Genome-wide association analysis identifies seven loci conferring resistance to multiple wheat foliar diseases, including yellow and brown rust resistance originating from Aegilops ventricosa

Genome-wide association analysis identifies seven loci conferring resistance to multiple wheat foliar diseases, including yellow and brown rust resistance originating from Aegilops ventricosa | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genome-wide association analysis identifies seven loci conferring resistance to multiple wheat foliar diseases, including yellow and brown rust resistance originating from Aegilops ventricosa Keith A. Gardner, Bethany Love, Pauline Bansept Basler, Tobias Barber, and 27 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6145769/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jun, 2025 Read the published version in Theoretical and Applied Genetics → Version 1 posted 5 You are reading this latest preprint version Abstract We assembled a European bread wheat (Triticum aestivum L.) association mapping panel (n=480) genotyped using a 90,000 single nucleotide polymorphism array, with the aim of identifying genetic loci controlling resistance to four fungal diseases: yellow (stripe) rust (YR), brown (leaf) rust (BR), Septoria tritici blotch (ST) and powdery mildew (PM). Simulations showed our panel to have good power to detect genetic loci, with >50% probability of identifying loci controlling as little as 5% of the variance when heritability was 0.6 or more. Using disease infection data collected across 31 trials undertaken in five European countries, genome-wide association studies (GWAS) identified 34 replicated genetic loci (20 for YR, 12 for BR, two for PM, 0 for ST), with seven loci associated with resistance to two or more diseases. Construction and analysis of eight bi-parental populations enabled two selected genetic loci, yellow rust resistance locus YR_2A010 (chromosome 2A) and YR_6A610 (6A), to be independently cross-validated, along with the development of genetic markers to track resistance alleles at these loci. Notably, the chromosome 2A yellow and brown rust resistance locus corresponds to the 2NvS introgression from the wild wheat species, Aegilops ventricosa. We found evidence of strong selection for 2NvS over recent breeding history, being present in 48% of the most recent cultivars in our panel. Collectively, we define the genetic architectures controlling resistance to four major fungal diseases of wheat under European field environments, and provide resources to exploit these for the development of new wheat cultivars with improved disease resistance. Genome-wide association study (GWAS) adult plant disease resistance (APR) single nucleotide polymorphism (SNP) high-density genotyping Quantitative Trait Locus (QTL) yellow (stripe) rust brown (leaf) rust Septoria tritici blotch (STB) powdery mildew yield protection Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Diseases of wheat ( Triticum aestivum L.) can have significant impact on grain quality and yield, with an estimated potential yield loss of 20% per year (Wulff & Krattinger, 2022 ). Accordingly, growers employ various methods to help prevent or control disease in their crops. Ideally, integrated pest management approaches are applied, which combine action thresholds with disease monitoring, prevention and control measures. Disease prevention via growth of cultivars with good genetic resistance is a key component of such strategies. This is particularly true in situations where the cost of fungicides and pesticides are restricting factors, or where regulations restrict use of specific chemical control options. Indeed, legislative regulation is likely to become increasingly focused on encouraging sustainable agricultural approaches, therefore promoting efficient exploitation of genetic sources of crop resistance. For example, the recent Farm to Fork Strategy, a central component of the European Green Deal, aims to encourage food systems that are fair, healthy and environmentally friendly (European Commission communication COM ( 2020 ) 381 final). Target wheat diseases: yellow rust, brown rust, powdery mildew and Septoria tritici blotch In north-western Europe, four of the most damaging fungal diseases of wheat are yellow rust (YR, also known as stripe rust; caused by Puccinia striiformis Westend f. sp. tritici , hereafter termed Pst ), brown rust (BR, also known as leaf rust; caused by Puccinia triticina Erikss., Pt ), Setporia tritici blotch (caused by Zymoseptoria tritici , Zt ) and powdery mildew (caused by Blumeria graminis f. sp. tritici , Bgt ). Pst , Pt and Bgt are obligate biotrophic fungi that require living host tissue to complete their lifecycle. In contrast, Zt has been classified as a latent necrotroph, initially growing asymptomatically in host tissue after which a necrotrophic phase is initiated during which host cell death is rapidly induced (Sanchez-Vallet et al. 2015 ). All four diseases predominantly result in infection of wheat leaves, and can result in notable reductions in grain yield and quality if left unchecked. The causal agents of yellow rust and brown rust belong to the same fungal genus, and have complicated lifecycles involving numerous spore stages as well as multiple plant host species for completion of their lifecycles (reviewed by Bouvet et al. 2022a ; Ren et al. 2023 ). Their asexual stages are undertaken on wheat, with infection initiated via wind-blown spores (termed urediniospores for Pst , and urediospores for Pt ) resulting in the development of pustules on the surfaces of infected wheat leaves that release spores which can reinfect wheat plants, so continuing the asexual lifecycle phase. For yellow rust, the yellow or orange pustules are arranged in stripes along the leaf blade, while brown rust pustules are brown and are arranged without specific pattern. Although brown rust tends to develop later in the season than yellow rust, both diseases lead to loss of green leaf area, thus affecting yield.. Powdery mildew is characterised by pale pink asexual colonies on the surfaces of infected wheat leaves, with infection most prominent in years with mild temperatures and high humidity. Release of conidia from these colonies can lead to reinfection cycles as quick as five days (Rana et al. 2022 ). Septoria tritici blotch results from the infection of a hemi-trophic fungus. Thus, while Zt initially requires living host tissue for infection, the fungus subsequently kills and takes up nutrients from the dead host tissues (Gupta et al. 2023 ). The visual symptoms of Septoria tritici blotch include elongated chlorotic or necrotic lesions on the leaves, which because they are restricted by the leaf veins, are typified by rectangular appearance. Within-season spread of Septoria tritici blotch infection is typically mediated via rain splash spread of pycnidiospores asexually produced from the characteristic small black fruiting bodies (pycnidia) that form on infected areas. Wheat genetic resistance to target fungal diseases Wheat genetic resistance to fungal infection is typically classified as either all-stage resistance (also termed ‘race-specific resistance’ or ‘seedling resistance’) or adult plant resistance (‘race nonspecific resistance’). All-stage resistance is expressed at the seedling stage and extends throughout plant development. It is underpinned by the gene-for-gene model (Flor, 1956 ), with the underlying genes in wheat typically encoding nucleotide-binding site, leucine-rice repeat (NBS-LRR) proteins. Use of cultivars with low numbers of all-stage resistance genes over large areas of cultivation can result in high pathogen selection pressures, leading to the evolution of pathogen races able to overcome specific sources of all-stage resistance - presumably via mutation or deletion of the pathogen effector proteins that specific NBS-LRR proteins detect. In Europe, a recent example is the breakdown of the yellow rust resistance conferred by Yr17 , resulting in growers rapidly shifting to cultivars that carried other sources of resistance (Bayles et al. 2000 ). Such cycles of ‘boom and bust’ can be ameliorated by the use of cultivars that pyramid multiple all-stage resistance loci and carry sources of adult plant resistance. Indeed, adult plant resistance loci typically provide more durable resistance, which while quantitative in nature, are less prone to being overcome by fungal pathogens. Some sources of adult plant resistance confer resistance to multiple fungal pathogens. For example, resistance to yellow rust, leaf rust ( Lr ), stem rust ( Sr ) and powdery mildew ( Pm ) is conferred by the same resistance gene Yr18/Lr34/Sr67Pm38 and has often been used in cultivars developed via the CIMMYT international breeding programme (Singh et al. 2005 ). While relatively few adult plant resistance genes have been cloned, they do not belong to a single class of gene: Yr36 encodes a protein with a kinase and a START lipid-binding domain (Fu et al. 2009 ), Yr18/Lr34 encodes an ABC transporter (Krattinger et al. 2009 ) and Yr46/Lr67 encodes a hexose transporter (Moore et al. 2015 ). Additionally, some wheat genes play an essential role in pathogen colonization and their mutation/deletion can result in increased resistance. Examples include mildew resistance locus ( Mlo ) (Buschges et al. 1997 ), a branched-chain amino acid aminotransferase termed TaBCAT1 (Corredor-Moreno et al. 2021 ) and a cytoplasmic protein kinase termed TaPsIPK1 (Wang et al. 2022 ). The majority of adult plant resistance, however, is conferred by genes with small individual effects but which collectively provide effective disease control. For further details of the genes and genetics of wheat resistance to the four fungal pathogens investigated here, see Bouvet et al. ( 2022a ) (yellow rust), Ren et al. ( 2023 ) (brown rust), Bapela et al. ( 2023 ) (powdery mildew) and Ababa ( 2023 ) (Septoria tritici blotch). Safeguarding future wheat production: understanding the genetics of resistance in current cultivars Knowledge of which disease resistance loci are deployed in current wheat cultivars helps inform resistance breeding strategies. For many cloned genes, molecular markers are now available that allow resistance loci to be tracked within breeding programmes (e.g. Rasheed et al. 2016 ). However, systematic understanding of the full repertoire of resistance loci deployed within elite wheat genepool will provide a framework from which informed resistance breeding can be conducted and helps safeguard against the sudden collapse of genetic resistance in contemporary cultivars. For example, sources of yellow rust adult plant resistance identified in a multi-founder wheat population have been shown to be rare in north-west European germplasm (Bouvet et al. 2022b , 2022c ), indicating their wider deployment in new cultivars could aid resistance durability. In the European context, additional factors such as the rapid change in genetic diversity and virulence of the yellow rust fungus Pst since the year 2000 (Hovmøller et al. 2007) which from 2011 began to largely replace the previously clonal Pst isolates (Hovmøller et al. 2016 ; Hubbard et al. 2015 ), and the relatively low number of assayed resistance loci conferring brown rust resistance in current surveys of UK Pt isolates (UKCPVS 2022) further highlight the need to optimise understanding and deployment of sources of wheat genetic resistance. Moreover, although powdery mildew resistance is relatively high in UK wheat and Septoria tritici resistance has increased over the past 30 years, little is known the genetic structure of resistance to either disease (Brown, 2021 ). With current winter wheat UK Recommended List varieties averaging resistance rating scores of around 6 for powdery mildew and Septoria tritici blotch (on a 1–9 non-linear scale, where 1 = susceptible. AHDB, 2024), there is of course scope for further genetic improvement despite the successes of the past. Genome-wide association studies (GWAS) allow the genetic architecture of target traits to be undertaken in large collections of contemporary germplasm (e.g. Mellers et al. 2020 ) and can offer superior mapping precision compared to conventional segregating populations (Gardiner et al. 2020 ). Here, we assembled an association mapping panel of 480 predominantly European winter wheat cultivars released between 1916 and 2007 and genotyped using a 90,000 feature single nucleotide polymorphism (SNP) array. We then assessed the panel for resistance to four fungal diseases - yellow rust, brown rust, powdery mildew and Septoria tritici blotch - via 31 field trials across five European countries, allowing identification of resistance loci by GWAS. Finally, we selected two genetic loci for independent validation in eight bi-parental populations, and provided genetic markers for further investigation and molecular tracking of the loci. Methods Association mapping panel and genotyping A panel of 480 mainly winter wheat cultivars and breeding lines that represent the North-Western European wheat elite breeding genepool of recent decades was assembled from previous germplasm collections and participating breeding companies (Supplementary Table S1 ). For each accession, a single seed was grown, genomic DNA extracted (Fulton et al. 1995 ), and self-fertilised seed produced for downstream research. Genotyping was performed using a wheat Illumina iSelect 90,000 feature SNP array (Wang et al. 2014 ), with genotypes called with GenomeStudio (Illumina). All genotypes were scored as 0 (A:A) or 1 (B:B), with the very rare cases of heterozygotes (A:B) were treated as "NA". The resulting genotypic dataset was processed to remove markers with missing data ≥ 10%, before the remaining missing values in the genotypic data were imputed using the R package missForest (Stekhoven and Buehlmann, 2012 ) with 200 trees. Markers with a minor allele frequency ≤ 2.5% were then removed in the imputed dataset. Forming a pseudo genetic map The 90k marker probe DNA sequences (Wang et al. 2014 ) were used as queries against the wheat reference genome of cultivar Chinese Spring (RefSeq v1.0; IWGSC, 2018) via BLAST + 2.7.1 using default parameters (Camacho et al. 2009 ). For each hit, the median base pair between the start and stop locations were taken as the physical position of the marker. The MAGIC 90k genetic linkage map from Gardner et al. ( 2016 ) was used to aid the marker anchoring to physical genome locations. Using R (R Core Team, 2020 ), three steps were applied to anchor markers: (1) If a marker had a singular physical hit for the same chromosome mapped in the genetic linkage map, that hit was taken as the anchored position. (2) For each marker not anchored in the first step, pairwise correlation ( r 2 ) was calculated with all markers already anchored to find the pair that yielded the highest r 2 . If the r 2 value was above a determined threshold ( r 2 > 0.35) and the unanchored marker had at least one physical hit on the same chromosome as the anchored marker, then the closest physical hit to the anchored marker was taken as the anchored position. (3) A backwards control step was implemented where every marker ( m 1 ) was correlated with the next two markers along the chromosome ( m 2 and m 3 ). If r 2 between m 1 and m 3 was > 0.7, r 2 between m 1 and m 2 was < 0.35 and r 2 between m 2 and m 3 was < 0.35, then m 2 was excluded from the anchored markers. Finally, the R package LDheatmap (Shin et al. 2006 ) was used to inspect the resulting LD between the final 20,166 anchored markers. These markers, along with the 5,366 unanchored SNPs, were then ‘skimmed’ to remove markers that were 100% correlated to each other, using a custom R script. The skimming approach involved removing a marker in each pair of markers with an absolute correlation coefficient ( r ) = 1. This resulted in 11,858 markers (8,962 anchored SNPs and 2,896 unmapped SNPs). All genotypic data is available online at www.niab.com/resources/ . Field trials, phenotypic data and trials analysis Phenotypic data were collected from 31 autumn-sown field trials (Table 1 ), grown in the UK (25 trials), Germany (5), Denmark (4), France (1) and Sweden (1) over four years (harvest years 2012, 2013, 2014 and 2015). For all but one trial (ST_4), two replicate plots for each entry were grown per trial, with inclusion of susceptible control cultivars at higher replicate number. Entries were randomised between two main blocks, typically with inclusion of additional sub-blocks. Further details of all trials are provided in Supplementary Table 2, including information on trial design (including entry number, replication number, control variety number, and total number of trial plots), trial location (country, latitude and longitude), sowing date, trial infection type (and pathogen isolate information where relevant), soil type, and the crops grown on the trial site in the previous 1–3 years. Trials were grown following standard local agronomic practices, but without the application of fungicides active against the target diseases. Disease infection was scored visually at the plot-level on between 1–3 timepoints in the season, depending on the trial, scored between the end of booting (growth stage 45–49; Zadoks et al. 1974 ) and the hard-dough stage (growth stage 87). Scores were recorded using either percentage infection, or via a 1–9 scale that was subsequently converted to percentage infection. Summary statistics (mean, median, standard deviation, and variance) were calculated using GenStat 19th edition (VSN International). Best linear unbiased estimates (BLUEs) were calculated using a linear mixed approach in REML using GenStat. For subsequent GWAS, all disease scores were transformed as log 10 (value + 1). Broad sense heritability ( H 2 ) was estimated using the method of Cullis et al. (2006). Table 1 Summary of winter-sown disease field trials. Year designation = harvest year. * No powdery mildew infection occurred in this trial. † No significant genome wide association study (GWAS) hits identified. DEU = Germany, DNK = Denmark, FRA = France, SWE = Sweeden, UK = United Kingdom. Trial code Trial operator Country Year Disease score (S) number Inf. range (%) Inf. mean (%) Heritability ( H 2 ) Yellow rust trials YR_1 DSV UK 2012 YR_1_S1 0–38.5 4.9 0.77 YR_1_S2 0–76.0 10.8 0.87 YR_2 ELS UK 2012 YR_2_S1 0–73.8 8.4 0.90 YR_2_S2 0–100 13.2 0.94 YR_3 LSW SWE 2012 YR_3_S1 0–55.7 6.9 0.51 YR_3_S2 0–100 36.8 0.85 YR_4 LIM UK 2012 YR_4_S1 0–79.3 7.5 0.92 YR_4_S2 0–100 14.9 0.95 YR_5 SEJ DNK 2012 YR_5_S1 0–77.0 5.9 0.71 YR_5_S2 0–100 13.2 0.93 YR_6 SEJ DNK 2013 YR_6_S1 0–92.8 10.3 0.90 YR_6_S2 0–100 19.9 0.94 YR_7 ELS UK 2014 YR_7_S1 0–100 8.4 0.90 YR_8 JIC M UK 2014 YR_8_S1 0–75.0 2.6 0.83 YR_9 JIC B UK 2014 YR_9_S1 0–76.5 4.0 0.73 YR_10 JIC T UK 2014 YR_10_S1 0–50.0 2.3 0.20 YR_11 RAGT UK 2014 YR_11_S1 0–50.4 2.1 0.84 YR_12 SYN UK 2014 YR_12_S1 0–100 11.8 0.97 YR_13 SYN FRA 2014 YR_13_S1 0–100 10.5 0.89 YR_13_S2 0–100 16.8 0.88 Other trials with yellow rust scores BR_2 NOR DEU 2012 BR_2_YR_S1 0–20.5 0.3 0.67 ST_4 SYN UK 2014 ST_4_YR_S1 0–100 12.6 0.38 PM_4* NIAB UK 2012 PM_4_YR_S1 0–60.5 6.0 0.88 PM_4_YR_S2 0–80.6 8.9 0.93 Brown rust trials BR_1 † LSW DEU 2012 BR_1_S1 0–6 0.7 0.26 BR_1_S2 0–32.7 4.7 0.43 BR_1_S3 0–36.3 11.2 0.38 BR_2 NOR DEU 2012 BR_2_S1 0–12.3 1.5 0.5 BR_3 RAGT UK 2012 BR_3_S1 0–51 1.0 0.86 BR_3_S2 0–50.5 1.0 0.81 BR_4 DSV DEU 2013 BR_4_S1 0–18 5.9 0.64 BR_4_S2 0–50.4 9.5 0.66 BR_5 † KWS UK 2013 BR_5_S1 0–27.5 0.4 0.15 BR_6 LSW DEU 2013 BR_6_S1 0–68.3 10.0 0.64 BR_7 RAGT UK 2013 BR_7_S1 0–40.5 2.9 0.83 BR_7_S2 0–76.8 8.4 0.86 BR_7_S3 0–91.7 17.3 0.88 Other trials with brown rust scores PM_4* NIAB UK 2012 PM_4_BR_S1 0–10.2 0.8 0.80 Septoria tritici blotch trials ST_1 † ELS UK 2012 ST_1_S1 2.70–99.7 46.5 0.12 ST_2 † KWS UK 2012 ST_2_S1 2.1–98.7 44.7 0.27 ST_3 LIM UK 2012 ST_3_S1 0–92.1 21.6 0.62 ST_4 † SYN UK 2012 ST_4_S1 0.1–25.0 5.9 NA (1 rep) ST_5 KWS UK 2013 ST_5_S1 0.6–34.0 6.2 0.6 ST_5_S2 1.9–73.9 16.4 0.4 ST_6 † LIM UK 2013 ST_6_S1 0–60.3 13.2 0.61 ST_7 † SEJ DNK 2013 ST_7_S1 20.5–74.7 51.8 0.65 ST_8 KWS UK 2014 ST_8_S1 1.3–74.3 26.2 0.61 Other trials with Septoria tritici blotch scores BR_3 † RAGT UK 2012 BR_3_ST_S1 0–81.8 24.5 0.63 YR_11 RAGT UK 2014 YR_11_ST_S1 0–15.2 4.4 0.67 Powdery mildew trials PM_1 SEJ DNK 2014 PM_1_S1 0–25.0 1.0 0.85 PM_2 JIC UK 2015 PM_2_S1 0–39.6 6.1 0.62 PM_2_S2 0–77.6 14.6 0.69 PM_2_S3 0–79.7 21.3 0.70 PM_2_S4 0–79.7 26.3 0.71 PM_2_S5 0–89.5 32.0 0.66 Other trials with powdery mildew scores BR_4 DSV DEU 2013 BR_4_PM_S1 0–50.3 6.6 0.81 ST_5 KWS UK 2013 ST_5_PM_S1 0.4–75 10.9 0.82 ST_6 † LIM UK 2013 ST_6_PM_S1 0–67.5 1.5 0.66 Statistical analysis Principal coordinate analysis (PCoA) was conducted in R using the package ape (Paradis and Schliep, 2019 ) with 3,563 markers that had been ‘skimmed’ to remove a SNP in each pair with an absolute correlation of r ≥ 0.7. Linkage disequilibrium was estimated as the r 2 between all pairs of unique anchored SNPs (8,962) using the R package sommer (Covarrubias-Pazaran, 2016 ). The LD decay was determined by plotting the r 2 values against physical distance (Mbp), and for each of the A, B and D sub-genomes a trend line was calculated by locally-weighted polynomial regression (LOESS) curve in R. The physical distance of LD decay to a threshold of r 2 = 0.2 was inspected for each genome. GWAS was performed using the R package GWASpoly (Rosyara et al. 2016 ), which identified marker-trait associations using the Mixed Linear Model (MLM) (Yu et al. 2006 ). The GWAS accounted for population structure (principal components = 5) and kinship as fixed and random effects, respectively. Using GWASpoly, the kinship matrix was calculated using a subset of 4,023 SNPs ‘skimmed’ from the 11,858 mapped and unmapped SNPs to remove a marker in each pair with an absolute r ≥ 0.75. The significance of marker-trait associations was determined using two thresholds: (1) the false discovery rate (FDR) (Benjamini and Hochberg, 1995 ) using a q -value cut-off of q = 0.05, and (2) the permutation threshold (Churchill and Doerge, 1994 ), using 1,000 permutations and α = 0.05. In cases where the FDR threshold was too lenient (under 2.9) just the permutation threshold was used. Markers in Manhattan plots were ordered according to the anchored physical positions from the wheat reference genome, with unmapped markers at the end. Covariate variables were included in successive iterations of GWAS. Marker trait associations (MTAs) were consolidated into discrete quantitative trait loci (QTL) by taking the mapped significant markers, organising them by physical and genetic distance, and choosing QTL cut-offs by taking into account linkage disequilibrium decay. QTL were named using the highest scoring physically mapped marker in the defined region. GWAS results were subsequently drawn in a chromosomal ideogram using R package LinkageMapView (Ouelette et al. 2017). Replicated GWAS hits between two or more diseases that were located within 