculum evenly across the leaf surface. A sterile solution of 0.05% v/v Tween 20 in distilled water was used to mock inoculation as a negative control. KWS Cashel, a wheat variety lacking established \u003cem\u003eStb\u003c/em\u003e resistance genes, was used as a susceptible control.\u003c/p\u003e\n\u003cp\u003eAfter inoculation, each half-tray containing inoculated plants was placed in a high-humidity box for three days. For each wheat genotype \u0026times; \u003cem\u003eZ. tritici\u003c/em\u003e isolate interaction, leaves from a minimum of four individual plants were inoculated. Plants were maintained for 29 days post inoculation (dpi) to allow full assessment of disease development. Phenotyping was conducted in an average of twelve independent experimental batches per isolate, with approximately 5\u0026ndash;7 plants per genotype \u0026times; isolate combination in each batch. Plants were screened without randomisation.\u003c/p\u003e\n\u003ch3\u003ePhenotypic analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eVisual phenotypic trait evaluation\u003c/h2\u003e\n \u003cp\u003eDisease symptom development on inoculated leaves was scored visually at 2\u0026ndash;3-day intervals from 10- to 29-dpi. Percentage leaf coverage by necrosis, chlorosis, and pycnidia was scored in 20% increments from 0 to 100%. Representative examples illustrating necrosis and pycnidia are shown in Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe area under the disease progress curve (AUDPC) was calculated for each plant\u0026ndash;pathogen interaction according to Simko et al. (2012):\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:AUDPC=\\:\\frac{\\sum\\:({t}_{i+1}-{t}_{i})({y}_{i}+{y}_{i+1})}{2}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003ei+1\u003c/em\u003e\u003c/sub\u003e are the percentages of disease severity at observations \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ei\u0026thinsp;+\u0026thinsp;1\u003c/em\u003e and (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{t}_{i+1}-{t}_{i}\\)\u003c/span\u003e\u003c/span\u003e) is the number of days between observations. AUDPC values were calculated separately for necrosis (AUDPC_N), and pycnidia (AUDPC_P). The same scoring schedule was applied across all experimental batches.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eNon-parametric analysis of AUDPC data\u003c/h2\u003e\n \u003cp\u003eAUDPC values for pycnidia (AUDPC_P) and necrosis (AUDPC_N) were analysed using non-parametric statistical methods because the data were non-normally distributed and showed heterogeneity of variance. All analyses were conducted in R v.4.3.2 (R Core Team \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To assess isolate-specific virulence across contrasting host backgrounds, AUDPC values were analysed separately for three host groups: the susceptible control variety KWS Cashel, the susceptible parent Robigus, and the SHW founders together with their derived NAM lines. For each host group and trait, overall differences among the five \u003cem\u003eZ. tritici\u003c/em\u003e isolates were tested using Kruskal-Wallis rank-sum tests. Where significant global effects were detected (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), pairwise isolate comparisons were performed using Dunn\u0026rsquo;s post hoc test with Benjamini-Hochberg correction. Family-level differences in disease severity were analysed using the same approach, comparing AUDPC values across eight genetic groups comprising the susceptible controls, the SHW founders, and the three NAM families.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eEstimation of genotypic effects (BLUEs)\u003c/h3\u003e\n\u003cp\u003eBest linear unbiased estimates (BLUEs) were calculated for necrosis (AUDPC_N) and pycnidia development (AUDPC_P) using linear mixed models implemented in lme4 (Bates et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), with inference supported by lmerTest (Kuznetsova et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and marginal means obtained using emmeans (Lenth \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For BLUE estimation, genotype was fitted as a fixed effect to obtain adjusted genotype means for each isolate.\u003c/p\u003e\n\u003cp\u003eBLUEs were estimated separately for each \u003cem\u003eZ. tritici\u003c/em\u003e isolate (HT-18, HT-44, HT-53, HT-74, and HT-96) using the following mixed-effects model:\u003c/p\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:{y}_{ij}=\\mu\\:+{g}_{i}+{B}_{j}+(g\\times\\:B{)}_{ij}+{e}_{ij}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{ij\\:}\\)\u003c/span\u003e\u003c/span\u003eis the AUDPC value of the \u003cem\u003ei-\u003c/em\u003eth genotype in the \u003cem\u003ej-\u003c/em\u003eth batch, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:\\:\\)\u003c/span\u003e\u003c/span\u003eis the overall mean, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{g}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003eis the fixed effect of genotype, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{B}_{j}\\)\u003c/span\u003e\u003c/span\u003e is the random effect of batch, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:(g\\times\\:B{)}_{ij}\\)\u003c/span\u003e\u003c/span\u003e is the random genotype-by-batch interaction, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{e}_{ij}\\)\u003c/span\u003e\u003c/span\u003e is the residual error term. Models were fitted using restricted maximum likelihood (REML), and BLUEs were extracted using emmeans. For each isolate, BLUEs were calculated from untransformed AUDPC values and used in downstream analyses. Relationships among BLUEs were evaluated using Spearman\u0026rsquo;s rank correlation coefficients. Correlation analysis and significance testing (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were performed using Hmisc (Harrell et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and visualised with corrplot (Wei et al. 2024). For heritability estimation, the same model structure was used, but with genotype fitted as a random effect, enabling extraction of best linear unbiased predictors (BLUPs) and the corresponding variance components.\u003c/p\u003e\n\u003ch3\u003eBroad-sense heritability estimation\u003c/h3\u003e\n\u003cp\u003eBroad-sense heritability (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}^{2}\\)\u003c/span\u003e\u003c/span\u003e) was estimated separately for each isolate and trait using variance components from the mixed model, with genotype treated as a random effect to obtain BLUPs and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{G}^{2}\\)\u003c/span\u003e\u003c/span\u003e. Heritability was calculated using the generalised heritability approach of Cullis et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e):\u003c/p\u003e\n\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$\\:{H}_{\\text{Cullis}}^{2}=1-\\frac{\\stackrel{\\text{⃐}}{\\text{PEV}}}{2{\\sigma\\:}_{G}^{2}}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\text{⃐}}{\\text{PEV}}\\:\\)\u003c/span\u003e\u003c/span\u003eis the mean prediction error variance of pairwise differences between genotype BLUPs, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{G}^{2}\\:\\)\u003c/span\u003e\u003c/span\u003eis the estimated genotypic variance. This approach provides robust heritability estimates under unbalanced experimental design by accounting for unequal replication and missing data. All heritability analyses were conducted in R using sommer (Covarrubias-Pazaran \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eGenotypic analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003eDNA extraction and genotyping\u003c/h2\u003e\n \u003cp\u003eGenomic DNA was previously extracted from seedling leaf tissue of the NAM population and parental lines using a modified CTAB protocol (Fulton et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) as described in Wright et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Genotyping was conducted at the University of Bristol using the Axiom 35K Wheat Breeders\u0026rsquo; SNP array (Thermo Fisher Scientific), which assays 35,143 SNP markers (Allen et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). All 201 NAM lines were genotyped at the BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e5\u003c/sub\u003e generation, together with their recurrent parent Robigus and the three SHW donor parents, SHW.035, SHW.054, and SHW.075.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eInitial genotype calling and marker filtering\u003c/h2\u003e\n \u003cp\u003eGenotype calling and initial quality control were performed in Axiom Analysis Suite v.5.4 (Thermo Fisher Scientific) following procedures adapted from Wright et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). An inbred penalty of 4 was applied to account for the highly inbred nature of the material. Samples failing standard quality thresholds (DishQC\u0026thinsp;\u0026ge;\u0026thinsp;0.80, call rate\u0026thinsp;\u0026ge;\u0026thinsp;95%) were excluded. Markers were filtered on the basis of Axiom cluster classification and genotype class representation. Monomorphic markers, markers with poor call rates, and off-target variants were excluded. Segregating markers were retained only when clear genotype clustering was observed, and the minor genotype class was represented by at least eight individuals. All retained markers were visually inspected to confirm clustering quality, and markers showing ambiguous clustering were discarded. Following these filtering steps, the dataset comprised 8,868 SNP markers across 202 genotypes that passed the quality control thresholds. These 202 genotypes included NAM lines with genotype files available, Robigus, and the three SHW donor parents, and were counted before the removal of additional genotypes during downstream quality control steps outside Axiom Analysis Suite.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eDownstream quality control and imputation\u003c/h2\u003e\n \u003cp\u003eFurther genotype quality control was conducted in R v.4.3.2 (R Core Team \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). SNP markers were removed if they exhibited\u0026thinsp;\u0026gt;\u0026thinsp;7% missing data, \u0026gt; 7% heterozygosity, or fewer than 12 individuals homozygous for the minor allele (\u003cstrong\u003eSupplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). Genotypes exceeding the same thresholds for missing data or heterozygosity were also excluded.