Global perspective on the genetic architecture of susceptibility to spot form net blotch in barley

Global perspective on the genetic architecture of susceptibility to spot form net blotch in barley | 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 Global perspective on the genetic architecture of susceptibility to spot form net blotch in barley Michele C. Malvestiti, Sefunmi Alaofin, Ryan Skiba, Shengming Yang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8961109/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Apr, 2026 Read the published version in Theoretical and Applied Genetics → Version 1 posted 9 You are reading this latest preprint version Abstract The necrotrophic fungal pathogen Pyrenophora teres f. maculata causes spot form net blotch (SFNB), a global disease of barley. This fungus uses effector proteins to promote infection, which act in an inverse gene-for-gene manner by targeting dominant host susceptibility genes. Currently, there is a general understanding of the genetics of resistance/susceptibility in the host; however, there are still gaps in our understanding of global pathogen virulence and how it has evolved to target the host and cause disease. Because the P. teres f. maculata -barley interaction conforms to an inverse gene-for-gene model, we crossed three different susceptible barley lines (Hockett, TR 326, and PI 392501) with the resistant line PI 67381 and developed and mapped recombinant inbred populations to characterize the susceptibility in these lines based on their response to ten pathogen isolates collected from globally diverse barley growing regions on five continents. Four independent quantitative trait loci (QTL) showed associations with susceptibility and mapped to barley chromosomes (Chr) 2HS, 4HS, 4HL, and 7HL. In all three populations, the same genomic position on Chr7H was associated with the highest susceptibility levels and was targeted by seven of the ten fungal isolates. The QTL identified on Chr2HS mapped to the same position in two populations and was also targeted by seven of the ten isolates. However, the Chr4HS and Chr4HL susceptibilities were targeted by only three and two of the global isolates, respectively. This work shows that pathogen populations under different host selection pressures can evolve to target different barley susceptibilities. Barley Pyrenophora teres f. maculata Spot form net blotch QTL mapping Susceptibility loci Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Key message Global populations of f. have evolved to target multiple dominant barley susceptibility loci, highlighting the risk of widespread disease under local selection pressures. Introduction Barley ( Hordeum vulgare , Poaceae, L.) is an economically important cereal crop with a total cultivation area of 48.1 million acres worldwide (Mittal 2022 ). Barley cultivation is affected by several pests and diseases, of which net blotch is one of the most destructive. Barley net blotch is a foliar disease caused by the filamentous Ascomycete Pyrenophora teres [anamorph Drechslera teres (Sacc.) Shoem]. Its emergence and spread coincide with the history of crop domestication and cultivation (Taliadoros et al. 2024 ). Nowadays, net blotch affects barley cultivation worldwide and has been reported in Africa (Lammari et al. 2020 ; Ababa et al. 2024 ; El Yousfi and Brahim, 2001 ), Australia (McLean et al. 2010a ), Europe (Tini et al. 2022 ; Jørgensen et al. 2000 ; Smedegard-Petersen, 1971 ), North America (Adhikari et al. 2020 ; Akhavan et al. 2016 ) and Western Asia (Dokhanchi et al. 2022 ; Vasighzadeh et al. 2021 ; Oğuz et al. 2019 ). Barley net blotch occurs in two distinct forms that can be distinguished by symptom development. Net form net blotch (NFNB), caused by Pyrenophora teres f. teres , is characterized by longitudinal and transverse dark-brown striations of necrotic tissue, forming a net-like pattern. By contrast, spot form net blotch (SFNB), caused by Pyrenophora teres f. maculata , is characterized by circular to elliptical dark-brown spot-like necrotic lesions, which are surrounded by a yellowish chlorotic area (Backes et al. 2021 ; Clare et al. 2020 ; Ellwood and Wallwork 2018 ; Liu et al. 2011 ; Lightfoot and Able, 2010 ; McLean et al. 2009 ). In the last decades, the incidence of SFNB has increased considerably in barley growing regions, posing a major threat to barley production (Tomić et al. 2024 ; Lammari et al. 2020 ; Akhavan et al. 2017 ; Marshall et al. 2015 ; McLean et al. 2014 and 2010a ; Liu et al. 2010). As a necrotrophic pathogen, P. teres f. maculata secretes effector proteins to manipulate host immunity for successful infection and disease development (Liu et al. 2011 ; Liu et al. 2015 ; Carlsen et al. 2017 ; Clare et al. 2022 ; Skiba et al. 2022 ). As shown in other interactions between plants and necrotrophic fungal pathogens, effectors often act in an inverse gene-for-gene manner (Friesen et al. 2007 ; Oliver and Solomon, 2010 ; Oliver et al. 2012 ; Faris and Friesen, 2020 ; Kariyawasam et al. 2023 ). Effectors target susceptibility genes in the host to activate programmed cell death (PCD), thereby leading to plant susceptibility and allowing host colonization (Lorang et al. 2007 ; Liu et al. 2009 ; Faris et al. 2010 ; Liu et al. 2012 ; Lorang et al. 2012 ; Shi et al. 2016 ; Richards et al. 2022 ). The strongest evidence of an inverse gene-for-gene interaction in the barley- P. teres f. maculata pathosystem, came from a study conducted by Skiba et al. ( 2022 ), where it was shown that single loci associated with fungal virulence targeted a corresponding dominant locus on barley associated with SFNB susceptibility, thereby promoting host colonization. Because of the high rate of sexual recombination in natural populations of P. teres f. maculata , the fungal effector repertoire is rapidly evolving, enabling the pathogen to overcome host resistance and to adapt to new environmental conditions (Gupta and Loughman, 2001 ; Arabi et al. 2003 ; McLean et al. 2014 ; Akhavan et al. 2015 ). Several efforts have been made to decipher the genetic complexity of the barley- P. teres f. maculata interaction (reviewed in Clare et al. 2020 ). On the pathogen side, Carlsen et al. ( 2017 ) phenotyped a P. teres f. maculata mapping population derived from a cross between an Australian and a North American isolate to identify six independent loci associated with virulence, with some virulence traits conferred by each parent. Moreover, a collection of North American P. teres f. maculata isolates was screened on thirty SFNB differential barley lines. Subsequent association mapping identified thirty distinct loci associated with fungal virulence, whereby one of the identified loci exhibited reciprocal virulence/avirulence with one haplotype mostly present in fungal isolates collected from Idaho (Clare et al. 2022 ). On the host side, initial studies relied on mapping of biparental barley populations to identify genetic loci associated with resistance or susceptibility to P. teres f. maculata . The first identified locus associated with resistance to P. teres f. maculata was the Rpt4 locus, located on the long arm of barley chromosome (Chr) 7H (Williams et al. 1999 and 2003 ; Grewal et al. 2008 ). Later, additional loci associated with resistance to P. teres f. maculata were identified, such as the Rpt6 locus located on the short arm of Chr5H (Manninen et al. 2006 ) and the Rpt8 locus located on the short arm of Chr4H (Franckowiak and Platz, 2013 ; Friesen et al. 2006 ). Subsequently, five independent studies using a GWAS approach were conducted on distinct barley collections from different geographical regions that identified a total of 27 (Tamang et al. 2015 ), 29 (Wang et al. 2015 ), 11 (Burlakoti et al. 2017 ), one (Daba et al. 2019) and four (Clare et al. 2021 ) independent loci, respectively, associated with resistance/susceptibility to P. teres f. maculata . Among the loci showing an association with barley Chr7H, four major QTL ( QRptm7-4 , QRptm7-6 , QRptm7-7 and QRptm7-8 ) mapped to a 36 cM region (Tamang et al. 2015 ; Wang et al. 2015 ). Even though analysis of linkage decay of the QTL identified on Chr7H might suggest that the four QTL are independent of each other, it remained unclear whether QTL on Chr7H were multiple linked loci or represented different alleles of a single gene (Tamang et al. 2015 ; Wang et al. 2015 ). Burlakoti et al. ( 2017 ) studied the effect of two-rowed and six-rowed barley from the Upper Midwest breeding programs and identified two novel QTL associated with resistance to P. teres f. maculata on Chr2H, SFNB-2H-38.08 and SFNB-2H-8-10 . The second QTL encompassed three SNP markers within a 2 cM region, and the marker 12_31497 was detected in all three populations, explaining the highest R 2 . Two more recent studies performed QTL mapping on biparental barley populations segregating for susceptibility to SFNB, using a set of P. teres f. maculata isolates (Tamang et al. 2019 , Skiba et al. 2022 ) and identified QTL on Chr2H, Chr4H, Chr6H, and Chr7H, whereby the QTL identified on Chr2H and Chr7H mapped to the same genomic positions in the different biparental populations (Tamang et al. 2019 ; Skiba et al. 2022 ). Despite significant advances in identifying host loci associated with resistance or susceptibility to SFNB, the barley genetics governing the P. teres f. maculata -barley interaction appear more complex than initially thought, with reports of dominant, recessive, and partial forms of both resistance and susceptibility. Given the increasing concern over P. teres f. maculata in barley production areas, intensified research is needed to thoroughly understand the genetic basis of host susceptibility within barley germplasm and how the host genetics correspond to the variation in pathogen virulence. To fill this gap in our understanding of this host-pathogen interaction, we used three recombinant inbred mapping populations developed by crossing a single resistant line that lacks susceptibility with three barley lines with known SFNB susceptibility. A set of 10 geographically diverse P. teres f. maculata isolates, collected over five continents, was then used to phenotype these three barley mapping populations. The genetic maps, together with the phenotypic data, were used to perform QTL analyses to identify barley genomic regions that contribute to disease. These analyses enabled us to determine which pathogen isolates targeted the corresponding susceptibility loci, providing a more integrated view of both host susceptibility and the global distribution of pathogen virulence. Materials and Methods Barley population development and genetic map construction One hundred and twenty-five F 2:6 recombinant inbred lines were developed by single-seed descent from a cross between the resistant Ethiopian barley two-row breeding line PI 67381 (Muñoz-Amatriaín et al. 2014; Neupane et al. 2015 ) and the susceptible two-row Australian breeding line TR 326 (McLean et al. 2014 ), resulting in the TR 326 × PI 67381 RIL population. One hundred and twenty-four F 2:6 recombinant inbred lines were also developed by single-seed descent from a cross between the same resistant Ethiopian barley line PI 67381 and the susceptible South African two-row breeding line PI 392501 (Neupane et al. 2015 ), resulting in the PI 392501 × PI 67381 RIL population. TR 326 and PI 392501 were selected as parental lines due to their frequent use as differential lines in assessing the virulence of local and global P. teres f. maculata populations, indicating that they differ for SFNB resistance/susceptibility (Carlsen et al. 2017 ; Clare et al. 2022 ). DNA extraction and genotyping for barley parental and progeny lines were performed by the North Central Small Grains Genotyping Lab (Fargo, ND, USA). Genotyping was carried out using the barley 50K Illumina iSelect single-nucleotide polymorphism (SNP) array, and genotype calling was performed using GenomeStudio software v2.0 ( https://support.illumina.com/array/array_software/genomestudio/downloads.html ) developed by Illumina (San Diego, CA, USA). Illumina genotyping of parental and progeny lines yielded 44,040 SNP markers for both the TR 326 × PI 67381 and the PI 392501 × PI 67381 population. Markers were filtered based on undetermined genotypes, heterozygous calls, segregation distortion (relative abundance of either parent’s genotype at each locus using a maximum allelic ratio of 3:1 and a minimum allelic ratio of 1:3 as cutoffs), and missing data (using a cutoff > 30%). Mapping of the two new RIL populations, TR 326 × PI 67381 and PI 392501 × PI 67381, was performed using MapDisto v2.1.7 ( http://mapdisto.free.fr/Download_Soft/ ) (Heffelfinger et al. 2017). Markers were assembled into genetic linkage groups using the ‘FindGroup’ command in MapDisto with a logarithm of odds (LOD) cutoff of 3.0 and a rmax of 0.3. Markers lacking chromosomal designations were assigned to their corresponding genomic positions according to the physical map of the H. vulgare “MorexV3” barley genome (Cantalapiedra et al. 2015 ). The ‘AutoOrder’ command was used to determine the initial marker order in each linkage group. The ‘AutoCheckInversion’, ‘AutoRipple’ and ‘DropLocus’ commands were used to refine and validate the final marker order. Co-segregating markers were identified from genetic maps. A single marker within each co-segregating block showing the least amount of missing data was selected and retained, and the redundant co-segregating markers were removed. The Hockett × PI 67381 RIL population and the genetic map used in this study were generated by Skiba et al. ( 2022 ). P. teres f. maculata isolates, inoculation assays and disease symptom analysis Fungal isolates used in this study and their geographical origin are listed in Table 1 . The isolates P-A14 (Montana, USA), C-A17 (Montana, USA), FGOB10-Ptm1 (North Dakota, USA), ID220 (Idaho, USA), Den2.6 (Denmark), NZKF2 (New Zealand) and SG1 (Australia) were chosen as they represent previously used reference isolates (Carlsen et al. 2017 ; Tamang et al. 2019 ; Clare et al. 2020 ; Wyatt and Friesen, 2021 ; Skiba et al. 2022 ). Additionally, three new isolates were identified from collections made in Iran (G76S), Azerbaijan (AZB_Ptm20), and Morocco (Mor4-2). These isolates were selected based on their geographic diversity and high level of virulence in a prescreening of the susceptible parental lines used in this study (data not shown). Collectively, these isolates represent a wide range of barley-growing regions, spanning North America, Australia, Europe, Western Asia, and North Africa. Table 1 P. teres f. maculata isolates used in this study and their geographical origin. P. teres f. maculata isolate Origin Reference/supplier P-A14 Montana, USA Wyatt and Friesen, 2021 C-A17 Montana, USA Wyatt and Friesen, 2021 FGOB10Ptm-1 North Dakota, USA Carlsen et al. 2017 ID220 Idaho, USA Clare et al. 2022 Den2.6 Denmark Wyatt and Friesen, 2021 NZKF2 New Zeeland Wyatt and Friesen, 2021 G76S Bandare torkman Golestan, Iran Eva Stukenbrock SG1 Australia (Provided by Simon Ellwood, Curtin University) Carlsen et al. 2017 AZB_Ptm20 Azerbaijan (Qobustan Center) Robert Brueggeman Mor4-2 Choiya, Morocco Sajid Rehman Fungal inoculum was prepared as follows. Fungi were grown for 5 days in darkness at 20°C on solid medium consisting of ddH 2 O (75% v/v), V8 vegetable juice (25% v/v, The Campbell Company, USA), Potato Dextrose Agar (10 g × L − 1 , Difco, USA), BactoAgar (10 g × L − 1 , Criterion, USA) and CaCO 3 (3 g × L − 1 , ThermoScientific, USA). Conidiogenesis was induced upon 24-h constant exposure to white light at 20°C, followed by 24-h incubation in darkness at 15°C. Conidia were harvested in sterile ddH 2 O and adjusted to a density of 2000 conidia × mL − 1 . To prevent spore clumping, 70 µL of Tween 20 (J.T. Baker Chemical Co.) was added for each 50 mL of conidial suspension. Barley seedlings (parental and progeny lines) were planted in racks containing 96 containers (Stuewe & Sons, Inc.), with barley cultivar “ND-Genesis” planted in the outside border to reduce edge effect. Barley seedlings were grown in a greenhouse for 15 days, or until secondary leaves were fully expanded. One hundred eighteen progeny lines of the Hockett × PI 67381 and TR 326 × PI 67381 populations, and one hundred fifteen lines of the PI 392501 × PI 67381 population, along with the respective parental lines, were homogenously sprayed with 100 mL of conidial suspension using a paint sprayer (DeVilbiss, model# SRIPRO-635G-10). After inoculation, plants were placed in mist chambers at 100% relative humidity and 21 ℃ under 24 h of light. After 24 h, plants were transferred to growth chambers and incubated under a 12-h light cycle (500 µmol/m²/s Photosynthetic Photon Flux Density) at 21 ℃. Disease reaction type was scored on secondary leaves at 7 days post-inoculation (dpi) based on a 1 (highly resistant) to 5 (highly susceptible) scale developed by Neupane et al. ( 2015 ). Each fungal isolate was inoculated on each barley population (Hockett × PI 67381, TR 326 × PI 67381, and PI 392501 × PI 67381) in three independent replicates, and the mean of the three replicates was used for further analysis. QTL and statistical analysis QTL analyses for each of the three barley RIL populations by each of the ten P. teres f. maculata isolates were performed with genetic maps constructed in MapDisto, and the corresponding average disease reaction type data using Qgene v4.4.0 ( https://www.qgene.org/qgene/download.php ) (Joehanes and Nelson, 2008). QTL analyses were performed using simple interval mapping (scan interval = 10). To establish critical logarithm of odds (LOD) thresholds at a significance level of α = 0.01, permutation tests of 1,000 iterations were performed three times for each P. teres f. maculata isolate–barley population combination. A LOD threshold was determined for each RIL population as the average value of the results obtained from the three permutation tests. The final LOD threshold for identifying significant QTL was calculated as the average LOD across all RIL populations. LOD values were plotted as line graphs using Microsoft Excel for figure presentation. The position of the identified QTL on the respective chromosome arm was determined according to the centromere position reported by Navrátilová et al. ( 2022 ). Genotypic categories and statistical analysis To evaluate the contribution of each QTL to SFNB disease, we grouped progeny lines from each RIL population into genotypic categories according to the parental allele for each locus using the most significant marker associated with each QTL. Disease reaction type scores were plotted on a box-plot diagram using ggplot2 (package 4.0.2) in RStudio (version 4.4.1). Fisher’s least significant difference (LSD) test was used to assess whether the genotypic categories differed significantly in reaction type for each P. teres f. maculata isolate used. A one-way ANOVA was performed in Microsoft Excel, and the resulting data were used to determine least significant differences at α = 0.05. Annotation of candidate genes in the QTL regions Using the H. vulgare pangenome database as a reference for annotated barley genes (Jayakodi et al. 2024 ), we obtained a list of candidate genes for each identified QTL region. To establish the QTL confidence interval, we employed composite interval mapping with forward cofactor selection in Qgene v4.4.0 ( https://www.qgene.org/qgene/download.php ) (Joehanes and Nelson, 2008), thereby refining the genomic regions associated with SFNB disease. The two outermost significant markers were selected to define the genomic boundaries, and the region between them was used