25 Mbp of each other were termed here ‘multi-resistance loci’ (this interval was arbitrarily set). Power analyses were undertaken using previously described methods (Wright et al. 2021 ), using simulated phenotypes with different H 2 (0.25, 0.50, 0.75, 0.90 or 0.99) and simulated focal QTL explaining different amounts of the variance (5%, 10%, 25%, 50% and 100%). For each combination of H 2 and percentage variance, 1000 simulations were run. Validation of GWAS hits A subset of the SNPs identified as significant in our GWAS analysis were converted from the 90k array to the Kompetitive Allele-Specific PCR (KASP) platform (LGC Genomics, UK) for subsequent use for validation via independent bi-parental populations, termed BP1 to BP8, provided by the breeding companies involved. KASP primer design was undertaken using PolyMarker (Ramirez-Gonzalez et al. 2015 ), with primers listed in Supplementary Table S3 . DNA for KASP genotyping was extracted from a set of 95 cultivars selected from the GWAS panel using the DNEasy Kit (Qiagen) and KASP genotyping undertaken using KASP V4.0 2x Master Mix (LGC Biosciences) using a ProFlex PCR System Thermocycler (Applied Biosystems) with the following settings: 1 cycle at 94°C for 15 mins; 10 cycles at 94°C for 20s, 65°C for 60s with a touchdown of -0.8°C/cycle to 57°C; 35 cycles at 94°C for 20s, 57°C for 60s; final hold at 10°C. Fluorescence of VIC and FAM fluorophore 5’ end labelled PCR products were subsequently read using a Scientific QuantStudio™ 12K Flex Real-time PCR System (Thermo Fisher Scientific). ROX was used as a passive fluorescent reference to allow normalisation of variations in signal caused by differences in well-to-well liquid volume, following the manufacturer’s instructions (LGC Genomics). Results were visualised using SNP Viewer v.1.99 ( http://lgcgenomics.com/ ). KASP markers confirmed as co-dominant were used to validate GWAS hits in bi-parental populations constructed either by single seed descent, or by the doubled haploid approach. Boxplots showing the distribution of the resistant and susceptible alleles and percentage of yellow rust infection recorded from field trials undertaken in the UK (using populations BP1, BP3, BP5-BP7), France (BP1), Denmark (BP2, BP4) and Germany (BP8) in 2015 were plotted using ggplot2 (Wickham, 2016 ) and significance tested via a one-way ANOVA in R. To analyse significance per QTL across trials, a two-way ANOVA was performed with independent variables of KASP score and experiment. For YR_2A010, the two KASP markers used were treated as the same as there was no evidence in our datasets that the introgression has been broken up by recombination; for YR_6A610, when KASP was added in to the model the effect was not significant. Trials where there were on average less than 5% yellow rust infection were excluded from the validation set. Haploblock and pedigree analysis Genotypic data was used to create haploblocks and their corresponding haplotypes using Haploview v4.2 (Barrett et al. 2005 ) with additional manual curation. Where required, genotype calls at SNPs defining the haploblock were also determined in the genome assembly of T. aestivum cultivar ‘Jagger’ (Walkowiak et al. 2020 ) via BLASTn using Ensembl Plants (Yates et al. 2022 ). Plots of the wheat pedigree were constructed with Helium v1.19.09.03 (Shaw et al. 2014 ) using the pedigree published by Fradgley et al. ( 2019 ). Results Characteristics of the association mapping panel: population substructure, linkage disequilibrium and experimental power We assembled a wheat association mapping panel, termed here the ‘WAGTAIL’ panel. It consisted of 480 European wheat cultivars released across 10 countries between 1916 and 2007. The cultivars predominantly originated from the United Kingdom (UK, 70%), France (12%) and Denmark (8%), and the majority were winter type (93%) (Supplementary Table S1 ). Genotyping the panel with a 90,000 feature SNP array, resulted in 26,015 polymorphic genetic markers (See Supplementary Text 1 for additional details). After removing 359 markers with > 10% missing data, and 124 markers with a minor allele frequency > 2.5%, 25,532 markers remained. Of these, we were able to anchor 20,166 markers to the wheat physical map, leaving 5,366 unmapped markers. Duplicated SNPs (based on 100% correlation) were then removed from both the mapped and unmapped datasets, resulting in 8,962 mapped markers and 2,896 unmapped markers. Therefore, the final SNP data-matrix consisted of 11,858 markers genotyped across 480 cultivars (Supplementary Table S1 ). Mean genetic marker density for the A and B sub-genomes was similar, at 1.55 and 2.05 markers/Mbp, but was lower on the D sub-genome (0.64 markers/Mbp). Genetic marker number per chromosome ranged from 2,196 (chromosome 1B) to 85 (chromosome 4D) (Supplementary Table S4 ). Principal coordinate analysis (PCoA) identified relatively limited genetic substructure (Fig. 1 ), with the first two principal coordinates (PC) accounting for 11.3% of the variation (PC1 = 7.1%, PC2 = 4.2%). While the majority of the cultivars in the panel were from the UK, the German (DEU) and Dutch (NLD) cultivars formed clusters in the PCoA plots. Overlaying ‘winter’ and ‘spring’ seasonal growth habit designations found spring cultivars to form a loose subcluster within the overall plot, determined predominantly by PC1. Furthermore, year of cultivar release showed a notable visual trend for newer varieties to be further separated from the spring cultivars in PCoA space. Using the 8,962 skimmed and anchored marker set, we then investigated the distribution and extent of linkage disequilibrium within all chromosomes via linkage disequilibrium decay plots, with a trend line for each sub-genome A, B and D calculated by locally-weighted polynomial regression (LOESS) (Fig. 2 ). The intersection between the LOESS curve and r 2 = 0.2 suggested that linkage disequilibrium decayed at a relatively low rate within chromosomes, returning distances for the A, B and D sub-genomes of 20 Mbp, 36 Mbp and 41 Mbp, respectively. To further explore the suitability of the panel for genome-wide association studies, we used our data-matrix of 480 cultivars and 8,962 SNPs to undertake power analyses, whereby the probability of identifying a simulated QTL was investigated when heritability ( H 2 ) and percentage variance explained by the QTL was varied (Fig. 3 ). As the percentage variance explained by the QTL and H 2 increased, the probability of finding the QTL increased. Where H 2 was high (≥ 0.75), the probability of QTL detection was close to 1.0 irrespective of the percent variance explained. At more modest levels of H 2 (0.50) and when the percentage variance explained by the QTL was ≥ 10%, the probability of QTL detection remained relatively high (> 66%). However, the probability of identifying QTL fell to below 20% when low values for both heritability ( H 2 = 0.25) and percent variance explained (≤ 0.25) were modelled. For QTL explaining 5% of variance, a 50% detection ability was achieved when H 2 was over about 0.6. Disease resistance phenotyping We used the association mapping panel to conduct a total of 31 disease assessment trials between 2012 and 2015, located in the UK, Germany, Denmark, Sweden and France. Of these trials, 13 were for yellow rust (YR), seven for brown rust (BR), eight for Septoria tritici blotch (ST), and three for powdery mildew (PM) (Table 1 ). In some trials, more than one disease was scored where the opportunity arose and where both diseases could be scored without mutual interference, and in one PM trial no PM infection occurred, such that in total 39 disease-trial combinations and 58 disease-trial-timepoint combinations were scored (Table 1 ). For yellow rust and brown rust, where percentage disease infection scores were measured in all trials on two or three separate dates (indicated in the trait names using the postscript ‘_S1’, ‘_S2’ or ‘_S3’), the percentage infection at later score dates were consistently higher than on the first. Overall, Septoria tritici blotch showed the highest infection based on mean percentage infection across all trials (25.9%), followed by yellow rust (11.2%), powdery mildew (10.6%), and brown rust (6.5%). Boxplots for disease resistance at the last score date for each trial-trait-score date combination are shown in Fig. 4 . For the majority of YR, BR, ST and PM datasets, percentage disease was skewed towards low infection – accordingly, Best Linear Unbiased Estimates (BLUEs) were log transformed for all downstream analyses (Supplementary Table S5 ). Heritabilities for yellow rust trials were mostly high (20/24 disease-trial-timepoint combinations H 2 > 0.7). For brown rust, heritability was variable (6/14 disease-trial-timepoint combinations H 2 > 0.7, 5/14 combinations H 2 ≤ 0.5). For Septoria, heritability for 0/11 disease-trial-timepoint combinations were H 2 > 0.7 and 3/11 were H 2 < 0.5) (Table 1 ). For powdery mildew, 5/9 disease-trial-timepoint combinations were H 2 ≥ 0.7. For each disease, there was significant positive correlation between variety means from trial-trait-score combinations for the majority of comparisons investigated (Supplementary Fig. S1 ). Correlations within disease datasets were highest for yellow rust datasets, and powdery mildew datasets, for which all comparisons were significant at P ≤ 0.001. Disease score correlations between all but one Septoria tritici blotch trial-trait-score combinations were significant at P ≥ 0.05. For brown rust, while the majority of correlations were significant at P ≤ 0.001, non-significant positive correlations were identified for four trial comparisons (BR_1/BR_7, BR_1/BR_3, BR_2/BR_3 and BR_2/BR_5). Several notable inter-disease correlations were observed. This included positive significant correlations (P ≤ 0.05) between yellow rust and powdery mildew (53 of 80 comparisons, 66%), yellow rust and brown rust (20 of 128 comparisons, 16%) and brown rust and powdery mildew (18 of 40 comparisons, 45%). Conversely, significant negative correlations between trial-trait-score date combinations were notable between subsets of Septoria trials and those for powdery mildew (14 of 50 comparisons, 28%) and brown rust (20 of 80 comparisons, 25%). Notably, most of the significant negative correlation (P ≤ 0.05) reported between Septoria and brown rust trials originated via comparisons with three brown rust trials (BR_3, BR_4, BR_6, 18 of 30 trial comparisons; Supplementary Fig. S1 ). Marker-trait associations We used the 58 trial-disease-timepoint combinations (subsequently termed here, ‘traits’) to undertake genome-wide association studies (GWAS), initially employing a false discovery rate significance threshold of FDR = 0.05. GWAS identified a total of 2,054 marker-trait associations (MTAs), of which 1,702 involved genetically mapped markers and 352 involved unmapped markers (Supplementary Table 6). Considering the mapped markers only, and taking into account LD decay, physical and genetic distance, these MTAs resolved into the following number of distinct genetic loci: 43 for YR, 34 for BR, 5 for ST, and 14 for PM (Fig. 5 ) (Supplementary Table 7, Supplementary Fig. S2 ). For each disease, GWAS hits with the highest significance are shown in Fig. 6 a-e (Manhattan plots for all GWAS analyses are shown in Supplementary Fig. S2 ). Yellow rust: GWAS hits were identified in all 16 field trials in which yellow rust was scored (Fig. 5 ) (Supplementary Table 7). The majority of loci located on the A and B subgenomes (23 and 17, respectively), while just three were found on the D subgenome. The highest number of YR resistance loci were located on chromosomes 2B (6), 2A (4), 3B (4), 4A (4) and 5A (4). Of the 43 yellow rust resistance genetic loci which were statistically significant in at least one disease trial, 20 were identified in two or more trials (Table 2 ; highlighted in red in Fig. 5 ) of which 12 were identified using the more stringent permutated P = 0.05 significance threshold (underlined in Fig. 5 ). The most replicated YR resistance locus was on chromosome 4A at ~ 738 Mbp (termed here, YR_4A738), which was identified in eight separate trials between 2012–2014 (five in the UK, two in Denmark and one in France). Overall, the YR resistance loci with the highest significance values (-log 10 P ≥ 8.21) were identified in the UK in 2014: YR_2A010 on the short arm of chromosome 2A (identified in five trials) and YR_6A610 on the long arm of chromosome 6A (identified in six trials) (Fig. 6 a). These two loci had very high significance values (Fig. 6 a) but relatively small SNP effect sizes (Table 2 ). Table 2 Summary of the 39 significant disease resistance genetic loci identified via genome wide association study (GWAS) in two or more trials. The six multi-resistance genetic loci in which replicated resistance loci were identified for two or more diseases within 25 Mbp, are listed as MT25Mb_1 to _6. Chromosome locations are based on the wheat reference genome (IWGSC, 2018). Effect on phenotype is indicated as change in disease leaf infection percentage conferred by the reference allele. Chr. = chromosome, No. = number, Pheno. = phenotype, Pos. = position. * Complete marker name: wsnp_Ex_rep_c101457_86817938 . Genetic locus Multi-resistance locus Chr. Interval (Mbp) Peak SNP name Peak SNP pos. (Mbp) Peak SNP sig. (-log 10 P) Effect on pheno (% inf.). No. trials Brown rust (total no. trials = 8) BR_1B117 1B 1.20-11.31 BS00009715_51 112.4 6.44 -0.50 5 BR_2A015 MT25Mb_1 2A 2.23–24.28 BS00004089_51 14.8 13.95 -0.67 2 BR_2A704 2A 703.98-717.17 Excalibur_c40617_983 704.0 3.58 0.83 2 BR_2B026 MT25Mb_2 2B 26.30-33.84 Kukri_c40764_367 26.3 5.80 -0.48 2 BR_2B640 2B 214.59-640.79 BS00069685_51 640.8 3.83 -0.36 2 BR_2B777 MT25Mb_3 2B 777.33 RAC875_c19685_944 777.3 3.57 0.47 2 BR_2D014 MT25Mb_4 2D 14.40 BobWhite_c15073_502 14.4 3.34 -0.27 2 BR_3A134 3A 134.25-471.36 BobWhite_c35303_192 134.2 4.04 -0.44 2 BR_3A741 MT25Mb_5 3A 736.39-741.24 Tdurum_contig5009_349b 741.2 3.61 -0.28 2 BR_3B826 3B 810.33–826.1 Tdurum_contig42131_1300 826.1 3.47 1.18 2 BR_4A714 MT25Mb_6 4A 698.04-743.98 BobWhite_c20306_111 713.5 16.27 -0.75 2 BR_6A016 6A 11.41–23.72 Excalibur_rep_c105463_330 15.7 4.30 -0.38 3 Powdery mildew (total no. trials = 5) PM_4A734 MT25Mb-6 4A 610.49-742.09 BS00110758_51a 734.0 5.22 -0.62 2 PM_6B664 6B 663.68-681.92 RAC875_c5129_280 663.7 5.04 0.68 2 Yellow rust (total no. trials = 16) YR_1B164 1B 14.85-163.78 Excalibur_rep_c92475_275 163.8 4.82 0.26 2 YR_2A010 MT25Mb_1 2A 0.40-24.28 wsnp_Ra_c8771_14786376b 9.5 12.48 -0.77 5 YR_2B034 MT25Mb_2 2B 24.91–33.84 BobWhite_rep_c64429_660a 33.8 10.61 1.96 4 YR_2B047 2B 47.43–47.43 BS00041587_51 47.4 5.95 0.81 6 YR_2B155 2B 133.7-154.99 Kukri_c36783_91 155.0 4.17 -0.57 2 YR_2B763 MT25Mb_3 2B 0.00-763.09 BS00070301_51a 763.1 4.54 -0.30 2 YR_2D014 MT25Mb_4 2D 0.00-13.99 RAC875_c90426_151 14.0 5.96 1.24 3 YR_3A008 3A 7.43–8.87 BS00037189_51 7.8 4.86 0.73 2 YR_3A746 MT25Mb_5 3A 746.17 wsnp_Ex_c60462_60905848 7746.2 5.69 -0.68 2 YR_3B739 3B 738.75-750.36 wsnp_Ex_rep_c101457 * 739.1 4.85 -0.48 2 YR_4A738 MT25Mb_6 4A 734.00-738.78 Excalibur_c65272_341 737.5 6.70 -0.62 8 YR_5A030 5A 15.85–29.51 Excalibur_rep_c90275_262 29.5 6.07 0.77 2 YR_5A560 5A 559.52-568.27 BS00021955_51 559.5 4.21 -0.60 2 YR_5A677 5A 677.13-684.94 BS00022867_51 677.1 5.07 -0.46 4 YR_6A002 6A 0.29–13.07 Excalibur_c50323_215 1.9 5.83 -0.44 5 YR_6A610 6A 596.58–11.13 GENE-4021_496 610.3 12.91 0.54 6 YR_7A090 7A 89.84–90.65 Kukri_c5757_530 90.7 4.17 1.23 2 YR_7A626 7A 625.74-632.59 BS00105558_51 625.7 5.73 -0.48 3 YR_7A730 7A 730.43-730.43 Kukri_c11451_1882 730.4 4.97 -0.63 2 YR_7B701 7B 700.57-705.73 Excalibur_c7338_563 700.8 6.04 0.51 3 Brown rust: Of the eight trials undertaken (four in the UK and four in Germany), GWAS hits for BR resistance were identified in six trials (Fig. 5 ) (Supplementary Table 7). No significant associations were identified in trials BR_1 (Germany 2012, low BR infection pressure) and BR_5 (UK, 2012) (Table 1 ). The 34 BR resistance genetic loci identified were distributed predominantly on the A and B subgenomes (16 and 15 loci, respectively), with just three found on the D subgenome. The group 2 chromosomes possessed notably high numbers: five for chromosome 2A and six for chromosome 2B. Twelve of the 34 BR resistance loci were replicated in two or more trials (Table 2 ; Supplementary Table S7; highlighted in red in Fig. 5 ), with two loci being by far the most significant (-log 10 P-value above the FDR = 0.05 significance threshold > 11): BR_2A015 on chromosome 2A at ~ 15 Mbp) and BR_4A714 on chromosome 4A at ~ 714 Mbp (Fig. 6 b). The BR resistance genetic locus identified in the highest number of trials was BR_1B177 on chromosome 1B at ~ 177 Mbp, found in five trials in Germany and the UK in 2012 and 2013 (trials BR_3, BR_4, BR_6, BR_7, PM_4). Septoria tritici blotch: GWAS hits for Septoria tritici blotch (ST) resistance were identified in three of the ten trials: ST_3 (UK, 2012), ST_5 (UK, 2013) and YR_11_ST (UK 2014) (Fig. 5 ) (Supplementary Table 7). Five genetic loci were identified, distributed across the A (1 loci), B (2) and D (2) subgenomes. Of these loci, none were replicated (Table 2 ; Fig. 5 ). The most significant ST resistance locus was ST_7D015, located on the short arm of chromosome 7D at ~ 15 Mbp, identified in trial ST_5 (UK, 2013). Powdery mildew: GWAS hits were identified in all five field trials in which powdery mildew infection was scored (Fig. 5 ) (Supplementary Table 7). In total, 14 genetic loci were identified across 11 chromosomes. Resistance loci were more common on the A and B subgenomes (7 and 6 loci, respectively) than on the D subgenome (1 locus), with chromosome 4A, 5A and 5B possessing two resistance QTL each. Two replicated resistance loci were identified, PM_4A734 in trials PM_1 and PM2, and PM_6B664 in trials PM_2 and ST_6 (Table 2 ; highlighted in red in Fig. 5 ). The most significant PM resistance loci identified were PM_2A762 (chromosome 2A at ~ 762 Mbp, identified in trial BR_4) (example Manhattan plot shown in Fig. 6 e) and PM_4B010 (chromosome 4B at ~ 10 Mbp, identified in trial ST_5), both of which returned -log 10 P values ≥ 1.98 above the FDR = 0.05 significance thresholds applied in their relevant trials. As with ST but in contrast to YR and BR, -log 10 P-values for even the most significant PM loci were not especially high. Chromosomal distribution of disease resistance genetic loci The occurrence of disease resistance genetic loci across the genome was enriched towards chromosome ends. The few loci that were located in pericentromeric regions, as defined by comparison of the physical and genetic maps (Supplementary Table 8), were typified by large physical interval sizes, due to reduced genetic recombination (Supplementary Table 7). Close physical linkage between the peak GWAS hits for resistance to two or more target diseases was observed, most commonly for yellow rust and brown rust (caused by related biotrophic fungal pathogens), but also with powdery mildew or Septoria tritici blotch. For example, considering replicated GWAS hits only, seven genetic loci clusters were predicted to be located within 25 Mbp of each other (termed here ‘multi-resistance loci’), of which five were within 11 Mbp (on chromosome 2A: YR_2A010/BR_2A015; chromosome 2B: BR_2B026/YR_2B034, chromosome 2D: YR_2D014/BR_2D014; Chromosome 3A: BR_3A741/YR_3A746; chromosome 6A: YR_6A002/BR_6A016) (Fig. 5 ; Supplementary Table 9). Notably, this included our second most significant hits for yellow rust resistance (YR_2A010) and brown rust resistance (BR_2A015), for which the most significant markers were located within ~ 5 Mb of each other on the short arm of chromosome 2A. YR_2A010 and BR_2A015 are located in a region previously reported to carry a ~ 32 Mbp introgression from Aegilops ventricosa chromosome 2N v S (Gao et al. 2021 ). Analysis of our GWAS panel identified 5 haploblocks towards the end of chromosome 2AS, encompassing 211 SNPs across ~ 37 Mbp. Of these, an unusually large haploblock consisting of 162 SNPs was present at the start of the chromosome arm (haploblock-1), within which a single haplotype was present at a frequency of 32% (153 of the 480 cultivars) (Fig. 7 a) (Supplemental Table 10). Anchoring these SNPs to the genome assembly of the German wheat cultivar ‘Jagger’, previously reported as carrying the 2N v S introgression (Gao et al. 2021 ), found that all SNPs were located within the 32.53 Mbp introgressed chromosomal segment and carry the ‘Jagger’ SNP variant (Supplemental Table 10). Of these 162 SNPs, 67 were found to each uniquely serve as a tag for the extended putative 2N v S haplotype (Supplemental Table 10) and resulted in highly significant GWAS P-values for yellow rust and brown rust (≥ 9.28 above the FDR). The 2N v S introgression was introduced into the wheat pedigree via the cultivar ‘VPM1’ (Dyck and Lukow, 1988 ). Cross referencing the presence of the 2N v S haplotype in our association mapping panel with a recently published pedigree of European wheat (Fradgley et al. 2019 ) found 97 of the 153 2N v S haplotype carriers to have ‘VPM1’ in their known pedigree (Fig. 7 b), with most of the remaining 56 cultivars lacking sufficient pedigree information detail to determine whether ‘VPM1’ was in their pedigree. Plotting the occurrence of the 2N v S introgression against cultivar commercial release date shows its frequency has significantly increased over time since its introduction via ‘VPM1’ in the early 1980s (Fig. 7 c), with 48% of the most recent cultivars in our panel carrying the introgression (years 2008–2010). Of the two replicated powdery mildew resistance loci, PM_4A734 was located within 20 Mbp of replicated resistance loci for yellow rust (YR_4A738) and brown rust (BR_4A714) on the long arm of chromosome 4A. No replicated resistance loci were identified for Septoria tritici blotch. Validation of yellow rust GWAS hits We selected the two most significant yellow rust resistance genetic loci identified by GWAS, YR_2A010 and YR_6A610, for independent validation in a series of eight bi-parental populations (termed BP1 to BP8). Parental lines were selected so that each bi-parental population was predicted to segregate for contrasting alleles at one or both of the target resistance loci (Supplementary Table 11), based on the parental genotypes in our GWAS dataset. The populations were phenotyped for percentage yellow rust infection in the field and the target loci genotyped using KASP markers for selected 90k array SNPs identified by GWAS in the association mapping panel (YR_2A010: SNPs Kukri_c18149_581 and Excalibur_c25599_358 , genotyped on populations segregating for this locus, BP1-BP6. For YR_6A610: SNPs GENE_4021_496 and Tdurum_contig29607_413 , genotyped on populations BP4-BP8). Meta-analyses of the bi-parental population datasets relevant to each of the two loci found highly significant association with yellow rust resistance scores for both YR_2A010 (P < 0.001) and YR_6A610 (P < 