\u003c/p\u003e\n \u003cp\u003ePrincipal coordinate analysis (PCoA) was used to identify potential outlier genotypes, both across the full NAM population and within each nested family (\u003cstrong\u003eSupplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/strong\u003e). The Pearson correlation coefficients among genotype pairs were used to detect highly similar or potentially duplicated samples, including selfed individuals or lines excessively similar to parental controls. Erroneous genotypes were identified and removed.\u003c/p\u003e\n \u003cp\u003eFor the remaining lines, missing genotype data were imputed using the random forest algorithm implemented in missForest (Stekhoven et al. 2012), using 200 trees per forest. After imputation, the final dataset comprised 8,040 SNP markers across 187 NAM genotypes, together with four parental controls.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003ePhysical marker positioning and LD-based reordering\u003c/h2\u003e\n \u003cp\u003ePhysical positions for 5,327 SNP markers were obtained from the IWGSC RefSeq v.1.0 wheat genome assembly (IWGSC 2018) using coordinates from the dataset described by Wright et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For the remaining 2,714 unmapped markers, SNP probe sequences from the Axiom 35K Wheat Breeders\u0026rsquo; array were retrieved from CerealsDB (Wilkinson et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and aligned to the reference genome using BLAST+ (Camacho et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For markers with a single high-confidence BLAST hit, physical positions were assigned as the midpoint between alignment coordinates, enabling placement of 496 additional markers. Remaining unmapped markers were anchored using linkage disequilibrium (LD) with mapped markers, assigning positions when strong LD (\u003cem\u003eR\u003c/em\u003e\u0026sup2; \u0026gt; 0.7) and consistent BLAST hits supported a chromosomal location. Marker placement was further refined using LD-based binning (\u003cem\u003eR\u003c/em\u003e\u0026sup2; \u0026gt; 0.5) to improve collinearity. Markers lacking reliable placement were excluded from downstream analyses. Marker ordering and map consistency were evaluated using LD heatmaps (\u003cstrong\u003eSupplementary Fig. S3, S4\u003c/strong\u003e) were generated using LDheatmap (Shin et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). After marker positioning and filtering, 7,032 SNP markers were retained for analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003ePopulation structure analysis\u003c/h2\u003e\n \u003cp\u003ePopulation structure within the NAM population, including the recurrent parent Robigus and the SHW founders, was assessed using genome-wide SNP data. To reduce marker redundancy, markers were thinned by removing one marker from each pair with strong correlation (|\u003cem\u003er\u003c/em\u003e| \u0026ge; 0.9). Population structure was visualised using principal component analysis (PCA) based on a genetic distance matrix calculated from the filtered marker set. Analyses were conducted in R and plots were generated using ggplot2 (Wickham \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Principal components and pedigree information were used to define covariates included in the genome-wide association study (GWAS).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eGenome-wide association study (GWAS)\u003c/h2\u003e\n \u003cp\u003eGWAS was performed on 171 phenotyped NAM lines, for which high-quality genotype data were available, together with the four parental controls (\u003cstrong\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). GWAS was performed using an additive Q\u0026thinsp;+\u0026thinsp;K mixed model implemented in GWASpoly (Rosyara et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Population structure (Q) was accounted for using fixed-effect covariates corresponding to family and tetraploid donor, together with the first ten principal components (\u003cstrong\u003eSupplementary Fig. S5\u003c/strong\u003e), while genetic relatedness (K) was controlled using a marker-derived kinship matrix estimated with the GWASpoly function \u003cem\u003eset.K\u003c/em\u003e. Leave-one-chromosome-out (LOCO) correction was not applied. Prior to GWAS, markers were skimmed to remove non-unique markers (|\u003cem\u003er\u003c/em\u003e| = 1) to reduce redundancy and avoid inflation of kinship estimates (\u003cstrong\u003eSupplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/strong\u003e). Model suitability was assessed by inspection of quantile\u0026ndash;quantile (QQ) plots and estimation of genomic inflation factors (\u003cem\u003e\u0026lambda;\u003c/em\u003e; Devlin et al. (1999)), and the final model settings were applied consistently across all traits. Significance thresholds were determined using the GWASpoly \u003cem\u003eset.threshold\u003c/em\u003e function, applying both false discovery rate (FDR; \u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05) (Benjamini et al. 1995) and permutation-derived thresholds based on 1,000 permutations (\u003cem\u003e\u0026alpha;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05) (Churchill et al. 1994). Candidate QTLs were defined based on \u0026minus;log10(\u003cem\u003ep\u003c/em\u003e) values, allele effect direction (using Robigus as the reference allele), and physical marker position. LD between markers was calculated as \u003cem\u003er\u0026sup2;\u003c/em\u003e using the GWASpoly \u003cem\u003eLD.plot\u003c/em\u003e function. LD decay was assessed by plotting \u003cem\u003er\u0026sup2;\u003c/em\u003e against physical distance, and the LD decay threshold was defined as the physical distance at which \u003cem\u003er\u0026sup2;\u003c/em\u003e declined to 0.2 (\u003cstrong\u003eSupplementary Fig. S6\u003c/strong\u003e). Final candidate QTLs were defined as the most significant SNP within \u0026plusmn;\u0026thinsp;LD-decay distance (Mb) of each association peak.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eIdentification of candidate genes\u003c/h2\u003e\n \u003cp\u003eCandidate genes underlying significant QTLs were identified by analysing gene content within QTL intervals defined by the outermost significant flanking SNPs and extended to include one additional marker on each side. Physical coordinates were based on the IWGSC RefSeq v.1.1 wheat genome assembly. Gene models were retrieved from the Ensembl Plants BioMart database using biomaRt (Durinck \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), extracting gene identifiers, genomic coordinates, functional descriptions, and InterPro domain annotations. Candidate genes were prioritised on the basis of resistance-associated domains, including nucleotide-binding leucine-rich repeat proteins (NLRs), wall-associated kinase-like proteins (WAKs), cysteine-rich receptor-like kinases (CRKs), lectin receptor-like kinases (LecRKs), leucine-rich repeat receptor-like kinases (LRR-RLKs), receptor-like proteins (RLPs), and other kinase-containing proteins, whereas genes lacking diagnostic domains were classified as other or unknown.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eDevelopment and screening of the F\u003csub\u003e2\u003c/sub\u003e mapping population\u003c/h2\u003e\n \u003cp\u003eTo investigate the genetic architecture of resistance identified in the NAM.144 family, genotype SHW.BC.144.12.1.4.1 (BC₁F₇; SHW.035 \u0026times; Robigus) was selected as the resistant parent on the basis of its consistent resistance to multiple \u003cem\u003eZ. tritici\u003c/em\u003e isolates. SHW.BC.144.12.1.4.1 was crossed with the susceptible parent Robigus (female) under glasshouse conditions, and F₁ plants were selfed to produce an F₂ population.\u003c/p\u003e\n \u003cp\u003eA total of 256 F\u003csub\u003e2\u003c/sub\u003e seedlings, derived from a single crossing event, were evaluated for resistance alongside the parental controls SHW.BC.144.12.1.4.1 (resistant parent) and Robigus (susceptible parent). The population was inoculated with \u003cem\u003eZ. tritici\u003c/em\u003e isolate HT-74 and disease progression was assessed at 29-dpi. Phenotyping was conducted in a single experimental batch, with twelve parental control plants included. Quantitative phenotypic data recorded at 29-dpi were converted to binary classes based on parental phenotype distributions. Plants with \u0026lt;\u0026thinsp;20% leaf coverage were classified as resistant for pycnidia, and plants with \u0026le;\u0026thinsp;20% leaf coverage were classified as resistant for necrosis. Segregation ratios for each trait were tested against expected Mendelian ratios using a chi-squared (\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e) goodness-of-fit test.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePhenotypic analysis of resistance to \u003cem\u003eZ. tritici\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResistance to STB was assessed in a three-family NAM population under controlled growth-room conditions following separate inoculations with five \u003cem\u003eZ. tritici\u003c/em\u003e isolates (HT-18, HT-44, HT-53, HT-74, and HT-96). Disease progression was assessed at several timepoints between 10- and 29-dpi by scoring the percentage leaf area covered by pycnidia and necrosis. These scores were used to calculate area under the disease progress curve values for pycnidia (AUDPC_P) and necrosis (AUDPC_N).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIsolate-specific virulence across host backgrounds\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClear isolate-specific differences in virulence were observed across host genetic backgrounds (\u003cstrong\u003eFig. 2\u003c/strong\u003e), with a broadly consistent virulence ranking across genotypes. Successful inoculation was confirmed for all five isolates, each of which induced substantial infection on the susceptible control variety KWS Cashel (\u003cstrong\u003eFig. 2\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the susceptible parent Robigus, significant differences in virulence among isolates were observed for both pycnidia and necrosis (\u003cstrong\u003eFig. 2b,e\u003c/strong\u003e). HT-74 was the most virulent isolate for pycnidia, whereas HT-96 consistently induced the lowest levels. Differences in necrosis were less pronounced, with only HT-74 inducing greater necrosis than HT-96.