to identify candidate genes. A list of the identified candidate genes is provided in Supplementary Information, Table S1 , Table S2, Table S3 and Table S4. Results Barley population development and genetic mapping To achieve the goal of understanding the genetic factors governing barley susceptibility to SFNB and how they correspond to global P. teres f. maculata virulence, we used three barley RIL populations derived from crossing the same resistant parent (PI 67381) with three distinct susceptible parents. Two of the susceptible parents (TR 326 and PI 392501) have been used extensively as SFNB differential sources (Carlsen et al. 2017 ; Clare et al. 2022 ), and one (Hockett) is a popular barley cultivar in Montana, USA (Blake 2008 ). Both the TR 326 × PI 67381 and the PI 392501 × PI 67381 populations consisted of one hundred and eighteen F 2:6 progeny lines. A total of 44,040 SNP markers were identified through genotyping of parental and progeny lines for both the TR 326 × PI 67381 and the PI 392501 × PI 67381 population. Markers for which genotypes could not be determined, as well as those with heterozygous calls, were removed, leaving 14,539 markers for the TR 326 × PI 67381 and 14,454 markers for the PI 392501 × PI 67381 population. Subsequent filtering for segregation distortion and missing data further reduced the marker set to 14,516 markers for the TR 326 × PI 67381 population and 14,399 for the PI 392501 × PI 67381 population, indicating a high marker coverage in both populations. Using the software MapDisto, markers were assembled into seven linkage groups corresponding to the seven barley chromosomes, and markers without chromosomal designation were added to the corresponding chromosomal position according to a previously generated barley map (Cantalapiedra et al. 2015 ). After refining the marker order, we obtained an initial draft of the genetic map for each chromosome. Subsequently, co-segregating markers were identified in the genetic maps and removed, yielding 1,276 markers for the TR 326 × PI 67381 population (Table S5 and Table S6) and 1,285 markers for the PI 392501 × PI 67381 population (Table S7 and Table S8) for QTL analysis. The total map size for the TR 326 × PI 67381 was 909.67 cM with an average marker density of one marker per 1.40 cM (Table 2 a), while the total map size for the PI 392501 × PI 67381 population was 966.30 cM with an average marker density of one marker per 1.33 cM (Table 2 b). Table 2 Mapping statistics in the TR 326 × PI 67381 (a) and PI 392501 × PI 67381 (b) RIL populations. a. TR 326 × PI 67381. Chromosome Markers Size (cM) 1H 149 121.26 2H 202 150.25 3H 218 138.53 4H 154 116.40 5H 222 148.96 6H 137 104.24 7H 195 130.03 Total 1,276 909.67 b. PI 392501 × PI 67381. Chromosome Markers Size (cM) 1H 148 125.30 2H 206 145.32 3H 221 148.20 4H 137 107.77 5H 247 193.96 6H 136 101.70 7H 190 144.05 Total 1,285 966.30 Infection assays and phenotypic analysis To assess differences in disease reaction types within barley germplasm and in fungal virulence across a global pathogen isolate collection, we first inoculated a set of P. teres f. maculata isolates on the parental barley lines Hockett, TR 326, PI 392501 and PI 67381 (Fig. 1 , Table 3 and Supplementary Information Table S9, Table S10 and Table S11). Table 3 Average disease reaction type scoring upon inoculation of ten P. teres f. maculata isolates on the four barley parental lines at 7 dpi. Data are presented as the mean and standard deviation, (±SD) for three independent scoring (biological replicates) for each isolate-barley combination. \(\text{a}\text{n}\text{d}\) \((\pm\) P. teres f. maculata isolate Hockett TR 326 PI 392501 PI 67381 P-A14 4.17 ( \(\pm\) 0.23) 3.00 ( \(\pm\) 0) 3.33 ( \(\pm\) 0.23) 1.50 ( \(\pm\) 0) C-A17 3.33 ( \(\pm\) 0.23) 2.67 ( \(\pm\) 0.23) 3.00 ( \(\pm\) 0) 1.33 ( \(\pm0.23\) ) FGOB10Ptm-1 3.33 ( \(\pm\) 0.23) 3.00 ( \(\pm\) 0) 2.83 ( \(\pm\) 0.23) 1.50 ( \(\pm\) 0) ID220 2.17 ( \(\pm\) 0.23) 2.17 ( \(\pm\) 0.23) 2.33 ( \(\pm\) 0.23) 1.17 ( \(\pm\) 0.23) Den2.6 2.17 ( \(\pm\) 0.23) 2.33 ( \(\pm\) 0.23) 2.33 ( \(\pm\) 0.23) 1.17 ( \(\pm\) 0.23) NZKF2 2.33 ( \(\pm\) 0.23) 2.33 ( \(\pm\) 0.23) 2.33 ( \(\pm\) 0.23) 1.67 ( \(\pm\) 0.23) G76S 4.33 ( \(\pm\) 0.23) 3.33 ( \(\pm\) 0.23) 3.33 ( \(\pm\) 0.23) 1.67 ( \(\pm\) 0.23) SG1 3.33 ( \(\pm\) 0.23) 2.50 ( \(\pm\) 0) 3.00 ( \(\pm\) 0) 1.25 ( \(\pm\) 0.25) AZB_Ptm20 3.50 ( \(\pm\) 0) 2.83 ( \(\pm\) 0.23) 3.00 ( \(\pm\) 0) 1.00 ( \(\pm\) 0) Mor4-2 3.33 ( \(\pm\) 0.23) 2.67 ( \(\pm\) 0.23) 2.83 ( \(\pm\) 0.23) 1.50 ( \(\pm\) 0) At 7 dpi, it was observed that parental barley lines Hockett, TR 326, and PI 392501 showed more severe symptoms compared to the resistant parental barley line PI 67381, regardless of the fungal isolate used. The average disease reaction type derived from the scoring of all fungal isolates ranged from 2.17 to 4.33 for Hockett, from 2.17 to 3.33 for TR 326, and from 2.33 to 3.33 for PI 392501, whereas the average disease reaction type for PI 67381 ranged from 1.00 to 1.67 (Table 3 ). On the susceptible lines Hockett, TR 326, and PI 392501, inoculated leaves showed dark brown, ellipsoidal, dry, necrotic spots surrounded by a yellow area of chlorotic tissue. With disease progression, the lesions expanded until they coalesced, causing collapse of the entire lamina (Fig. 1 ). By contrast, inoculated leaves of the resistant line PI 67381 exhibited tiny, pinpoint brown necrotic spots that did not expand, and no surrounding tan necrotic or yellow chlorotic areas were observed (Fig. 1 ). In addition, variation in disease reaction type was observed among P. teres f. maculata isolates (Table 3 ). On the susceptible lines Hockett, TR 326, and PI 39250, the most virulent isolates G76S and P-A14 showed disease reaction types ranging from 3.00 to 4.33, whereas the intermediate isolates C-A17, FGOB10Ptm-1, SG1, Mor4-2, and AZB_Ptm20 showed disease reaction type averages of 2.50 to 3.50. By contrast, the least virulent isolates NZKF2, Den2.6, and ID220 showed disease reaction type averages of 2.17 to 2.33 on the susceptible lines Hockett, TR 326, and PI 39250 (Table 3 ). QTL Analysis Subsequently, the developed genetic maps were used in combination with disease reaction type scores to perform QTL analysis for each P. teres f. maculata isolate-barley population combination. An overview of the observed QTL pattern is shown in Fig. 2 , where only barley chromosomes showing a significant association are presented. A LOD threshold of 3.5 at a significance level of α = 0.01 was established as the average value obtained from three permutation tests conducted on each fungal isolate-barley population combination (Hockett × PI 67381, LOD = 3.54; TR 326 × PI 67381, LOD = 3.48; PI 392501 × PI 67381, LOD = 3.51). Across all three barley populations, a significant QTL was identified on Chr7H following inoculation with P. teres f. maculata isolates P-A14, C-A17, FBOB10Ptm-1, G76S, SG1, AZB_Ptm20, and Mor4-2 (Fig. 2 c). According to the centromere position reported by Navrátilová et al. ( 2022 ), the Chr7H QTL was located on the long arm, and therefore, it is referred to as Chr7HL. The QTL identified on Chr7HL in the Hockett × PI 67381 population accounted for 34.4% (LOD = 10.51, P-A14), 24.1% (LOD = 6.81, C-A17), 32.3% (LOD = 9.99, FGOB10Ptm-1), 37.1% (LOD = 11.79, G76S), 38.3% (LOD = 11.96, SG1), 23.2% (LOD = 6.69, AZB_Ptm20) and 42.4% (LOD = 14.14, Mor4-2) of the variation in average disease reaction type (Table 4 , and Supplementary Information Table S12). The QTL identified on Chr7HL in the TR 326 × PI 67381 population accounted for 62.6% (LOD = 25.64, P-A14), 64.6% (LOD = 27.06, C-A17), 46.1% (LOD = 15.83, FGOB10Ptm-1), 76.2% (LOD = 36.78, G76S), 62.1% (LOD = 25.30, SG1), 45.9% (LOD = 15.73, AZB_Ptm20) and 64.1% (LOD = 26.28, Mor4-2) of the disease variation (Table 5 and Table S13). The QTL identified on Chr7HL in the PI 392501 × PI 67381 population accounted for 53.7% (LOD = 19.04, P-A14), 53.5% (LOD = 19.10, C-A17), 49.1% (LOD = 16.86, FGOB10Ptm-1), 66.1% (LOD = 27.10, G76S), 57.6% (LOD = 21.41, SG1), 49% (LOD = 16.67, AZB_Ptm20) and 57.4% (LOD = 21.3, Mor4-2) of the disease variation (Table 6 and Table S14). In all fungal isolate-barley population interactions, the identified QTL were located at the same genomic position on Chr7HL (Fig. 2 c, Supplementary Information, Table S12, Table S13 and Table S14). This result, along with the dominant susceptibility data reported by Skiba et al. ( 2022 ), indicates that the same Chr7HL dominant susceptibility gene is present in all three susceptible parents across the three RIL populations but is absent in PI67381. Isolates ID220, NZKF2, and Den2.6, collected from Idaho, USA, New Zealand, and Denmark, respectively, three geographically diverse barley-growing regions, do not target Chr7HL susceptibility, indicating that they lack the P. teres f. maculata chromosome 2 virulence reported by Skiba et al. ( 2022 ). In the populations Hockett × PI 67381 and TR 326 × PI 67381, a similarly positioned QTL was identified on Chr2H upon inoculation with P. teres f. maculata isolates P-A14, FGOB10Ptm-1, ID220, Den2.6, NZKF2, and AZB_Ptm20 (Fig. 2 a), but no significant association with Chr2H was found for the isolates G76S, SG1, or Mor4-2. According to the centromeric position reported by Navrátilová et al. ( 2022 ), the Chr2H QTL was located on the short arm and is therefore referred to as Chr2HS. In the Hockett × PI 67381 population the QTL identified on Chr2HS accounted for 14.4% (LOD = 3.88, P-A14), 26.8% (LOD = 8.00, FGOB10Ptm-1), 31.2% (LOD = 9.51, ID220), 27.4% (LOD = 8.07, Den2.6), 14.5% (LOD = 4.07, NZKF2) and 35.9% (LOD = 11.28, AZB_Ptm20) of the disease variation (Table 4 and table S12). In the TR 326 × PI 67381 population, the QTL identified on Chr2HS was located at the same genomic position as in the Hockett × PI 67381 and accounted for 12.7% (LOD = 3.54 P-A14), 22.5% (LOD = 6.52, FGOB10Ptm-1), 36.9% (LOD = 11.79, ID220), 13.5% (LOD = 3.77, Den2.6), 14.5% (LOD = 4.07, NZKF2) and 21.7% (LOD = 6.27, AZB_Ptm20) of the disease variation (Fig. 2 a, Table 5 , and Supplementary Information, Table S12 and Table S13). Notably, the isolate C-A17 showed a significant association with Chr2HS on the Hockett × PI 67381, accounting for 15.1% (LOD = 4.05) of the disease variation, but no significant corresponding QTL associated with Chr2HS was detected when C-A17 was inoculated on the TR 326 × PI 67381 population (Table 4 and Table 5 ). No significant association with Chr2HS was identified in the PI 392501 × PI 67381 population (Fig. 2 a, Table 6 and Table S14). Collectively, these results indicate that isolates P-A14, C-A17, FGOB10Ptm-1, ID220, Den2.6, NZKF2, and AZB_Ptm20 each harbor the P. teres f. maculata chromosome 1 virulence targeting Chr2HS present in Hockett (Skiba et al. 2022 ), and that the corresponding barley Chr2HS susceptibility gene is also present in TR 326, but absent in PI 392501. In the TR 326 × PI 67381 and PI 392501 × PI 67381 populations, independent QTL were identified at different genomic positions on Chr4H upon inoculation with P. teres f. maculata isolates Den2.6 and NZKF2. However, none of the fungal isolates used in this study showed a significant association with Chr4H when the Hockett × PI 67381 population was inoculated (Fig. 2 b). In the PI 392501 × PI 67381 population, the Chr4H QTL was located on the short arm, according to the centromere position reported by Navrátilová et al. ( 2022 ) and therefore, it is referred to as Chr4HS. By contrast, in the TR 326 × PI 67381 population, the QTL was located on the distal end of the long arm of Chr4H and is therefore referred to as Chr4HL (Navrátilová et al. 2022 ). The Chr4HL QTL in the TR 326 × PI 67381 population accounted for 41.2% (LOD = 13.84, Den2.6) and 24.4% (LOD = 7.28, NZKF2) of the disease variation (Fig. 2 b, Table 5 and Table S13), whereas in the PI 392501 × PI 67381 population, the QTL identified on Chr4HS accounted for 25.7% (LOD = 7.67, Den2.6) and 26.8% (LOD = 9.96, NZKF2) of the disease variation (Fig. 2 b, Table 6 and Table S14). Notably, the P. teres f. maculata Idaho isolate ID220 also showed a significant association with the Chr4HS locus in the PI 392501 × PI 67381 population, accounting for 35.1% (LOD = 11.64) of the disease variation (Fig. 2 b, Table 6 and Table S14). However, no significant association with Chr4HL was identified when ID220 was inoculated on the TR 326 × PI 67381 population (Fig. 2 b). The results indicate that ID220, Den2.6, and NZKF2 produce the same effector that targets the Chr4HS susceptibility allele harbored by PI 392501, whereas an independent effector produced only by NZKF2 and Den2.6 targets the susceptibility allele on Chr4HL contributed by TR 326. Table 4 R2 and LOD values (LOD values in parentheses) calculated for the significant QTL at the = 0.01 level (calculated LOD threshold = 3.5), identified on Chr2HS, Chr4HS, and Chr7HL, respectively, upon inoculation of each P. teres f. maculata isolate on the Hockett × PI 67381 population. R2 and LOD values of non-significant (NS) associations are shown in Supplementary Information, Table S12. α P. teres f. maculata isolate Chr2HS R 2 (LOD) Chr4HS R 2 (LOD) Chr7HL R 2 (LOD) P-A14 0.144 (3.88) NS 0.344 (10.51) C-A17 0.151 (4.05) NS 0.241% (6.81) FGOB10Ptm-1 0.225 (6.52) NS 0.323 (9.99) ID220 0.369 (11.79) NS NS Den2.6 0.274 (8.07) NS NS NZKF2 0.362 (11.10) NS NS G76S NS NS 0.371 (11.79) SG1 NS NS 0.383 (11.96) AZB_Ptm20 0.359 (11.28) NS 0.232 (6.69) Mor4-2 NS NS 0.424 (14.14) Table 5 R2 and LOD values (LOD values in parenthesis) calculated for the significant QTL at the = 0.01 level (calculated LOD threshold = 3.5) identified on Chr2HS, Chr4HL, and Chr7HL, respectively, upon inoculation of each P. teres f. maculata isolate on the TR 326 × PI 67381 population. R2 and LOD values of non-significant (NS) associations are shown in Supplementary Information, Table S13. α P. teres f. maculata isolate Chr2HS R 2 (LOD) Chr4HL R 2 (LOD) Chr7HL R 2 (LOD) P-A14 0.127 (3.54) NS 0.626 (25.64) C-A17 NS NS 0.646 (27.06) FGOB10Ptm-1 0.268 (8.00) NS 0.461 (15.83) ID220 0.312 (9.51) NS NS Den2.6 0.135 (3.77) 0.412 (13.84) NS NZKF2 0.145 (4.07) 0.244 (7.28) NS G76S NS NS 0.762 (36.78) SG1 NS NS 0.621 (25.30) AZB_Ptm20 0.217 (6.27) NS 0.459 (15.73) Mor4-2 NS NS 0.641 (26.28) Table 6 R2 and LOD values (LOD values in parenthesis) calculated for the significant QTL at the = 0.01 level (calculated LOD threshold = 3.5) identified on Chr2HS, Chr4HS, and Chr7HL, respectively, upon inoculation of each P. teres f. maculata isolate on the PI 392501 × PI 67381 population. R2 and LOD values of non-significant (NS) associations are shown in Supplementary Information, Table S14. α P. teres f. maculata isolate Chr2HS R 2 (LOD) Chr4HS R 2 (LOD) Chr7HL R 2 (LOD) P-A14 NS NS 0.537 (19.04) C-A17 NS NS 0.535 (19.10) FGOB10Ptm-1 NS NS 0.491 (16.86) ID220 NS 0.351 (11.64) NS Den2.6 NS 0.257 (7.67) NS NZKF2 NS 0.268 (9.96) NS G76S NS NS 0.661 (27.10) SG1 NS NS 0.576 (21.41) AZB_Ptm20 NS NS 0.49 (16.67) Mor4-2 NS NS 0.574 (21.32) Identification of candidate genes in the QTL regions Genetic mapping showed that the QTL region identified on Chr2HS was at the same genomic position in both the Hockett × PI 67381 and TR326 × PI 67381 populations, and the Chr7HL QTL mapped to the same genomic position in all three barley populations (Fig. 2 ). In the TR 326 × PI 67381 population, the Chr2HS QTL region spanned a confidence interval of 1.1 Mb (data from inoculation with isolate ID220), with the most significant marker associated with variation in disease reaction being JHI-Hv50k-2016-67492 (Table S1 3). In this QTL interval, twenty-four genes were annotated, which included genes predicted to encode cell membrane-located nitrogen, magnesium and sugar transporters, proteins involved in sugar and protein metabolism, signal transduction-related proteins, two transcription factors, a terpene synthase, and one receptor protein of nucleotide binding site-leucin rich repeat (NBS-LRR) type associated with disease (accession number HORVU.MOREX.r3.2HG0102530.1), which represented the most likely gene candidate (Supplementary Information, Table S1 ). On Chr4H, the Chr4HS QTL identified in the PI 392501 × PI 67381 population was the largest of the QTL intervals, spanning 46.4 Mb (data from inoculation with isolate ID220). The most significant marker associated with disease reaction in this region was BOPA1_3644 − 1483 (Supplementary Information, Table S14). This QTL region contained three hundred eighty-one annotated genes which were predicted to encode proteins involved in diverse cellular processes, including sugar, protein, and lipid metabolism, light and salt stress responses, biosynthesis and modification of cell wall components, DNA replication and transcriptional activity, ion uptake, intracellular vesicle trafficking, biosynthesis of secondary metabolites, and immunity (Supplementary Information, Table S2). Of these, the most likely candidates included genes encoding proteins involved in Shikimate pathways, such as the chalcone synthase B (accession number HORVU.MOREX.r3.4HG0389560.1) and a chorismate synthase (accession number HORVU.MOREX.r3.4HG0391140.1) (Table S2). The Chr4HL QTL region identified in the TR 326 × PI 67381 population spanned 3.4 Mb (data from inoculation with Den2.6), and the most significant marker associated with variation in disease reaction was JHI-Hv50k-2016-272241 (Table S13). This QTL region harbored sixty-seven annotated genes predicted to encode proteins involved in cell division, jasmonic acid and auxin signaling, nuclear and cytoskeletal structure, post-translational modifications and lipid metabolism, sugar and ion transporters, three transcription factors, and two genes annotated as receptor-like proteins. The two genes annotated as receptor-like proteins represent the most likely candidates (accession numbers HORVU.MOREX.r3.4HG0415260.1 and HORVU.MOREX.r3.4HG0415460.1) (Table S3). The Chr7HL QTL region identified in the TR326 × PI 67381 was the most significant association among the three populations evaluated. The QTL region in the TR326 × PI 67381 population spanned 1.0 Mb (data from inoculation with isolate G76S), and the most significant marker associated with variation in disease reaction was JHI-Hv50k-2016-501105 (Supplementary Information, Table S13). This QTL region contained twenty-seven annotated genes predicted to encode proteins involved in the metabolism and signaling of plant hormones, three proteases from different classes, proteins related to heavy metal stress tolerance and secondary metabolic pathways, and six proteins related to immunity (Supplementary Information, Table S4). Of these genes, the most likely candidates encode proteins related to disease resistance, such as the Enhanced Disease Resistance 2 protein (accession number HORVU.MOREX.r3.7HG0747430.1) and an NBS-LRR type receptor protein (accession number HORVU.MOREX.r3.7HG0747370.1) (Table S4). The identified loci contribute differently to SFNB susceptibility The QTL analysis identified four distinct loci associated with susceptibility to SFNB, with some fungal isolates showing significant associations with multiple loci within a specific barley population (Fig. 2 ). Therefore, we assessed the contribution of each QTL to disease, both individually and in combination. Progeny lines were first grouped into genotypic categories based on their parental alleles for each significant QTL, and average disease reaction types were compared across the different genotypic categories in a “head-to-head” fashion. This comparison was conducted only for fungal isolate-barley population combinations showing an association with two distinct