0.001) (Supplementary Table 11; Supplementary Fig. S3 ). Accordingly, bi-parental analysis undertaken provided independent validation of both YR_2A010 and YR_6A610. Discussion Properties of the wheat association mapping panel We assembled and genotyped an association mapping panel of 480 wheat cultivars, representing a valuable resource for dissecting the underpinning genetics of North-west European wheat germplasm developed across ~ 90 years of crop breeding. Our population was relatively large in comparison to other published wheat association mapping panels (e.g. the median population size of the 17 wheat panels used for GWAS cited in this manuscript is 273). Linkage disequilibrium in the WAGTAIL panel decayed at rates comparable to that typically observed in other inbred cereal crop species (e.g. Roncallo et al. 2021 ). While these rates are around an order of magnitude higher than that observed in outbreeding cereal crops such as maize ( Zea mays ) (e.g. 0.34 Mbp at a genome-wide level, Ertiro et al. 2020 ), the development of new cultivars via crossing means that association mapping panels consisting of collections of cultivars and breeding lines can be considered as pseudo-outbreeding populations that have been subjected to strong selection for beneficial alleles and allelic combinations (Rostoks et al. 2006). Thus, while lower genome-wide genetic marker numbers are required to identify genetic loci compared to an outbreeding crop like maize, the pseudo-outbreeding nature of the panel due to the crosses made by breeders results in elevation in genetic recombination levels of throughout much of the genome compared to purely inbreeding species. Population substructure was evident in the panel, predominantly due to a combination of year of cultivar release, cultivar country of origin and spring/winter seasonal growth habit phenotype. Such substructure is a common feature of wheat association mapping panels (e.g. Bentley et al. 2014 , Mellers et al. 2020 , Walkowiak et al. 2020 ) and related cereal crops such as barley (e.g. Cockram et al. 2010 ), and is due to historic and/or recent similarities in the shared ancestry of the lines. If this is not accounted for, the frequency of false-positive associations can increase, due to causes other than close linkage between the genetic marker and QTL (Cockram and Mackay, 2018 ). After statistical adjustment for substructure and kinship, plots of expected versus observed marker-trait significances for our disease traits indicated that the population stratification present in our panel was adequately accounted for. Power and precision to detect marker-trait associations in association mapping panels via GWAS relies on numerous factors, including population size and the amount of historic genetic recombination captured (Cockram and Mackay, 2018 ). Estimation of the power of an association mapping panel to detect marker-trait associations provides a priori expectations of experimental design. While this is standard practice in human studies, it is not commonly applied in crops. Our power analyses indicated that the association mapping had relatively good power to detect loci, even when the percentage of the variation explained by a given locus was relatively low, indicating that association mapping panels of this size or greater are likely required for detection of quantitative sources of resistance in modern wheat cultivars. The genetic architecture of disease resistance in North-Western European wheat Our analysis indicated that field resistance to the four target foliar diseases were under complex genetic control, with 34 replicated resistance loci identified across three of the four target diseases. Of the seven ‘multi-resistance’ genetic loci identified, six controlled resistance to two or all three of the target biotrophic diseases. A total of 87 permanently named loci have been identified for yellow rust (Rosewarne et al. 2013 ; Wang and Chen, 2017 ; Catalogue of Gene Symbols of Wheat – 2024 edition) and 85 for brown rust (Koláriková et al. 2023 ; Catalogue of Gene Symbols of Wheat – 2024 edition), with many additional loci reported that have yet to be given formal Yr or Lr nomenclature. Resistance alleles at 70 named powdery mildew resistance genes ( Pm ) have been reported (Catalogue of Gene Symbols of Wheat – 2024 edition. See also review by Zou et al. 2023 ). Some wheat adult plant resistance genes confer resistance against two or more biotrophic pathogens, a characteristic that has been suggested to be an indicator of the durability of resistance. These include the following three loci, each conferring resistance to yellow rust, brown rust, stem rust and/or powdery mildew: Yr18/Lr34/Pm38/ Sr67 (Spielmeyer et al. 2005 ; Lillemo et al. 2008 ), Yr29/Lr46/Sr58/Pm39 (Lagudah, 2011 ), Yr30/Lr27/Sr2 (Mago et al. 2011 ) and Yr46/Lr67/Sr55/Pm46 (Herrera-Foessel et al. 2014 ; Moore et al. 2015 ) (although we found no evidence for these presence of these loci in our European wheat panel). The ‘multi-resistance’ genetic loci identified here are defined as linked genetic loci rather than a single underlying gene. However, it may be possible that for some, the underlying gene may confer resistance to more than one disease. The most notable of our ‘multi-resistance’ loci, based on GWAS significance and number of trials identified in, included: (1) Yellow/brown rust locus YR_2A010/BR_2A015: Resistance at this locus on the short arm of wheat chromosome 2A was conferred by the Ae. ventricosa 2N v S introgression. Thirty-two percent of the cultivars in our GWAS panel carried this introgression on chromosome 2A, as defined by our 162-SNP haplotype. This introgression is a well know source of resistance to multiple diseases, including yellow rust ( Yr17 ) (Fang et al. 2011 ), brown rust ( Lr37 ) (Xu et al. 2018), stem rust ( Sr7a , Sr38 ) (Turner et al. 2016 ), eyespot (Doussinault et al. 1983 ), wheat blast (Cruz et al. 2016 ; Wu et al. 2022 ) and cereal cyst nematode resistance (Jahier et al. 2008 ). While rust resistance conferred by Yr17 and Lr37 have been widely overcome (Bayles et al. 2000 ; UKCPVS, 2022), the 33 Mbp Ae. ventricosa segment is rich in NLR genes, with increased numbers of NLRs relative to the equivalent region in the wheat reference genome assembly (Gao et al. 2021 ). Indeed, our results indicate that 2N v S carries effective sources of yellow and brown rust resistance in addition to the previously overcome resistance genes Yr17 and Lr37 , agreeing with recent reports by Wang et al. ( 2023 ). This introgression has also been associated with increased grain yield (Gao et al. 2021 ; Juliana et al. 2019 ) and reduced lodging (Gao et al. 2021 ). Directional selection for 2N v S was evident in our panel, with notable change in frequency of the 2N v S haplotype in wheat pedigree over time: first introduced via ‘VPM1’ in the early 1980s (Dyck and Lukow, 1988 ), it was passed onto several cultivars, including ‘Rendezvous’ - a frequently used parent in the European pedigree (Fig. 7 b) (Fradgley et al. 2019 ). ‘Rendezvous’ is a parent of subsequent parents that are frequently used in the pedigree, such as ‘Lynx’, ‘Hussar’ and ‘Tofrida’ (Fig. 7 b-c) (Fradgley et al. 2019 ). Notably, while we found ‘Aardvark’ to lack 2N v S, it was a parent for seven cultivars which possess the introgression. Analysis of the pedigrees of these seven lines indicates that ‘Aardvark’ most likely carries 2N v S. Previous studies have noted genotypic discrepancies for ‘Aardvark’ (Corsi et al. 2020 ), indicating that either the incorrect germplasm was used here, or that residual heterozygosity was present within the cultivar when it was being used by breeding companies for crossing within the pedigree. The 2N v S introgression has previously been identified as a possible explanation for the very strong signals for directional selection in winter wheat across more than seventy years in the United States (Ayalew et al. 2020 ). Indeed, 48% of the most recent cultivars in our panel (from 2008–2010) contained the introgression. Similar strong selection is reported in wheat cultivars developed by CIMMYT in Mexico: frequency across all CIMMYT genotypes released between the 1990s to the early 2010s is ~ 24%, increasing to ~ 90% in lines released after 2015 (Gao et al. 2021 ; Juliana et al. 2019 ; Juliana et al. 2020 ). Thus, the combination of multiple sources of disease resistance and beneficial yield traits may explain the continued strong selection for the 2N v S introgression in wheat breeding programmes over these periods. Rare putative (He et al. 2020 ; Xue et al. 2018 ) or observed (Wang et al. 2023 ) recombination between the 2N v S introgression and the native wheat chromosome 2A have previously been reported. However, we found no evidence for recombination within 2N v S in the 480 cultivars studied here. Thus, the two KASP genetic markers we developed ( Kukri_c18149_581 and Excalibur_c25599_358 ) are each capable of serving as a diagnostic tag for the extended putative 2N v S haplotype in our panel of cultivars, providing researchers and breeders with resources with which to track and manipulate this agronomically important genomic feature. (2) Yellow rust/brown rust locus YR_2B763/BR_2B777. Three named leaf rust resistance genes ( Lr50 , Brown-Guedira et al. 2003 ; Lr58 , Kuraparthy et al. 2011 ; both originating from T. timpoheevi; Lr82 from a wheat landrace, Bariana et al. 2022 ) and three named yellow rust resistance genes ( Yr5, Yr7 and YrSP , Marchal et al. 2018 ) are located on the long arm of chromosome 2B. Of these, physical map location based on anchoring to the wheat reference genome rules out all but Lr50, Lr58 and Lr82 – although it is currently unclear whether our locus represents resistance via all-stage or adult plant mechanisms. Accordingly, the chromosome 2B locus identified here may represent a novel yellow rust resistance gene in relatively close linkage to one or more brown rust resistance loci. Interestingly, a genetic locus controlling grain yield ( YLD_2B.4 , peak marker anchored on chromosome 2B at 766 Mbp) has recently been identified at this location in European wheat (White et al. 2021 ), indicating this genomic region may carry other beneficial alleles of agronomic relevance. (3) Brown rust/powdery mildew/yellow rust locus BR_4A714/PM_4A734/YR_4A738 (replicated in two, two and eight trials, respectively) located close to the telomere on the long arm of chromosome 4A. This region has recently been reported to confer resistance to both rust diseases (Liu et al. 2020 ; Kale et al. 2022 ) and to powdery mildew (Liang et al. 2022 ). Of the named resistance loci, the all-stage brown rust resistance gene Lr28 effective against numerous Pt pathotypes (e.g. Bipinraj et al. 2011 ) is located in this region. Lr28 is thought to have originated in wild wheat species, having been found in Aegilops speltoides (Naik et al. 1998 ), Ae. crassa, Ae. juvenalis , Ae. triuncialis and T. timpoheevii (Koláriková et al. 2023 ). The leaf rust resistance locus on the long arm of bread wheat chromosome 4A identified by Kale et al. ( 2022 ) in the European cultivar ‘Attraktion’ was noted to be in a genomic region shown to carry a 26 Mbp region of high sequence divergence with the wheat reference genome sequence (indicative of a chromosomal introgression from a wheat relative), and was identical by descent to an introgression carried in the UK cultivar ‘Robigus’. Indeed, via SNP array genotyping and analysis of pedigree records, this region in ‘Robigus’ has previously been reported to likely to originate from a wild wheat relative, potentially T. dicoccoides (Przewieslik-Allen et al. 2021 ). ‘Robigus’ is notable in its prominence in the UK wheat pedigree (Fradgley et al. 2019 ), highlighting the usefulness of alien chromosome introgression in European bread wheat resistance genetics, and the potential that Lr28 may underlie the GWAS hit BR_4A714. (4) The yellow/brown rust locus YR_6A002/BR_6A016 on the short arm of chromosome 6A was replicated in five and three trials, respectively, and was validated in our study in bi-parental populations. This locus has recently been identified as a source of good yellow rust resistance at the adult plant stage in a UK wheat multi-founder population ( QYr.niab-6A.1 , based on peak SNP BS00011010_51 on 6A at 19 Mbp, Bouvet et al. 2022c ), further supporting the efficacy of this locus for rust resistance in the field. Additional genetic loci conferring strong resistance In addition to the replicated GWAS hits that clustered into ‘multi-resistance’ loci, replicated genetic loci conferring resistance to single diseases were also identified. Notable amongst these were: PM_1A003: Based on physical map location, PM_1A003 (chromosome 1A at ~ 3 Mbp) likely corresponds to the cloned powdery mildew resistance gene Pm3 (Yahiaoui et al. 2004 ), located at 4.5 Mbp on chromosome 1A in the wheat reference genome. Previous work on a limited number of wheat cultivars indicated that of the ~ 10 known Pm3 resistant alleles ( PM3a - Pm3j ), European wheat commonly carries Pm3d and Pm3g (Tommasini et al. 2006 ). Our findings that PM_1A004 confers field resistance to powdery mildew in both the UK and Denmark, combined with the recent finding that Pm3a was also the likely source of powdery mildew field resistance in a European multi-founder wheat population assessed in field trials in Germany (Stadlmeier et al. 2019 ), indicates that allelic variation at Pm3 remains a good source of field resistance in European environments. Given Pm3 alleles have been deployed in modern wheat cultivars for around 90 years (Hsam et al. 2002), characterisation of the Pm3 alleles present in current wheat cultivars will help protect against breakdown in resistance, and could also help inform the use of parental lines carrying contrasting Pm3 alleles for F 1 hybrid varietal development. YR_6A610: Based on its physical map location ( chromosome 6A at ~ 610 Mbp) and its notably strong effect on resistance, YR_6A610 likely corresponds to a resistance locus recently identified in a European wheat multi-founder population ( QYr.niab-6A.3 , Bouvet et al. 2022c ), as well as in smaller European GWAS panels grown in Europe (Germany and Austria; based on SNP Tdurum_contig29607_413 , Shahinnia et al. 2022 ) and beyond (Norway, Austria, China; QYr.nmbu.6A , Lin et al. 2023 ). Further, we independently validated this locus via construction and analysis of bespoke bi-parental populations. Thus with trials spanning 2012–2021, these datasets (Bouvet et al. 2022c , Shahinnia et al. 2022 , Lin et al. 2023 , and the work we present here) collectively indicate that YR_6A610 has provided a strong source of yellow rust field resistance in European environments for at least ten years. Here we provide KASP genetic markers to track this locus for breeding and research purposes. Of the five Septoria tritici blotch genetic loci identified, none were replicated. This reflects in some ways previous studies that find ST resistance to be controlled by numerous loci of small effect (Brown et al. 2015 ), and so reported effects of individual genetic loci may not be replicated between trials, years (e.g. Stadlmeier et al. 2019 ) or separate studies, even though varietal resistance is largely repeatable (e.g. Supplementary Fig. 1). The complexity of the wheat genetics is also compounded by interaction with the high levels of standing genetic variation present in Zt populations (McDonald et al. 2022 ). Of the unreplicated Septoria tritici blotch loci, ST_2B150 was located within 5 Mbp of the yellow rust resistance locus YR_2B155. Located close by is the hybrid necrosis gene Necrosis 2 ( Ne2 , Hewitt et al. 2022 ), an intracellular nucleotide binding leucine-rich repeat (NLR) immune receptor – allelic variation within is allelic to both the leaf rust resistance gene Lr17 (Hewitt et al. 2022 ) and the yellow rust resistance gene Yr27 (Athiyannan et al. 2022 ) and whose equivalent gene in the wheat reference genome is located at 157.7 Mbp ( TraesCS2B02G182800 ). Depending on allele and genetic background, deletions of portions of the Ne2 gene result in loss of disease resistance while retaining a necrotic phenotype (Hewitt et al. 2022 ), highlighting possible links between biotrophic and necrotrophic disease response. Breeding utility of loci identified in this study The utility of the identified loci for disease resistance breeding is partly determined by their effect sizes, by the number, geographic breadth and temporal range of trials in which they were found to be significant, and by whether they have been successfully validated. However, the distribution of the alleles in the panel is also of considerable importance. If the resistance allele is very common, then investing in a marker-based selection strategy is less likely to be beneficial to a breeder, especially if the rare susceptible alleles are largely found in the older material in the panel (as we found here for yellow rust). On the other hand, if the resistant allele is rare, it is likely to be more useful for future breeding efforts, all other factors being equal. The distribution of alleles across the frequency spectrum is shown in Table 3 . Across diseases, there are similar proportions of rare (resistance allele frequency ≤ 10%) and common (resistance allele frequency > 90%) resistance alleles, 15/96 and 16/96, respectively. Similarly, there are approximately equal proportions of moderately common (33/96) and moderately rare (32/96) resistance alleles in the association mapping panel (Table 3 ).. Many of the rarest alleles (< 10% frequency) were only detected in one or two trials, possibly due in part to their rarity making detection harder. Of these, the replicated resistance loci that contribute to ‘multi-resistance’ locus MT25Mb-6 were of particular note. This included PM_4A734 (resistance allele frequency 6%), one of only two replicated powdery mildew resistance QTL identified, and BR_4A714 (resistance allele frequency = 3%), one of the most highly significant brown rust resistance genetic loci identified and introduced first into the pedigree via cv. Robigus in 2002. These examples highlight the potential of exploiting currently rare disease resistance alleles for forward selection in breeding programmes. Table 3 Grouping the 96 quantitative trait loci (QTL) identified by genome-wide association study (GWAS) by the frequency of resistance alleles in the association mapping panel. Brown rust (BR), powdery mildew (PM), Septoria tritici blotch (ST) and yellow rust (YR). Disease Frequency of resistance allele 0–10% 11–50% 51–90% 91–100% BR 4 13 14 3 PM 4 5 2 3 ST 0 3 1 1 YR 7 11 16 9 Total 15 32 33 16 Concluding remarks Here we define numerous quantitative sources of disease resistance within elite wheat germplasm released over a 90-year period, finding chromosomal regions conferring resistance to more than one disease, as well as highlighting the role of chromosomal introgressions from wild wheat relatives in the resistance profiles of modern wheat. Notably, the first incursions of genetically diverse Pst isolates that swept across the European agricultural landscape from 2011 (e.g. Hubbard et al. 2015 ; Hovmøller et al. 2016 ; UKCPVS 2016) resulting in rapid changes in YR resistance due to break down of previously effective durable sources of resistance, were beginning to occur across the duration of our YR field trials. Thus, our YR results catalogue the effective sources of resistance to these new endemic Pst races. Finally, none of the three adult plant rust resistance genes cloned to date, Yr18/Lr34/Sr67/Pm38, Yr36 (Fu et al. 2009 ) and Yr46/Lr67/Sr55/Pm46 (estimated here as being located on the wheat reference genome on chromosome 7D:474 Mbp, 6B:136 Mbp and 4D:405 Mbp, respectively), were identified as sources of resistance in our panel. If they are indeed absent, this may be due to their origin from unadapted germplasm ( Yr18 and Yr46 originated from Chinese landraces and central American wheat, respectively) (Krattinger et al. 2009 ; Singh et al. 1998 ) or different wheat species ( Yr36 ), and suggests biotrophic fungal pathogen resistance could be rapidly enhanced in the European genepool via use of these loci. Collectively, the information generated here will help optimise sources of genetic resistance present in elite wheat, so providing a baseline from which new resistance loci can be introduced. Declarations Competing interests Following Theoretical and Applied Genetics (TAG) guidelines, here we declare that James Cockram is a member of the TAG Editorial Board. No other competing interests are declared. Funding This work was supported by the United Kingdom Biotechnology and Biological Sciences Research Council (BBSRC) through LINK programme grant BB/J002542/1, ‘Wheat Association Genetics for Trait Improvement in Lineages’, and by in-kind contributions from the participating industrial partners: DSV UK Ltd, Elsoms Wheat Ltd, KWS UK Ltd, Limagrain UK Ltd, RAGT Seeds Ltd and Syngenta. JC’s time was additionally supported by BBSRC grant APP2449. Author contributions DO, JB and IM conceived of the study. DO and JB were awarded project funding. DO, JB, JC, KG, IM and TW designed research. NB, SB, PJ, MK, JL, SS and PW generated bi-parental populations. PB, RB, SB, DF, PF, NG, CH, TH, PJ, MK, JL, LN, JS, SS, PV and additional members of the WAGTAIL Consortium undertook field trials and provided associated phenotypic data. PB, TB, JB, MC, JC, GR and NG undertook additional germplasm, glasshouse and phenotyping work. KG undertook trials analysis and calculated heritabilities. KG, BL and TW performed genetic and statistical analyses. JC, TW and CZ undertook bioinformatic analyses. JC and RS undertook haploblock analysis. PB and JC undertook molecular genetics. PB, KG, BL, JC, and TW analysed data. JB, JC, IM and DO managed the project. JC wrote the manuscript, with contributions from BL, KG and TW. All authors edited and approved the manuscript. Data availability All datasets used are either included as supplementary materials, or are publicly available. Acknowledgements We thank Lawrence Percival-Alwyn (NIAB) for support with sequence alignments and Margaret Corbitt (JIC) for assistance with trials at JIC, UK. In caring memory of Prof. Ian Mackay, from all of his colleagues. References Ababa G (2023) Biology, taxonomy, genetics, and management of Zymoseptoria tritici: the causal agent of wheat leaf blotch. 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Front Plant Sci 14:1269498 Supplementary Files SUPPLEMENTARYTABLESvD.xlsx SupplementaryFigureS1.docx SupplementaryFigureS2.docx SupplementaryFigureS3.docx SupplementaryText1.docx Cite Share Download PDF Status: Published Journal Publication published 02 Jun, 2025 Read the published version in Theoretical and Applied Genetics → Version 1 posted Editorial decision: Accept 16 Apr, 2025 Reviewers agreed at journal 12 Apr, 2025 Reviewers invited by journal 11 Apr, 2025 Editor assigned by journal 11 Apr, 2025 First submitted to journal 10 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6145769","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":441882790,"identity":"7c257847-0545-4c08-b64e-72105021a1b5","order_by":0,"name":"Keith A. 