\u003c/p\u003e\n\u003cp\u003eAcross the combined SHW founders and NAM families, significant isolate-specific differences were also observed for both pycnidia and necrosis (\u003cstrong\u003eFig. 2c,f\u003c/strong\u003e). Pycnidia development followed a consistent virulence hierarchy across these genetic backgrounds (HT-74 \u0026gt; HT-53 \u0026gt; HT-44 \u0026gt; HT-18 \u0026gt; HT-96), with HT-74 inducing significantly greater AUDPC_P than all other isolates (\u003cstrong\u003eFig. 2c\u003c/strong\u003e). Necrosis severity also differed significantly among isolates, although separation was less marked than for pycnidia; HT-44 and HT-74 generally induced the highest AUDPC_N values, whereas HT-18- and HT-96-induced necrosis were among the least severe (\u003cstrong\u003eFigure 2f\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNAM family-level variation in pycnidia and necrosis severity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the contribution of genetic wheat background to STB resistance, AUDPC_P and AUDPC_N were compared across eight genetic groups: KWS Cashel, Robigus, three SHW founders (SHW.035, SHW.054, and SHW.075), and three corresponding SHW-derived NAM families (NAM.144, NAM.212, and NAM.233) (\u003cstrong\u003eFig. 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eHighly significant differences among genetic groups were detected for both pycnidia development and necrosis (\u003cstrong\u003eFig. 3\u003c/strong\u003e). As expected, the susceptible control variety KWS Cashel exhibited the highest AUDPC_P values, significantly exceeding those of all other groups. Robigus developed fewer pycnidia than KWS Cashel but remained clearly susceptible, displaying significantly higher AUDPC_P values than the SHW founders or NAM lines (\u003cstrong\u003eFig. 3a\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eIn contrast, the SHW founders showed near-complete suppression of pycnidia development (\u003cstrong\u003eFig. 3a\u003c/strong\u003e). SHW.035 had virtually no visible pycnidia, whereas SHW.054 and SHW.075 exhibited only rare and low-severity sporulation. Despite strong restriction of pathogen reproduction, SHW backgrounds still developed some degree of necrosis, particularly SHW.075 (\u003cstrong\u003eFig. 3b\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe SHW-derived NAM populations displayed intermediate phenotypes consistent with their mixed genetic origin. All three NAM populations showed strong suppression of pycnidia relative to their susceptible parent Robigus, with significantly lower AUDPC_P values than the susceptible controls (\u003cstrong\u003eFig. 3a\u003c/strong\u003e). In contrast, necrosis severity differed markedly among the NAM populations: NAM.144 and NAM.212 exhibited moderate necrosis development despite low AUDPC_P, whereas NAM.233 displayed necrosis levels comparable to those of Robigus (\u003cstrong\u003eFig. 3b\u003c/strong\u003e). Together, these patterns indicate that genetic factors limiting fungal reproduction may segregate independently from those modulating host tissue damage.\u003c/p\u003e\n\u003cp\u003eIn addition to these family-level trends, several individual SHW-derived NAM lines exhibited consistently low necrosis and near-complete suppression of pycnidia across all five isolates. This stable multi-isolate response suggests that resistance is mediated either by a major locus with broad-spectrum effects or by a combination of loci acting together to provide stable resistance across diverse pathogen genotypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeritability of isolate-specific disease responses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBroad-sense heritability of disease responses was estimated separately for each \u003cem\u003eZ. tritici\u003c/em\u003e isolate and disease component using the generalised heritability approach of Cullis et al. (2006), based on genotype BLUPs obtained from mixed-model analysis (\u003cstrong\u003eTable 2\u003c/strong\u003e). Heritability for AUDPC_N was consistently moderate to high across isolates, ranging from 0.76 to 0.88. In contrast, heritability for AUDPC_P was lower overall, with estimates ranging from 0.65 to 0.79. For AUDPC_P, the highest heritability was observed for isolate HT-74 (\u003cem\u003eH\u003c/em\u003e\u0026sup2; = 0.79), whereas the lowest was observed for HT-96 (\u003cem\u003eH\u003c/em\u003e\u0026sup2; = 0.65). Across all isolates, heritability estimates were consistently higher for AUDPC_N than for AUDPC_P. Corresponding estimates of genotypic variance (\u003cem\u003eV\u003csub\u003eg\u003c/sub\u003e\u003c/em\u003e) and mean prediction error variance (PEV) differed among isolates and traits and are presented in \u003cstrong\u003eTable 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation between AUDPC_N and AUDPC_P phenotypes across isolates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpearman rank correlation analysis using phenotype BLUEs calculated for each isolate revealed moderate positive correlations among necrosis traits across isolates (\u003cem\u003e\u0026rho;\u003c/em\u003e = 0.57\u0026ndash;0.80, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), indicating that genotypes exhibiting high necrosis to one isolate tended to respond similarly to others (\u003cstrong\u003eFig. 4\u003c/strong\u003e). Correlations among AUDPC_P BLUEs were weaker but consistently positive (\u003cem\u003e\u0026rho;\u003c/em\u003e \u0026asymp; 0.32\u0026ndash;0.59)\u003cstrong\u003e.\u003c/strong\u003e Correlations between necrosis and pycnidia were generally weaker still (\u003cem\u003e\u0026rho;\u003c/em\u003e = 0.18\u0026ndash;0.46) and were often non-significant. This pattern indicates that, although necrosis responses show moderate consistency across isolates, substantial isolate-specific variation remains, and cross-trait associations are limited. The decoupling of necrosis and pycnidia was particularly evident for HT-18 and HT-96, where necrosis severity showed little association with pycnidia development. Collectively, these findings suggest that individual isolates cannot fully predict symptom expression to others and that necrosis and pycnidia represent partially independent components of STB resistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenotypic analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGenomic variation, population structure, and linkage disequilibrium\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of SNP marker distribution across the three wheat subgenomes revealed a clear disparity in polymorphism (\u003cstrong\u003eTable 3\u003c/strong\u003e). The B subgenome exhibited the highest marker density, averaging 486.1 SNPs per chromosome, whereas the D subgenome showed significantly lower variation, averaging 191.1 SNPs per chromosome. This pattern is consistent with the reduced genetic diversity of the D subgenome, resulting from the evolutionary bottleneck associated with hexaploid wheat formation. However, the lower proportion of retained D-subgenome markers relative to their representation on the 35K array suggests that ascertainment bias in marker design may also have contributed to this disparity (\u003cstrong\u003eTable 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eMarker distribution across the physical length of all 21 chromosomes indicated sufficient density for downstream LD analysis and GWAS. Prior to genetic analyses, markers were pruned to minimise redundancy by removing SNPs in perfect LD (\u003cem\u003er\u0026sup2;\u003c/em\u003e = 1). Because LD-based pruning filters markers on the basis of statistical correlation rather than physical position, the resulting pruned datasets does not accurately reflect physical genome coverage. Consequently, chromosome and subgenome coverage were evaluated using the unpruned dataset, whereas the LD-pruned dataset was used exclusively to reduce marker redundancy for downstream population structure and association analyses (\u003cstrong\u003eFig. 5\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003ePCA revealed clear population structure within the NAM panel, driven primarily by the synthetic founders (\u003cstrong\u003eFig. 6\u003c/strong\u003e). PC1 and PC2 together explained 13.69% of the total genetic variation (PC1 = 7.06%, PC2 = 6.63%). The four founders (SHW.035, SHW.054, SHW.075, and Robigus) formed distinct clusters. Progeny derived from SHW.035 and SHW.054 formed partially overlapping clusters, consistent with their shared tetraploid donor (\u003cstrong\u003eTable 1\u003c/strong\u003e). Subgenome-specific comparison of founder SNPs showed that SHW.035 and SHW.054 exhibited minimal differentiation across the A and B subgenomes but extensive differentiation across the D subgenome (\u003cstrong\u003eSupplementary Fig. S6\u003c/strong\u003e). Differentiating SNPs between these founders were distributed across all D-subgenome chromosomes rather than being confined to specific regions, consistent with their distinct \u003cem\u003eAe. tauschii\u003c/em\u003e lineage donors (L1 for SHW.035 and L2 for SHW.054). In contrast, SHW.075 displayed broader genome-wide differentiation, reflecting divergence in both tetraploid and D-subgenome ancestry. As expected for backcross-derived families, NAM lines clustered closer to Robigus than to their SHW parents. Overall, the PCA confirms strong founder-driven population structure shaped primarily by D-subgenome lineage differences among the SHW donors\u003cem\u003e.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGenome-wide association study (GWAS)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGWAS was conducted to identify QTLs controlling resistance to \u003cem\u003eZ. tritici\u003c/em\u003e in the NAM population. Analyses used BLUEs calculated for two disease metrics, AUDPC_N and AUDPC_P, across five isolates (HT-18, HT-44, HT-53, HT-74, and HT-96) (\u003cstrong\u003eSupplementary Table S3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eGenome-wide LD decay analysis showed that the correlation coefficient (\u003cem\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e) declined to 0.2 at approximately 50 Mb (\u003cstrong\u003eSupplementary Fig. S7\u003c/strong\u003e). This relatively slow LD decay is consistent with the backcross-derived nature of the population, limited historical recombination, and relatedness among lines. This distance was therefore used to delineate independent QTL regions by selecting the most significant SNP within each 50 Mb window following computation of marker significance as -log10\u003cem\u003e(p)\u003c/em\u003e. Across traits and isolates, several significant QTLs were identified (\u003cstrong\u003eTable 4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGenome-wide association mapping for necrosis (AUDPC_N)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA major, highly reproducible QTL, designated \u003cem\u003eqSTB-3D.1\u003c/em\u003e, was detected on 3DL in response to four of the five tested isolates (HT-18, HT-53, HT-74, and HT-96) (\u003cstrong\u003eTable 4\u003c/strong\u003e, \u003cstrong\u003eFig. 7, Supplementary Fig. S8\u003c/strong\u003e). No significant association at this locus was detected in response to isolate HT-44 (\u003cstrong\u003eSupplementary Fig. S8\u003c/strong\u003e). In all cases, the SHW-derived allele significantly reduced AUDPC_N, with estimated effect sizes ranging from approximately \u0026minus;199 to \u0026minus;382. These consistently large negative effects indicate that \u003cem\u003eqSTB-3D.1\u003c/em\u003e represents a major-effect resistance QTL.