loci. In the Hockett × PI 67381 population, independent associations were identified at Chr2HS and Chr7HL (Fig. 2 , Table 4 ). Accordingly, progeny lines were grouped into four categories based on the alleles at Chr2HS and Chr7HL contributed by the parental lines PI 67381 and Hockett. These groups included progeny lines harboring neither susceptibility allele (Chr2HS PI 67381 -Chr7HL PI 6738 ), one or the other susceptibility allele (Chr2HS Hockett -Chr7HL PI 67381 or Chr2HS PI 67381 -Chr7HL Hockett ), or both susceptibility alleles (Chr2HS Hockett -Chr7HL Hockett ) (Fig. 3 ). Upon inoculation of the Hockett × PI 67381 population with isolates targeting both Chr2HS and Chr7HL, P-A14, C-A17, FGOB10Ptm-1 and AZB_Ptm20, it was observed that, except for P-A14, Chr2HS PI 67381 -Chr7HL Hockett lines, showed no significant difference in disease reaction (average score P-A14 = 2.83; C-A17 = 2.51; FGOB10Ptm-1 = 2.47; AZB_Ptm20 = 2.54) when compared to Chr2HS Hockett -Chr7HL PI 67381 lines (average score P-A14 = 2.39; C-A17 = 2.40; FGOB10Ptm-1 = 2.40; AZB_Ptm20 = 2.70) (Fig. 3 , Fig. 4 , and Table S15). However, disease reactions were significantly higher in progeny lines possessing both susceptibility alleles (Chr2HS Hockett -Chr7HL Hockett lines average score P-A14 = 3.37; C-A17 = 2.91; FGOB10Ptm-1 = 2.95; AZB_Ptm20 = 3.11), compared to lines harboring only one susceptibility allele (Fig. 3 , Fig. 4 , and Table S15). In the TR 326 × PI 67381 population, three distinct QTL were identified, although none of the isolates showed a significant association with more than two loci (Fig. 2 , Table 5 ). Therefore, for the isolates targeting Chr2HS and Chr7HL, P-A14, FGOB10Ptm-1, and AZB_Ptm20, progeny lines were grouped into four categories based on the alleles at Chr2HS and Chr7HL contributed by the parental lines PI 67381 and TR 326. These groups included progeny lines harboring neither susceptibility allele (Chr2HS PI 67381 -Chr7HL PI 67381 ), one or the other susceptibility allele for Chr2HS and Chr7HL (Chr2HS TR 326 -Chr7HL PI 67381 or Chr2HS PI 67381 -Chr7HL TR 326 ), or both susceptibility alleles (Chr2HS TR 326 -Chr7HL TR 326 ) (Fig. 5 ). Upon inoculation of the TR 326 × PI 67381 population with isolates targeting both Chr2HS and Chr7HL (P-A14, FGOB10Ptm-1 and AZB_Ptm20) progeny lines carrying the Chr7HL susceptibility allele alone (Chr2HS PI 67381 -Chr7HL TR 326 ) showed significantly higher disease reaction (average score P-A14 = 3.01; FGOB10Ptm-1 = 2.63; AZB_Ptm20 = 2.68) than lines carrying only the Chr2HS susceptibility allele (Chr2HS TR326 -Chr7HL PI 67381 ) (average score P-A14 = 2.37; FGOB10Ptm-1 = 2.41; AZB_Ptm20 = 2.20) (Fig. 5 , Fig. 6 and Table S16). As observed in the Hockett × PI 67381 population, progeny lines possessing both susceptibility alleles (Chr2HS TR 326 -Chr7HL TR 326 ) exhibited significantly higher disease reactions than lines harboring only one allele (Fig. 5 , Fig. 6 , and Table S16). Using the same strategy, for the isolates targeting Chr2HS and Chr4HL (Den2.6 and NZKF2), progeny lines were grouped into four categories based on these alleles contributed by the parental lines PI 67381 and TR 326. These groups included progeny lines harboring neither susceptibility allele (Chr2HS PI 67381 -Chr4HL PI 67381 ), one or the other susceptibility allele for Chr2HS or Chr4HL (Chr2HS TR 326 -Chr4HL PI 67381 and Chr2HS PI 67381 -Chr4HL TR 326 ), or both susceptibility alleles (Chr2HS TR 326 -Chr4HL TR 326 ) (Fig. 7 , Fig. 8 , and Table S17). When the isolate Den2.6 was inoculated on the TR326 × PI 67381 population, progeny lines carrying the Chr4HL susceptibility allele alone (Chr2HS PI 67381 -Chr4HL TR 326 ) showed an average disease reaction of 2.08, which was significantly higher compared to an average disease reaction of 1.77 scored for Chr2HS TR 326 -Chr4HL PI 67381 lines (Fig. 7 ). By contrast, upon inoculation with NZKF2, no significant difference was observed between lines carrying either single susceptibility allele (Fig. 7 and Fig. 8 ), indicating a comparable contribution of the loci. For both isolates, progeny lines possessing both susceptibility alleles showed higher disease reaction (Chr2HS TR 326 -Chr4HL TR 326 lines average score Den2.6 = 2.31; NZKF2 = 2.17) compared to lines harboring only one of the susceptibility alleles (Fig. 7 , Fig. 8 , and Table S17). Collectively, these observations suggest that the four loci (Chr2HS, Chr4HS, Chr4HL, and Chr7HL) act independently but are synergistic in their contribution to susceptibility when present in the same progeny line. Our results also suggest that, in these populations, the Chr7HL locus contributes more to disease than the Chr2HS, Chr4HS, and Chr4HL loci (Figs. 3 , 4 , 5 , 6 , 7 , and 8 ). Different susceptibility loci contribute differently to leaf symptoms Our results suggest that Chr7HS susceptibility provided a quantitatively greater contribution to disease than the loci associated with susceptibility mapping to Chr2HS, Chr4HS, and Chr4HL, respectively (Fig. 3 , Fig. 4 , Fig. 5 , and Fig. 6 ). Additionally, in the PI 392501 × PI 67381 population, we observed distinct symptom types associated with the different susceptibility loci. When progeny lines harboring Chr4HS susceptibility were inoculated with an isolate targeting Chr4HS (e.g., Den2.6), leaves showed tiny dot-like brown lesions surrounded by an area of yellow, pale chlorotic tissue that expanded over time, covering most of the lamina (Fig. 9 b and Fig. 9 d). By contrast, when progeny lines harboring Chr7HL susceptibility were inoculated with an isolate targeting Chr7HL (e.g., P-A14), leaves showed circular to elliptical brown, dry necrotic lesions that expanded with time (Fig. 9 c and Fig. 9 d). This might indicate that, in the PI 392501 × PI 67381 population, Chr4HS susceptibility was associated with yellow leaf chlorosis, whereas Chr7HL susceptibility was associated with brown leaf necrosis. Therefore, Chr4HS and Chr7HL susceptibility might be targeted by fungal effectors that trigger distinct physiological host responses upon effector recognition. Discussion SFNB poses a significant threat to barley production. Disease management strategies rely on the application of protective chemicals and biocontrol agents (reviewed in Backes et al. 2021 ; Abebe, 2021 ; Helps et al. 2024 ). However, both strategies have limitations: chemical control can lead to the development of fungicide resistance in the pathogen population, while biocontrol agents can be negatively affected by environmental factors and require lengthy, complex experiments for assessment (Backes et al. 2021 ; El-Saadony et al. 2022 ). Effective and durable resistance can be achieved by deploying barley cultivars that harbor SFNB resistance or lack susceptibility genes. However, both the complex nature of this host-pathogen genetic interaction, as well as the genetic diversity in the P. teres f. maculata population at a global level, have been significant obstacles to identifying, characterizing, and maintaining durable sources of genetic resistance (reviewed in Clare et al. 2020 ; Clare et al. 2022 ; Carlsen et al. 2017 ; Wang et al. 2015 ). Because we previously showed that the SFNB system predominantly conforms to an inverse gene-for-gene interaction where pathogen effectors target specific dominant susceptibility loci (Tamang et al. 2019 ; Skiba et al.2022), we generated three barley RIL mapping populations using three distinct susceptible barley lines, each crossed to a single resistant line that lacked susceptibility for use in this study. These segregating mapping populations were evaluated for resistance/susceptibility with a set of 10 globally collected P. teres f. maculata isolates spanning five continents, including two isolates collected near the barley center of diversity (Taliadoros et al. 2024 ). We used these isolates to obtain disease reactions across the barley populations, followed by QTL analysis to identify host loci significantly associated with SFNB susceptibility/resistance. TR 326 and PI 392501 were selected as susceptible parental lines since they represented new differential lines that showed variation in disease reaction upon inoculation with different P. teres f. maculata isolates (Carlsen et al. 2017 ; Clare et al. 2022 ), whereas Hockett is a popular North American barley cultivar due to its valuable agronomic traits (Blake 2008 ). This experimental design was chosen to determine whether the three susceptible lines possessed the same or different susceptibility genes. P. teres f. maculata isolates collected from geographically diverse barley growing regions were chosen because the effector repertoire of geographically distinct fungal populations evolves to overcome the selection pressure imposed by the host resistance/susceptibility genes present in the locally planted barley cultivars (Karki and Sharp 1986 ; Tekauz 1990 ; Arabi et al. 2003 ; Gupta et al. 2011; McLean et al. 2010b). We observed diversity in QTL patterns across fungal isolate-barley population combinations and identified both isolate- and line-specific associations with susceptibility loci, with contributions to disease severity varying quantitatively in some cases. In total, four independent loci associated with SFNB susceptibility were identified, with these quantitative associations mapping to barley Chr2HS, Chr4HS, Chr4HL, and Chr7HL. All three RIL populations showed a significant association with Chr7HL at the same genomic position, whereby the most significant markers associated with phenotypic variation were JHI-Hv50k-2016-502956 for the Hockett × PI 67381 population (Table S12), JHI-Hv50k-2016-501105 for the TR 326 × PI 67381 (Table S13), and JHI-Hv50k-2016-501140 for the PI 392501 × PI 67381 (Table S14). This indicates that the same susceptibility gene is likely present in the three susceptible parents, Hockett, TR 326, and PI 392501. The long arm of Chr7H has long been known to harbor an important genetic locus involved in the barley- P. teres f. maculata interaction. The first locus associated with phenotypic variation in SFNB disease reaction, Rpt4 , was reported on the long arm of Chr7H and mapped to the same genetic position as our Chr7HL QTL (Williams et al. 1999 , 2003 ). In their research, Williams et al. defined Rpt4 as a dominant resistance locus effective against SFNB, based on phenotyping F 1 individuals. The data were not presented, so we do not know how many individuals were tested, nor do we have information on the phenotypic response (Williams et al. 1999 ). Conversely, more recent studies have shown that, when the F 2 progenies were evaluated on two populations (Tamang et al. 2019 ) and an individual population (Skiba et al. 2022 ), the Chr7H locus segregated in a 3:1 (susceptible: resistant) ratio, indicating that Rpt4 represents a dominant locus associated with SFNB susceptibility, rather than resistance (Tamang et al. 2019 ; Skiba et al. 2022 ). This observation is in line with the findings reported in the interactions between wheat and the closely related necrotrophic fungal pathogens Pyrenophora tritici-repentis and Parastagonospora nodorum , where it was shown that the genetics of these pathosystems was governed primarily by dominant susceptibility loci in the host (reviewed in Kariyawasam et al. 2023 ; Peters-Haugrud et al. 2022 ; Friesen and Faris 2021 ; Faris and Friesen 2020 ). Four additional studies have identified a QTL associated with SFNB susceptibility mapping to Chr7H at the Rpt4 locus, including QRpt7 reported by Grewal et al. ( 2008 ), QRptm7-3 reported by Wang et al. ( 2015 ), the QRptm-7H-119-137 reported by Tamang et al. ( 2019 ), and the QRptm-7H-96-107 QTL reported by Alhashel et al. ( 2021 ). Moreover, the Chr7H QTL reported by Tamang et al. ( 2019 ) was identified in two RIL populations derived from crosses between the same resistant line used in this study, PI 67381, and two susceptible cultivars, Pinnacle and Tradition, indicating segregation of the same gene associated with SFNB susceptibility. According to Skiba et al. ( 2022 ), Chr7H susceptibility was associated with a virulence that was mapped to P. teres f. maculata chromosome 2. In the current study, except for the Idaho, Danish, and New Zealand isolates, all P. teres f. maculata isolates used in this study showed an association with barley Chr7HL in all three RIL populations, indicating that these isolates harbor the same effector gene located on P. teres f. maculata chromosome 2 as identified by Skiba et al. ( 2022 ). A significant QTL on Chr2HS was identified in the Hockett × PI 67381 and TR 326 × PI 67381 populations, with each QTL mapping to the same genetic position. The most significant markers associated with phenotypic variation were JHI-Hv50k-2016-65583 for the Hockett × PI 67381 population (Table S12) and JHI-Hv50k-2016-67492 for the TR 326 × PI 67381 (Table S13). The Chr2HS QTL localized to the same region as the SFNB-2H-8-10 QTL reported in an association mapping study that included breeding lines from the Upper Midwestern US breeding programs (Burlakoti et al. 2017 ). In addition, our Chr2HS QTL mapped to the same position as the QRptm-2H-1-31 QTL identified by Tamang et al. ( 2019 ) in three different RIL populations and as the Chr2H QTL detected by Skiba et al. ( 2022 ) in the QTL analysis on Hockett × PI 67381. Consequently, despite their diverse geographic origins, all isolates targeting Chr2HS in Hockett × PI 67381 and TR 326 × PI 67381, namely P-A14, FGOB10Ptm-1, AZB_Ptm20, and C-A17, only in the Hockett × PI 67381 population, likely harbor the same effector gene on P. teres f. maculata chromosome 1 as reported by Skiba et al. ( 2022 ). We noticed that the Chr2HS QTL identified in the Hockett × PI 67381 population accounted for a larger phenotypic variation compared to the same locus identified in the TR 326 × PI 67381 population when the RIL populations were inoculated with isolates P-A14, ID220, and AZB_Ptm20 (Fig. 2 , Table 4 and Table 5 ). This indicates that the barley lines Hockett and TR 326 likely possess the same gene but distinct alleles conferring Chr2HS susceptibility, with the Hockett allele providing a more substantial contribution to disease development, possibly due to a stronger pathogen effector-host target association. Alternatively, the effects of the Chr2HS gene may be influenced by differences in genetic background across populations. Uniquely, the Azerbaijan isolate AZB_Ptm20 was the only isolate to induce a more significant QTL associated with the Chr2HS locus than the Chr7HL locus, suggesting that the P. teres f. maculata effector alleles at Chr1 and Chr2 may differ in this isolate compared to the other isolates used here. Cloning and validation of these genes would allow us to characterize the variability at these effector gene loci as well as provide insight on the evolution at these loci. In the Hockett × PI 67381 population, both isolates, P-A14 and C-A17, showed a significant association with Chr2HS (Fig. 2 and Table 4 ). In the TR 326 × PI 67381 population P-A14 also showed a significant association with the same Chr2HS locus; however, no association with Chr2HS was observed when C-A17 was inoculated on this population (Fig. 2 and Table 5 ). This might suggest that, although both isolates originated in Montana (USA), the virulence alleles at the chromosome 1 locus likely differ between C-A17 and P-A14, with the P-A14 allele contributing more to virulence than the C-A17 allele. This situation has been shown previously in a study on the allelic diversity of the P. nodorum necrotrophic effector gene SnTox5 , in which isolates harboring different SnTox5 alleles showed significantly different levels of virulence on wheat lines carrying the corresponding susceptibility locus Snn5 (Kariyawasam et al. 2022 ). Validation of this hypothesis will require the cloning and functional characterization of this gene and its protein. Two significant associations with Chr4H were identified in this study. One of them, mapping to the short arm of Chr4H, was detected only in the PI 392501 × PI 67381 population after inoculation with the P. teres f. maculata isolates Den2.6, NZKF2, and ID220, with BOPA1_3644 − 1483 as the most closely associated marker (Table S14). Given the same genetic position on Chr4HS and the use of the same isolates Den2.6 and NZKF2, this locus likely corresponds to the QRptm-4H-120-125 QTL identified on Chr4H in the RIL population Tradition × PI 67381 (Tamang et al. 2019 ). Moreover, the Chr4HS QTL was mapped to a similar position as the QRpts4 locus identified by Grewal et al. ( 2008 ), which was associated with resistance against both forms of P. teres . Den2.6, NZKF2 and ID220 were collected in Denmark, New Zealand, and Idaho, USA, respectively. This indicates that virulence targeting 4HS occurs in pathogen populations across three continents and could become widespread if barley cultivars with the corresponding 4HS susceptibility were broadly grown. Analogously, the QTL detected on the long arm of Chr4H in the TR 326 × PI 67381 population, with JHI-Hv50k-2016-272241 as the most significant marker (Table S13), likely corresponds to the QTL QRptm-4H-120-125 identified by Tamang et al. ( 2019 ) in the Pinnacle × PI 67381 population that was detected when the population was inoculated with the P. teres f. maculata isolates Den2.6 and NZKF2. This indicates that Den2.6, NZKF2, and ID220 secrete an effector that targets the same susceptibility present on the short arm of Chr4H in the barley lines PI 392501 and Tradition, whereas a different effector produced by Den2.6 and NZKF2 targets a different susceptibility gene on the long arm of Chr4H in the barley lines TR 326 and Pinnacle. Our results showed that each of the four identified QTL represented an independent association with SFNB susceptibility, but they did not contribute equally to symptom development. P. teres f. maculata isolates targeting Chr7HL, including P-A14, C-A17, FGOB10Ptm-1, G76S, SG1, Mor4-2, and AZB_Ptm20, were more virulent on all three susceptible parental lines than isolates showing no association with Chr7HL (ID220, Den2.6, and NZKF2) (Fig. 1 , Fig. 2 and Table 3 ). In addition, for the TR 326 × PI 67381 populations, when average disease reaction was compared between the different genotypic categories, progeny lines harboring only the Chr7HL susceptible allele showed significantly higher average disease reaction compared to progeny lines harboring only the Chr2HS susceptible allele (Fig. 3 , Fig. 4 , Fig. 5 and Fig. 6 ). Therefore, these data indicate that the association with Chr7HL susceptibility made the greatest contribution to SFNB symptom development relative to any of the other associations and that the most virulent P. teres f. maculata isolates harbor a conserved effector that targets Chr7HL susceptibility. Analysis of Tradition × PI 67381 (Tamang et al. 2019 ) and Hockett × PI 67381 (Skiba et al. 2022 ) F 2 populations previously showed that, at the barley Chr7HL and Chr2HS loci, progeny lines segregated in a 3:1 (susceptible to resistant) ratio at both the Chr7H and Chr2H loci following inoculation with P. teres f. maculata isolates P-A14 and FGOB10-Ptm1, indicating that the Chr7HL and Chr2HS genes are dominant for susceptibility. In addition, when analyzing phenotypic data of RIL populations showing multiple loci associated with susceptibility, we observed that the different QTL acted synergistically, where progeny lines harboring both susceptibility alleles showed significantly higher disease reaction scores than lines harboring only one susceptibility allele (Fig. 3 , Fig. 4 , Fig. 5 , Fig. 3 , and Fig. 5 ). These observations support the hypothesis that the P. teres f. maculata -barley pathosystem