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O’Sullivan","email":"","orcid":"","institution":"NIAB: National Institute of Agricultural Botany","correspondingAuthor":false,"prefix":"","firstName":"Donal","middleName":"M.","lastName":"O’Sullivan","suffix":""}],"badges":[],"createdAt":"2025-03-03 11:35:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6145769/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6145769/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00122-025-04907-x","type":"published","date":"2025-06-02T15:56:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80633591,"identity":"6e433605-6129-471d-b462-7a58df7cecec","added_by":"auto","created_at":"2025-04-15 12:02:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":362406,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal coordinate analysis of the WAGTAIL association mapping panel. A subset of 3,563 markers were used, ‘skimmed’ from the overall marker set to remove a SNP in every pair with an absolute r\u003csup\u003e2\u003c/sup\u003e ≥ 0.7. The two principal coordinates are shown, overlaid with (a) country of origin, and (b) the year of release and seasonal growth habit (spring, S ▲; winter, W ■)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6145769/v1/fa4914818d79150e92f7a681.png"},{"id":80633597,"identity":"41f547b5-e7bb-49f5-bae9-8a8967ac5704","added_by":"auto","created_at":"2025-04-15 12:02:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":10873196,"visible":true,"origin":"","legend":"\u003cp\u003eLinkage disequilibrium decay plot for the wheat association mapping panel (n = 480). Pairwise correlation (r\u003csup\u003e2\u003c/sup\u003e) between markers (8,962) on all 21 wheat nuclear chromosomes was calculated as a metric for LD. The locally estimated scatterplot smoothing (LOESS) curves summarising LD on the A (green), B (yellow) and D (pink) sub-genomes are shown. The blue horizontal line indicates the r\u003csup\u003e2\u003c/sup\u003e threshold of 0.2\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6145769/v1/5611f219f5451858ef4e977d.png"},{"id":80634466,"identity":"d9182339-3d31-481f-9f78-cdd067e53b94","added_by":"auto","created_at":"2025-04-15 12:10:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":189945,"visible":true,"origin":"","legend":"\u003cp\u003ePower analyses conducted with the WAGTAIL association mapping panel. Five heritability values were used to simulate phenotypes linked to quantitative trait loci (QTL) explaining four different amounts of phenotypic variance. For each combination of heritability and explained phenotypic variance, 1,000 simulations were completed and the frequency of how many times the focal QTL was found above the FDR threshold of q = 0.05 in each association mapping scan determined the probability of finding QTL\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6145769/v1/dda188ecbf94f1f7b6fa26a9.png"},{"id":80634461,"identity":"595858b5-2c71-4250-b40f-f54f1477b0de","added_by":"auto","created_at":"2025-04-15 12:10:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":185242,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots showing percentage disease infection in the WAGTAIL association mapping panel as assessed on the last scoring date measured at each field trial. Values represent untransformed Best Linear Unbiased Estimates (BLUEs). Trials are arranged by year (between 2012 and 2014), with trial abbreviations as listed in Table 1. The line within the box represents the median, bottom and top of boxes represent upper and lower quartiles and lines below and above box minimum and maximum values, respectively. Dots show outliers. YR = yellow rust. BR = brown rust. ST = Septoria tritici blotch, PM = powdery mildew\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6145769/v1/fee63f122bfc77cf9edc49a3.png"},{"id":80633593,"identity":"2d2b4f68-7811-4c57-af42-d48a340d9c78","added_by":"auto","created_at":"2025-04-15 12:02:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":418007,"visible":true,"origin":"","legend":"\u003cp\u003eChromosomal ideogram showing the distribution across the wheat reference genome of all genetic resistance loci identified via genome-wide association studies (GWAS). Black horizontal lines within each chromosome represent positions of those genetic markers significant above the false discovery rate (FDR) q= 0.05 significance threshold, with the physical location displayed as a scale on the left-hand side in megabase pairs (Mbp). Genetic loci identified using genetic markers significant above the FDR q = 0.05 threshold are labelled on the ideogram. YR: yellow rust, BR: brown rust, PM: powdery mildew, ST: Septoria tritici. Genetic loci identified in two or more trials are highlighted in red, and those with one or more instances of significance above the permutated α=0.05 significance threshold are underlined\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6145769/v1/8e50cc030c0e2bd7a10e8446.png"},{"id":80636198,"identity":"315e9460-8edd-4776-b376-96cf789620dd","added_by":"auto","created_at":"2025-04-15 12:26:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1340753,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plots (i) and quantile-quantile (Q-Q) plots (ii) showing examples of the results of genome-wide association study (GWAS) analysis of the four disease resistance traits scored in our wheat association mapping panel. For Manhattan plots, each datapoint represents a single marker trait association (MTA); markers are plotted using physical map location on the wheat reference genome. Two significance thresholds are shown: the false discovery rate (FDR) q=0.05 threshold, and the more stringent α=0.05 threshold as determined via permutation. Genetic loci above the permutation threshold are labelled on each Manhattan plot. (A) Disease score 1 from yellow rust resistance trial YR_12, undertaken in the UK in 2014. (B) Disease score 2 from brown rust resistance trial BR_4, undertaken in Germany in 2013. (C) Disease score 3 from brown rust resistance trial BR_7, undertaken in the UK in 2013. (D) Septoria tritici blotch disease score 1 from trial YR_11, undertaken in the UK in 2014. (E) Disease score 1 for powdery mildew resistance, as scored in brown rust trial BR_4, undertaken in Germany in 2013. Q-Q plots compare observed (O) versus expected (E) significance values. The red dashed line denotes where O = E. All results shown are based on log transformed phenotypic data\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6145769/v1/e37189743565483eca47a52f.png"},{"id":80634470,"identity":"e41bb162-4412-4077-91fa-58ae7f688f0d","added_by":"auto","created_at":"2025-04-15 12:10:01","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":295780,"visible":true,"origin":"","legend":"\u003cp\u003eFeatures of the \u003cem\u003eAegilops ventricosa \u003c/em\u003e2N\u003csup\u003ev\u003c/sup\u003eS chromosomal introgression in our wheat association mapping panel (n = 480). (A) A notably large haploblock consisting of 162 SNPs (Block 1) was identified on the short arm of chromosome 2A. The most common haplotype within this haploblock was identified in 153 of the 480 cultivars investigated (32\u0026nbsp;%), including ‘Rendezvous’, the earliest 2N\u003csup\u003ev\u003c/sup\u003eS carrier in our panel. ‘VPM1’ is the first published 2N\u003csup\u003ev\u003c/sup\u003eS carrier, and ‘Rendezvous’ has ‘VPM1’ in its pedigree (Virtue x [Maris-Hobbit sib x VPM1]). (B) Inheritance of the 2N\u003csup\u003ev\u003c/sup\u003eS haplotype for 97 cultivars in the wheat pedigree based on the 162-SNP haplotype. For some cultivars, assumptions have been made on whether they either carry (purple) or lack (light blue) 2N\u003csup\u003ev\u003c/sup\u003eS based on the haplotypes of their parents (or in the case of ‘VPM1’ and its \u003cem\u003eAe. ventricosa \u003c/em\u003eparent, based on the literature). Cultivars in the pedigree not present in our association mapping panel are shown in grey. Cultivar size is proportional to the frequency of its contribution in the pedigree. 1 = ‘Hussar’, 2 = ‘Torfrida’, 3 = ‘Equinox’, 4 = ‘Aardvark’ (highlighted in green, as our data suggests the ‘Aardvark’ genotype is anomalous, agreeing with similar conclusions by Corsi et al. (2020). (C) Rocket plot, illustrating changes in occurrence of the 2N\u003csup\u003ev\u003c/sup\u003eS introgression over time in our GWAS panel based on genotype call at the 2N\u003csup\u003ev\u003c/sup\u003eS diagnostic SNP, BobWhite_c18101_540. Each data point represents a cultivar, plotted against year of cultivar release (plotted positions of 2N\u003csup\u003ev\u003c/sup\u003eS carriers are offset on y-axis for visual clarity)\u003c/p\u003e","description":"","filename":"Figure72NvScaps31.png","url":"https://assets-eu.researchsquare.com/files/rs-6145769/v1/6b79f307c01e587fef688a57.png"},{"id":84242366,"identity":"ce89d131-82f8-47b6-9771-a798a66fc84b","added_by":"auto","created_at":"2025-06-09 16:06:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13824119,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6145769/v1/136c4014-8140-44f7-a3eb-e1eb97b0f085.pdf"},{"id":80633633,"identity":"ad41e45f-7d4f-4f96-a7e4-4d9c6e7bc43d","added_by":"auto","created_at":"2025-04-15 12:02:03","extension":"xlsx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":47033178,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYTABLESvD.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6145769/v1/f2d84b0c620c22826efa2bc1.xlsx"},{"id":80634471,"identity":"b98d9595-5f46-4372-8e58-c235efbcfbaf","added_by":"auto","created_at":"2025-04-15 12:10:02","extension":"docx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":286041,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6145769/v1/a19b0dbc72506528cfa6eadc.docx"},{"id":80633620,"identity":"69ccb26c-88aa-4cc9-880c-88400ec2b6f2","added_by":"auto","created_at":"2025-04-15 12:02:02","extension":"docx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":8994487,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6145769/v1/0222d9a8dbfb26b820f79ec5.docx"},{"id":80633609,"identity":"fd479abb-27bd-42e6-bdbb-807ef386bb66","added_by":"auto","created_at":"2025-04-15 12:02:02","extension":"docx","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":175402,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6145769/v1/f46006f322006b666f748236.docx"},{"id":80634477,"identity":"a290f0f9-7136-4b92-b889-6d0068a53de4","added_by":"auto","created_at":"2025-04-15 12:10:02","extension":"docx","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":13948,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryText1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6145769/v1/4f5a98a4a4d959e38ca08bc4.docx"}],"financialInterests":"","formattedTitle":"Genome-wide association analysis identifies seven loci conferring resistance to multiple wheat foliar diseases, including yellow and brown rust resistance originating from Aegilops ventricosa","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiseases of wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) can have significant impact on grain quality and yield, with an estimated potential yield loss of 20% per year (Wulff \u0026amp; Krattinger, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Accordingly, growers employ various methods to help prevent or control disease in their crops. Ideally, integrated pest management approaches are applied, which combine action thresholds with disease monitoring, prevention and control measures. Disease prevention via growth of cultivars with good genetic resistance is a key component of such strategies. This is particularly true in situations where the cost of fungicides and pesticides are restricting factors, or where regulations restrict use of specific chemical control options. Indeed, legislative regulation is likely to become increasingly focused on encouraging sustainable agricultural approaches, therefore promoting efficient exploitation of genetic sources of crop resistance. For example, the recent Farm to Fork Strategy, a central component of the European Green Deal, aims to encourage food systems that are fair, healthy and environmentally friendly (European Commission communication COM (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) 381 final).\u003c/p\u003e\n\u003ch3\u003eTarget wheat diseases: yellow rust, brown rust, powdery mildew and Septoria tritici blotch\u003c/h3\u003e\n\u003cp\u003eIn north-western Europe, four of the most damaging fungal diseases of wheat are yellow rust (YR, also known as stripe rust; caused by \u003cem\u003ePuccinia striiformis\u003c/em\u003e Westend f. sp. \u003cem\u003etritici\u003c/em\u003e, hereafter termed \u003cem\u003ePst\u003c/em\u003e), brown rust (BR, also known as leaf rust; caused by \u003cem\u003ePuccinia triticina\u003c/em\u003e Erikss., \u003cem\u003ePt\u003c/em\u003e), Setporia tritici blotch (caused by \u003cem\u003eZymoseptoria tritici\u003c/em\u003e, \u003cem\u003eZt\u003c/em\u003e) and powdery mildew (caused by \u003cem\u003eBlumeria graminis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e, \u003cem\u003eBgt\u003c/em\u003e). \u003cem\u003ePst\u003c/em\u003e, \u003cem\u003ePt\u003c/em\u003e and \u003cem\u003eBgt\u003c/em\u003e are obligate biotrophic fungi that require living host tissue to complete their lifecycle. In contrast, \u003cem\u003eZt\u003c/em\u003e has been classified as a latent necrotroph, initially growing asymptomatically in host tissue after which a necrotrophic phase is initiated during which host cell death is rapidly induced (Sanchez-Vallet et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). All four diseases predominantly result in infection of wheat leaves, and can result in notable reductions in grain yield and quality if left unchecked. The causal agents of yellow rust and brown rust belong to the same fungal genus, and have complicated lifecycles involving numerous spore stages as well as multiple plant host species for completion of their lifecycles (reviewed by Bouvet et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Ren et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Their asexual stages are undertaken on wheat, with infection initiated via wind-blown spores (termed urediniospores for \u003cem\u003ePst\u003c/em\u003e, and urediospores for \u003cem\u003ePt\u003c/em\u003e) resulting in the development of pustules on the surfaces of infected wheat leaves that release spores which can reinfect wheat plants, so continuing the asexual lifecycle phase. For yellow rust, the yellow or orange pustules are arranged in stripes along the leaf blade, while brown rust pustules are brown and are arranged without specific pattern. Although brown rust tends to develop later in the season than yellow rust, both diseases lead to loss of green leaf area, thus affecting yield.. Powdery mildew is characterised by pale pink asexual colonies on the surfaces of infected wheat leaves, with infection most prominent in years with mild temperatures and high humidity. Release of conidia from these colonies can lead to reinfection cycles as quick as five days (Rana et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Septoria tritici blotch results from the infection of a hemi-trophic fungus. Thus, while \u003cem\u003eZt\u003c/em\u003e initially requires living host tissue for infection, the fungus subsequently kills and takes up nutrients from the dead host tissues (Gupta et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The visual symptoms of Septoria tritici blotch include elongated chlorotic or necrotic lesions on the leaves, which because they are restricted by the leaf veins, are typified by rectangular appearance. Within-season spread of Septoria tritici blotch infection is typically mediated via rain splash spread of pycnidiospores asexually produced from the characteristic small black fruiting bodies (pycnidia) that form on infected areas.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eWheat genetic resistance to target fungal diseases\u003c/h2\u003e \u003cp\u003eWheat genetic resistance to fungal infection is typically classified as either all-stage resistance (also termed \u0026lsquo;race-specific resistance\u0026rsquo; or \u0026lsquo;seedling resistance\u0026rsquo;) or adult plant resistance (\u0026lsquo;race nonspecific resistance\u0026rsquo;). All-stage resistance is expressed at the seedling stage and extends throughout plant development. It is underpinned by the gene-for-gene model (Flor, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1956\u003c/span\u003e), with the underlying genes in wheat typically encoding nucleotide-binding site, leucine-rice repeat (NBS-LRR) proteins. Use of cultivars with low numbers of all-stage resistance genes over large areas of cultivation can result in high pathogen selection pressures, leading to the evolution of pathogen races able to overcome specific sources of all-stage resistance - presumably via mutation or deletion of the pathogen effector proteins that specific NBS-LRR proteins detect. In Europe, a recent example is the breakdown of the yellow rust resistance conferred by \u003cem\u003eYr17\u003c/em\u003e, resulting in growers rapidly shifting to cultivars that carried other sources of resistance (Bayles et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Such cycles of \u0026lsquo;boom and bust\u0026rsquo; can be ameliorated by the use of cultivars that pyramid multiple all-stage resistance loci and carry sources of adult plant resistance. Indeed, adult plant resistance loci typically provide more durable resistance, which while quantitative in nature, are less prone to being overcome by fungal pathogens. Some sources of adult plant resistance confer resistance to multiple fungal pathogens. For example, resistance to yellow rust, leaf rust (\u003cem\u003eLr\u003c/em\u003e), stem rust (\u003cem\u003eSr\u003c/em\u003e) and powdery mildew (\u003cem\u003ePm\u003c/em\u003e) is conferred by the same resistance gene \u003cem\u003eYr18/Lr34/Sr67Pm38\u003c/em\u003e and has often been used in cultivars developed via the CIMMYT international breeding programme (Singh et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). While relatively few adult plant resistance genes have been cloned, they do not belong to a single class of gene: \u003cem\u003eYr36\u003c/em\u003e encodes a protein with a kinase and a START lipid-binding domain (Fu et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), \u003cem\u003eYr18/Lr34\u003c/em\u003e encodes an ABC transporter (Krattinger et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and \u003cem\u003eYr46/Lr67\u003c/em\u003e encodes a hexose transporter (Moore et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Additionally, some wheat genes play an essential role in pathogen colonization and their mutation/deletion can result in increased resistance. Examples include \u003cem\u003emildew resistance locus\u003c/em\u003e (\u003cem\u003eMlo\u003c/em\u003e) (Buschges et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), a branched-chain amino acid aminotransferase termed \u003cem\u003eTaBCAT1\u003c/em\u003e (Corredor-Moreno et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and a cytoplasmic protein kinase termed \u003cem\u003eTaPsIPK1\u003c/em\u003e (Wang et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The majority of adult plant resistance, however, is conferred by genes with small individual effects but which collectively provide effective disease control. For further details of the genes and genetics of wheat resistance to the four fungal pathogens investigated here, see Bouvet et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e) (yellow rust), Ren et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (brown rust), Bapela et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (powdery mildew) and Ababa (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (Septoria tritici blotch).\u003c/p\u003e \u003cp\u003e \u003cem\u003eSafeguarding future wheat production: understanding the genetics of resistance in current\u003c/em\u003e cultivars\u003c/p\u003e \u003cp\u003eKnowledge of which disease resistance loci are deployed in current wheat cultivars helps inform resistance breeding strategies. For many cloned genes, molecular markers are now available that allow resistance loci to be tracked within breeding programmes (e.g. Rasheed et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, systematic understanding of the full repertoire of resistance loci deployed within elite wheat genepool will provide a framework from which informed resistance breeding can be conducted and helps safeguard against the sudden collapse of genetic resistance in contemporary cultivars. For example, sources of yellow rust adult plant resistance identified in a multi-founder wheat population have been shown to be rare in north-west European germplasm (Bouvet et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022c\u003c/span\u003e), indicating their wider deployment in new cultivars could aid resistance durability. In the European context, additional factors such as the rapid change in genetic diversity and virulence of the yellow rust fungus \u003cem\u003ePst\u003c/em\u003e since the year 2000 (Hovm\u0026oslash;ller et al. 2007) which from 2011 began to largely replace the previously clonal \u003cem\u003ePst\u003c/em\u003e isolates (Hovm\u0026oslash;ller et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hubbard et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and the relatively low number of assayed resistance loci conferring brown rust resistance in current surveys of UK \u003cem\u003ePt\u003c/em\u003e isolates (UKCPVS 2022) further highlight the need to optimise understanding and deployment of sources of wheat genetic resistance. Moreover, although powdery mildew resistance is relatively high in UK wheat and Septoria tritici resistance has increased over the past 30 years, little is known the genetic structure of resistance to either disease (Brown, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). With current winter wheat UK Recommended List varieties averaging resistance rating scores of around 6 for powdery mildew and Septoria tritici blotch (on a 1\u0026ndash;9 non-linear scale, where 1\u0026thinsp;=\u0026thinsp;susceptible. AHDB, 2024), there is of course scope for further genetic improvement despite the successes of the past.