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSegregation of this QTL was highly family specific: a small subset of NAM.144 lines carried the resistance-associated allele, whereas the other two NAM families were fixed for the susceptible allele, consistent with SHW.035 being the sole donor of this resistance. Across all four responsive isolates, significant associations mapped to a consistent genomic interval spanning 587.09\u0026ndash;610.79\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eMb on chromosome 3D. The same peak SNP (AX-95090074; 609.21 Mb) was identified for all four isolates. All associations fell within a single extended LD block, indicating that these signals represent the same underlying QTL.\u003c/p\u003e\n\u003cp\u003eTo investigate the genetic basis of \u003cem\u003eqSTB-3D.1\u003c/em\u003e, the 23.7 Mb interval spanning 587.09\u0026ndash;610.79 Mb on chromosome 3D was examined in detail. A total of 425 high-confidence genes were identified within this interval (\u003cstrong\u003eSupplementary Table S4\u003c/strong\u003e). Consistent with known mechanisms of resistance to \u003cem\u003eZ. tritici\u003c/em\u003e, the search prioritised genes encoding immune receptor-related proteins, including WAKs, CRKs, LecRKs, and NLRs. The most prominent feature of the interval was a dense cluster of seven CRKs located between 590.04 and 590.31 Mb. This cluster includes the cloned resistance gene \u003cem\u003eStb16q\u003c/em\u003e (TraesCS3D02G500800) (Ghaffary et al. 2012; Saintenac et al. 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough the strongest statistical association across all isolates, SNP AX-95090074, occurred distally at approximately 609 Mb, this peak resides within the same extended LD block as the CRK cluster. The peak is located near a WAK gene (TraesCS3D02G533100) at 608.92 Mb. Although additional WAK, RLKs, NLRs, and RLPs are present within the broader interval, these represent lower-priority candidates than the \u003cem\u003eStb16q\u003c/em\u003e-containing CRK cluster. A summary of annotated candidate gene classes within the interval is provided in \u003cstrong\u003eTable 5.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIsolate-specific QTLs for necrosis (AUDPC_N)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to the major \u003cem\u003eqSTB-3D.1\u003c/em\u003e locus, several necrosis QTLs were detected in an isolate-specific manner (\u003cstrong\u003eTable 4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eTwo additional loci were identified in response to isolate HT-18 (\u003cstrong\u003eFig. 7a\u003c/strong\u003e). The first, \u003cem\u003eqSTB-1B.1\u003c/em\u003e (28.56\u0026ndash;32.77 Mb), was restricted to the SHW.075-derived family and conferred a substantial reduction in necrosis (ALT allele effect: \u0026minus;395.24), explaining 8.89% of the phenotypic variance. Candidate gene analysis within this 1BS interval identified a WAK gene (TraesCS1B02G050100) at 29.92 Mb, located proximal to the peak SNP (\u003cstrong\u003eSupplementary Table S5\u003c/strong\u003e). The second locus, \u003cem\u003eqSTB-6D.1\u003c/em\u003e, was detected at approximately 27 Mb on chromosome 6D. Unlike the 1BS locus, the resistance allele for \u003cem\u003eqSTB-6D.1\u003c/em\u003e was present in all three SHW parents. This QTL explained 6.14% of the phenotypic variance (effect size: \u0026minus;282.01) and is located near the WAK candidate gene TraesCS6D02G056600 (27.06 Mb) (\u003cstrong\u003eSupplementary Table S6\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eA QTL on chromosome 3A, designated, \u003cem\u003eqSTB-3A.1\u003c/em\u003e, was detected exclusively in response to isolate HT-53 (\u003cstrong\u003eFig. 7b\u003c/strong\u003e, \u003cstrong\u003eSupplementary Fig. S8\u003c/strong\u003e). This locus mapped to an approximately 11.41 Mb region, had a substantial negative effect on AUDPC_N (effect size: \u0026minus;334.58), and explained 10.53% of the phenotypic variance (effect size: \u0026minus;374.94. The resistance-associated allele was restricted to the SHW.075-derived family. The interval contains ten WAK genes, including the cloned resistance gene \u003cem\u003eStb6\u003c/em\u003e (TraesCS3A02G049500) (\u003cstrong\u003eTable 5\u003c/strong\u003e,\u003cstrong\u003e\u0026nbsp;Supplementary Table S7\u003c/strong\u003e) (Saintenac et al. 2018).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, a single isolate-specific QTL was identified for HT-74 on chromosome 2D (\u003cem\u003eqSTB-2D.1\u003c/em\u003e) (\u003cstrong\u003eFig. 7c\u003c/strong\u003e). Spanning a narrow 2.09 Mb interval (641.11\u0026ndash;643.19 Mb), this locus accounted for 8.71% of the phenotypic variance, with an effect size of \u0026ndash;329.67. The resistance allele was present in all three SHW parents. Within this region, an CRK gene, TraesCS2D02G579600, was identified as a primary candidate, together with two additional kinase domain-encoding genes (\u003cstrong\u003eTable 5\u003c/strong\u003e,\u003cstrong\u003e\u0026nbsp;Supplementary Table S8\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGWAS for pycnidia development (AUDPC_P)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn contrast to necrosis, GWAS identified no major or reproducible QTLs for pycnidia coverage (AUDPC_P) across the five tested isolates. Significant associations were detected only in response to isolate HT-96, which revealed a single modest-effect locus on the long arm of chromosome 1B, designated \u003cem\u003eqSTB-1B.2\u003c/em\u003e (~647.23 Mb) (\u003cstrong\u003eTable 4, Fig. 8\u003c/strong\u003e). This QTL explained 7.41% of the phenotypic variance, and the resistance-associated allele was contributed by Robigus. The \u003cem\u003eqSTB-1B.2\u003c/em\u003e interval did not contain any annotated genes encoding obvious immune receptor-like proteins (\u003cstrong\u003eSupplementary Table S9\u003c/strong\u003e). No significant associations were detected for the remaining four isolates (\u003cstrong\u003eSupplementary Fig. S9\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSegregation of resistance phenotypes in a SHW.BC.144.12.1.4.1 \u0026times; Robigus F\u003csub\u003e2\u003c/sub\u003e population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn F\u003csub\u003e2\u003c/sub\u003e population derived from a cross between the highly resistant NAM line SHW.BC.144.12.1.4.1, a member of NAM.144 developed from SHW.035 \u0026times; Robigus) and the susceptible variety Robigus was assessed for resistance to \u003cem\u003eZ. tritici\u003c/em\u003e isolate HT-74. The principal disease components, percentage leaf coverage by pycnidia and by necrosis, were scored at 29-dpi (\u003cstrong\u003eFig. 9\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe distribution of percentage leaf coverage by pycnidia in the F\u003csub\u003e2\u003c/sub\u003e population was strongly skewed towards resistance (\u003cstrong\u003eFig. 9a\u003c/strong\u003e). Most individuals showed no or very low visible pycnidia, resembling the resistant parent, whereas fewer individuals exhibited moderate or high levels. A small number of F\u003csub\u003e2\u003c/sub\u003e individuals displayed higher pycnidia coverage than Robigus, indicating transgressive segregation. When individuals were classified into resistant (0 %) and susceptible (\u0026gt;0 %) categories, the observed segregation (191 R: 65 S) did not deviate from a 3:1 expectation (\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e = 0.021, \u003cem\u003ep\u003c/em\u003e = 0.885), consistent with control by a single dominant locus.\u003c/p\u003e\n\u003cp\u003eFor percentage leaf coverage by necrosis, individuals showing 0\u0026ndash;20% classed as resistant and those showing \u0026gt;20% as susceptible (\u003cstrong\u003eFig. 9b\u003c/strong\u003e). T The observed segregation (111 R : 145 S) among scored individuals was consistent with a 7:9 ratio (\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e = 0.016, \u003cem\u003edf\u003c/em\u003e = 1, \u003cem\u003ep\u003c/em\u003e = 0.90), but inconsistent with simple single-gene models, including a 3:1 ratio (\u003cem\u003e\u0026chi;\u003c/em\u003e\u0026sup2; = 136.69, \u003cem\u003edf\u003c/em\u003e = 1, \u003cem\u003ep\u003c/em\u003e \u0026lt; 2.2 \u0026times; 10⁻\u0026sup1;⁶) and a 1:3 ratio (\u003cem\u003e\u0026chi;\u003c/em\u003e\u0026sup2; = 46.02, \u003cem\u003edf\u003c/em\u003e = 1, \u003cem\u003ep\u003c/em\u003e = 1.17 \u0026times; 10⁻\u0026sup1;\u0026sup1;). These results indicate more complex genetic control of this trait in this population.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study used a SHW-derived NAM population to dissect resistance to \u003cem\u003eZ. tritici\u003c/em\u003e across multiple isolates differing in virulence. By integrating multi-isolate phenotyping with genetic mapping, the study identified both broad-spectrum and isolate-specific resistance loci derived from SHW and introgressed into an elite UK background. Importantly, the results show that genetic factors limiting pathogen reproduction are at least partly distinct from those controlling host tissue damage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIsolate-specific virulence and host response variation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe five \u003cem\u003eZ. tritici\u003c/em\u003e isolates used in this study exhibited a range of aggressiveness across host genetic backgrounds. HT-74 consistently induced the highest levels of disease, whereas HT-96 was markedly less aggressive, and this hierarchy was broadly maintained across both susceptible elite wheat controls and SHW-derived material (\u003cstrong\u003eFig. 2\u003c/strong\u003e). This range of virulence and aggressiveness reflects the extensive genetic and effector diversity characteristic of natural \u003cem\u003eZ. tritici\u003c/em\u003e populations (McDonald et al. 