is governed by multiple inverse gene-for-gene interactions analogous to the P. nodorum- wheat and P. tritici-repentis- wheat interactions, whereby multiple necrotrophic effectors target a corresponding dominant susceptibility gene product to promote disease development in an additive manner (reviewed in Kariyawasam et al. 2022 ; Peters-Haugrud et al. 2022 ; Friesen and Faris 2021 ; Faris and Friesen 2020 ). Further analysis of disease phenotypes revealed locus-specific susceptibility responses. Association with Chr2HS and Chr7HL each resulted in expanding brown necrotic spots in susceptible progeny lines across all three RIL populations (Fig. 3 , Fig. 4 , and Fig. 6 ). A possible explanation for this phenotype can be inferred from the observation that barley leaf cells accumulate polyphenolic compounds upon fungal infection (Ishihara et al. 2017 ; Ube et al. 2019 and 2021 ). During PCD, polyphenolic compounds are released from the vacuole and oxidized into brown melanin pigments through phenoloxidase activity (Boeckx et al. 2015 ; Tilley et al. 2023 ; reviewed in Hatsugai et al. 2015 ; Mayer 2006 ). Therefore, based on this information, we could hypothesize that the necrotic phenotype associated with Chr2HS and Chr7HL susceptibility relies on a physiological response that initiates with the induction of PCD, followed by the release and oxidation of phenolic compounds, leading to the formation of melanin that becomes visible as tissue browning. Further work is needed to validate this hypothesis. Both the Chr2HS and Chr7HL QTL contain barley genes encoding proteins related to disease resistance, such as Toll/interleukin-1/Nucleotide-binding site-leucine-rich repeat (TIR/NBS-LRR) receptor-like proteins, which represent interesting candidate genes capable of triggering host PCD induction upon recognition of the cognate fungal effector (Table S1 and Table S4). Ongoing work includes fine-scale genetic mapping of susceptibility genes on Chr2HS and Chr7HL and functional analyses to determine the roles of these candidate genes (Alhashel et al. 2023 ). In contrast to Chr2HS and Chr7HL susceptibility, the association with Chr4HS observed in progeny lines of the PI 392501 × PI 67381 population resulted in the development of tiny brown necrotic spots surrounded by an expanding area of yellow chlorotic tissue (Fig. 6 ). This phenotype is reminiscent of the light-dependent chlorotic response (Strelkov et al. 1998 ) caused by the interaction between the host susceptibility gene Tsc2 and the effector Ptr ToxB produced by the wheat fungal pathogen P. tritici-repentis (Friesen and Faris, 2004 ). However, to the best of our knowledge, no Ptr ToxB orthologs are present in P. teres f. maculata , and no Tsc2 orthologs have been annotated in barley. Intriguingly, our results indicate that the most significant marker associated with the Chr4HS QTL mapped to a barley gene annotated as chorismate synthase (accession number HORVU.MOREX.r3.4HG0391140.1; Table S2). Chorismate synthases are involved in the biosynthesis of photoactive pigments and defense-related secondary metabolites (reviewed in Hubrich et al. 2021 ; Hu et al. 2009 ; Kretschmer et al. 2017 ). If indeed this is the targeted gene in barley, it could be hypothesized that the P. teres f. maculata effector targeting chorismate synthase on Chr4HS could perturb physiological processes involved in early host defense responses, thereby promoting fungal colonization during the initial phase of infection and leading to the formation of chlorotic lesions. Functional analysis of the barley chorismate synthase will help to better understand the mechanism of the host response. In conclusion, our study showed that the genetic architecture of SFNB susceptibility is governed by the independent action of multiple dominant host genes. Even though some of these genes might be conserved at specific loci across different barley lines, they do not contribute equally to disease development, and allelic variants of the same gene may have evolved because of diversifying selection during coevolution with the pathogen. Among the loci identified in this study, we showed that the Chr7HL locus is generally the largest contributor to SFNB susceptibility and is targeted by seven of ten globally collected P. teres f. maculata isolates spanning four continents. The identification and elimination of susceptibility genes in host germplasm represents the most straightforward strategy for developing barley cultivars that harbor genetic resistance to necrotrophic pathogens (Oliver et al. 2012 and 2014 ; Downie et al. 2021 ). Therefore, the elimination of Chr7HL susceptibility should be prioritized in barley breeding programs. Although the Chr7HL susceptibility had the largest effects across these three populations, the other three susceptibility loci on Chr2HS, Chr4HS, and Chr4HL also contributed significantly to disease and should also be eliminated. Even if the local P. teres f. maculata populations do not currently harbor the corresponding effectors, unknowingly introgressing the susceptibility targets on Chr2HS, Chr4HS, and Chr4HL would put selection pressure on the pathogen population to adapt to these susceptibilities. As examples of potential evolution in barley-growing regions of the world, the Chr2HS susceptibility was targeted by seven of the 10 isolates, covering four continents; Chr4HS was targeted by three isolates spanning three continents; and Chr4HL was targeted by two isolates spanning two continents. It appears that the effector genes targeting the four barley susceptibility genes presented here are distributed globally, facilitating easy adaptation to locally planted barley cultivars. Therefore, monitoring for these barley susceptibility genes as well as the local P. teres f. maculata populations is critical to managing this global disease. This study provides barley breeders with valuable tools, including germplasm and potential markers, to develop new resistant cultivars via marker-assisted selection. Additionally, the barley research community can use the knowledge presented here to validate genes and conduct functional studies to characterize the molecular mechanisms underlying SFNB susceptibility. Declarations Author contribution statement MCM and TLF designed the study. RS and JDF generated sequencing data for the barley markers. MCM, RS and SA performed mapping, phenotyping and QTL analysis. MCM, RS and TLF analyzed the data. MCM and TLF wrote the manuscript. SY and ZL contributed to review the manuscript. All authors edited and approved the manuscript. Disclaimer The mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. Funding This work was supported by the U.S. Department of Agriculture, Agricultural Research Service, through CRIS project 3060-22000-051-000D. Author Contribution MCM and TLF designed the study. RS and JDF generated sequencing data for the barley markers. MCM, RS and SA performed mapping, phenotyping and QTL analysis. MCM, RS and TLF analyzed the data. MCM and TLF wrote the manuscript. SY and ZL contributed to review the manuscript. All authors edited and approved the manuscript. Acknowledgement The authors are grateful to Danielle Holmes, Dr. Alyssa Flobinus, Zoie Gilpin, and Garret Kuhn for their assistance with plant care and phenotype data collection, to Mary Osenga’s technical work on generating the barley population markers. We would also like to thank Drs. Robert Brueggeman, Karl Effertz, Sajid Rehman, and Eva Stukenbrock for contributing diseased leaf samples and/or P. teres f maculata isolates for use in this study. Data Availability All data used in this study is available in the supplementary files. References Ababa G, Hailu W, Shiferaw T, Fekadu W, Alamerew S (2024) Adult-plant resistance to leaf scald and net form net blotch in food barley genotypes at a hot spot location in Ethiopia. Heliyon, 10(22). 10.1016/j.heliyon.2024.e40529 Abebe W (2021) Barley net blotch disease management: A review. Int J Environ Agric Res 7(9), 69–81. Adhikari A, Steffenson BJ, Smith KP, Smith M, Dill-Macky R (2020) Identification of quantitative trait loci for net form net blotch resistance in contemporary barley breeding germplasm from the USA using genome-wide association mapping. Theor Appl Genet 133, 1019–1037. https://doi.org/10.1007/s00122-019-03528-5 Akhavan A, Turkington TK, Kebede B, Tekauz A, Kutcher HR, Kirkham C, Xi K, Kumar K, Tucker JR, Strelkov SE (2015) Prevalence of mating type idiomorphs in Pyrenophora teres f. teres and P. teres f. maculata populations from the Canadian prairies. Can J Plant Pathol 37:52–60. https://doi.org/10.1080/07060661.2014.995710 Akhavan A, Turkington TK, Askarian H, Tekauz A, Xi K, Tucker JR, Kutcher HR, Strelkov SE (2016). Virulence of Pyrenophora teres populations in western Canada. Can J Plant Pathol 38, 183–196. https://doi.org/10.1080/07060661.2016.1159617 Akhavan A, Turkington TK, Askarian H, Tekauz A, Legge WG, Xi K, Kutcher R, Tucker JR, Kirkham C, Kumar K, Strelkov S (2017) Assessing the genetic and pathogenic variability of Pyrenophora teres f. maculata (spot form net blotch of barley) and its ability to overcome currently-used sources of resistance on the Canadian prairies. Soils and Crops Workshop 2017 Mar 6. Alhashel AF, Sharma Poudel R, Fiedler J, Carlson CH, Rasmussen J, Baldwin T, Friesen TL, Brueggeman RS, Yang S (2021) Genetic mapping of host resistance to the Pyrenophora teres f. maculata isolate 13IM8. 3. G3, 11(12), jkab341. https://doi.org/10.1093/g3journal/jkab341 Alhashel AF, Fiedler JD, Nandety RS, Skiba RM, Bruggeman RS, Baldwin T, Friesen TL, Yang S (2023) Genetic and physical localization of a major susceptibility gene to Pyrenophora teres f. maculata in barley. Theor Appl Genet 136(5), 118. https://doi.org/10.1007/s00122-023-04367-1 Arabi M, Al-Safadi IE, Charbaji BT (2003) Pathogenic variation among isolates of Pyrenophora teres the causal agent of barley net blotch. J Phytopathol 151:376–382. https://doi.org/10.1046/j.1439-0434.2003.00734.x Backes A, Guerriero G, Ait Barka E, Jacquard C (2021) Pyrenophora teres : taxonomy, morphology, interaction with barley, and mode of control. Front Plant Sci 12, 614951. https://doi.org/10.3389/fpls.2021.614951 Blake T (2008) Hockett: Release and Recommendation Notices, Planning Conference January 2008. Montana State University, Barley Breeding Program. Boeckx T, Winters AL, Webb KJ, Kingston-Smith AH (2015) Polyphenol oxidase in leaves: is there any significance to the chloroplastic localization? J Exp Bot 66(12), 3571–3579. https://doi.org/10.1093/jxb/erv141 Burlakoti RR, Gyawali S, Chao S, Smith KP, Horsley RD, Cooper B, Muehlbauer GJ, Neate SM (2017) Genome-wide association study of spot form of net blotch resistance in the Upper Midwest barley breeding programs. Phytopathology 107(1), 100–108. https://doi.org/10.1094/PHYTO-03-16-0136-R Cantalapiedra CP, Boudiar R, Casas AM, Igartua E, Contreras-Moreira B (2015) BARLEYMAP: physical and genetic mapping of nucleotide sequences and annotation of surrounding loci in barley. Mol Breeding 35:13 http://doi.org/10.1007/s11032-015-0253-1 Carlsen SA, Neupane A, Wyatt NA, Richards JK, Faris JD, Xu SS, Brueggeman RS, Friesen TL (2017) Characterizing the Pyrenophora teres f. maculata –barley interaction using pathogen genetics. G3 7(8), 2615–2626. https://doi.org/10.1534/g3.117.043265 Clare SJ, Duellman KM, Richards JK, Poudel RS, Merrick LF, Friesen TL, Brueggeman RS (2022) Association mapping reveals a reciprocal virulence/avirulence locus within diverse US Pyrenophora teres f. maculata isolates. BMC Genom 23(1), 285. https://doi.org/10.1186/s12864-022-08529-1 Clare SJ, Wyatt NA, Brueggeman RS, Friesen TL (2020) Research advances in the Pyrenophora teres –barley interaction. Mol Plant Path 21(2), 272–288. https://doi.org/10.1111/mpp.12896 Clare SJ, Oğuz AÇ, Effertz K, Sharma R, Deven See P, Karakaya A, Brueggeman RS (2021) Genome-wide association mapping of Pyrenophora teres f. maculata and Pyrenophora teres f. teres resistance loci utilizing natural Turkish wild and landrace barley populations. G3 11(11), 269. https://doi.org/10.1093/g3journal/jkab269 Dokhanchi H, Babai-Ahari A, Arzanlou M, Abed-Ashtiani F (2022) Characterization of Pyrenophora , Bipolaris , Curvularia and Exserohilum species associated with leaf spot diseases on barley in East Azerbaijan province, using morphological and molecular data. J Appl Res Plant Protect 11(1), 1–18. Downie RC, Lin M, Corsi B, Ficke A, Lillemo M, Oliver RP, Phan HT, Tan KC, Cockram J (2021) Septoria nodorum blotch of wheat: disease management and resistance breeding in the face of shifting disease dynamics and a changing environment. Phytopathology 111(6), 906–920. https://doi.org/10.1094/PHYTO-07-20-0280-RVW El-Saadony MT, Saad AM, Soliman SM, Salem HM, Ahmed AI, Mahmood M, El-Tahan AM, Ebrahim AA, Abd El-Mageed TA, Negm SH, Selim S (2022) Plant growth-promoting microorganisms as biocontrol agents of plant diseases: Mechanisms, challenges and future perspectives. Front Plant Sci 13: 923880. https://doi.org/10.3389/fpls.2022.923880 El Yousfi B, Brahim E (2001) Net blotch in semi arid regions of Morocco. I Epidemiology. Field Crops Research 73.1: 35–46. https://doi.org/10.1016/S0378-4290(01)00180-0 Ellwood SR, Wallwork H (2018) Diseases affecting barley: net blotches. In: Integrated disease management of wheat and barley (pp. 191–202). Burleigh Dodds Science Publishing. Faris JD, Friesen TL (2020) Plant genes hijacked by necrotrophic fungal pathogens. Curr Opin Plant Biol 56, 74–80. https://doi.org/10.1016/j.pbi.2020.04.003 Faris JD, Zhang Z, Lu H, Lu S, Reddy L, Cloutier S, Fellers JP, Meinhardt SW, Rasmussen JB, Xu SS, Friesen TL (2010) A unique wheat disease resistance-like gene governs effector-triggered susceptibility to necrotrophic pathogens. PNAS 107: 13544–13549. https://doi.org/10.1073/pnas.1004090107 Franckowiak JD, Platz GJ (2013) International database for barley genes and barley genetic stocks. Barley Genet Newsl 43, 48–223. Friesen TL, Faris JD (2004) Molecular mapping of resistance to Pyrenophora tritici-repentis race 5 and sensitivity to Ptr ToxB in wheat. Theor Appl genet 109(3), 464–471. https://doi.org/10.1016/j.pbi.2020.04.003 Friesen TL, Faris JD, Lai Z, Steffenson BJ (2006) Identification and chromosomal location of major genes for resistance to Pyrenophora teres in a doubled-haploid barley population. Genome 49, 855–859. https://doi.org/10.1139/g06-024 Friesen TL, Meinhardt SW, Faris JD (2007) The Stagonospora nodorum- wheat pathosystem involves multiple proteinaceous host‐selective toxins and corresponding host sensitivity genes that interact in an inverse gene‐for‐gene manner. Plant J 51(4), 681–692. https://doi.org/10.1111/j.1365-313X.2007.03166.x Friesen TL, Faris JD (2021) Characterization of effector–target interactions in necrotrophic pathosystems reveals trends and variation in host manipulation. Annu Rev Phytopathol 59(1), 77–98. https://doi.org/10.1146/annurev-phyto-120320-012807 Grewal TS, Rossnagel BG, Pozniak CJ, Scoles GJ (2008) Mapping quantitative trait loci associated with barley net blotch resistance. Theor Appl Genet 116, 529–539. https://doi.org/10.1007/s00122-007-0688-9 Gupta S, Loughman R (2001) Current virulence of Pyrenophora teres on barley in Western Australia. Plant Dis 85:960–966. https://doi.org/10.1094/PDIS.2001.85.9.960 Hatsugai N, Yamada K, Goto-Yamada S, Hara-Nishimura I (2015) Vacuolar processing enzyme in plant programmed cell death. Front Plant Sci 6, 234. https://doi.org/10.3389/fpls.2015.00234 Helps J, Lopez-Ruiz F, Zerihun A, van den Bosch F (2024) Do growers using solo fungicides affect the durability of disease control of growers using mixtures and alternations? The case of spot-form net blotch in western Australia. Phytopathology 114(3), 590–602. https://doi.org/10.1094/PHYTO-02-23-0050-R Hu P, Meng Y, Wise RP (2009) Functional contribution of chorismate synthase, anthranilate synthase, and chorismate mutase to penetration resistance in barley–powdery mildew interactions. MPMI 22(3), 311–320. https://doi.org/10.1094/MPMI-22-3-0311 Hubrich F, Müller M, Andexer JN (2021) Chorismate-and isochorismate converting enzymes: versatile catalysts acting on an important metabolic node. Chem Commun 57(20), 2441–2463. https://doi.org/10.1039/D0CC08078K Ishihara A, Kumeda R, Hayashi N, Yagi Y, Sakaguchi N, Kokubo Y, Ueno K (2017). Induced accumulation of tyramine, serotonin, and related amines in response to Bipolaris sorokiniana infection in barley. Biosci Biotechnol Biochem 81(6), 1090–1098. https://doi.org/10.1080/09168451.2017.1290520 Jayakodi M, Lu Q, Pidon H, Rabanus-Wallace MT, Bayer M, Lux T, Guo Y, Jaegle B, Badea A, Bekele W, Brar GS (2024). Structural variation in the pangenome of wild and domesticated barley. Nature 636(8043), 654–662. https://doi.org/10.1038/s41586-024-08187-1 Jørgensen JH, Bech C, Jensen J (2000) Reaction of European spring barley varieties to a population of the net blotch fungus. Plant Breed 119(1), 43–46. https://doi.org/10.1046/j.1439-0523.2000.00455.x Karki CB, Sharp EL (1986) Pathogenic variation in some isolates of Pyrenophora teres f. sp. maculata on barley. Plant Dis 70: 684. https://doi.org/10.1094/PD-70-684 Kariyawasam GK, Richards JK, Wyatt NA, Running KL, Xu SS, Liu Z, Borowicz P, Faris JD, Friesen TL (2022) The Parastagonospora nodorum necrotrophic effector SnTox5 targets the wheat gene Snn5 and facilitates entry into the leaf mesophyll. New Phytol 233, 1: 409–426. https://doi.org/10.1111/nph.17602 Kariyawasam, GK, Nelson, AC, Williams, SJ, Solomon, PS, Faris, JD, Friesen, TL (2023) The necrotrophic pathogen Parastagonospora nodorum is a master manipulator of wheat defense. MPMI 36 (12), 764–773. https://doi.org/10.1094/MPMI-05-23-0067-IRW Kretschmer M, Croll D, Kronstad JW (2017) Chloroplast-associated metabolic functions influence the susceptibility of maize to Ustilago maydis . MPP 18(9), 1210–1221. https://doi.org/10.1111/mpp.12485 Lammari HI, Rehfus A, Stammler G, Fellahi ZEA, Benbelkacem A, Benslimane H (2020) Occurrence and frequency of spot form and net form of net blotch disease of barley in Algeria. J Plant Dis Prot 127, 35–42. https://doi.org/10.1007/s41348-019-00278-w Lightfoot DJ, Able AJ (2010) Growth of Pyrenophora teres in planta during barley net blotch disease. Australas Plant Pathol 39: 499–507. https://doi.org/10.1071/AP10121 Liu Z, Faris JD, Oliver RP, Tan KC, Solomon PS, McDonald MC, McDonald BA, Nunez A, Lu S, Rasmussen JB, Friesen TL (2009) SnTox3 acts in effector triggered susceptibility to induce disease on wheat carrying the Snn3 gene. PLoS Pathog 5 (9), e1000581. Liu Z, Friesen TL (2010) Identification of Pyrenophora teres f. maculata , causal agent of spot type net blotch of barley in North Dakota. Plant Dis 94, 4: 480–480. Liu Z, Ellwood, SR, Oliver, RP, Friesen TL (2011) Pyrenophora teres : profile of an increasingly damaging barley pathogen. MPP 12, 1–19. https://doi.org/10.1111/j.1364-3703.2010.00649.x Liu Z, Zhang Z, Faris JD, Oliver RP, Syme R, McDonald MC, and Friesen TL (2012) The cysteine rich necrotrophic effector SnTox1 produced by Stagonospora nodorum triggers susceptibility of wheat lines harboring Snn1 . PLoS Pathog 8 (1), e1002467. Liu Z, Holmes DJ, Faris JD, Chao S, Brueggeman RS, Edwards MC, Friesen TL (2015) Necrotrophic effector-triggered susceptibility (NETS) underlies the barley– Pyrenophora teres f. teres interaction specific to chromosome 6H. MPP 16(2), 188–200. Lorang JM, Sweat TA, Wolpert TJ (2007) Plant disease susceptibility conferred by a “resistance” gene. PNAS 104(37), 14861–14866. Lorang J, Kidarsa T, Bradford CS, Gilbert B, Curtis M, Tzeng SC, Wolpert TJ (2012) Tricking the guard: exploiting plant defense for disease susceptibility. Science 338 (6107), 659–662. Manninen OM, Jalli M, Kalendar R, Schulman A, Afanasenko O and Robinson J (2006) Mapping of major spot-type and net-type net blotch resistance genes in the Ethiopian barley line CI 9819. Genome 49, 1564–1571. Marshall JM, Kinzer K, Brueggeman RS (2015) First report of Pyrenophora teres f. maculata the cause of spot form net blotch of barley in Idaho. Plant Dis 99(12), 1860. Mayer AM (2006) Polyphenol oxidases in plants and fungi: going places? A review. Phytochemistry 67(21), 2318–2331. Mclean MS, Howlett BJ, Hollaway GJ (2009) Epidemiology and control of spot form of net blotch ( Pyrenophora teres f. maculata ) of barley: a review. Crop Past Sci 60, 303–315. https://doi.org/10.1071/CP08173 . Mclean MS, Howlett BJ, Hollaway GJ (2010a) Spot form of net blotch, caused by Pyrenophora teres f. maculata , is the most prevalent foliar disease of barley in Victoria, Australia. Australas. Plant Pathol 39, 46–49 https://doi.org/10.1071/AP09054 . McLean MS, FJ Keiper, GJ Hollaway (2010b) Genetic and pathogenic diversity in Pyrenophora teres f. maculata in barley crops of Victoria, Australia. Australas Plant Pathol 39: 319–325. https://doi.org/10.1071/AP09097 McLean MS, Martin A, Gupta S, Sutherland MW, Hollaway GJ, Platz GJ (2014) Validation of a new spot form of net blotch differential set and evidence for hybridisation between the spot and net forms of net blotch in Australia. Australas Plant Pathol 43, 223–233. https://doi.org/10.1007/s13313-014-0285-8 Mittal S (2022) Wheat and Barley Production Trends and Research Priorities: A Global Perspective. In: Kashyap, P.L. New Horizons in Wheat and Barley Research. Springer Singapore. https://doi.org/10.1007/978-981-16-4449-8_1 Navrátilová P, Toegelová H, Tulpová Z, Kuo YT, Stein N, Doležel J, Houben A, Šimková H, Mascher M (2022) Prospects of telomere-to‐telomere assembly in barley: Analysis of sequence gaps in the MorexV3 reference genome. Plant Biotechnol J 20(7), 1373–1386. https://doi.org/10.1111/pbi.13816 Neupane A, Tamang P, Brueggeman RS, Friesen TL (2015) Evaluation of a barley core collection for spot form net blotch reaction reveals distinct genotype-specific pathogen virulence and host susceptibility. Phytopathology 105 (4), 509–517. https://doi.org/10.1094/PHYTO-04-14-0107-R Oğuz AÇ, Ölmez F, Karakaya A (2019) Genetic diversity of net blotch pathogens of barley in Turkey. Int J Agric Biol 21:1089–1096. https://doi.org/10.17957/IJAB/15.0998 Oliver RP, Solomon PS (2010) New developments in pathogenicity and virulence of necrotrophs. Curr Opin Plant Biol 13(4), 415–419. https://doi.org/10.1016/j.pbi.2010.05.003 Oliver RP, Friesen TL, Faris JD, Solomon PS (2012) Stagonospora nodorum : from pathology to genomics and host resistance. Annu Rev Phytopathol 50(1), 23–43. https://doi.org/10.1146/annurev-phyto-081211-173019 Oliver R, Lichtenzveig J, Tan KC, Waters O, Rybak K, Lawrence J, Friesen T, Burgess P (2014) Absence of detectable yield penalty associated with insensitivity to Pleosporales necrotrophic effectors in wheat grown in the West Australian wheat belt. Plant Pathology, 63(5), 1027–1032. https://doi.org/10.1111/ppa.12191 Peters-Haugrud AR, Zhang Z, Friesen TL, Faris JD (2022) Genetics of resistance to septoria nodorum blotch in wheat. Theor Appl Genet 135, 11: 3685–3707. https://doi.org/10.1007/s00122-022-04036-9 Richards JK, Kariyawasam GK, Seneviratne S, Wyatt NA, Xu SS, Liu Z, Friesen TL (2022) A triple threat: the Parastagonospora nodorum SnTox267 effector exploits three distinct host genetic factors to cause disease in wheat. New Phytol 233 (1), 427–442. https://doi.org/10.1111/nph.17601 Shi G, Zhang Z, Friesen TL, Raats D, Fahima T, Brueggeman RS, Lu S, Trick HN, Liu Z, Chao W, Faris JD (2016) The hijacking of a receptor kinase-driven pathway by a wheat fungal pathogen leads to disease. Sci Adv 2 (10). https://doi.org/10.1126/sciadv.1600822 Skiba RM, Wyatt NA, Kariyawasam GK, Fiedler JD, Yang S, Brueggeman RS, Friesen TL (2022) Host and pathogen genetics reveal an inverse gene-for-gene association in the P. teres f. maculata –barley pathosystem. Theor Appl Genet 135(10), 3597–3609. https://doi.org/10.1007/s00122-022-04204-x Smedegard-Petersen V (1971) Pyrenophora teres f. teres and Pyrenophora teres f. maculata on barley in Denmark. Arskrift den kgle Vet-Landbohojsk 971, 124–144. Strelkov SE, Lamari L, Ballance GM (1998) Induced chlorophyll degradation by a chlorosis toxin from Pyrenophora tritici-repentis . Can J plant pathol 20: 428–435. https://doi.org/10.1080/07060669809500417 Taliadoros D, Feurtey A, Wyatt N, Barrès B, Gladieux P, Friesen TL, Stukenbrock EH (2024) Emergence and spread of the barley net blotch pathogen coincided with crop domestication and cultivation history. PLoS Genet 20(1), e1010884. https://doi.org/10.1371/journal.pgen.1010884 Tamang P, Neupane A, Mamidi S, Friesen TL, Brueggeman RS (2015) Association mapping of seedling resistance to spot form net blotch in a worldwide collection of barley. Phytopathology 105(4), 500–508. https://doi.org/10.1094/PHYTO-04-14-0106-R Tamang P, Richards JK, Alhashal A, Sharma Poudel R, Horsley RD, Friesen TL, Brueggeman RS (2019) Mapping of barley susceptibility/resistance QTL against spot form net blotch caused by Pyrenophora teres f. maculata using RIL populations. Theor Appl Genet 132, 1953–1963. https://doi.org/10.1007/s00122-019-03328-x . Tekauz A (1990) Characterization and distribution of pathogenic variation in Pyrenophora teres f. teres and P. teres f. maculata from western Canada. Can J Plant Pathol 12(2), 141–148. https://doi.org/10.1080/07060669009501017 Tilley A, McHenry MP, McHenry JA, Solah V, Bayliss K (2023) Enzymatic browning: The role of substrates in polyphenol oxidase mediated browning. Curr Res Food Sci 7, 100623. https://doi.org/10.1016/j.crfs.2023.100623 Tini F, Covarelli L, Ricci G, Balducci E, Orfei M, Beccari G (2022) Management of Pyrenophora teres f. teres , the causal agent of net form net blotch of barley, in a two-year field experiment in central Italy. Pathogens 11(3), 291 https://doi.org/10.3390/pathogens11030291 Tomić A, Trkulja V, Matić S, Trkulja N, Iličić R, Scortichini M, Popović Milovanović T (2024) Net blotch ( Pyrenophora teres Drechsler): An increasingly significant threat to barley production. Plant Prot Sci 60(1), 1–30. https://doi.org/10.17221/122/2023-PPS Ube N, Yabuta Y, Tohnooka T, Ueno K, Taketa S, Ishihara A (2019) Biosynthesis of phenylamide phytoalexins in pathogen-infected barley. Int J Mol Sci 20(22), 5541. https://doi.org/10.3390/ijms20225541 Ube N, Katsuyama Y, Kariya K, Tebayashi SI, Sue M, Tohnooka T, Ueno K, Taketa S, Ishihara A (2021) Identification of methoxylchalcones produced in response to CuCl2 treatment and pathogen infection in barley. Phytochemistry 184, 112650. https://doi.org/10.1016/j.phytochem.2020.112650 Vasighzadeh A, Sharifnabi B, Javan-Nikkhah M, Seifollahi E, Landermann‐Habetha D, Feurtey A, Holtgrewe‐Stukenbrock E (2021) Population genetic structure of four regional populations of the barley pathogen Pyrenophora teres f. maculata in Iran is characterized by high genetic diversity and sexual recombination. Plant Pathol 70(3), 735–744. https://doi.org/10.1111/ppa.13326 Wyatt NA, Friesen TL (2021) Four reference quality genome assemblies of Pyrenophora teres f. maculata : a resource for studying the barley spot form net blotch interaction. MPMI 34(1), 135–139. https://doi.org/10.1094/MPMI-08-20-0228-A Wang X, Mace ES, Platz GJ, Hunt CH, Hickey LT, Franckowiak JD, Jordan DR (2015) Spot form of net blotch resistance in barley is under complex genetic control. Theor Appl Genet 128, 489–499. https://doi.org/10.1007/s00122-014-2447-z Williams KJ, Lichon A, Gianquitto P, Kretschmer JM, Karakousis A, Manning S, Langridge P, Wallwork H (1999) Identification and mapping of a gene conferring resistance to the spot form of net blotch ( Pyrenophora teres f. maculata ) in barley. Theor Appl Genet 99:323–327. https://doi.org/10.1007/s001220051239 Williams KJ, Platz GJ, Barr AR, Cheong J, Willsmore K, Cakir M, Wallwork H (2003). A comparison of the genetics of seedling and adult plant resistance to the spot form of net blotch ( Pyrenophora teres f. maculata ). Aust J Agric Res 54(12), 1387–1394. https://doi.org/10.1071/AR03028 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTablesS1S17.xlsx Cite Share Download PDF Status: Published Journal Publication published 28 Apr, 2026 Read the published version in Theoretical and Applied Genetics → Version 1 posted Editorial decision: Revision requested 31 Mar, 2026 Reviews received at journal 31 Mar, 2026 Reviews received at journal 18 Mar, 2026 Reviewers agreed at journal 08 Mar, 2026 Reviewers agreed at journal 04 Mar, 2026 Reviewers invited by journal 01 Mar, 2026 Editor assigned by journal 28 Feb, 2026 Submission checks completed at journal 25 Feb, 2026 First submitted to journal 24 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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-8961109","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600735483,"identity":"18fd5322-8a28-4401-9dab-9e1696b52da9","order_by":0,"name":"Michele C. Malvestiti","email":"","orcid":"","institution":"North Dakota State University","correspondingAuthor":false,"prefix":"","firstName":"Michele","middleName":"C.","lastName":"Malvestiti","suffix":""},{"id":600735484,"identity":"b69f2201-a37b-4d92-a872-e07ddc0e4749","order_by":1,"name":"Sefunmi Alaofin","email":"","orcid":"","institution":"North Dakota State University","correspondingAuthor":false,"prefix":"","firstName":"Sefunmi","middleName":"","lastName":"Alaofin","suffix":""},{"id":600735485,"identity":"7b590908-2fe0-48f5-9a72-0e4bfa46da4d","order_by":2,"name":"Ryan Skiba","email":"","orcid":"","institution":"USDA-ARS","correspondingAuthor":false,"prefix":"","firstName":"Ryan","middleName":"","lastName":"Skiba","suffix":""},{"id":600735486,"identity":"47440de1-1614-47af-b3ea-9f682a7e2e58","order_by":3,"name":"Shengming Yang","email":"","orcid":"","institution":"USDA-ARS","correspondingAuthor":false,"prefix":"","firstName":"Shengming","middleName":"","lastName":"Yang","suffix":""},{"id":600735487,"identity":"56047f84-3938-4015-8a4e-89274a424d2c","order_by":4,"name":"Jason D. Fiedler","email":"","orcid":"","institution":"USDA-ARS","correspondingAuthor":false,"prefix":"","firstName":"Jason","middleName":"D.","lastName":"Fiedler","suffix":""},{"id":600735488,"identity":"af172b52-66c4-4075-968b-3d5f2a3ffdbe","order_by":5,"name":"Zhaohui Liu","email":"","orcid":"","institution":"North Dakota State University","correspondingAuthor":false,"prefix":"","firstName":"Zhaohui","middleName":"","lastName":"Liu","suffix":""},{"id":600735489,"identity":"64b245af-60c9-4d4f-9a51-641f8c490cc5","order_by":6,"name":"Timothy L. Friesen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYDACZiBmbGBgYG8AkQwMckBsgFcHD0jLQaBingMQLcaEtTDAtUAEEhsIabFn5058/HHHYQYe6cOND39U3EnfcP7wxg8Mv2wSG3A6jHezwcEzQC18ic3GPGee5W64kVYswdiXhk/LNomDbYcZ7HkY26QZ2w4DtfAYSDD2HDbG6ReYFh4exvafP/8dTjc4f8b4B7Fa2hh4Gw4nGBzIMZNg+HFYDqeWw0C/nG1L5wFqaZbmOXbYcOaNtDKLxIY0nFrY+89ufFDZZi3Hw8P+8OOPmsPyfOcPb77x4Y8NDy4tUNCMpiCxjYAGBoY6dIE/BLWMglEwCkbByAEAlTJaq63IVg0AAAAASUVORK5CYII=","orcid":"","institution":"USDA-ARS","correspondingAuthor":true,"prefix":"","firstName":"Timothy","middleName":"L.","lastName":"Friesen","suffix":""}],"badges":[],"createdAt":"2026-02-24 21:23:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8961109/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8961109/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00122-026-05250-5","type":"published","date":"2026-04-28T15:58:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":104102865,"identity":"7132eb80-e79c-40ff-924a-0dfb94cf55ac","added_by":"auto","created_at":"2026-03-06 20:28:31","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":528142,"visible":true,"origin":"","legend":"\u003cp\u003eSFNB symptoms observed at 7dpi on secondary leaves of barley parental lines Hockett, TR 326, PI 392501 and PI 67381 upon inoculation with a set of \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates originating from different geographical areas. Inoculation of each fungal isolate on Hockett, TR 326 and PI 392501 resulted in host susceptibility, whereas inoculation on PI 67381 resulted in host resistance.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8961109/v1/374810b476b443a9af41b5ef.jpg"},{"id":104403188,"identity":"cb057abe-ba08-4fba-ac96-95703e795f1c","added_by":"auto","created_at":"2026-03-11 12:17:41","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":306598,"visible":true,"origin":"","legend":"\u003cp\u003eQTL pattern showing significant associations with susceptibility loci identified on Chr2H (\u003cstrong\u003ea\u003c/strong\u003e), Chr4H (\u003cstrong\u003eb\u003c/strong\u003e) and Chr7H (\u003cstrong\u003ec\u003c/strong\u003e) upon inoculation of a set of \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates originating from different geographical areas on the barley populations Hockett × PI 67381, TR 326 × PI 67381 and PI 392501 × PI 67381. QTL analysis was performed using single interval mapping. The Y-axis indicates the LOD value calculated for a given marker position. The X-axis shows the physical position of the markers on the corresponding chromosome. Each isolate is represented by a solid-colored line: red P-A14, pink C-A17, yellow FGOB10Ptm-1, purple ID220, light blue Den2.6, dark blue NZKF2, light green G76S, dark green SG1, orange AZB_Ptm20, grey Mor4-2. R\u003csup\u003e2\u003c/sup\u003e, (in %) and LOD values (in parenthesis) are presented in the figure legends next to each isolate name. Isolates showing a significant association with the corresponding chromosome are highlighted in bold. The black dashed line represents the calculated critical LOD threshold (α = 0.01) of 3.5. Only chromosomes with significant QTL are shown.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8961109/v1/92f7a65a68b6e4c97a9258da.jpg"},{"id":104102868,"identity":"717763f0-d67a-47d0-a9f3-51b133a15dd4","added_by":"auto","created_at":"2026-03-06 20:28:32","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":175881,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of average disease reaction type between genotypic categories derived from the Hockett × PI 67381 population harboring the susceptible (Hockett) or resistant (PI 67381) allele at Chr2HS and Chr7HL upon inoculation with \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates P-A14, C-A17, FGOB10Ptm-1, and AZB_Ptm20. Scores of each category are shown as blue boxes. The X-axis shows the genotype of the categories harboring both resistant alleles (2HS\u003csup\u003ePI 67381\u003c/sup\u003e-7HL\u003csup\u003ePI 67381\u003c/sup\u003e), one resistant and one susceptible allele (2HS\u003csup\u003eHockett\u003c/sup\u003e-7HL\u003csup\u003ePI 67381\u003c/sup\u003e; 2HS\u003csup\u003ePI 67381\u003c/sup\u003e-7HL\u003csup\u003eHockett\u003c/sup\u003e) or both susceptible alleles (2HS\u003csup\u003eHockett\u003c/sup\u003e-7HL\u003csup\u003eHockett\u003c/sup\u003e). The Y-axis shows the disease reaction type score. The solid horizontal line in each box indicates the median while average disease reaction is indicated by a solid triangle. Categories with different letters following their average scores differed significantly at the 0.05 level of probability using Fisher’s least significant difference (LSD) test.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8961109/v1/c4cc02a13a52323980bf4078.jpg"},{"id":104402995,"identity":"ec1a80a7-3c29-4296-8ded-ee09aadff886","added_by":"auto","created_at":"2026-03-11 12:17:07","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":286248,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in disease reaction type\u003cstrong\u003e \u003c/strong\u003eat 7 dpi on leaves of progeny lines of the Hockett x PI 67381 population inoculated with \u003cem\u003eP. teres \u003c/em\u003ef. \u003cem\u003emaculata\u003c/em\u003e isolate P-A14. Each panel shows two representative leaves of three lines belonging to the different genotypic categories harboring at Chr2HS and Chr7HL both alleles derived from the resistant parental line PI 67381 (\u003cstrong\u003ea\u003c/strong\u003e), the allele at Chr2HS derived from the susceptible parental line Hockett and the allele at Chr7HL derived from the resistant parental line (\u003cstrong\u003eb\u003c/strong\u003e), the allele at Chr2HS derived from the resistant parental line and the allele at Chr7HL derived from the susceptible parental line (\u003cstrong\u003ec\u003c/strong\u003e), and both alleles derived from the susceptible parental line (\u003cstrong\u003ed\u003c/strong\u003e). Progeny line numbers are shown in white on the left edge of the leaves.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8961109/v1/adcdbdd1a891ba5ef0a9efdb.jpg"},{"id":104403292,"identity":"63affabe-4b40-4b47-b2c7-8e6089b49905","added_by":"auto","created_at":"2026-03-11 12:17:57","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":92744,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of average disease reaction type between genotypic categories derived from the TR 326 × PI 67381 population harboring the susceptible (TR 326) or resistant (PI 67381) allele at Chr2HS and Chr7HL upon inoculation with \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates P-A14, FGOB10Ptm-1, and AZB_Ptm20. Scores of each category are shown as blue boxes. The X-axis shows the genotype of the categories harboring both resistant alleles (2HS\u003csup\u003ePI 67381\u003c/sup\u003e-7HL\u003csup\u003ePI 67381\u003c/sup\u003e), one resistant and one susceptible allele (2HS\u003csup\u003eTR 326\u003c/sup\u003e-7HL\u003csup\u003ePI 67381\u003c/sup\u003e; 2HS\u003csup\u003ePI 67381\u003c/sup\u003e-7HL\u003csup\u003eTR 326\u003c/sup\u003e) or both susceptible alleles (2HS\u003csup\u003eTR 326\u003c/sup\u003e-7HL\u003csup\u003eTR 326\u003c/sup\u003e). The Y-axis shows the disease reaction type score. The solid horizontal line in each box indicates the median while average disease reaction is indicated by a solid triangle. Categories with different letters following their average scores differed significantly at the 0.05 level of probability using Fisher’s least significant difference (LSD) test.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8961109/v1/f3ca1e77cd6d12bf653f8278.jpg"},{"id":104403248,"identity":"cf9f4626-6154-4b22-b86e-fdd286ee29c8","added_by":"auto","created_at":"2026-03-11 12:17:50","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":226208,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in disease reaction type\u003cstrong\u003e \u003c/strong\u003eat 7 dpi on leaves of progeny lines of the TR 326 x PI 67381 population inoculated with \u003cem\u003eP. teres \u003c/em\u003ef. \u003cem\u003emaculata\u003c/em\u003e isolate P-A14. Each panel shows two representative leaves of three lines belonging to the different genotypic categories harboring at Chr2HS and Chr7HL both alleles derived from the resistant parental line PI 67381 (\u003cstrong\u003ea\u003c/strong\u003e), the allele at Chr2HS derived from the susceptible parental line TR 326 and the allele at Chr7HL derived from the resistant parental line (\u003cstrong\u003eb\u003c/strong\u003e), the allele at Chr2HS derived from the resistant parental line and the allele at Chr7HL derived from the susceptible parental line (\u003cstrong\u003ec\u003c/strong\u003e), and both alleles derived from the susceptible parental line (\u003cstrong\u003ed\u003c/strong\u003e). Progeny line numbers are shown in white on the left edge of the leaves.