\u003c/p\u003e \u003cp\u003eGenome-wide association studies (GWAS) allow the genetic architecture of target traits to be undertaken in large collections of contemporary germplasm (e.g. Mellers et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and can offer superior mapping precision compared to conventional segregating populations (Gardiner et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Here, we assembled an association mapping panel of 480 predominantly European winter wheat cultivars released between 1916 and 2007 and genotyped using a 90,000 feature single nucleotide polymorphism (SNP) array. We then assessed the panel for resistance to four fungal diseases - yellow rust, brown rust, powdery mildew and Septoria tritici blotch - via 31 field trials across five European countries, allowing identification of resistance loci by GWAS. Finally, we selected two genetic loci for independent validation in eight bi-parental populations, and provided genetic markers for further investigation and molecular tracking of the loci.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAssociation mapping panel and genotyping\u003c/h2\u003e \u003cp\u003eA panel of 480 mainly winter wheat cultivars and breeding lines that represent the North-Western European wheat elite breeding genepool of recent decades was assembled from previous germplasm collections and participating breeding companies (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). For each accession, a single seed was grown, genomic DNA extracted (Fulton et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), and self-fertilised seed produced for downstream research. Genotyping was performed using a wheat Illumina iSelect 90,000 feature SNP array (Wang et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), with genotypes called with GenomeStudio (Illumina). All genotypes were scored as 0 (A:A) or 1 (B:B), with the very rare cases of heterozygotes (A:B) were treated as \"NA\". The resulting genotypic dataset was processed to remove markers with missing data\u0026thinsp;\u0026ge;\u0026thinsp;10%, before the remaining missing values in the genotypic data were imputed using the R package missForest (Stekhoven and Buehlmann, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) with 200 trees. Markers with a minor allele frequency\u0026thinsp;\u0026le;\u0026thinsp;2.5% were then removed in the imputed dataset.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eForming a pseudo genetic map\u003c/h3\u003e\n\u003cp\u003eThe 90k marker probe DNA sequences (Wang et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) were used as queries against the wheat reference genome of cultivar Chinese Spring (RefSeq v1.0; IWGSC, 2018) via BLAST\u0026thinsp;+\u0026thinsp;2.7.1 using default parameters (Camacho et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For each hit, the median base pair between the start and stop locations were taken as the physical position of the marker. The MAGIC 90k genetic linkage map from Gardner et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) was used to aid the marker anchoring to physical genome locations. Using R (R Core Team, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), three steps were applied to anchor markers: (1) If a marker had a singular physical hit for the same chromosome mapped in the genetic linkage map, that hit was taken as the anchored position. (2) For each marker not anchored in the first step, pairwise correlation (\u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) was calculated with all markers already anchored to find the pair that yielded the highest \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e. If the \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e value was above a determined threshold (\u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.35) and the unanchored marker had at least one physical hit on the same chromosome as the anchored marker, then the closest physical hit to the anchored marker was taken as the anchored position. (3) A backwards control step was implemented where every marker (\u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e) was correlated with the next two markers along the chromosome (\u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e). If \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e between \u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e was \u0026gt;\u0026thinsp;0.7, \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e between \u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e was \u0026lt;\u0026thinsp;0.35 and \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e between \u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e was \u0026lt;\u0026thinsp;0.35, then \u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e was excluded from the anchored markers. Finally, the R package LDheatmap (Shin et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) was used to inspect the resulting LD between the final 20,166 anchored markers. These markers, along with the 5,366 unanchored SNPs, were then \u0026lsquo;skimmed\u0026rsquo; to remove markers that were 100% correlated to each other, using a custom R script. The skimming approach involved removing a marker in each pair of markers with an absolute correlation coefficient (\u003cem\u003er\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;1. This resulted in 11,858 markers (8,962 anchored SNPs and 2,896 unmapped SNPs). All genotypic data is available online at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.niab.com/resources/\" target=\"_blank\"\u003ewww.niab.com/resources/\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.niab.com/resources/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eField trials, phenotypic data and trials analysis\u003c/h3\u003e\n\u003cp\u003ePhenotypic data were collected from 31 autumn-sown field trials (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), grown in the UK (25 trials), Germany (5), Denmark (4), France (1) and Sweden (1) over four years (harvest years 2012, 2013, 2014 and 2015). For all but one trial (ST_4), two replicate plots for each entry were grown per trial, with inclusion of susceptible control cultivars at higher replicate number. Entries were randomised between two main blocks, typically with inclusion of additional sub-blocks. Further details of all trials are provided in Supplementary Table\u0026nbsp;2, including information on trial design (including entry number, replication number, control variety number, and total number of trial plots), trial location (country, latitude and longitude), sowing date, trial infection type (and pathogen isolate information where relevant), soil type, and the crops grown on the trial site in the previous 1\u0026ndash;3 years. Trials were grown following standard local agronomic practices, but without the application of fungicides active against the target diseases. Disease infection was scored visually at the plot-level on between 1\u0026ndash;3 timepoints in the season, depending on the trial, scored between the end of booting (growth stage 45\u0026ndash;49; Zadoks et al. \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e1974\u003c/span\u003e) and the hard-dough stage (growth stage 87). Scores were recorded using either percentage infection, or via a 1\u0026ndash;9 scale that was subsequently converted to percentage infection. Summary statistics (mean, median, standard deviation, and variance) were calculated using GenStat 19th edition (VSN International). Best linear unbiased estimates (BLUEs) were calculated using a linear mixed approach in REML using GenStat. For subsequent GWAS, all disease scores were transformed as log\u003csub\u003e10\u003c/sub\u003e(value\u0026thinsp;+\u0026thinsp;1). Broad sense heritability (\u003cem\u003eH\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) was estimated using the method of Cullis et al. (2006).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of winter-sown disease field trials. Year designation\u0026thinsp;=\u0026thinsp;harvest year. \u003csup\u003e*\u003c/sup\u003eNo powdery mildew infection occurred in this trial. \u003csup\u003e\u0026dagger;\u003c/sup\u003e No significant genome wide association study (GWAS) hits identified. DEU\u0026thinsp;=\u0026thinsp;Germany, DNK\u0026thinsp;=\u0026thinsp;Denmark, FRA\u0026thinsp;=\u0026thinsp;France, SWE\u0026thinsp;=\u0026thinsp;Sweeden, UK\u0026thinsp;=\u0026thinsp;United Kingdom.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrial code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrial operator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDisease score (S) number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInf. range (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eInf. mean (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eHeritability (\u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYellow rust trials\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYR_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDSV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYR_1_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYR_1_S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;76.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYR_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eELS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYR_2_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;73.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYR_2_S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYR_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSWE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYR_3_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;55.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYR_3_S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e36.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYR_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLIM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYR_4_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;79.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYR_4_S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYR_5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSEJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYR_5_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;77.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYR_5_S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYR_6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSEJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYR_6_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;92.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYR_6_S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYR_7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eELS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYR_7_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYR_8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJIC M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYR_8_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;75.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYR_9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJIC B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYR_9_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;76.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYR_10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJIC T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYR_10_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYR_11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRAGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYR_11_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;50.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYR_12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSYN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYR_12_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYR_13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSYN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYR_13_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYR_13_S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOther trials with yellow rust scores\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBR_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBR_2_YR_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSYN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eST_4_YR_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM_4*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNIAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePM_4_YR_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;60.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePM_4_YR_S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;80.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBrown rust trials\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBR_1\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBR_1_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBR_1_S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;32.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBR_1_S3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;36.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBR_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBR_2_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBR_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRAGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBR_3_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBR_3_S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;50.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBR_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDSV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBR_4_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBR_4_S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;50.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBR_5\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKWS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBR_5_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;27.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBR_6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBR_6_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;68.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBR_7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRAGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBR_7_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;40.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBR_7_S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;76.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBR_7_S3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;91.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOther trials with brown rust scores\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM_4*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNIAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePM_4_BR_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeptoria tritici blotch trials\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST_1\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eELS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eST_1_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.70\u0026ndash;99.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e46.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST_2\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKWS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eST_2_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.1\u0026ndash;98.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e44.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLIM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eST_3_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;92.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e21.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST_4\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSYN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eST_4_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1\u0026ndash;25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eNA (1 rep)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST_5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKWS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eST_5_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u0026ndash;34.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eST_5_S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.9\u0026ndash;73.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST_6\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLIM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eST_6_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;60.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST_7\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSEJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eST_7_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.5\u0026ndash;74.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e51.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST_8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKWS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eST_8_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3\u0026ndash;74.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e26.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOther trials with Septoria tritici blotch scores\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBR_3\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRAGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBR_3_ST_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;81.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYR_11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRAGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYR_11_ST_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePowdery mildew trials\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSEJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePM_1_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePM_2_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;39.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePM_2_S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;77.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePM_2_S3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;79.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePM_2_S4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;79.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePM_2_S5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;89.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e32.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOther trials with powdery mildew scores\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBR_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDSV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBR_4_PM_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;50.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST_5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKWS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eST_5_PM_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4\u0026ndash;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST_6\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLIM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eST_6_PM_S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;67.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003ePrincipal coordinate analysis (PCoA) was conducted in R using the package ape (Paradis and Schliep, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) with 3,563 markers that had been \u0026lsquo;skimmed\u0026rsquo; to remove a SNP in each pair with an absolute correlation of \u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.7. Linkage disequilibrium was estimated as the \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e between all pairs of unique anchored SNPs (8,962) using the R package sommer (Covarrubias-Pazaran, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The LD decay was determined by plotting the \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e values against physical distance (Mbp), and for each of the A, B and D sub-genomes a trend line was calculated by locally-weighted polynomial regression (LOESS) curve in R. The physical distance of LD decay to a threshold of \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.2 was inspected for each genome. GWAS was performed using the R package GWASpoly (Rosyara et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which identified marker-trait associations using the Mixed Linear Model (MLM) (Yu et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The GWAS accounted for population structure (principal components\u0026thinsp;=\u0026thinsp;5) and kinship as fixed and random effects, respectively. Using GWASpoly, the kinship matrix was calculated using a subset of 4,023 SNPs \u0026lsquo;skimmed\u0026rsquo; from the 11,858 mapped and unmapped SNPs to remove a marker in each pair with an absolute \u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.75. The significance of marker-trait associations was determined using two thresholds: (1) the false discovery rate (FDR) (Benjamini and Hochberg, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) using a \u003cem\u003eq\u003c/em\u003e-value cut-off of \u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05, and (2) the permutation threshold (Churchill and Doerge, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), using 1,000 permutations and \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05. In cases where the FDR threshold was too lenient (under 2.9) just the permutation threshold was used. Markers in Manhattan plots were ordered according to the anchored physical positions from the wheat reference genome, with unmapped markers at the end. Covariate variables were included in successive iterations of GWAS. Marker trait associations (MTAs) were consolidated into discrete quantitative trait loci (QTL) by taking the mapped significant markers, organising them by physical and genetic distance, and choosing QTL cut-offs by taking into account linkage disequilibrium decay. QTL were named using the highest scoring physically mapped marker in the defined region. GWAS results were subsequently drawn in a chromosomal ideogram using R package LinkageMapView (Ouelette et al. 2017). Replicated GWAS hits between two or more diseases that were located within 25 Mbp of each other were termed here \u0026lsquo;multi-resistance loci\u0026rsquo; (this interval was arbitrarily set). Power analyses were undertaken using previously described methods (Wright et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), using simulated phenotypes with different \u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e (0.25, 0.50, 0.75, 0.90 or 0.99) and simulated focal QTL explaining different amounts of the variance (5%, 10%, 25%, 50% and 100%). For each combination of \u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e and percentage variance, 1000 simulations were run.