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhereas the susceptible controls KWS Cashel and Robigus developed substantial disease in response to all isolates, SHW-derived lines showed markedly reduced disease severity. A striking feature of these synthetic backgrounds was the frequent uncoupling of necrosis and pycnidia development (\u003cstrong\u003eFig. 3\u003c/strong\u003e). All three SHW-derived NAM families exhibited consistently low AUDPC_P, often approaching a complete absence of visible pycnidia, even when necrotic lesions (AUDPC_N) were present. This phenotype was most pronounced in the SHW.075-derived family, which displayed substantial necrosis despite strong suppression of pathogen reproduction. This response may reflect early activation of host defence mechanisms that limit fungal proliferation before pycnidia formation. Although principal component analysis showed NAM lines clustered genetically closer to the recurrent parent Robigus, their disease phenotypes, particularly for AUDPC_P, more closely resembled those of their respective SHW founders.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis uncoupling suggests the presence of resistance mechanisms that restrict pathogen development rather than preventing initial infection, a recognised feature of the wheat\u0026ndash;\u003cem\u003eZ. tritici\u003c/em\u003e interaction (Fones et al. 2023). Similar phenotypes have been described in wheat lines carrying \u003cem\u003eStb3\u003c/em\u003e, \u003cem\u003eStb6\u003c/em\u003e, or \u003cem\u003eStb5\u003c/em\u003e, where fungal ingress occurs but subsequent pathogen growth and reproduction are effectively arrested by host defence responses (Tidd et al. 2023). From an epidemiological perspective, suppression of pycnidia is especially important because it directly limits the production of secondary inoculum and subsequent spread within the crop canopy.\u003c/p\u003e\n\u003cp\u003eSegregation analysis in the F\u003csub\u003e2\u003c/sub\u003e population derived from the cross between the highly resistant NAM line SHW.BC.144.12.1.4.1 and susceptible Robigus supported this interpretation. Resistance to pycnidia formation followed a clear 3:1 segregation ratio, consistent with control by a single dominant resistance factor. In contrast, necrosis severity did not conform to a simple Mendelian ratio and instead displayed a more complex distribution, suggesting quantitative control and/or a contribution from host physiological responses to infection. Together, these results suggest that resistance in this SHW-derived material is organised around a major factor that strongly restricts pathogen reproduction, with additional loci influencing the extent of host cell death. This resistance architecture is consistent with receptor-mediated recognition mechanisms, potentially involving WAKs or other RLKs that activate defence signalling upon pathogen detection. Such combination of a strong primary resistance factor with polygenic background effects may contribute to increased durability, as proposed in other plant\u0026ndash;pathogen systems (Palloix et al. 2009).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBroad-spectrum resistance on chromosome 3D\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe major QTL on chromosome 3D, \u003cem\u003eqSTB-3D.1\u003c/em\u003e, was detected in response to four of the five \u003cem\u003eZ. tritici\u0026nbsp;\u003c/em\u003eisolates tested and explained a substantial proportion of the variation in necrosis severity. The resistance-associated allele was contributed exclusively by the D-genome founder of SHW.035 (\u003cem\u003eAe. tauschii\u0026nbsp;\u003c/em\u003eaccession WX224) and was absent from the other NAM families.\u003c/p\u003e\n\u003cp\u003eThe physical interval underlying \u003cem\u003eqSTB-3D.1\u003c/em\u003e overlaps a cluster of CRK genes, including \u003cem\u003eStb16q\u003c/em\u003e, a cloned STB resistance gene first identified in a synthetic hexaploid wheat M3 line (W-7976) developed by CIMMYT (Ghaffary et al. 2012; Saintenac et al. 2021). \u003cem\u003eStb16q\u003c/em\u003e confers broad-spectrum resistance by restricting stomatal penetration and inhibiting early fungal growth (Battache et al. 2022), which is consistent with the multi-isolate effect observed here. Although no significant effect was detected for isolate HT-44, this may reflect partial virulence, a smaller effect size, or experimental variation.\u003c/p\u003e\n\u003cp\u003eTwo local maxima were observed within the broader 3DL association interval: one near 588 Mb, close to the reported position of \u003cem\u003eStb16q\u003c/em\u003e, and a second near 609 Mb adjacent to a WAK gene. Resistance-associated regions spanning this interval have been reported in several independent studies, with uncertainty as to whether they correspond to \u003cem\u003eStb16q\u003c/em\u003e itself or to additional tightly linked loci. For example, Odilbekov et al. (2019) identified a resistance QTL spanning approximately 593.4\u0026ndash;614.4 Mb and noted ambiguity regarding its relationship to \u003cem\u003eStb16q\u003c/em\u003e. More recently, Binalf et al. (2024) also highlighted the region around 609 Mb as being associated with STB resistance.\u003c/p\u003e\n\u003cp\u003eClarifying the genetic basis of this resistance will require targeted sequencing of the \u003cem\u003eStb16q\u003c/em\u003e-associated interval in SHW.035 to determine whether it corresponds to the previously characterised \u003cem\u003eStb16q\u003c/em\u003e allele now deployed in some European varieties or represents distinct allelic variation relative to the current European bread wheat gene pool. The emergence of \u003cem\u003eZ. tritici\u003c/em\u003e isolates virulent towards \u003cem\u003eStb16q-\u003c/em\u003econtaining wheat varieties (such as Cellule) in European populations further underlines the importance of evaluating the effectiveness of this locus against more recently sampled field isolates (Kildea et al. 2020; Orellana‐Torrejon et al. 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIsolate-specific resistance loci on chromosomes 1B, 2D, 3A and 6D\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral isolate-specific resistance loci were detected, consistent with gene-for-gene interactions between NAM founders and individual \u003cem\u003eZ. tritici\u003c/em\u003e isolates. The locus \u003cem\u003eqSTB-1B.1\u003c/em\u003e, detected in response to isolate HT-18, maps to 1BS within a region known to harbour multiple STB resistance genes, including \u003cem\u003eStb2\u003c/em\u003e (Liu et al. 2013), \u003cem\u003eStb11\u003c/em\u003e (Chartrain et al. 2005b), and several \u003cem\u003eStbWW\u003c/em\u003e loci (Raman et al. 2009). However, the interval identified here lies outside the previously defined major resistance clusters and the \u003cem\u003eQStb.wai.1B.1\u003c/em\u003e region (Yang et al. 2022), suggesting that \u003cem\u003eqSTB-1B.1\u003c/em\u003e may represent either a distinct locus or allelic variation within this broader resistance gene-rich region. Candidate gene analysis identified a WAK (TraesCS1B02G050100) within the peak interval.\u003c/p\u003e\n\u003cp\u003eA second HT-18-specific locus, \u003cem\u003eqSTB-6D.1\u003c/em\u003e, was identified on chromosome 6D. This region partially overlaps the reported position of \u003cem\u003eStb18\u003c/em\u003e, an isolate-specific resistance gene (Tabib Ghaffary et al. 2011). However, the peak association detected here falls outside the currently defined \u003cem\u003eStb18\u003c/em\u003e interval, leaving open the possibility that \u003cem\u003eqSTB-6D.1\u003c/em\u003e represents either \u003cem\u003eStb18\u003c/em\u003e itself or a closely linked resistance factor. Notably, all other isolates tested were virulent on the variety Balance, which carries \u003cem\u003eStb6\u003c/em\u003e and \u003cem\u003eStb18\u003c/em\u003e (Tidd et al. 2023), supporting the isolate-specific nature of the HT-18 response.\u003c/p\u003e\n\u003cp\u003eOn chromosome 3A, \u003cem\u003eqSTB-3A.1\u003c/em\u003e was detected exclusively in response to isolate HT-53 and was contributed by SHW.075. This region overlaps the location of \u003cem\u003eStb6\u003c/em\u003e, which encodes a WAK mediating gene-for-gene resistance (Brading et al. 2002; Saintenac et al. 2018). Although \u003cem\u003eStb6\u003c/em\u003e is present in many European varieties, including Robigus (Chartrain et al. 2005a; Goudemand et al. 2013), the resistance-associated allele at \u003cem\u003eqSTB-3A.1\u003c/em\u003e was contributed by SHW.075 in this study. This suggests either allelic variation at the \u003cem\u003eStb6\u003c/em\u003e locus or the presence of a tightly linked resistance gene within the same WAK cluster, producing a highly isolate-specific interaction with HT-53.\u003c/p\u003e\n\u003cp\u003eThe locus \u003cem\u003eqSTB-2D.1\u003c/em\u003e, detected in response to isolate HT-74, is of particular interest because no major \u003cem\u003eStb\u003c/em\u003e genes have been reported on chromosome 2D. This region overlaps previously reported STB resistance QTLs identified in synthetic-derived germplasm (Naz et al. 2015; Riaz et al. 2020), suggesting that chromosome 2D may represent a recurrent target for STB resistance. The peak interval contains a CRK-encoding gene (TraesCS2D02G579600), consistent with receptor-mediated defence mechanisms. The contribution of this allele by all three SHW founders further underlines the importance of synthetic wheat germplasm as a reservoir of novel STB resistance diversity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic architecture of pycnidia resistance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn contrast to necrosis severity, GWAS identified very few loci associated with AUDPC_P. Only one minor-effect QTL was detected in response to isolate HT-96, and no significant associations were identified for the more aggressive isolates (\u003cstrong\u003eTable 4\u003c/strong\u003e). Although broad-sense heritability for resistance to pycnidia development was moderate to high, within-family variance was limited because sporulation was strongly suppressed in most SHW-derived NAM families. This restricted phenotypic variation likely reduced the power of association mapping to detect loci underlying pycnidia resistance (\u003cstrong\u003eFig. 2,3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eConsistent with this interpretation, candidate gene analysis provided little evidence for major resistance determinants underlying the detected AUDPC_P QTL. No obvious immune receptor-like genes were identified within the \u003cem\u003eqSTB-1B.2\u003c/em\u003e interval. This suggests that \u003cem\u003eqSTB-1B.2\u003c/em\u003e represents a minor, background-dependent effect rather than a primary determinant of resistance to pycnidia formation in this NAM population.