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8961109/v1/cf9cd9d2b9a17d37c5c98e34.jpg"},{"id":104102866,"identity":"345dc57b-6eac-493a-850d-9fa0998930d6","added_by":"auto","created_at":"2026-03-06 20:28:31","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":102161,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of average disease reaction type between genotypic categories derived from the TR 326 × PI 67381 population harboring the susceptible (TR 326) or resistant (PI 67381) allele at Chr2HS and Chr4HL upon inoculation with \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates Den2.6 and NZKF2. Scores of each category are shown as blue boxes. The X-axis shows the genotypic class harboring both resistant alleles (2HS\u003csup\u003ePI 67381\u003c/sup\u003e-7HL\u003csup\u003ePI 67381\u003c/sup\u003e), one resistant and one susceptible allele (2HS\u003csup\u003eTR 326\u003c/sup\u003e-4HL\u003csup\u003ePI 67381\u003c/sup\u003e; 2HS\u003csup\u003ePI 67381\u003c/sup\u003e-4HL\u003csup\u003eTR 326\u003c/sup\u003e) or both susceptible alleles (2HS\u003csup\u003eTR 326\u003c/sup\u003e-4HL\u003csup\u003eTR 326\u003c/sup\u003e). The Y-axis shows the disease reaction type score. The solid horizontal line in each box indicates the median while average disease reaction is indicated by a solid triangle. Categories with different letters following their average scores differed significantly at the 0.05 level of probability using Fisher’s least significant difference (LSD) test.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8961109/v1/3b6b158d04f8d44bdaff834b.jpg"},{"id":104102871,"identity":"df8236b0-7769-44a8-941b-b41a39151fc2","added_by":"auto","created_at":"2026-03-06 20:28:32","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":282071,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in disease reaction type\u003cstrong\u003e \u003c/strong\u003eat 7dpi on leaves of progeny lines of the TR 326 x PI 67381 population inoculated with \u003cem\u003eP. teres \u003c/em\u003ef. \u003cem\u003emaculata\u003c/em\u003e isolate Den2.6. Each panel shows two representative leaves of three lines belonging to the different genotypic categories harboring at Chr2HS and Chr4HL both alleles derived from the resistant parental line PI 67381 (\u003cstrong\u003ea\u003c/strong\u003e), the allele at Chr2HS derived from the susceptible parental line TR 326 and the allele at Chr4HL derived from the resistant parental line (\u003cstrong\u003eb\u003c/strong\u003e), the allele at Chr2HS derived from the resistant parental line and the allele at Chr4HL derived from the susceptible parental line (\u003cstrong\u003ec\u003c/strong\u003e), and both alleles derived from the susceptible parental line (\u003cstrong\u003ed\u003c/strong\u003e). Progeny line numbers are shown in white on the left edge of the leaves.\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8961109/v1/9613e7e6a56f8113f6919b26.jpg"},{"id":104403737,"identity":"aa8984b7-6807-4def-bc74-d586ecda6050","added_by":"auto","created_at":"2026-03-11 12:18:57","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":545629,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in symptom appearance at 7 dpi on leaves of progeny lines of the PI 392501 × PI 67381 population inoculated with P. \u003cem\u003eteres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolate Den2.6 and P-A14. Each panel show two representative leaves of three lines belonging to the different genotypic categories harboring at Chr4HS and Chr7HL either both alleles derived from the resistant parental line PI 67381 (\u003cstrong\u003ea\u003c/strong\u003e), the allele at Chr4HS derived from the susceptible parental line PI 392501 and the allele at Chr7HL derived from the resistant parental line (\u003cstrong\u003eb\u003c/strong\u003e), the allele at Chr4HS derived from the resistant parental line and the allele at Chr7HL derived from the susceptible parental line (\u003cstrong\u003ec\u003c/strong\u003e), and both alleles derived from the susceptible parental line (\u003cstrong\u003ed\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8961109/v1/fd0ba05f35ab0c66b41bbc9c.jpg"},{"id":108437864,"identity":"59af9c27-1977-4cf7-b16b-f923e47a2046","added_by":"auto","created_at":"2026-05-04 16:03:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3384253,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8961109/v1/5cf629f8-6dfe-4279-b7c5-cc9cfee94c1e.pdf"},{"id":104102874,"identity":"ac5f5cb2-70e3-40f0-888f-bfb3844cdd84","added_by":"auto","created_at":"2026-03-06 20:28:32","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1396220,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesS1S17.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8961109/v1/240a3a96e22ad51acc67b039.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global perspective on the genetic architecture of susceptibility to spot form net blotch in barley","fulltext":[{"header":"Key message","content":"\u003cp\u003eGlobal populations of f. have evolved to target multiple dominant barley susceptibility loci, highlighting the risk of widespread disease under local selection pressures.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eBarley (\u003cem\u003eHordeum vulgare\u003c/em\u003e, Poaceae, L.) is an economically important cereal crop with a total cultivation area of 48.1\u0026nbsp;million acres worldwide (Mittal \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Barley cultivation is affected by several pests and diseases, of which net blotch is one of the most destructive. Barley net blotch is a foliar disease caused by the filamentous Ascomycete \u003cem\u003ePyrenophora teres\u003c/em\u003e [anamorph \u003cem\u003eDrechslera teres\u003c/em\u003e (Sacc.) Shoem]. Its emergence and spread coincide with the history of crop domestication and cultivation (Taliadoros et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nowadays, net blotch affects barley cultivation worldwide and has been reported in Africa (Lammari et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ababa et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; El Yousfi and Brahim, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), Australia (McLean et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e), Europe (Tini et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; J\u0026oslash;rgensen et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Smedegard-Petersen, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e1971\u003c/span\u003e), North America (Adhikari et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Akhavan et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Western Asia (Dokhanchi et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Vasighzadeh et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Oğuz et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Barley net blotch occurs in two distinct forms that can be distinguished by symptom development. Net form net blotch (NFNB), caused by \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003eteres\u003c/em\u003e, is characterized by longitudinal and transverse dark-brown striations of necrotic tissue, forming a net-like pattern. By contrast, spot form net blotch (SFNB), caused by \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e, is characterized by circular to elliptical dark-brown spot-like necrotic lesions, which are surrounded by a yellowish chlorotic area (Backes et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Clare et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ellwood and Wallwork \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lightfoot and Able, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; McLean et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the last decades, the incidence of SFNB has increased considerably in barley growing regions, posing a major threat to barley production (Tomić et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lammari et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Akhavan et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Marshall et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; McLean et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e and \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e; Liu et al. 2010).\u003c/p\u003e \u003cp\u003eAs a necrotrophic pathogen, \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e secretes effector proteins to manipulate host immunity for successful infection and disease development (Liu et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Carlsen et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Clare et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Skiba et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As shown in other interactions between plants and necrotrophic fungal pathogens, effectors often act in an inverse gene-for-gene manner (Friesen et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Oliver and Solomon, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Oliver et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Faris and Friesen, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kariyawasam et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Effectors target susceptibility genes in the host to activate programmed cell death (PCD), thereby leading to plant susceptibility and allowing host colonization (Lorang et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Faris et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Lorang et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Shi et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Richards et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The strongest evidence of an inverse gene-for-gene interaction in the barley-\u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e pathosystem, came from a study conducted by Skiba et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), where it was shown that single loci associated with fungal virulence targeted a corresponding dominant locus on barley associated with SFNB susceptibility, thereby promoting host colonization.\u003c/p\u003e \u003cp\u003eBecause of the high rate of sexual recombination in natural populations of \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e, the fungal effector repertoire is rapidly evolving, enabling the pathogen to overcome host resistance and to adapt to new environmental conditions (Gupta and Loughman, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Arabi et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; McLean et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Akhavan et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Several efforts have been made to decipher the genetic complexity of the barley-\u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e interaction (reviewed in Clare et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). On the pathogen side, Carlsen et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) phenotyped a \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e mapping population derived from a cross between an Australian and a North American isolate to identify six independent loci associated with virulence, with some virulence traits conferred by each parent. Moreover, a collection of North American \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates was screened on thirty SFNB differential barley lines. Subsequent association mapping identified thirty distinct loci associated with fungal virulence, whereby one of the identified loci exhibited reciprocal virulence/avirulence with one haplotype mostly present in fungal isolates collected from Idaho (Clare et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the host side, initial studies relied on mapping of biparental barley populations to identify genetic loci associated with resistance or susceptibility to \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e. The first identified locus associated with resistance to \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e was the \u003cem\u003eRpt4\u003c/em\u003e locus, located on the long arm of barley chromosome (Chr) 7H (Williams et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e1999\u003c/span\u003e and \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Grewal et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Later, additional loci associated with resistance to \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e were identified, such as the \u003cem\u003eRpt6\u003c/em\u003e locus located on the short arm of Chr5H (Manninen et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and the \u003cem\u003eRpt8\u003c/em\u003e locus located on the short arm of Chr4H (Franckowiak and Platz, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Friesen et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Subsequently, five independent studies using a GWAS approach were conducted on distinct barley collections from different geographical regions that identified a total of 27 (Tamang et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), 29 (Wang et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), 11 (Burlakoti et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), one (Daba et al. 2019) and four (Clare et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) independent loci, respectively, associated with resistance/susceptibility to \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e. Among the loci showing an association with barley Chr7H, four major QTL (\u003cem\u003eQRptm7-4\u003c/em\u003e, \u003cem\u003eQRptm7-6\u003c/em\u003e, \u003cem\u003eQRptm7-7\u003c/em\u003e and \u003cem\u003eQRptm7-8\u003c/em\u003e) mapped to a 36 cM region (Tamang et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Even though analysis of linkage decay of the QTL identified on Chr7H might suggest that the four QTL are independent of each other, it remained unclear whether QTL on Chr7H were multiple linked loci or represented different alleles of a single gene (Tamang et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Burlakoti et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) studied the effect of two-rowed and six-rowed barley from the Upper Midwest breeding programs and identified two novel QTL associated with resistance to \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e on Chr2H, \u003cem\u003eSFNB-2H-38.08\u003c/em\u003e and \u003cem\u003eSFNB-2H-8-10\u003c/em\u003e. The second QTL encompassed three SNP markers within a 2 cM region, and the marker 12_31497 was detected in all three populations, explaining the highest \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e. Two more recent studies performed QTL mapping on biparental barley populations segregating for susceptibility to SFNB, using a set of \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates (Tamang et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Skiba et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and identified QTL on Chr2H, Chr4H, Chr6H, and Chr7H, whereby the QTL identified on Chr2H and Chr7H mapped to the same genomic positions in the different biparental populations (Tamang et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Skiba et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite significant advances in identifying host loci associated with resistance or susceptibility to SFNB, the barley genetics governing the \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e-barley interaction appear more complex than initially thought, with reports of dominant, recessive, and partial forms of both resistance and susceptibility. Given the increasing concern over \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e in barley production areas, intensified research is needed to thoroughly understand the genetic basis of host susceptibility within barley germplasm and how the host genetics correspond to the variation in pathogen virulence.\u003c/p\u003e \u003cp\u003eTo fill this gap in our understanding of this host-pathogen interaction, we used three recombinant inbred mapping populations developed by crossing a single resistant line that lacks susceptibility with three barley lines with known SFNB susceptibility. A set of 10 geographically diverse \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates, collected over five continents, was then used to phenotype these three barley mapping populations. The genetic maps, together with the phenotypic data, were used to perform QTL analyses to identify barley genomic regions that contribute to disease. These analyses enabled us to determine which pathogen isolates targeted the corresponding susceptibility loci, providing a more integrated view of both host susceptibility and the global distribution of pathogen virulence.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBarley population development and genetic map construction\u003c/h2\u003e \u003cp\u003eOne hundred and twenty-five F\u003csub\u003e2:6\u003c/sub\u003e recombinant inbred lines were developed by single-seed descent from a cross between the resistant Ethiopian barley two-row breeding line PI 67381 (Mu\u0026ntilde;oz-Amatria\u0026iacute;n et al. 2014; Neupane et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and the susceptible two-row Australian breeding line TR 326 (McLean et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), resulting in the TR 326 \u0026times; PI 67381 RIL population. One hundred and twenty-four F\u003csub\u003e2:6\u003c/sub\u003e recombinant inbred lines were also developed by single-seed descent from a cross between the same resistant Ethiopian barley line PI 67381 and the susceptible South African two-row breeding line PI 392501 (Neupane et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), resulting in the PI 392501 \u0026times; PI 67381 RIL population. TR 326 and PI 392501 were selected as parental lines due to their frequent use as differential lines in assessing the virulence of local and global \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e populations, indicating that they differ for SFNB resistance/susceptibility (Carlsen et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Clare et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDNA extraction and genotyping for barley parental and progeny lines were performed by the North Central Small Grains Genotyping Lab (Fargo, ND, USA). Genotyping was carried out using the barley 50K Illumina iSelect single-nucleotide polymorphism (SNP) array, and genotype calling was performed using GenomeStudio software v2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://support.illumina.com/array/array_software/genomestudio/downloads.html\u003c/span\u003e\u003cspan address=\"https://support.illumina.com/array/array_software/genomestudio/downloads.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) developed by Illumina (San Diego, CA, USA). Illumina genotyping of parental and progeny lines yielded 44,040 SNP markers for both the TR 326 \u0026times; PI 67381 and the PI 392501 \u0026times; PI 67381 population. Markers were filtered based on undetermined genotypes, heterozygous calls, segregation distortion (relative abundance of either parent\u0026rsquo;s genotype at each locus using a maximum allelic ratio of 3:1 and a minimum allelic ratio of 1:3 as cutoffs), and missing data (using a cutoff\u0026thinsp;\u0026gt;\u0026thinsp;30%). Mapping of the two new RIL populations, TR 326 \u0026times; PI 67381 and PI 392501 \u0026times; PI 67381, was performed using MapDisto v2.1.7 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mapdisto.free.fr/Download_Soft/\u003c/span\u003e\u003cspan address=\"http://mapdisto.free.fr/Download_Soft/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Heffelfinger et al. 2017). Markers were assembled into genetic linkage groups using the \u0026lsquo;FindGroup\u0026rsquo; command in MapDisto with a logarithm of odds (LOD) cutoff of 3.0 and a rmax of 0.3. Markers lacking chromosomal designations were assigned to their corresponding genomic positions according to the physical map of the \u003cem\u003eH. vulgare\u003c/em\u003e \u0026ldquo;MorexV3\u0026rdquo; barley genome (Cantalapiedra et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The \u0026lsquo;AutoOrder\u0026rsquo; command was used to determine the initial marker order in each linkage group. The \u0026lsquo;AutoCheckInversion\u0026rsquo;, \u0026lsquo;AutoRipple\u0026rsquo; and \u0026lsquo;DropLocus\u0026rsquo; commands were used to refine and validate the final marker order.