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eValidation of GWAS hits\u003c/h3\u003e\n\u003cp\u003eA subset of the SNPs identified as significant in our GWAS analysis were converted from the 90k array to the Kompetitive Allele-Specific PCR (KASP) platform (LGC Genomics, UK) for subsequent use for validation via independent bi-parental populations, termed BP1 to BP8, provided by the breeding companies involved. KASP primer design was undertaken using PolyMarker (Ramirez-Gonzalez et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), with primers listed in Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e. DNA for KASP genotyping was extracted from a set of 95 cultivars selected from the GWAS panel using the DNEasy Kit (Qiagen) and KASP genotyping undertaken using KASP V4.0 2x Master Mix (LGC Biosciences) using a ProFlex PCR System Thermocycler (Applied Biosystems) with the following settings: 1 cycle at 94\u0026deg;C for 15 mins; 10 cycles at 94\u0026deg;C for 20s, 65\u0026deg;C for 60s with a touchdown of -0.8\u0026deg;C/cycle to 57\u0026deg;C; 35 cycles at 94\u0026deg;C for 20s, 57\u0026deg;C for 60s; final hold at 10\u0026deg;C. Fluorescence of VIC and FAM fluorophore 5\u0026rsquo; end labelled PCR products were subsequently read using a Scientific QuantStudio\u0026trade; 12K Flex Real-time PCR System (Thermo Fisher Scientific). ROX was used as a passive fluorescent reference to allow normalisation of variations in signal caused by differences in well-to-well liquid volume, following the manufacturer\u0026rsquo;s instructions (LGC Genomics). Results were visualised using SNP Viewer v.1.99 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://lgcgenomics.com/\u003c/span\u003e\u003cspan address=\"http://lgcgenomics.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). KASP markers confirmed as co-dominant were used to validate GWAS hits in bi-parental populations constructed either by single seed descent, or by the doubled haploid approach. Boxplots showing the distribution of the resistant and susceptible alleles and percentage of yellow rust infection recorded from field trials undertaken in the UK (using populations BP1, BP3, BP5-BP7), France (BP1), Denmark (BP2, BP4) and Germany (BP8) in 2015 were plotted using ggplot2 (Wickham, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and significance tested via a one-way ANOVA in R. To analyse significance per QTL across trials, a two-way ANOVA was performed with independent variables of KASP score and experiment. For YR_2A010, the two KASP markers used were treated as the same as there was no evidence in our datasets that the introgression has been broken up by recombination; for YR_6A610, when KASP was added in to the model the effect was not significant. Trials where there were on average less than 5% yellow rust infection were excluded from the validation set.\u003c/p\u003e\n\u003ch3\u003eHaploblock and pedigree analysis\u003c/h3\u003e\n\u003cp\u003eGenotypic data was used to create haploblocks and their corresponding haplotypes using Haploview v4.2 (Barrett et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) with additional manual curation. Where required, genotype calls at SNPs defining the haploblock were also determined in the genome assembly of \u003cem\u003eT. aestivum\u003c/em\u003e cultivar \u0026lsquo;Jagger\u0026rsquo; (Walkowiak et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) via BLASTn using Ensembl Plants (Yates et al. \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Plots of the wheat pedigree were constructed with Helium v1.19.09.03 (Shaw et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) using the pedigree published by Fradgley et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the association mapping panel: population substructure, linkage disequilibrium and experimental power\u003c/h2\u003e \u003cp\u003eWe assembled a wheat association mapping panel, termed here the \u0026lsquo;WAGTAIL\u0026rsquo; panel. It consisted of 480 European wheat cultivars released across 10 countries between 1916 and 2007. The cultivars predominantly originated from the United Kingdom (UK, 70%), France (12%) and Denmark (8%), and the majority were winter type (93%) (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Genotyping the panel with a 90,000 feature SNP array, resulted in 26,015 polymorphic genetic markers (See Supplementary Text 1 for additional details). After removing 359 markers with \u0026gt;\u0026thinsp;10% missing data, and 124 markers with a minor allele frequency\u0026thinsp;\u0026gt;\u0026thinsp;2.5%, 25,532 markers remained. Of these, we were able to anchor 20,166 markers to the wheat physical map, leaving 5,366 unmapped markers. Duplicated SNPs (based on 100% correlation) were then removed from both the mapped and unmapped datasets, resulting in 8,962 mapped markers and 2,896 unmapped markers. Therefore, the final SNP data-matrix consisted of 11,858 markers genotyped across 480 cultivars (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMean genetic marker density for the A and B sub-genomes was similar, at 1.55 and 2.05 markers/Mbp, but was lower on the D sub-genome (0.64 markers/Mbp). Genetic marker number per chromosome ranged from 2,196 (chromosome 1B) to 85 (chromosome 4D) (Supplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Principal coordinate analysis (PCoA) identified relatively limited genetic substructure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with the first two principal coordinates (PC) accounting for 11.3% of the variation (PC1\u0026thinsp;=\u0026thinsp;7.1%, PC2\u0026thinsp;=\u0026thinsp;4.2%). While the majority of the cultivars in the panel were from the UK, the German (DEU) and Dutch (NLD) cultivars formed clusters in the PCoA plots. Overlaying \u0026lsquo;winter\u0026rsquo; and \u0026lsquo;spring\u0026rsquo; seasonal growth habit designations found spring cultivars to form a loose subcluster within the overall plot, determined predominantly by PC1. Furthermore, year of cultivar release showed a notable visual trend for newer varieties to be further separated from the spring cultivars in PCoA space.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing the 8,962 skimmed and anchored marker set, we then investigated the distribution and extent of linkage disequilibrium within all chromosomes via linkage disequilibrium decay plots, with a trend line for each sub-genome A, B and D calculated by locally-weighted polynomial regression (LOESS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The intersection between the LOESS curve and \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.2 suggested that linkage disequilibrium decayed at a relatively low rate within chromosomes, returning distances for the A, B and D sub-genomes of 20 Mbp, 36 Mbp and 41 Mbp, respectively. To further explore the suitability of the panel for genome-wide association studies, we used our data-matrix of 480 cultivars and 8,962 SNPs to undertake power analyses, whereby the probability of identifying a simulated QTL was investigated when heritability (\u003cem\u003eH\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) and percentage variance explained by the QTL was varied (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). As the percentage variance explained by the QTL and \u003cem\u003eH\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e increased, the probability of finding the QTL increased. Where \u003cem\u003eH\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e was high (\u0026ge;\u0026thinsp;0.75), the probability of QTL detection was close to 1.0 irrespective of the percent variance explained. At more modest levels of \u003cem\u003eH\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (0.50) and when the percentage variance explained by the QTL was \u0026ge;\u0026thinsp;10%, the probability of QTL detection remained relatively high (\u0026gt;\u0026thinsp;66%). However, the probability of identifying QTL fell to below 20% when low values for both heritability (\u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.25) and percent variance explained (\u0026le;\u0026thinsp;0.25) were modelled. For QTL explaining 5% of variance, a 50% detection ability was achieved when \u003cem\u003eH\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e was over about 0.6.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDisease resistance phenotyping\u003c/h2\u003e \u003cp\u003eWe used the association mapping panel to conduct a total of 31 disease assessment trials between 2012 and 2015, located in the UK, Germany, Denmark, Sweden and France. Of these trials, 13 were for yellow rust (YR), seven for brown rust (BR), eight for Septoria tritici blotch (ST), and three for powdery mildew (PM) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In some trials, more than one disease was scored where the opportunity arose and where both diseases could be scored without mutual interference, and in one PM trial no PM infection occurred, such that in total 39 disease-trial combinations and 58 disease-trial-timepoint combinations were scored (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For yellow rust and brown rust, where percentage disease infection scores were measured in all trials on two or three separate dates (indicated in the trait names using the postscript \u0026lsquo;_S1\u0026rsquo;, \u0026lsquo;_S2\u0026rsquo; or \u0026lsquo;_S3\u0026rsquo;), the percentage infection at later score dates were consistently higher than on the first. Overall, Septoria tritici blotch showed the highest infection based on mean percentage infection across all trials (25.9%), followed by yellow rust (11.2%), powdery mildew (10.6%), and brown rust (6.5%). Boxplots for disease resistance at the last score date for each trial-trait-score date combination are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. For the majority of YR, BR, ST and PM datasets, percentage disease was skewed towards low infection \u0026ndash; accordingly, Best Linear Unbiased Estimates (BLUEs) were log transformed for all downstream analyses (Supplementary Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). Heritabilities for yellow rust trials were mostly high (20/24 disease-trial-timepoint combinations \u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.7). For brown rust, heritability was variable (6/14 disease-trial-timepoint combinations \u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.7, 5/14 combinations \u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.5). For Septoria, heritability for 0/11 disease-trial-timepoint combinations were \u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.7 and 3/11 were \u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.5) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For powdery mildew, 5/9 disease-trial-timepoint combinations were \u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.7. For each disease, there was significant positive correlation between variety means from trial-trait-score combinations for the majority of comparisons investigated (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Correlations within disease datasets were highest for yellow rust datasets, and powdery mildew datasets, for which all comparisons were significant at P\u0026thinsp;\u003cem\u003e\u0026le;\u003c/em\u003e\u0026thinsp;0.001. Disease score correlations between all but one Septoria tritici blotch trial-trait-score combinations were significant at P\u0026thinsp;\u0026ge;\u0026thinsp;0.05. For brown rust, while the majority of correlations were significant at P\u0026thinsp;\u003cem\u003e\u0026le;\u003c/em\u003e\u0026thinsp;0.001, non-significant positive correlations were identified for four trial comparisons (BR_1/BR_7, BR_1/BR_3, BR_2/BR_3 and BR_2/BR_5). Several notable inter-disease correlations were observed. This included positive significant correlations (P\u0026thinsp;\u003cem\u003e\u0026le;\u003c/em\u003e\u0026thinsp;0.05) between yellow rust and powdery mildew (53 of 80 comparisons, 66%), yellow rust and brown rust (20 of 128 comparisons, 16%) and brown rust and powdery mildew (18 of 40 comparisons, 45%). Conversely, significant negative correlations between trial-trait-score date combinations were notable between subsets of Septoria trials and those for powdery mildew (14 of 50 comparisons, 28%) and brown rust (20 of 80 comparisons, 25%). Notably, most of the significant negative correlation (P\u0026thinsp;\u003cem\u003e\u0026le;\u003c/em\u003e\u0026thinsp;0.05) reported between Septoria and brown rust trials originated via comparisons with three brown rust trials (BR_3, BR_4, BR_6, 18 of 30 trial comparisons; Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMarker-trait associations\u003c/h2\u003e \u003cp\u003eWe used the 58 trial-disease-timepoint combinations (subsequently termed here, \u0026lsquo;traits\u0026rsquo;) to undertake genome-wide association studies (GWAS), initially employing a false discovery rate significance threshold of FDR\u0026thinsp;=\u0026thinsp;0.05. GWAS identified a total of 2,054 marker-trait associations (MTAs), of which 1,702 involved genetically mapped markers and 352 involved unmapped markers (Supplementary Table\u0026nbsp;6). Considering the mapped markers only, and taking into account LD decay, physical and genetic distance, these MTAs resolved into the following number of distinct genetic loci: 43 for YR, 34 for BR, 5 for ST, and 14 for PM (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) (Supplementary Table\u0026nbsp;7, Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). For each disease, GWAS hits with the highest significance are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-e (Manhattan plots for all GWAS analyses are shown in Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eYellow rust: GWAS hits were identified in all 16 field trials in which yellow rust was scored (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) (Supplementary Table\u0026nbsp;7). The majority of loci located on the A and B subgenomes (23 and 17, respectively), while just three were found on the D subgenome. The highest number of YR resistance loci were located on chromosomes 2B (6), 2A (4), 3B (4), 4A (4) and 5A (4). Of the 43 yellow rust resistance genetic loci which were statistically significant in at least one disease trial, 20 were identified in two or more trials (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; highlighted in red in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) of which 12 were identified using the more stringent permutated P\u0026thinsp;=\u0026thinsp;0.05 significance threshold (underlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The most replicated YR resistance locus was on chromosome 4A at ~\u0026thinsp;738 Mbp (termed here, YR_4A738), which was identified in eight separate trials between 2012\u0026ndash;2014 (five in the UK, two in Denmark and one in France). Overall, the YR resistance loci with the highest significance values (-log\u003csub\u003e10\u003c/sub\u003eP\u0026thinsp;\u0026ge;\u0026thinsp;8.21) were identified in the UK in 2014: YR_2A010 on the short arm of chromosome 2A (identified in five trials) and YR_6A610 on the long arm of chromosome 6A (identified in six trials) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). These two loci had very high significance values (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) but relatively small SNP effect sizes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the 39 significant disease resistance genetic loci identified via genome wide association study (GWAS) in two or more trials. The six multi-resistance genetic loci in which replicated resistance loci were identified for two or more diseases within 25 Mbp, are listed as MT25Mb_1 to _6. Chromosome locations are based on the wheat reference genome (IWGSC, 2018). Effect on phenotype is indicated as change in disease leaf infection percentage conferred by the reference allele. Chr. = chromosome, No. = number, Pheno. = phenotype, Pos. = position. \u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003eComplete marker name: \u003cem\u003ewsnp_Ex_rep_c101457_86817938\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGenetic locus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMulti-resistance locus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChr.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInterval (Mbp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePeak SNP name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePeak SNP pos. (Mbp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePeak SNP sig. (-log\u003csub\u003e10\u003c/sub\u003eP)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEffect on pheno (% inf.).\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo. trials\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBrown rust (total no. trials\u0026thinsp;=\u0026thinsp;8)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBR_1B117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.20-11.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBS00009715_51\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e112.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBR_2A015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMT25Mb_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.23\u0026ndash;24.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBS00004089_51\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBR_2A704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e703.98-717.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eExcalibur_c40617_983\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e704.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBR_2B026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMT25Mb_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.30-33.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eKukri_c40764_367\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBR_2B640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e214.59-640.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBS00069685_51\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e640.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBR_2B777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMT25Mb_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e777.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eRAC875_c19685_944\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e777.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBR_2D014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMT25Mb_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBobWhite_c15073_502\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBR_3A134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e134.25-471.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBobWhite_c35303_192\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e134.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBR_3A741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMT25Mb_5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e736.39-741.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eTdurum_contig5009_349b\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e741.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBR_3B826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e810.33\u0026ndash;826.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eTdurum_contig42131_1300\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e826.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBR_4A714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMT25Mb_6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e698.04-743.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBobWhite_c20306_111\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e713.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBR_6A016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.41\u0026ndash;23.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eExcalibur_rep_c105463_330\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePowdery mildew (total no. trials\u0026thinsp;=\u0026thinsp;5)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePM_4A734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMT25Mb-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e610.49-742.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBS00110758_51a\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e734.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePM_6B664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e663.68-681.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eRAC875_c5129_280\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e663.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYellow rust (total no. trials\u0026thinsp;=\u0026thinsp;16)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYR_1B164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.85-163.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eExcalibur_rep_c92475_275\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e163.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYR_2A010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMT25Mb_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40-24.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ewsnp_Ra_c8771_14786376b\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYR_2B034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMT25Mb_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.91\u0026ndash;33.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBobWhite_rep_c64429_660a\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYR_2B047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.43\u0026ndash;47.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBS00041587_51\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYR_2B155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e133.7-154.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eKukri_c36783_91\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e155.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYR_2B763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMT25Mb_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00-763.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBS00070301_51a\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e763.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYR_2D014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMT25Mb_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00-13.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eRAC875_c90426_151\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYR_3A008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.43\u0026ndash;8.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBS00037189_51\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYR_3A746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMT25Mb_5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e746.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ewsnp_Ex_c60462_60905848\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7746.