\u003c/p\u003e\n\u003cp\u003eFurther support for this interpretation comes from segregation analysis in the independent F\u003csub\u003e2\u003c/sub\u003e population derived from the SHW.BC.144.12.1.4.1 \u0026times; Robigus cross, in which pycnidia presence or absence followed a clear 3:1 ratio, consistent with control by a single dominant resistance factor in that specific cross. However, this Mendelian segregation pattern cannot be assumed to extend across the wider NAM population, where the corresponding resistance-associated allele may be fixed within individual families or present at high frequency across multiple SHW-derived lineages. In a NAM framework, loci that are fixed within families or exhibit limited allelic contrast across the population are inherently difficult to detect by association-based approaches, even when they have strong phenotypic effects (Yu et al. 2008; McMullen et al. 2009). It is also plausible that strong pycnidia resistance alleles are shared across multiple SHW-derived families, resulting in limited segregation for this trait within the overall population. Under such circumstances, association mapping would be expected to have reduced power to detect the underlying loci because of insufficient allelic contrast among families (Korte et al. 2013).\u003c/p\u003e\n\u003cp\u003eIn addition, the resolution of the phenotyping scale may have further reduced sensitivity for detecting small genetic effects. Percentage leaf coverage by pycnidia was scored in 20% increments, producing a discrete ordinal phenotype that may compress variation, particularly when most genotypes cluster near the lower end of the scale.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations and future directions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile this study provides a detailed analysis of the genetic basis of STB resistance in a SHW-derived NAM population, several limitations should be acknowledged. First, disease phenotyping relied on visual scoring using discrete 20% intervals. Although this approach was sufficient to capture major differences in disease severity, particularly for necrosis, it is inherently subjective and may reduce sensitivity for detecting subtle phenotypic variation associated with minor-effect loci. The development of image-based phenotyping pipelines offers a clear opportunity to address this limitation by enabling continuous, high-resolution quantification of necrotic and sporulating tissue on living leaves over time. Such approaches are likely to improve both the power and precision of future genome-wide association analyses, particularly for traits governed by small-effect loci.\u003c/p\u003e\n\u003cp\u003eSecond, all phenotyping was conducted at the seedling stage under controlled conditions. Validation of the identified QTLs under field conditions at the adult-plant stage, particularly under high disease pressure, will therefore be essential to confirm their breeding relevance and to assess their stability across environments.\u003c/p\u003e\n\u003cp\u003eFinally, although the presence of major resistance factors within the SHW-derived material is well supported, the genetic basis of pycnidia suppression remains only partly resolved. The limited power of association mapping to detect loci controlling this trait suggests that targeted biparental populations may be better suited for further genetic dissection. In particular, the SHW.BC.144.12.1.4.1 \u0026times; Robigus population provided an opportunity to characterise resistance factors that are difficult to resolve within the NAM framework and to distinguish between control by a single dominant gene and multiple tightly linked loci. Because this population was generated by crossing a BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e6\u003c/sub\u003e donor line to the recurrent parent and then advancing to the F\u003csub\u003e2\u003c/sub\u003e generation, it is more accurately described as a BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u0026nbsp;\u003c/sub\u003epopulation than as a true F\u003csub\u003e2\u003c/sub\u003e. This makes it especially attractive for integration into commercial breeding programmes, since much of the donor genome has already been eliminated through backcrossing. Such material will be valuable for fine mapping, validation of the resistance mechanisms inferred from the NAM analysis, and more rapid deployment of resistance in adapted germplasm.\u003c/p\u003e\n\u003cp\u003eIn addition, further characterisation of the \u003cem\u003eStb16q\u003c/em\u003e-associated interval in SHW.035 will be important to determine allelic novelty and assess resistance durability. Given reports of \u003cem\u003eZ. tritici\u003c/em\u003e isolates virulent towards \u003cem\u003eStb16q\u003c/em\u003e in European populations, evaluating the effectiveness of this locus against contemporary field isolates will be critical for informing its potential deployment in breeding programmes. Overall, this study shows that resistance to STB in the investigated SHW-derived NAM population is governed by multiple genetic factors that differ in breadth and may involve distinct modes of action. With higher-resolution phenotyping, validation at the adult-plant stage, and targeted fine mapping, the resistance mechanisms identified here can be resolved more fully and translated into wheat improvement.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKK \u0026ndash; conceptualisation; AB \u0026ndash; experimental work, phenotyping, data analysis and writing; TICW \u0026ndash; guidance on SNP genotyping and quality control; FL, RH, PH \u0026ndash; research materials; AB, FL, KK, PH, RR, SR, and TICW \u0026ndash; review and editing; FL, KK, PH, RR, and SR \u0026ndash; supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAB was funded by the Biotechnology and Biological Sciences Research Council (BBSRC) through the Collaborative Training Program for Sustainable Agricultural Innovation (CTP-SAI) (BB/W009439/1), in partnership with The Morley Agricultural Foundation (TMAF) and the University of Nottingham. KK and PH acknowledge support from Defra through the Wheat Genetic Improvement Network (WGIN; contract C24770). PH was also supported by the BBSRC Institute Strategic Programme: Delivering Sustainable Wheat (DSW) \u0026ndash; partner grant BB/Y000064/1. RR and SR provided formal support to AB on behalf of the University of Nottingham and TMAF, respectively. We thank the Research \u0026amp; Scientific Computing teams at the James Hutton Institute for providing access to the UK Crop Diversity High-Performance Computing platform, which supported the analyses presented in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) through the Collaborative Training Program for Sustainable Agricultural Innovation (BB/W009439/1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data supporting the findings of this study are available within the paper and within its Supporting Information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdhikari T B, Anderson JM, Goodwin SB (2003) Identification and molecular mapping of a gene in wheat conferring resistance to Mycosphaerella graminicola. 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R Foundation for Statistical Computing, Vienna, Austria.\u003c/li\u003e\n\u003cli\u003eRaman R, Milgate A, Imtiaz M, Tan M-K, Raman H, Lisle C, Coombes N, Martin P (2009) Molecular mapping and physical location of major gene conferring seedling resistance to Septoria tritici blotch in wheat. Mol Breed 24:153-164. https://doi.org/10.1007/s11032-009-9280-0 \u003c/li\u003e\n\u003cli\u003eRiaz A, KockAppelgren P, Hehir JG, Kang J, Meade F, Cockram J, Milbourne D, Spink J, Mullins E, Byrne S (2020) Genetic analysis using a multi-parent wheat population identifies novel sources of Septoria tritici blotch resistance. Genes 11:887. https://doi.org/10.3390/genes11080887 \u003c/li\u003e\n\u003cli\u003eRosyara UR, De Jong WS, Douches DS, Endelman JB (2016) Software for genome-wide association studies in autopolyploids and its application to potato. Plant Genome 9: plantgenome2015.08.0073. https://doi.org/10.3835/plantgenome2015.08.0073 \u003c/li\u003e\n\u003cli\u003eSaintenac C, Lee WS, Cambon F, Rudd JJ, King RC, Marande W, Powers SJ, Berg\u0026egrave;s H, Phillips AL, Uauy C, Hammond-Kosack KE, Langin T, Kanyuka K (2018) Wheat receptor-kinase-like protein Stb6 controls gene-for-gene resistance to fungal pathogen Zymoseptoria tritici. Nat Genet 50:368-374. https://doi.org/10.1038/s41588-018-0051-x \u003c/li\u003e\n\u003cli\u003eSaintenac C, Cambon F, Aouini L, Verstappen E, Ghaffary SMT, Poucet T, Marande W, Berges H, Xu S, Jaouannet M, Favery B, Alassimone J, S\u0026aacute;nchez-Vallet A, Faris J, Kema G, Robert O, Langin T (2021) A wheat cysteine-rich receptor-like kinase confers broad-spectrum resistance against Septoria tritici blotch. Nat Commun 12:433. https://doi.org/10.1038/s41467-020-20685-0 \u003c/li\u003e\n\u003cli\u003eShin J, Blay S, Lewin-Koh N (2006) LDheatmap: Graphical display of pairwise linkage disequilibria between SNPs. R package version 0.2-3. https://sfustatgen.github.io/LDheatmap/index.html \u003c/li\u003e\n\u003cli\u003eSimko I, Piepho HP (2012) The area under the disease progress stairs: calculation, advantage, and application. Phytopathology 102:381-389. https://doi.org/10.1094/PHYTO-07-11-0216 \u003c/li\u003e\n\u003cli\u003eStekhoven DJ, Stekhoven MDJ (2012) MissForest: nonparametric missing value imputation for mixed-type data. Bioinformatics 28:112-118. https://doi.org/10.1093/bioinformatics/btr597 \u003c/li\u003e\n\u003cli\u003eGhaffary SM, Robert O, Laurent V, Lonnet P, Margal\u0026eacute; E, van der Lee TA, Visser RG, Kema GH (2011) Genetic analysis of resistance to septoria tritici blotch in the French winter wheat cultivars Balance and Apache. Theor Appl Genet 123:741-754. https://doi.org/10.1007/s00122-011-1623-7 \u003c/li\u003e\n\u003cli\u003eThauvin JN, G\u0026eacute;lisse S, Cambon F, Langin T; Breedwheat consortium; Marcel TC, Saintenac C (2024) The genetic architecture of resistance to Septoria tritici blotch in French wheat cultivars. BMC Plant Biol 24:1212. https://doi.org/10.1186/s12870-024-05898-5 \u003c/li\u003e\n\u003cli\u003eTidd H, Rudd JJ, Ray RV, Bryant R, Kanyuka K (2022) A large bioassay identifies Stb resistance genes that provide broad resistance against Septoria tritici blotch disease in the UK. Front Plant Sci 13:1070986. https://doi.org/10.3389/fpls.2022.1070986 \u003c/li\u003e\n\u003cli\u003eTorriani SF, Brunner PC, McDonald BA, Sierotzki H (2009) QoI resistance emerged independently at least 4 times in European populations of Mycosphaerella graminicola. Pest Manag Sci 65:155-162. https://doi.org/10.1002/ps.1662 \u003c/li\u003e\n\u003cli\u003eTorriani SF, Melichar JP, Mills C, Pain N, Sierotzki H, Courbot M (2015) Zymoseptoria tritici: A major threat to wheat production, integrated approaches to control. Fungal Genet Biol 79:8-12. https://doi.org/10.1016/j.fgb.2015.04.010 \u003c/li\u003e\n\u003cli\u003eWei T, Simko V (2024) Package \u0026lsquo;corrplot\u0026rsquo;: Visualization of a correlation matrix (Version 0.95). Statistician 56:e24. https://doi.org/10.32614/CRAN.package.corrplot \u003c/li\u003e\n\u003cli\u003eWickham H (2016) ggplot2: Elegant Graphics for Data Analysis. https://doi.org/10.32614/CRAN.package.ggplot2 \u003c/li\u003e\n\u003cli\u003eWilkinson PA, Winfield MO, Barker GL, Tyrrell S, Bian X, Allen AM, Burridge A, Coghill JA, Waterfall C, Caccamo M, Davey RP, Edwards KJ (2016) CerealsDB 3.0: expansion of resources and data integration. BMC Bioinformatics 17:256. https://doi.org/10.1186/s12859-016-1139-x \u003c/li\u003e\n\u003cli\u003eWright TIC, Horsnell R, Love B, Burridge AJ, Gardner KA, Jackson R, Leigh FJ, Ligeza A, Heuer S, Bentley AR, Howell P (2024) A new winter wheat genetic resource harbors untapped diversity from synthetic hexaploid wheat. Theor Appl Genet 137:73. https://doi.org/10.1007/s00122-024-04577-1 \u003c/li\u003e\n\u003cli\u003eYang N, McDonald MC, Solomon PS, Milgate AW (2018) Genetic mapping of Stb19, a new resistance gene to Zymoseptoria tritici in wheat. Theor Appl Genet 131:2765-2773. https://doi.org/10.1007/s00122-018-3189-0 \u003c/li\u003e\n\u003cli\u003eYang N, Ovenden B, Baxter B, McDonald MC, Solomon PS, Milgate A (2022) Multi-stage resistance to Zymoseptoria tritici revealed by GWAS in an Australian bread wheat diversity panel. Front Plant Sci 13:990915. https://doi.org/10.3389/fpls.2022.990915 \u003c/li\u003e\n\u003cli\u003eYu J, Holland JB, McMullen MD, Buckler ES (2008) Genetic design and statistical power of nested association mapping in maize.\u0026quot; Genetics 178:539-551. https://doi.org/10.1534/genetics.107.074245 \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Details of the three-family NAM population used in this study, including population size and parental origin.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"482\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003ePopulation name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003eTotal lines genotyped \u0026amp; phenotyped (\u003cem\u003en\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eSynthetic parent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 121px;\"\u003e\n \u003cp\u003eSHW\u003c/p\u003e\n \u003cp\u003eDD donor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 79px;\"\u003e\n \u003cp\u003eSHW\u003c/p\u003e\n \u003cp\u003eAABB donor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eNAM.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 125px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eSHW.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 121px;\"\u003e\n \u003cp\u003eWX224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 79px;\"\u003e\n \u003cp\u003eBiensur\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eNAM.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 125px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eSHW.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 121px;\"\u003e\n \u003cp\u003eEnt.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 79px;\"\u003e\n \u003cp\u003eBiensur\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eNAM.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 125px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eSHW.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 121px;\"\u003e\n \u003cp\u003eEnt.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 79px;\"\u003e\n \u003cp\u003eHoh-501\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\u003e\u003cstrong\u003eTable 2. Broad-sense heritability (\u003cem\u003eH\u003c/em\u003e\u0026sup2;\u003cem\u003e\u003csub\u003ecullis\u003c/sub\u003e\u003c/em\u003e) estimates for necrosis (AUDPC_N) and pycnidia (AUDPC_P).\u0026nbsp;\u003c/strong\u003eGenotypic variance (\u003cem\u003eV\u003csub\u003eg\u003c/sub\u003e\u003c/em\u003e) and mean prediction error variance (PEV) are also shown.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"477\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIsolate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrait\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u003csub\u003ecullis\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eV\u003csub\u003eg\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePEV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003eHT-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eAUDPC_N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e186910.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e64200.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003eHT-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eAUDPC_N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e203191.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e98541.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003eHT-53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eAUDPC_N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e166334.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e59308.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003eHT-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eAUDPC_N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e281812.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e66853.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003eHT-96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eAUDPC_N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e144762.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e47844.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003eHT-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eAUDPC_P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e18020.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e11743.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003eHT-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eAUDPC_P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e11047.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e5128.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003eHT-53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eAUDPC_P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e30859.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e16778.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003eHT-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eAUDPC_P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e60558.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e25641.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003eHT-96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eAUDPC_P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e4798.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e3326.06\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\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Physical coverage of SNP markers across wheat chromosomes.\u003c/strong\u003e Coverage was calculated using 5 Mb windows and reflects the proportion of each chromosome represented by at least one SNP. Values are given before LD pruning.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"676\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChromosome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNPs on 35k array (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNPs retained\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003cem\u003en\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNPs retained (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCovered interval (Mb)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChromosome size\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Mb)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenome coverage\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e4A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e4B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e4D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e5A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e5D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e6B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e6D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e7D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 91px;\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 101px;\"\u003e\n \u003cp\u003e639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Significant QTLs detected for AUDPC necrosis (AUDPC_N) and pycnidia (AUDPC_P) across five \u003cem\u003eZymoseptoria tritici\u003c/em\u003e isolates in the three-family wheat NAM population.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"794\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQTL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrait\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIsolate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSignificance threshold\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeak SNP\u003cbr\u003e\u0026nbsp;position\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Mb)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeak SNP\u003cbr\u003e\u0026nbsp;significance\u003cbr\u003e (\u003cem\u003e-log\u003csub\u003e10\u003c/sub\u003e(p)\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eALT allele\u0026nbsp;\u003cbr\u003e\u0026nbsp;effect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVar. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSTB-1B.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003eAUDPC_N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003eHT-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e4.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAX-94632775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e28.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-395.