\u003c/p\u003e \u003cp\u003eCo-segregating markers were identified from genetic maps. A single marker within each co-segregating block showing the least amount of missing data was selected and retained, and the redundant co-segregating markers were removed. The Hockett \u0026times; PI 67381 RIL population and the genetic map used in this study were generated by Skiba et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eP. teres\u003c/b\u003e \u003cb\u003ef.\u003c/b\u003e \u003cb\u003emaculata\u003c/b\u003e \u003cb\u003eisolates, inoculation assays and disease symptom analysis\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFungal isolates used in this study and their geographical origin are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The isolates P-A14 (Montana, USA), C-A17 (Montana, USA), FGOB10-Ptm1 (North Dakota, USA), ID220 (Idaho, USA), Den2.6 (Denmark), NZKF2 (New Zealand) and SG1 (Australia) were chosen as they represent previously used reference isolates (Carlsen et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tamang et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Clare et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wyatt and Friesen, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Skiba et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, three new isolates were identified from collections made in Iran (G76S), Azerbaijan (AZB_Ptm20), and Morocco (Mor4-2). These isolates were selected based on their geographic diversity and high level of virulence in a prescreening of the susceptible parental lines used in this study (data not shown). Collectively, these isolates represent a wide range of barley-growing regions, spanning North America, Australia, Europe, Western Asia, and North Africa.\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\u003eP. teres f. maculata isolates used in this study and their geographical origin.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrigin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference/supplier\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-A14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMontana, USA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWyatt and Friesen, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-A17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMontana, USA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWyatt and Friesen, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGOB10Ptm-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorth Dakota, USA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarlsen et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIdaho, USA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClare et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDen2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWyatt and Friesen, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNZKF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNew Zeeland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWyatt and Friesen, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG76S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBandare torkman Golestan, Iran\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEva Stukenbrock\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia (Provided by Simon Ellwood, Curtin University)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarlsen et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAZB_Ptm20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAzerbaijan (Qobustan Center)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobert Brueggeman\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMor4-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChoiya, Morocco\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSajid Rehman\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\u003eFungal inoculum was prepared as follows. Fungi were grown for 5 days in darkness at 20\u0026deg;C on solid medium consisting of ddH\u003csub\u003e2\u003c/sub\u003eO (75% v/v), V8 vegetable juice (25% v/v, The Campbell Company, USA), Potato Dextrose Agar (10 g \u0026times; L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, Difco, USA), BactoAgar (10 g \u0026times; L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, Criterion, USA) and CaCO\u003csub\u003e3\u003c/sub\u003e (3 g \u0026times; L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, ThermoScientific, USA). Conidiogenesis was induced upon 24-h constant exposure to white light at 20\u0026deg;C, followed by 24-h incubation in darkness at 15\u0026deg;C. Conidia were harvested in sterile ddH\u003csub\u003e2\u003c/sub\u003eO and adjusted to a density of 2000 conidia \u0026times; mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. To prevent spore clumping, 70 \u0026micro;L of Tween 20 (J.T. Baker Chemical Co.) was added for each 50 mL of conidial suspension. Barley seedlings (parental and progeny lines) were planted in racks containing 96 containers (Stuewe \u0026amp; Sons, Inc.), with barley cultivar \u0026ldquo;ND-Genesis\u0026rdquo; planted in the outside border to reduce edge effect. Barley seedlings were grown in a greenhouse for 15 days, or until secondary leaves were fully expanded. One hundred eighteen progeny lines of the Hockett \u0026times; PI 67381 and TR 326 \u0026times; PI 67381 populations, and one hundred fifteen lines of the PI 392501 \u0026times; PI 67381 population, along with the respective parental lines, were homogenously sprayed with 100 mL of conidial suspension using a paint sprayer (DeVilbiss, model# SRIPRO-635G-10). After inoculation, plants were placed in mist chambers at 100% relative humidity and 21 ℃ under 24 h of light. After 24 h, plants were transferred to growth chambers and incubated under a 12-h light cycle (500 \u0026micro;mol/m\u0026sup2;/s Photosynthetic Photon Flux Density) at 21 ℃. Disease reaction type was scored on secondary leaves at 7 days post-inoculation (dpi) based on a 1 (highly resistant) to 5 (highly susceptible) scale developed by Neupane et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Each fungal isolate was inoculated on each barley population (Hockett \u0026times; PI 67381, TR 326 \u0026times; PI 67381, and PI 392501 \u0026times; PI 67381) in three independent replicates, and the mean of the three replicates was used for further analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQTL and statistical analysis\u003c/h3\u003e\n\u003cp\u003eQTL analyses for each of the three barley RIL populations by each of the ten \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates were performed with genetic maps constructed in MapDisto, and the corresponding average disease reaction type data using Qgene v4.4.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.qgene.org/qgene/download.php\u003c/span\u003e\u003cspan address=\"https://www.qgene.org/qgene/download.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Joehanes and Nelson, 2008). QTL analyses were performed using simple interval mapping (scan interval\u0026thinsp;=\u0026thinsp;10). To establish critical logarithm of odds (LOD) thresholds at a significance level of α\u0026thinsp;=\u0026thinsp;0.01, permutation tests of 1,000 iterations were performed three times for each \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolate\u0026ndash;barley population combination. A LOD threshold was determined for each RIL population as the average value of the results obtained from the three permutation tests. The final LOD threshold for identifying significant QTL was calculated as the average LOD across all RIL populations. LOD values were plotted as line graphs using Microsoft Excel for figure presentation. The position of the identified QTL on the respective chromosome arm was determined according to the centromere position reported by Navr\u0026aacute;tilov\u0026aacute; et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eGenotypic categories and statistical analysis\u003c/h3\u003e\n\u003cp\u003eTo evaluate the contribution of each QTL to SFNB disease, we grouped progeny lines from each RIL population into genotypic categories according to the parental allele for each locus using the most significant marker associated with each QTL. Disease reaction type scores were plotted on a box-plot diagram using ggplot2 (package 4.0.2) in RStudio (version 4.4.1). Fisher\u0026rsquo;s least significant difference (LSD) test was used to assess whether the genotypic categories differed significantly in reaction type for each \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolate used. A one-way ANOVA was performed in Microsoft Excel, and the resulting data were used to determine least significant differences at α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e\n\u003ch3\u003eAnnotation of candidate genes in the QTL regions\u003c/h3\u003e\n\u003cp\u003eUsing the \u003cem\u003eH. vulgare\u003c/em\u003e pangenome database as a reference for annotated barley genes (Jayakodi et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), we obtained a list of candidate genes for each identified QTL region. To establish the QTL confidence interval, we employed composite interval mapping with forward cofactor selection in Qgene v4.4.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.qgene.org/qgene/download.php\u003c/span\u003e\u003cspan address=\"https://www.qgene.org/qgene/download.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Joehanes and Nelson, 2008), thereby refining the genomic regions associated with SFNB disease. The two outermost significant markers were selected to define the genomic boundaries, and the region between them was used to identify candidate genes. A list of the identified candidate genes is provided in Supplementary Information, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Table S2, Table S3 and Table S4.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBarley population development and genetic mapping\u003c/h2\u003e \u003cp\u003eTo achieve the goal of understanding the genetic factors governing barley susceptibility to SFNB and how they correspond to global \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e virulence, we used three barley RIL populations derived from crossing the same resistant parent (PI 67381) with three distinct susceptible parents. Two of the susceptible parents (TR 326 and PI 392501) have been used extensively as SFNB differential sources (Carlsen et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Clare et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and one (Hockett) is a popular barley cultivar in Montana, USA (Blake \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Both the TR 326 \u0026times; PI 67381 and the PI 392501 \u0026times; PI 67381 populations consisted of one hundred and eighteen F\u003csub\u003e2:6\u003c/sub\u003e progeny lines.\u003c/p\u003e \u003cp\u003eA total of 44,040 SNP markers were identified through genotyping of parental and progeny lines for both the TR 326 \u0026times; PI 67381 and the PI 392501 \u0026times; PI 67381 population. Markers for which genotypes could not be determined, as well as those with heterozygous calls, were removed, leaving 14,539 markers for the TR 326 \u0026times; PI 67381 and 14,454 markers for the PI 392501 \u0026times; PI 67381 population. Subsequent filtering for segregation distortion and missing data further reduced the marker set to 14,516 markers for the TR 326 \u0026times; PI 67381 population and 14,399 for the PI 392501 \u0026times; PI 67381 population, indicating a high marker coverage in both populations.\u003c/p\u003e \u003cp\u003eUsing the software MapDisto, markers were assembled into seven linkage groups corresponding to the seven barley chromosomes, and markers without chromosomal designation were added to the corresponding chromosomal position according to a previously generated barley map (Cantalapiedra et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). After refining the marker order, we obtained an initial draft of the genetic map for each chromosome. Subsequently, co-segregating markers were identified in the genetic maps and removed, yielding 1,276 markers for the TR 326 \u0026times; PI 67381 population (Table S5 and Table S6) and 1,285 markers for the PI 392501 \u0026times; PI 67381 population (Table S7 and Table S8) for QTL analysis. The total map size for the TR 326 \u0026times; PI 67381 was 909.67 cM with an average marker density of one marker per 1.40 cM (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), while the total map size for the PI 392501 \u0026times; PI 67381 population was 966.30 cM with an average marker density of one marker per 1.33 cM (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\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\u003eMapping statistics in the TR 326 \u0026times; PI 67381 (a) and PI 392501 \u0026times; PI 67381 (b) RIL populations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ea. TR 326 \u0026times; PI 67381.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSize (cM)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e909.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eb. PI 392501 \u0026times; PI 67381.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSize (cM)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e966.30\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\n\u003ch3\u003eInfection assays and phenotypic analysis\u003c/h3\u003e\n\u003cp\u003eTo assess differences in disease reaction types within barley germplasm and in fungal virulence across a global pathogen isolate collection, we first inoculated a set of \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates on the parental barley lines Hockett, TR 326, PI 392501 and PI 67381 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplementary Information Table S9, Table S10 and Table S11).\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\u003eAverage disease reaction type scoring upon inoculation of ten P. teres f. maculata isolates on the four barley parental lines at 7 dpi. Data are presented as the mean and standard deviation, (\u0026plusmn;SD) for three independent scoring (biological replicates) for each isolate-barley combination.\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\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{a}\\text{n}\\text{d}\\)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\\pm\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHockett\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTR 326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePI 392501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePI 67381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-A14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.17 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.33 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.50 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-A17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.33 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.67 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.00 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm0.23\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGOB10Ptm-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.33 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.83 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.50 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.17 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.17 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.33 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDen2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.17 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.33 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.33 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNZKF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.33 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.33 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.33 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.67 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG76S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.33 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.33 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.33 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.67 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.33 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.50 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.00 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAZB_Ptm20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.50 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.83 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.00 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMor4-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.33 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.67 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.83 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.50 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt 7 dpi, it was observed that parental barley lines Hockett, TR 326, and PI 392501 showed more severe symptoms compared to the resistant parental barley line PI 67381, regardless of the fungal isolate used. The average disease reaction type derived from the scoring of all fungal isolates ranged from 2.17 to 4.33 for Hockett, from 2.17 to 3.33 for TR 326, and from 2.33 to 3.33 for PI 392501, whereas the average disease reaction type for PI 67381 ranged from 1.00 to 1.67 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). On the susceptible lines Hockett, TR 326, and PI 392501, inoculated leaves showed dark brown, ellipsoidal, dry, necrotic spots surrounded by a yellow area of chlorotic tissue. With disease progression, the lesions expanded until they coalesced, causing collapse of the entire lamina (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). By contrast, inoculated leaves of the resistant line PI 67381 exhibited tiny, pinpoint brown necrotic spots that did not expand, and no surrounding tan necrotic or yellow chlorotic areas were observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition, variation in disease reaction type was observed among \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). On the susceptible lines Hockett, TR 326, and PI 39250, the most virulent isolates G76S and P-A14 showed disease reaction types ranging from 3.00 to 4.33, whereas the intermediate isolates C-A17, FGOB10Ptm-1, SG1, Mor4-2, and AZB_Ptm20 showed disease reaction type averages of 2.50 to 3.50. By contrast, the least virulent isolates NZKF2, Den2.6, and ID220 showed disease reaction type averages of 2.17 to 2.33 on the susceptible lines Hockett, TR 326, and PI 39250 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eQTL Analysis\u003c/h3\u003e\n\u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, the developed genetic maps were used in combination with