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYR_3B739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e738.75-750.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ewsnp_Ex_rep_c101457\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e739.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYR_4A738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMT25Mb_6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e734.00-738.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eExcalibur_c65272_341\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e737.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYR_5A030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.85\u0026ndash;29.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eExcalibur_rep_c90275_262\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYR_5A560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e559.52-568.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBS00021955_51\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e559.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYR_5A677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e677.13-684.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBS00022867_51\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e677.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYR_6A002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.29\u0026ndash;13.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eExcalibur_c50323_215\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYR_6A610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e596.58\u0026ndash;11.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eGENE-4021_496\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e610.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYR_7A090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.84\u0026ndash;90.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eKukri_c5757_530\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYR_7A626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e625.74-632.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBS00105558_51\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e625.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYR_7A730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e730.43-730.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eKukri_c11451_1882\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e730.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYR_7B701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e700.57-705.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eExcalibur_c7338_563\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e700.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBrown rust: Of the eight trials undertaken (four in the UK and four in Germany), GWAS hits for BR resistance were identified in six trials (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) (Supplementary Table\u0026nbsp;7). No significant associations were identified in trials BR_1 (Germany 2012, low BR infection pressure) and BR_5 (UK, 2012) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The 34 BR resistance genetic loci identified were distributed predominantly on the A and B subgenomes (16 and 15 loci, respectively), with just three found on the D subgenome. The group 2 chromosomes possessed notably high numbers: five for chromosome 2A and six for chromosome 2B. Twelve of the 34 BR resistance loci were replicated in two or more trials (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Supplementary Table S7; highlighted in red in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), with two loci being by far the most significant (-log\u003csub\u003e10\u003c/sub\u003eP-value above the FDR\u0026thinsp;=\u0026thinsp;0.05 significance threshold\u0026thinsp;\u0026gt;\u0026thinsp;11): BR_2A015 on chromosome 2A at ~\u0026thinsp;15 Mbp) and BR_4A714 on chromosome 4A at ~\u0026thinsp;714 Mbp (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). The BR resistance genetic locus identified in the highest number of trials was BR_1B177 on chromosome 1B at ~\u0026thinsp;177 Mbp, found in five trials in Germany and the UK in 2012 and 2013 (trials BR_3, BR_4, BR_6, BR_7, PM_4).\u003c/p\u003e \u003cp\u003eSeptoria tritici blotch: GWAS hits for Septoria tritici blotch (ST) resistance were identified in three of the ten trials: ST_3 (UK, 2012), ST_5 (UK, 2013) and YR_11_ST (UK 2014) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) (Supplementary Table\u0026nbsp;7). Five genetic loci were identified, distributed across the A (1 loci), B (2) and D (2) subgenomes. Of these loci, none were replicated (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The most significant ST resistance locus was ST_7D015, located on the short arm of chromosome 7D at ~\u0026thinsp;15 Mbp, identified in trial ST_5 (UK, 2013).\u003c/p\u003e \u003cp\u003ePowdery mildew: GWAS hits were identified in all five field trials in which powdery mildew infection was scored (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) (Supplementary Table\u0026nbsp;7). In total, 14 genetic loci were identified across 11 chromosomes. Resistance loci were more common on the A and B subgenomes (7 and 6 loci, respectively) than on the D subgenome (1 locus), with chromosome 4A, 5A and 5B possessing two resistance QTL each. Two replicated resistance loci were identified, PM_4A734 in trials PM_1 and PM2, and PM_6B664 in trials PM_2 and ST_6 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; highlighted in red in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The most significant PM resistance loci identified were PM_2A762 (chromosome 2A at ~\u0026thinsp;762 Mbp, identified in trial BR_4) (example Manhattan plot shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee) and PM_4B010 (chromosome 4B at ~\u0026thinsp;10 Mbp, identified in trial ST_5), both of which returned -log\u003csub\u003e10\u003c/sub\u003eP values\u0026thinsp;\u0026ge;\u0026thinsp;1.98 above the FDR\u0026thinsp;=\u0026thinsp;0.05 significance thresholds applied in their relevant trials. As with ST but in contrast to YR and BR, -log\u003csub\u003e10\u003c/sub\u003eP-values for even the most significant PM loci were not especially high.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eChromosomal distribution of disease resistance genetic loci\u003c/h2\u003e \u003cp\u003eThe occurrence of disease resistance genetic loci across the genome was enriched towards chromosome ends. The few loci that were located in pericentromeric regions, as defined by comparison of the physical and genetic maps (Supplementary Table\u0026nbsp;8), were typified by large physical interval sizes, due to reduced genetic recombination (Supplementary Table\u0026nbsp;7).\u003c/p\u003e \u003cp\u003eClose physical linkage between the peak GWAS hits for resistance to two or more target diseases was observed, most commonly for yellow rust and brown rust (caused by related biotrophic fungal pathogens), but also with powdery mildew or Septoria tritici blotch. For example, considering replicated GWAS hits only, seven genetic loci clusters were predicted to be located within 25 Mbp of each other (termed here \u0026lsquo;multi-resistance loci\u0026rsquo;), of which five were within 11 Mbp (on chromosome 2A: YR_2A010/BR_2A015; chromosome 2B: BR_2B026/YR_2B034, chromosome 2D: YR_2D014/BR_2D014; Chromosome 3A: BR_3A741/YR_3A746; chromosome 6A: YR_6A002/BR_6A016) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Supplementary Table\u0026nbsp;9). Notably, this included our second most significant hits for yellow rust resistance (YR_2A010) and brown rust resistance (BR_2A015), for which the most significant markers were located within ~\u0026thinsp;5 Mb of each other on the short arm of chromosome 2A. YR_2A010 and BR_2A015 are located in a region previously reported to carry a\u0026thinsp;~\u0026thinsp;32 Mbp introgression from \u003cem\u003eAegilops ventricosa\u003c/em\u003e chromosome 2N\u003csup\u003ev\u003c/sup\u003eS (Gao et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Analysis of our GWAS panel identified 5 haploblocks towards the end of chromosome 2AS, encompassing 211 SNPs across ~\u0026thinsp;37 Mbp. Of these, an unusually large haploblock consisting of 162 SNPs was present at the start of the chromosome arm (haploblock-1), within which a single haplotype was present at a frequency of 32% (153 of the 480 cultivars) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea) (Supplemental Table\u0026nbsp;10). Anchoring these SNPs to the genome assembly of the German wheat cultivar \u0026lsquo;Jagger\u0026rsquo;, previously reported as carrying the 2N\u003csup\u003ev\u003c/sup\u003eS introgression (Gao et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), found that all SNPs were located within the 32.53 Mbp introgressed chromosomal segment and carry the \u0026lsquo;Jagger\u0026rsquo; SNP variant (Supplemental Table\u0026nbsp;10). Of these 162 SNPs, 67 were found to each uniquely serve as a tag for the extended putative 2N\u003csup\u003ev\u003c/sup\u003eS haplotype (Supplemental Table\u0026nbsp;10) and resulted in highly significant GWAS P-values for yellow rust and brown rust (\u0026ge;\u0026thinsp;9.28 above the FDR). The 2N\u003csup\u003ev\u003c/sup\u003eS introgression was introduced into the wheat pedigree via the cultivar \u0026lsquo;VPM1\u0026rsquo; (Dyck and Lukow, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Cross referencing the presence of the 2N\u003csup\u003ev\u003c/sup\u003eS haplotype in our association mapping panel with a recently published pedigree of European wheat (Fradgley et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found 97 of the 153 2N\u003csup\u003ev\u003c/sup\u003eS haplotype carriers to have \u0026lsquo;VPM1\u0026rsquo; in their known pedigree (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb), with most of the remaining 56 cultivars lacking sufficient pedigree information detail to determine whether \u0026lsquo;VPM1\u0026rsquo; was in their pedigree. Plotting the occurrence of the 2N\u003csup\u003ev\u003c/sup\u003eS introgression against cultivar commercial release date shows its frequency has significantly increased over time since its introduction via \u0026lsquo;VPM1\u0026rsquo; in the early 1980s (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec), with 48% of the most recent cultivars in our panel carrying the introgression (years 2008\u0026ndash;2010).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOf the two replicated powdery mildew resistance loci, PM_4A734 was located within 20 Mbp of replicated resistance loci for yellow rust (YR_4A738) and brown rust (BR_4A714) on the long arm of chromosome 4A. No replicated resistance loci were identified for Septoria tritici blotch.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eValidation of yellow rust GWAS hits\u003c/h2\u003e \u003cp\u003eWe selected the two most significant yellow rust resistance genetic loci identified by GWAS, YR_2A010 and YR_6A610, for independent validation in a series of eight bi-parental populations (termed BP1 to BP8). Parental lines were selected so that each bi-parental population was predicted to segregate for contrasting alleles at one or both of the target resistance loci (Supplementary Table\u0026nbsp;11), based on the parental genotypes in our GWAS dataset. The populations were phenotyped for percentage yellow rust infection in the field and the target loci genotyped using KASP markers for selected 90k array SNPs identified by GWAS in the association mapping panel (YR_2A010: SNPs \u003cem\u003eKukri_c18149_581\u003c/em\u003e and \u003cem\u003eExcalibur_c25599_358\u003c/em\u003e, genotyped on populations segregating for this locus, BP1-BP6. For YR_6A610: SNPs \u003cem\u003eGENE_4021_496\u003c/em\u003e and \u003cem\u003eTdurum_contig29607_413\u003c/em\u003e, genotyped on populations BP4-BP8). Meta-analyses of the bi-parental population datasets relevant to each of the two loci found highly significant association with yellow rust resistance scores for both YR_2A010 (P\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) and YR_6A610 (P\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) (Supplementary Table\u0026nbsp;11; Supplementary Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Accordingly, bi-parental analysis undertaken provided independent validation of both YR_2A010 and YR_6A610.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eProperties of the wheat association mapping panel\u003c/h2\u003e \u003cp\u003eWe assembled and genotyped an association mapping panel of 480 wheat cultivars, representing a valuable resource for dissecting the underpinning genetics of North-west European wheat germplasm developed across ~\u0026thinsp;90 years of crop breeding. Our population was relatively large in comparison to other published wheat association mapping panels (e.g. the median population size of the 17 wheat panels used for GWAS cited in this manuscript is 273). Linkage disequilibrium in the WAGTAIL panel decayed at rates comparable to that typically observed in other inbred cereal crop species (e.g. Roncallo et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While these rates are around an order of magnitude higher than that observed in outbreeding cereal crops such as maize (\u003cem\u003eZea mays\u003c/em\u003e) (e.g. 0.34 Mbp at a genome-wide level, Ertiro et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the development of new cultivars via crossing means that association mapping panels consisting of collections of cultivars and breeding lines can be considered as pseudo-outbreeding populations that have been subjected to strong selection for beneficial alleles and allelic combinations (Rostoks et al. 2006). Thus, while lower genome-wide genetic marker numbers are required to identify genetic loci compared to an outbreeding crop like maize, the pseudo-outbreeding nature of the panel due to the crosses made by breeders results in elevation in genetic recombination levels of throughout much of the genome compared to purely inbreeding species.\u003c/p\u003e \u003cp\u003ePopulation substructure was evident in the panel, predominantly due to a combination of year of cultivar release, cultivar country of origin and spring/winter seasonal growth habit phenotype. Such substructure is a common feature of wheat association mapping panels (e.g. Bentley et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Mellers et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Walkowiak et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and related cereal crops such as barley (e.g. Cockram et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and is due to historic and/or recent similarities in the shared ancestry of the lines. If this is not accounted for, the frequency of false-positive associations can increase, due to causes other than close linkage between the genetic marker and QTL (Cockram and Mackay, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). After statistical adjustment for substructure and kinship, plots of expected versus observed marker-trait significances for our disease traits indicated that the population stratification present in our panel was adequately accounted for. Power and precision to detect marker-trait associations in association mapping panels via GWAS relies on numerous factors, including population size and the amount of historic genetic recombination captured (Cockram and Mackay, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Estimation of the power of an association mapping panel to detect marker-trait associations provides \u003cem\u003ea priori\u003c/em\u003e expectations of experimental design. While this is standard practice in human studies, it is not commonly applied in crops. Our power analyses indicated that the association mapping had relatively good power to detect loci, even when the percentage of the variation explained by a given locus was relatively low, indicating that association mapping panels of this size or greater are likely required for detection of quantitative sources of resistance in modern wheat cultivars.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eThe genetic architecture of disease resistance in North-Western European wheat\u003c/h2\u003e \u003cp\u003eOur analysis indicated that field resistance to the four target foliar diseases were under complex genetic control, with 34 replicated resistance loci identified across three of the four target diseases. Of the seven \u0026lsquo;multi-resistance\u0026rsquo; genetic loci identified, six controlled resistance to two or all three of the target biotrophic diseases. A total of 87 permanently named loci have been identified for yellow rust (Rosewarne et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wang and Chen, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Catalogue of Gene Symbols of Wheat \u0026ndash; \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e edition) and 85 for brown rust (Kol\u0026aacute;rikov\u0026aacute; et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Catalogue of Gene Symbols of Wheat \u0026ndash; \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e edition), with many additional loci reported that have yet to be given formal \u003cem\u003eYr\u003c/em\u003e or \u003cem\u003eLr\u003c/em\u003e nomenclature. Resistance alleles at 70 named powdery mildew resistance genes (\u003cem\u003ePm\u003c/em\u003e) have been reported (Catalogue of Gene Symbols of Wheat \u0026ndash; \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e edition. See also review by Zou et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Some wheat adult plant resistance genes confer resistance against two or more biotrophic pathogens, a characteristic that has been suggested to be an indicator of the durability of resistance. These include the following three loci, each conferring resistance to yellow rust, brown rust, stem rust and/or powdery mildew: \u003cem\u003eYr18/Lr34/Pm38/ Sr67\u003c/em\u003e (Spielmeyer et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Lillemo et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), \u003cem\u003eYr29/Lr46/Sr58/Pm39\u003c/em\u003e (Lagudah, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), \u003cem\u003eYr30/Lr27/Sr2\u003c/em\u003e (Mago et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and \u003cem\u003eYr46/Lr67/Sr55/Pm46\u003c/em\u003e (Herrera-Foessel et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Moore et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) (although we found no evidence for these presence of these loci in our European wheat panel). The \u0026lsquo;multi-resistance\u0026rsquo; genetic loci identified here are defined as linked genetic loci rather than a single underlying gene. However, it may be possible that for some, the underlying gene may confer resistance to more than one disease. The most notable of our \u0026lsquo;multi-resistance\u0026rsquo; loci, based on GWAS significance and number of trials identified in, included:\u003c/p\u003e \u003cp\u003e(1) Yellow/brown rust locus YR_2A010/BR_2A015: Resistance at this locus on the short arm of wheat chromosome 2A was conferred by the \u003cem\u003eAe. ventricosa\u003c/em\u003e 2N\u003csup\u003ev\u003c/sup\u003eS introgression. Thirty-two percent of the cultivars in our GWAS panel carried this introgression on chromosome 2A, as defined by our 162-SNP haplotype. This introgression is a well know source of resistance to multiple diseases, including yellow rust (\u003cem\u003eYr17\u003c/em\u003e) (Fang et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), brown rust (\u003cem\u003eLr37\u003c/em\u003e) (Xu et al. 2018), stem rust (\u003cem\u003eSr7a\u003c/em\u003e, \u003cem\u003eSr38\u003c/em\u003e) (Turner et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), eyespot (Doussinault et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1983\u003c/span\u003e), wheat blast (Cruz et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and cereal cyst nematode resistance (Jahier et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). While rust resistance conferred by \u003cem\u003eYr17\u003c/em\u003e and \u003cem\u003eLr37\u003c/em\u003e have been widely overcome (Bayles et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; UKCPVS, 2022), the 33 Mbp \u003cem\u003eAe. ventricosa\u003c/em\u003e segment is rich in NLR genes, with increased numbers of NLRs relative to the equivalent region in the wheat reference genome assembly (Gao et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Indeed, our results indicate that 2N\u003csup\u003ev\u003c/sup\u003eS carries effective sources of yellow and brown rust resistance in addition to the previously overcome resistance genes \u003cem\u003eYr17\u003c/em\u003e and \u003cem\u003eLr37\u003c/em\u003e, agreeing with recent reports by Wang et al. (\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This introgression has also been associated with increased grain yield (Gao et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Juliana et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and reduced lodging (Gao et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Directional selection for 2N\u003csup\u003ev\u003c/sup\u003eS was evident in our panel, with notable change in frequency of the 2N\u003csup\u003ev\u003c/sup\u003eS haplotype in wheat pedigree over time: first introduced via \u0026lsquo;VPM1\u0026rsquo; in the early 1980s (Dyck and Lukow, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), it was passed onto several cultivars, including \u0026lsquo;Rendezvous\u0026rsquo; - a frequently used parent in the European pedigree (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb) (Fradgley et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). \u0026lsquo;Rendezvous\u0026rsquo; is a parent of subsequent parents that are frequently used in the pedigree, such as \u0026lsquo;Lynx\u0026rsquo;, \u0026lsquo;Hussar\u0026rsquo; and \u0026lsquo;Tofrida\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb-c) (Fradgley et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Notably, while we found \u0026lsquo;Aardvark\u0026rsquo; to lack 2N\u003csup\u003ev\u003c/sup\u003eS, it was a parent for seven cultivars which possess the introgression. Analysis of the pedigrees of these seven lines indicates that \u0026lsquo;Aardvark\u0026rsquo; most likely carries 2N\u003csup\u003ev\u003c/sup\u003eS. Previous studies have noted genotypic discrepancies for \u0026lsquo;Aardvark\u0026rsquo; (Corsi et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), indicating that either the incorrect germplasm was used here, or that residual heterozygosity was present within the cultivar when it was being used by breeding companies for crossing within the pedigree. The 2N\u003csup\u003ev\u003c/sup\u003eS introgression has previously been identified as a possible explanation for the very strong signals for directional selection in winter wheat across more than seventy years in the United States (Ayalew et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Indeed, 48% of the most recent cultivars in our panel (from 2008\u0026ndash;2010) contained the introgression. Similar strong selection is reported in wheat cultivars developed by CIMMYT in Mexico: frequency across all CIMMYT genotypes released between the 1990s to the early 2010s is ~\u0026thinsp;24%, increasing to ~\u0026thinsp;90% in lines released after 2015 (Gao et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Juliana et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Juliana et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, the combination of multiple sources of disease resistance and beneficial yield traits may explain the continued strong selection for the 2N\u003csup\u003ev\u003c/sup\u003eS introgression in wheat breeding programmes over these periods. Rare putative (He et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xue et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) or observed (Wang et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) recombination between the 2N\u003csup\u003ev\u003c/sup\u003eS introgression and the native wheat chromosome 2A have previously been reported. However, we found no evidence for recombination within 2N\u003csup\u003ev\u003c/sup\u003eS in the 480 cultivars studied here. Thus, the two KASP genetic markers we developed (\u003cem\u003eKukri_c18149_581\u003c/em\u003e and \u003cem\u003eExcalibur_c25599_358\u003c/em\u003e) are each capable of serving as a diagnostic tag for the extended putative 2N\u003csup\u003ev\u003c/sup\u003eS haplotype in our panel of cultivars, providing researchers and breeders with resources with which to track and manipulate this agronomically important genomic feature.