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e8.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSTB-3D.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003eAUDPC_N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003eHT-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e4.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAX-95090074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e3D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e609.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e5.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-198.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e8.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSTB-6D.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003eAUDPC_N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003eHT-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e4.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAX-95175637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e6D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e27.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-282.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e6.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSTB-3A.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003eAUDPC_N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003eHT-53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e4.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAX-94766803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e3A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e26.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e5.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-374.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e10.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSTB-3D.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003eAUDPC_N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003eHT-53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e4.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAX-95090074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e3D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e609.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e5.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-334.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e7.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSTB-2D.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003eAUDPC_N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003eHT-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAX-94455930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e641.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-329.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e8.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSTB-3D.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003eAUDPC_N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003eHT-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAX-95090074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e3D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e609.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e5.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-175.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e11.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSTB-3D.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003eAUDPC_N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003eHT-96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e4.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAX-95090074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e3D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e609.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e6.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-382.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e8.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSTB-1B.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003eAUDPC_P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003eHT-96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAX-95193381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e647.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e4.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e101.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e7.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe table lists the chromosome (Chr), physical position (Mb), and \u0026minus;log10\u003cem\u003e(p)\u003c/em\u003e value of the peak SNP for each QTL. Significance thresholds were determined separately for each isolate and trait using 1,000 permutations at \u003cem\u003e\u0026alpha;\u003c/em\u003e = 0.05. Effect sizes represent the estimated change in phenotype per dosage of the alternative allele, with the Robigus allele used as the reference. \u0026lsquo;Var. (%)\u0026rsquo; indicates the percentage of total phenotypic variance explained by each QTL. Negative effect values indicate reduced necrosis or pycnidia associated with the alternative allele, whereas positive values indicate increased disease severity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Candidate resistance genes identified within QTL intervals.\u003c/strong\u003e Physical genomic coordinates and interval sizes (Mb) are based on the IWGSC RefSeq v.1.1 assembly. Candidate genes are categorised by functional class: CRK, cysteine-rich receptor-like kinase; WAK, wall-associated kinase; LecRLKs, lectin receptor-like kinase; LRR-RLK, leucine-rich repeat receptor-like kinase; NLR, nucleotide-binding leucine-rich repeat; Kinase, unspecified kinase; RLP, receptor-like protein.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"677\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQTL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStart position\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnd position\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWindow size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWAK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLecRK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRR-RLK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKinase\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRLP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSTB-1B.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 85px;\"\u003e\n \u003cp\u003e28560996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 85px;\"\u003e\n \u003cp\u003e32769214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSTB-1B.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 85px;\"\u003e\n \u003cp\u003e646172198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 85px;\"\u003e\n \u003cp\u003e651275182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSTB-2D.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 85px;\"\u003e\n \u003cp\u003e641106101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 85px;\"\u003e\n \u003cp\u003e643193656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSTB-3A.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 85px;\"\u003e\n \u003cp\u003e24057407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 85px;\"\u003e\n \u003cp\u003e35463415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e11.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 43px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eqSTB-3D.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 85px;\"\u003e\n \u003cp\u003e587096644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" 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style=\"width: 40px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Wheat, Disease resistance, Septoria tritici blotch, Synthetic hexaploid wheat, Nested association mapping, Genome-wide association study","lastPublishedDoi":"10.21203/rs.3.rs-9451480/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9451480/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSeptoria tritici blotch (STB), caused by \u003cem\u003eZymoseptoria tritici\u003c/em\u003e, remains a major constraint on wheat production worldwide. Erosion of host resistance and declining fungicide efficacy highlight the need to exploit the new sources of resistance, including synthetic hexaploid wheat (SHW). Here, we used a three-family nested association mapping (NAM) population derived from Niab SHW donors (SHW.035, SHW.054, and SHW.075) crossed with the elite UK variety Robigus to dissect the genetic basis of STB resistance under controlled conditions. Seedlings were challenged with five \u003cem\u003eZ. tritici\u003c/em\u003e isolates differing in virulence, and disease progress was quantified separately as necrosis (AUDPC_N) and pycnidia coverage (AUDPC_P). Many SHW-derived lines showed strong suppression of pycnidia despite visible necrosis, indicating partial genetic decoupling of pathogen reproduction and host tissue damage. Genome-wide association analysis identified a major broad-spectrum resistance locus on chromosome 3D (\u003cem\u003eqSTB-3D.1\u003c/em\u003e), contributed by SHW.035, that co-localised with the \u003cem\u003eStb16q \u003c/em\u003eregion. Additional isolate-specific loci were detected on chromosomes 1B, 2D, 3A, and 6D. In an F\u003csub\u003e2\u003c/sub\u003e population derived from a resistant SHW.035-derived NAM line, suppression of pycnidia segregated as a single dominant factor, whereas necrosis showed quantitative inheritance. These findings show that Niab SHWs are a valuable source of STB resistance and highlight the potential to breed for reduced pathogen reproduction independently of visible leaf damage.\u003c/p\u003e","manuscriptTitle":"Multi-isolate GWAS identifies broad-spectrum and isolate-specific Septoria tritici blotch resistance loci in synthetic hexaploid wheat","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-06 12:57:18","doi":"10.21203/rs.3.rs-9451480/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-27T09:33:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-20T20:03:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-18T19:00:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2026-04-17T16:46:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f4f150c9-864a-4062-b108-d6a972eaf3fb","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T12:57:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 12:57:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9451480","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9451480","identity":"rs-9451480","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}