\u003c/p\u003e \u003cp\u003e(2) Yellow rust/brown rust locus YR_2B763/BR_2B777. Three named leaf rust resistance genes (\u003cem\u003eLr50\u003c/em\u003e, Brown-Guedira et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; \u003cem\u003eLr58\u003c/em\u003e, Kuraparthy et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; both originating from \u003cem\u003eT. timpoheevi; Lr82\u003c/em\u003e from a wheat landrace, Bariana et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and three named yellow rust resistance genes (\u003cem\u003eYr5, Yr7\u003c/em\u003e and \u003cem\u003eYrSP\u003c/em\u003e, Marchal et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) are located on the long arm of chromosome 2B. Of these, physical map location based on anchoring to the wheat reference genome rules out all but \u003cem\u003eLr50, Lr58\u003c/em\u003e and \u003cem\u003eLr82\u003c/em\u003e \u0026ndash; although it is currently unclear whether our locus represents resistance via all-stage or adult plant mechanisms. Accordingly, the chromosome 2B locus identified here may represent a novel yellow rust resistance gene in relatively close linkage to one or more brown rust resistance loci. Interestingly, a genetic locus controlling grain yield (\u003cem\u003eYLD_2B.4\u003c/em\u003e, peak marker anchored on chromosome 2B at 766 Mbp) has recently been identified at this location in European wheat (White et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), indicating this genomic region may carry other beneficial alleles of agronomic relevance.\u003c/p\u003e \u003cp\u003e(3) Brown rust/powdery mildew/yellow rust locus BR_4A714/PM_4A734/YR_4A738 (replicated in two, two and eight trials, respectively) located close to the telomere on the long arm of chromosome 4A. This region has recently been reported to confer resistance to both rust diseases (Liu et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kale et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and to powdery mildew (Liang et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Of the named resistance loci, the all-stage brown rust resistance gene \u003cem\u003eLr28\u003c/em\u003e effective against numerous \u003cem\u003ePt\u003c/em\u003e pathotypes (e.g. Bipinraj et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) is located in this region. \u003cem\u003eLr28\u003c/em\u003e is thought to have originated in wild wheat species, having been found in \u003cem\u003eAegilops speltoides\u003c/em\u003e (Naik et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), \u003cem\u003eAe. crassa, Ae. juvenalis\u003c/em\u003e, \u003cem\u003eAe. triuncialis\u003c/em\u003e and \u003cem\u003eT. timpoheevii\u003c/em\u003e (Kol\u0026aacute;rikov\u0026aacute; et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The leaf rust resistance locus on the long arm of bread wheat chromosome 4A identified by Kale et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in the European cultivar \u0026lsquo;Attraktion\u0026rsquo; was noted to be in a genomic region shown to carry a 26 Mbp region of high sequence divergence with the wheat reference genome sequence (indicative of a chromosomal introgression from a wheat relative), and was identical by descent to an introgression carried in the UK cultivar \u0026lsquo;Robigus\u0026rsquo;. Indeed, via SNP array genotyping and analysis of pedigree records, this region in \u0026lsquo;Robigus\u0026rsquo; has previously been reported to likely to originate from a wild wheat relative, potentially \u003cem\u003eT. dicoccoides\u003c/em\u003e (Przewieslik-Allen et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). \u0026lsquo;Robigus\u0026rsquo; is notable in its prominence in the UK wheat pedigree (Fradgley et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), highlighting the usefulness of alien chromosome introgression in European bread wheat resistance genetics, and the potential that \u003cem\u003eLr28\u003c/em\u003e may underlie the GWAS hit BR_4A714.\u003c/p\u003e \u003cp\u003e(4) The yellow/brown rust locus YR_6A002/BR_6A016 on the short arm of chromosome 6A was replicated in five and three trials, respectively, and was validated in our study in bi-parental populations. This locus has recently been identified as a source of good yellow rust resistance at the adult plant stage in a UK wheat multi-founder population (\u003cem\u003eQYr.niab-6A.1\u003c/em\u003e, based on peak SNP BS00011010_51 on 6A at 19 Mbp, Bouvet et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022c\u003c/span\u003e), further supporting the efficacy of this locus for rust resistance in the field.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eAdditional genetic loci conferring strong resistance\u003c/h2\u003e \u003cp\u003eIn addition to the replicated GWAS hits that clustered into \u0026lsquo;multi-resistance\u0026rsquo; loci, replicated genetic loci conferring resistance to single diseases were also identified. Notable amongst these were:\u003c/p\u003e \u003cp\u003ePM_1A003: Based on physical map location, PM_1A003 (chromosome 1A at ~\u0026thinsp;3 Mbp) likely corresponds to the cloned powdery mildew resistance gene \u003cem\u003ePm3\u003c/em\u003e (Yahiaoui et al. \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), located at 4.5 Mbp on chromosome 1A in the wheat reference genome. Previous work on a limited number of wheat cultivars indicated that of the ~\u0026thinsp;10 known \u003cem\u003ePm3\u003c/em\u003e resistant alleles (\u003cem\u003ePM3a\u003c/em\u003e-\u003cem\u003ePm3j\u003c/em\u003e), European wheat commonly carries \u003cem\u003ePm3d\u003c/em\u003e and \u003cem\u003ePm3g\u003c/em\u003e (Tommasini et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Our findings that PM_1A004 confers field resistance to powdery mildew in both the UK and Denmark, combined with the recent finding that \u003cem\u003ePm3a\u003c/em\u003e was also the likely source of powdery mildew field resistance in a European multi-founder wheat population assessed in field trials in Germany (Stadlmeier et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), indicates that allelic variation at \u003cem\u003ePm3\u003c/em\u003e remains a good source of field resistance in European environments. Given \u003cem\u003ePm3\u003c/em\u003e alleles have been deployed in modern wheat cultivars for around 90 years (Hsam et al. 2002), characterisation of the \u003cem\u003ePm3\u003c/em\u003e alleles present in current wheat cultivars will help protect against breakdown in resistance, and could also help inform the use of parental lines carrying contrasting \u003cem\u003ePm3\u003c/em\u003e alleles for F\u003csub\u003e1\u003c/sub\u003e hybrid varietal development.\u003c/p\u003e \u003cp\u003eYR_6A610: Based on its physical map location ( chromosome 6A at ~\u0026thinsp;610 Mbp) and its notably strong effect on resistance, YR_6A610 likely corresponds to a resistance locus recently identified in a European wheat multi-founder population (\u003cem\u003eQYr.niab-6A.3\u003c/em\u003e, Bouvet et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022c\u003c/span\u003e), as well as in smaller European GWAS panels grown in Europe (Germany and Austria; based on SNP \u003cem\u003eTdurum_contig29607_413\u003c/em\u003e, Shahinnia et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and beyond (Norway, Austria, China; \u003cem\u003eQYr.nmbu.6A\u003c/em\u003e, Lin et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Further, we independently validated this locus via construction and analysis of bespoke bi-parental populations. Thus with trials spanning 2012\u0026ndash;2021, these datasets (Bouvet et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022c\u003c/span\u003e, Shahinnia et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Lin et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, and the work we present here) collectively indicate that YR_6A610 has provided a strong source of yellow rust field resistance in European environments for at least ten years. Here we provide KASP genetic markers to track this locus for breeding and research purposes.\u003c/p\u003e \u003cp\u003eOf the five Septoria tritici blotch genetic loci identified, none were replicated. This reflects in some ways previous studies that find ST resistance to be controlled by numerous loci of small effect (Brown et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and so reported effects of individual genetic loci may not be replicated between trials, years (e.g. Stadlmeier et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) or separate studies, even though varietal resistance is largely repeatable (e.g. Supplementary Fig.\u0026nbsp;1). The complexity of the wheat genetics is also compounded by interaction with the high levels of standing genetic variation present in \u003cem\u003eZt\u003c/em\u003e populations (McDonald et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Of the unreplicated Septoria tritici blotch loci, ST_2B150 was located within 5 Mbp of the yellow rust resistance locus YR_2B155. Located close by is the hybrid necrosis gene \u003cem\u003eNecrosis 2\u003c/em\u003e (\u003cem\u003eNe2\u003c/em\u003e, Hewitt et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), an intracellular nucleotide binding leucine-rich repeat (NLR) immune receptor \u0026ndash; allelic variation within is allelic to both the leaf rust resistance gene \u003cem\u003eLr17\u003c/em\u003e (Hewitt et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and the yellow rust resistance gene \u003cem\u003eYr27\u003c/em\u003e (Athiyannan et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and whose equivalent gene in the wheat reference genome is located at 157.7 Mbp (\u003cem\u003eTraesCS2B02G182800\u003c/em\u003e). Depending on allele and genetic background, deletions of portions of the \u003cem\u003eNe2\u003c/em\u003e gene result in loss of disease resistance while retaining a necrotic phenotype (Hewitt et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), highlighting possible links between biotrophic and necrotrophic disease response.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eBreeding utility of loci identified in this study\u003c/h2\u003e \u003cp\u003eThe utility of the identified loci for disease resistance breeding is partly determined by their effect sizes, by the number, geographic breadth and temporal range of trials in which they were found to be significant, and by whether they have been successfully validated. However, the distribution of the alleles in the panel is also of considerable importance. If the resistance allele is very common, then investing in a marker-based selection strategy is less likely to be beneficial to a breeder, especially if the rare susceptible alleles are largely found in the older material in the panel (as we found here for yellow rust). On the other hand, if the resistant allele is rare, it is likely to be more useful for future breeding efforts, all other factors being equal. The distribution of alleles across the frequency spectrum is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Across diseases, there are similar proportions of rare (resistance allele frequency\u0026thinsp;\u0026le;\u0026thinsp;10%) and common (resistance allele frequency\u0026thinsp;\u0026gt;\u0026thinsp;90%) resistance alleles, 15/96 and 16/96, respectively. Similarly, there are approximately equal proportions of moderately common (33/96) and moderately rare (32/96) resistance alleles in the association mapping panel (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).. Many of the rarest alleles (\u0026lt;\u0026thinsp;10% frequency) were only detected in one or two trials, possibly due in part to their rarity making detection harder. Of these, the replicated resistance loci that contribute to \u0026lsquo;multi-resistance\u0026rsquo; locus MT25Mb-6 were of particular note. This included PM_4A734 (resistance allele frequency 6%), one of only two replicated powdery mildew resistance QTL identified, and BR_4A714 (resistance allele frequency\u0026thinsp;=\u0026thinsp;3%), one of the most highly significant brown rust resistance genetic loci identified and introduced first into the pedigree via cv. Robigus in 2002. These examples highlight the potential of exploiting currently rare disease resistance alleles for forward selection in breeding programmes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGrouping the 96 quantitative trait loci (QTL) identified by genome-wide association study (GWAS) by the frequency of resistance alleles in the association mapping panel. Brown rust (BR), powdery mildew (PM), Septoria tritici blotch (ST) and yellow rust (YR).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eFrequency of resistance allele\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;10%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026ndash;50%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51\u0026ndash;90%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91\u0026ndash;100%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eConcluding remarks\u003c/h2\u003e \u003cp\u003eHere we define numerous quantitative sources of disease resistance within elite wheat germplasm released over a 90-year period, finding chromosomal regions conferring resistance to more than one disease, as well as highlighting the role of chromosomal introgressions from wild wheat relatives in the resistance profiles of modern wheat. Notably, the first incursions of genetically diverse \u003cem\u003ePst\u003c/em\u003e isolates that swept across the European agricultural landscape from 2011 (e.g. Hubbard et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hovm\u0026oslash;ller et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; UKCPVS 2016) resulting in rapid changes in YR resistance due to break down of previously effective durable sources of resistance, were beginning to occur across the duration of our YR field trials. Thus, our YR results catalogue the effective sources of resistance to these new endemic \u003cem\u003ePst\u003c/em\u003e races. Finally, none of the three adult plant rust resistance genes cloned to date, \u003cem\u003eYr18/Lr34/Sr67/Pm38, Yr36\u003c/em\u003e (Fu et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and \u003cem\u003eYr46/Lr67/Sr55/Pm46\u003c/em\u003e (estimated here as being located on the wheat reference genome on chromosome 7D:474 Mbp, 6B:136 Mbp and 4D:405 Mbp, respectively), were identified as sources of resistance in our panel. If they are indeed absent, this may be due to their origin from unadapted germplasm (\u003cem\u003eYr18\u003c/em\u003e and \u003cem\u003eYr46\u003c/em\u003e originated from Chinese landraces and central American wheat, respectively) (Krattinger et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Singh et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) or different wheat species (\u003cem\u003eYr36\u003c/em\u003e), and suggests biotrophic fungal pathogen resistance could be rapidly enhanced in the European genepool via use of these loci. Collectively, the information generated here will help optimise sources of genetic resistance present in elite wheat, so providing a baseline from which new resistance loci can be introduced.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFollowing Theoretical and Applied Genetics (TAG) guidelines, here we declare that James Cockram is a member of the TAG Editorial Board. No other competing interests are declared.\u003c/span\u003e \u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the United Kingdom Biotechnology and Biological Sciences Research Council (BBSRC) through LINK programme grant BB/J002542/1, \u0026lsquo;Wheat Association Genetics for Trait Improvement in Lineages\u0026rsquo;, and by in-kind contributions from the participating industrial partners: DSV UK Ltd, Elsoms Wheat Ltd, KWS UK Ltd, Limagrain UK Ltd, RAGT Seeds Ltd and Syngenta. JC\u0026rsquo;s time was additionally supported by BBSRC grant APP2449.\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eDO, JB and IM conceived of the study. DO and JB were awarded project funding. DO, JB, JC, KG, IM and TW designed research. NB, SB, PJ, MK, JL, SS and PW generated bi-parental populations. PB, RB, SB, DF, PF, NG, CH, TH, PJ, MK, JL, LN, JS, SS, PV and additional members of the WAGTAIL Consortium undertook field trials and provided associated phenotypic data. PB, TB, JB, MC, JC, GR and NG undertook additional germplasm, glasshouse and phenotyping work. KG undertook trials analysis and calculated heritabilities. KG, BL and TW performed genetic and statistical analyses. JC, TW and CZ undertook bioinformatic analyses. JC and RS undertook haploblock analysis. PB and JC undertook molecular genetics. PB, KG, BL, JC, and TW analysed data. JB, JC, IM and DO managed the project. JC wrote the manuscript, with contributions from BL, KG and TW. All authors edited and approved the manuscript.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eAll datasets used are either included as supplementary materials, or are publicly available.\u003c/p\u003e \u003cp\u003eAcknowledgements\u003c/p\u003e \u003cp\u003eWe thank Lawrence Percival-Alwyn (NIAB) for support with sequence alignments and Margaret Corbitt (JIC) for assistance with trials at JIC, UK. In caring memory of Prof. Ian Mackay, from all of his colleagues.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbaba G (2023) Biology, taxonomy, genetics, and management of Zymoseptoria tritici: the causal agent of wheat leaf blotch. 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Front Plant Sci 14:1269498\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Genome-wide association study (GWAS), adult plant disease resistance (APR), single nucleotide polymorphism (SNP), high-density genotyping, Quantitative Trait Locus (QTL), yellow (stripe) rust, brown (leaf) rust, Septoria tritici blotch (STB), powdery mildew, yield protection","lastPublishedDoi":"10.21203/rs.3.rs-6145769/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6145769/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"We assembled a European bread wheat (Triticum aestivum L.) association mapping panel (n=480) genotyped using a 90,000 single nucleotide polymorphism array, with the aim of identifying genetic loci controlling resistance to four fungal diseases: yellow (stripe) rust (YR), brown (leaf) rust (BR), Septoria tritici blotch (ST) and powdery mildew (PM). Simulations showed our panel to have good power to detect genetic loci, with \u0026gt;50% probability of identifying loci controlling as little as 5% of the variance when heritability was 0.6 or more. Using disease infection data collected across 31 trials undertaken in five European countries, genome-wide association studies (GWAS) identified 34 replicated genetic loci (20 for YR, 12 for BR, two for PM, 0 for ST), with seven loci associated with resistance to two or more diseases. Construction and analysis of eight bi-parental populations enabled two selected genetic loci, yellow rust resistance locus YR_2A010 (chromosome 2A) and YR_6A610 (6A), to be independently cross-validated, along with the development of genetic markers to track resistance alleles at these loci. Notably, the chromosome 2A yellow and brown rust resistance locus corresponds to the 2NvS introgression from the wild wheat species, Aegilops ventricosa. We found evidence of strong selection for 2NvS over recent breeding history, being present in 48% of the most recent cultivars in our panel. Collectively, we define the genetic architectures controlling resistance to four major fungal diseases of wheat under European field environments, and provide resources to exploit these for the development of new wheat cultivars with improved disease resistance.","manuscriptTitle":"Genome-wide association analysis identifies seven loci conferring resistance to multiple wheat foliar diseases, including yellow and brown rust resistance originating from Aegilops ventricosa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-15 12:01:56","doi":"10.21203/rs.3.rs-6145769/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accept","date":"2025-04-16T14:06:48+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-04-12T09:06:11+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-11T23:54:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-11T14:00:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2025-04-10T07:38:26+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":"56b7c838-1f6c-406c-ab28-213cbceb2e2d","owner":[],"postedDate":"April 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T15:58:44+00:00","versionOfRecord":{"articleIdentity":"rs-6145769","link":"https://doi.org/10.1007/s00122-025-04907-x","journal":{"identity":"theoretical-and-applied-genetics","isVorOnly":false,"title":"Theoretical and Applied Genetics"},"publishedOn":"2025-06-02 15:56:58","publishedOnDateReadable":"June 2nd, 2025"},"versionCreatedAt":"2025-04-15 12:01:56","video":"","vorDoi":"10.1007/s00122-025-04907-x","vorDoiUrl":"https://doi.org/10.1007/s00122-025-04907-x","workflowStages":[]},"version":"v1","identity":"rs-6145769","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6145769","identity":"rs-6145769","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}