disease reaction type scores to perform QTL analysis for each \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolate-barley population combination. An overview of the observed QTL pattern is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, where only barley chromosomes showing a significant association are presented. A LOD threshold of 3.5 at a significance level of α\u0026thinsp;=\u0026thinsp;0.01 was established as the average value obtained from three permutation tests conducted on each fungal isolate-barley population combination (Hockett \u0026times; PI 67381, LOD\u0026thinsp;=\u0026thinsp;3.54; TR 326 \u0026times; PI 67381, LOD\u0026thinsp;=\u0026thinsp;3.48; PI 392501 \u0026times; PI 67381, LOD\u0026thinsp;=\u0026thinsp;3.51). Across all three barley populations, a significant QTL was identified on Chr7H following inoculation with \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates P-A14, C-A17, FBOB10Ptm-1, G76S, SG1, AZB_Ptm20, and Mor4-2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). According to the centromere position reported by Navr\u0026aacute;tilov\u0026aacute; et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the Chr7H QTL was located on the long arm, and therefore, it is referred to as Chr7HL. The QTL identified on Chr7HL in the Hockett \u0026times; PI 67381 population accounted for 34.4% (LOD\u0026thinsp;=\u0026thinsp;10.51, P-A14), 24.1% (LOD\u0026thinsp;=\u0026thinsp;6.81, C-A17), 32.3% (LOD\u0026thinsp;=\u0026thinsp;9.99, FGOB10Ptm-1), 37.1% (LOD\u0026thinsp;=\u0026thinsp;11.79, G76S), 38.3% (LOD\u0026thinsp;=\u0026thinsp;11.96, SG1), 23.2% (LOD\u0026thinsp;=\u0026thinsp;6.69, AZB_Ptm20) and 42.4% (LOD\u0026thinsp;=\u0026thinsp;14.14, Mor4-2) of the variation in average disease reaction type (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and Supplementary Information Table S12). The QTL identified on Chr7HL in the TR 326 \u0026times; PI 67381 population accounted for 62.6% (LOD\u0026thinsp;=\u0026thinsp;25.64, P-A14), 64.6% (LOD\u0026thinsp;=\u0026thinsp;27.06, C-A17), 46.1% (LOD\u0026thinsp;=\u0026thinsp;15.83, FGOB10Ptm-1), 76.2% (LOD\u0026thinsp;=\u0026thinsp;36.78, G76S), 62.1% (LOD\u0026thinsp;=\u0026thinsp;25.30, SG1), 45.9% (LOD\u0026thinsp;=\u0026thinsp;15.73, AZB_Ptm20) and 64.1% (LOD\u0026thinsp;=\u0026thinsp;26.28, Mor4-2) of the disease variation (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Table S13). The QTL identified on Chr7HL in the PI 392501 \u0026times; PI 67381 population accounted for 53.7% (LOD\u0026thinsp;=\u0026thinsp;19.04, P-A14), 53.5% (LOD\u0026thinsp;=\u0026thinsp;19.10, C-A17), 49.1% (LOD\u0026thinsp;=\u0026thinsp;16.86, FGOB10Ptm-1), 66.1% (LOD\u0026thinsp;=\u0026thinsp;27.10, G76S), 57.6% (LOD\u0026thinsp;=\u0026thinsp;21.41, SG1), 49% (LOD\u0026thinsp;=\u0026thinsp;16.67, AZB_Ptm20) and 57.4% (LOD\u0026thinsp;=\u0026thinsp;21.3, Mor4-2) of the disease variation (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Table S14). In all fungal isolate-barley population interactions, the identified QTL were located at the same genomic position on Chr7HL (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, Supplementary Information, Table S12, Table S13 and Table S14). This result, along with the dominant susceptibility data reported by Skiba et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), indicates that the same Chr7HL dominant susceptibility gene is present in all three susceptible parents across the three RIL populations but is absent in PI67381. Isolates ID220, NZKF2, and Den2.6, collected from Idaho, USA, New Zealand, and Denmark, respectively, three geographically diverse barley-growing regions, do not target Chr7HL susceptibility, indicating that they lack the \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e chromosome 2 virulence reported by Skiba et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the populations Hockett \u0026times; PI 67381 and TR 326 \u0026times; PI 67381, a similarly positioned QTL was identified on Chr2H upon inoculation with \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates P-A14, FGOB10Ptm-1, ID220, Den2.6, NZKF2, and AZB_Ptm20 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), but no significant association with Chr2H was found for the isolates G76S, SG1, or Mor4-2. According to the centromeric position reported by Navr\u0026aacute;tilov\u0026aacute; et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the Chr2H QTL was located on the short arm and is therefore referred to as Chr2HS. In the Hockett \u0026times; PI 67381 population the QTL identified on Chr2HS accounted for 14.4% (LOD\u0026thinsp;=\u0026thinsp;3.88, P-A14), 26.8% (LOD\u0026thinsp;=\u0026thinsp;8.00, FGOB10Ptm-1), 31.2% (LOD\u0026thinsp;=\u0026thinsp;9.51, ID220), 27.4% (LOD\u0026thinsp;=\u0026thinsp;8.07, Den2.6), 14.5% (LOD\u0026thinsp;=\u0026thinsp;4.07, NZKF2) and 35.9% (LOD\u0026thinsp;=\u0026thinsp;11.28, AZB_Ptm20) of the disease variation (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and table S12). In the TR 326 \u0026times; PI 67381 population, the QTL identified on Chr2HS was located at the same genomic position as in the Hockett \u0026times; PI 67381 and accounted for 12.7% (LOD\u0026thinsp;=\u0026thinsp;3.54 P-A14), 22.5% (LOD\u0026thinsp;=\u0026thinsp;6.52, FGOB10Ptm-1), 36.9% (LOD\u0026thinsp;=\u0026thinsp;11.79, ID220), 13.5% (LOD\u0026thinsp;=\u0026thinsp;3.77, Den2.6), 14.5% (LOD\u0026thinsp;=\u0026thinsp;4.07, NZKF2) and 21.7% (LOD\u0026thinsp;=\u0026thinsp;6.27, AZB_Ptm20) of the disease variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, and Supplementary Information, Table S12 and Table S13). Notably, the isolate C-A17 showed a significant association with Chr2HS on the Hockett \u0026times; PI 67381, accounting for 15.1% (LOD\u0026thinsp;=\u0026thinsp;4.05) of the disease variation, but no significant corresponding QTL associated with Chr2HS was detected when C-A17 was inoculated on the TR 326 \u0026times; PI 67381 population (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). No significant association with Chr2HS was identified in the PI 392501 \u0026times; PI 67381 population (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Table S14). Collectively, these results indicate that isolates P-A14, C-A17, FGOB10Ptm-1, ID220, Den2.6, NZKF2, and AZB_Ptm20 each harbor the \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e chromosome 1 virulence targeting Chr2HS present in Hockett (Skiba et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and that the corresponding barley Chr2HS susceptibility gene is also present in TR 326, but absent in PI 392501.\u003c/p\u003e \u003cp\u003eIn the TR 326 \u0026times; PI 67381 and PI 392501 \u0026times; PI 67381 populations, independent QTL were identified at different genomic positions on Chr4H upon inoculation with \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates Den2.6 and NZKF2. However, none of the fungal isolates used in this study showed a significant association with Chr4H when the Hockett \u0026times; PI 67381 population was inoculated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). In the PI 392501 \u0026times; PI 67381 population, the Chr4H QTL was located on the short arm, according to the centromere position reported by Navr\u0026aacute;tilov\u0026aacute; et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and therefore, it is referred to as Chr4HS. By contrast, in the TR 326 \u0026times; PI 67381 population, the QTL was located on the distal end of the long arm of Chr4H and is therefore referred to as Chr4HL (Navr\u0026aacute;tilov\u0026aacute; et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The Chr4HL QTL in the TR 326 \u0026times; PI 67381 population accounted for 41.2% (LOD\u0026thinsp;=\u0026thinsp;13.84, Den2.6) and 24.4% (LOD\u0026thinsp;=\u0026thinsp;7.28, NZKF2) of the disease variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Table S13), whereas in the PI 392501 \u0026times; PI 67381 population, the QTL identified on Chr4HS accounted for 25.7% (LOD\u0026thinsp;=\u0026thinsp;7.67, Den2.6) and 26.8% (LOD\u0026thinsp;=\u0026thinsp;9.96, NZKF2) of the disease variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Table S14). Notably, the \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e Idaho isolate ID220 also showed a significant association with the Chr4HS locus in the PI 392501 \u0026times; PI 67381 population, accounting for 35.1% (LOD\u0026thinsp;=\u0026thinsp;11.64) of the disease variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Table S14). However, no significant association with Chr4HL was identified when ID220 was inoculated on the TR 326 \u0026times; PI 67381 population (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The results indicate that ID220, Den2.6, and NZKF2 produce the same effector that targets the Chr4HS susceptibility allele harbored by PI 392501, whereas an independent effector produced only by NZKF2 and Den2.6 targets the susceptibility allele on Chr4HL contributed by TR 326.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eR2 and LOD values (LOD values in parentheses) calculated for the significant QTL at the \u0026thinsp;=\u0026thinsp;0.01 level (calculated LOD threshold\u0026thinsp;=\u0026thinsp;3.5), identified on Chr2HS, Chr4HS, and Chr7HL, respectively, upon inoculation of each P. teres f. maculata isolate on the Hockett \u0026times; PI 67381 population. R2 and LOD values of non-significant (NS) associations are shown in Supplementary Information, Table S12.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChr2HS\u003c/p\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e (LOD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr4HS\u003c/p\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e (LOD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChr7HL\u003c/p\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e (LOD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-A14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.144 (3.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.344 (10.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-A17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.151 (4.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.241% (6.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGOB10Ptm-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.225 (6.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.323 (9.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.369 (11.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDen2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.274 (8.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNZKF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.362 (11.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG76S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.371 (11.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.383 (11.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAZB_Ptm20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.359 (11.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.232 (6.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMor4-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.424 (14.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eR2 and LOD values (LOD values in parenthesis) calculated for the significant QTL at the \u0026thinsp;=\u0026thinsp;0.01 level (calculated LOD threshold\u0026thinsp;=\u0026thinsp;3.5) identified on Chr2HS, Chr4HL, and Chr7HL, respectively, upon inoculation of each P. teres f. maculata isolate on the TR 326 \u0026times; PI 67381 population. R2 and LOD values of non-significant (NS) associations are shown in Supplementary Information, Table S13.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChr2HS\u003c/p\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e (LOD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr4HL\u003c/p\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e (LOD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChr7HL\u003c/p\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e (LOD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-A14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.127 (3.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.626 (25.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-A17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.646 (27.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGOB10Ptm-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.268 (8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.461 (15.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.312 (9.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDen2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.135 (3.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.412 (13.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNZKF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.145 (4.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.244 (7.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG76S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.762 (36.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.621 (25.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAZB_Ptm20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.217 (6.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.459 (15.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMor4-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.641 (26.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eR2 and LOD values (LOD values in parenthesis) calculated for the significant QTL at the \u0026thinsp;=\u0026thinsp;0.01 level (calculated LOD threshold\u0026thinsp;=\u0026thinsp;3.5) identified on Chr2HS, Chr4HS, and Chr7HL, respectively, upon inoculation of each P. teres f. maculata isolate on the PI 392501 \u0026times; PI 67381 population. R2 and LOD values of non-significant (NS) associations are shown in Supplementary Information, Table S14.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChr2HS\u003c/p\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e (LOD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr4HS\u003c/p\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e (LOD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChr7HL\u003c/p\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e (LOD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-A14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.537 (19.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-A17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.535 (19.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGOB10Ptm-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.491 (16.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.351 (11.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDen2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.257 (7.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNZKF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.268 (9.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG76S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.661 (27.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.576 (21.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAZB_Ptm20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49 (16.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMor4-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.574 (21.32)\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=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of candidate genes in the QTL regions\u003c/h2\u003e \u003cp\u003eGenetic mapping showed that the QTL region identified on Chr2HS was at the same genomic position in both the Hockett \u0026times; PI 67381 and TR326 \u0026times; PI 67381 populations, and the Chr7HL QTL mapped to the same genomic position in all three barley populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the TR 326 \u0026times; PI 67381 population, the Chr2HS QTL region spanned a confidence interval of 1.1 Mb (data from inoculation with isolate ID220), with the most significant marker associated with variation in disease reaction being JHI-Hv50k-2016-67492 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e3). In this QTL interval, twenty-four genes were annotated, which included genes predicted to encode cell membrane-located nitrogen, magnesium and sugar transporters, proteins involved in sugar and protein metabolism, signal transduction-related proteins, two transcription factors, a terpene synthase, and one receptor protein of nucleotide binding site-leucin rich repeat (NBS-LRR) type associated with disease (accession number HORVU.MOREX.r3.2HG0102530.1), which represented the most likely gene candidate (Supplementary Information, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn Chr4H, the Chr4HS QTL identified in the PI 392501 \u0026times; PI 67381 population was the largest of the QTL intervals, spanning 46.4 Mb (data from inoculation with isolate ID220). The most significant marker associated with disease reaction in this region was BOPA1_3644\u0026thinsp;\u0026minus;\u0026thinsp;1483 (Supplementary Information, Table S14). This QTL region contained three hundred eighty-one annotated genes which were predicted to encode proteins involved in diverse cellular processes, including sugar, protein, and lipid metabolism, light and salt stress responses, biosynthesis and modification of cell wall components, DNA replication and transcriptional activity, ion uptake, intracellular vesicle trafficking, biosynthesis of secondary metabolites, and immunity (Supplementary Information, Table S2). Of these, the most likely candidates included genes encoding proteins involved in Shikimate pathways, such as the chalcone synthase B (accession number HORVU.MOREX.r3.4HG0389560.1) and a chorismate synthase (accession number HORVU.MOREX.r3.4HG0391140.1) (Table S2).\u003c/p\u003e \u003cp\u003eThe Chr4HL QTL region identified in the TR 326 \u0026times; PI 67381 population spanned 3.4 Mb (data from inoculation with Den2.6), and the most significant marker associated with variation in disease reaction was JHI-Hv50k-2016-272241 (Table S13). This QTL region harbored sixty-seven annotated genes predicted to encode proteins involved in cell division, jasmonic acid and auxin signaling, nuclear and cytoskeletal structure, post-translational modifications and lipid metabolism, sugar and ion transporters, three transcription factors, and two genes annotated as receptor-like proteins. The two genes annotated as receptor-like proteins represent the most likely candidates (accession numbers HORVU.MOREX.r3.4HG0415260.1 and HORVU.MOREX.r3.4HG0415460.1) (Table S3).\u003c/p\u003e \u003cp\u003eThe Chr7HL QTL region identified in the TR326 \u0026times; PI 67381 was the most significant association among the three populations evaluated. The QTL region in the TR326 \u0026times; PI 67381 population spanned 1.0 Mb (data from inoculation with isolate G76S), and the most significant marker associated with variation in disease reaction was JHI-Hv50k-2016-501105 (Supplementary Information, Table S13). This QTL region contained twenty-seven annotated genes predicted to encode proteins involved in the metabolism and signaling of plant hormones, three proteases from different classes, proteins related to heavy metal stress tolerance and secondary metabolic pathways, and six proteins related to immunity (Supplementary Information, Table S4). Of these genes, the most likely candidates encode proteins related to disease resistance, such as the Enhanced Disease Resistance 2 protein (accession number HORVU.MOREX.r3.7HG0747430.1) and an NBS-LRR type receptor protein (accession number HORVU.MOREX.r3.7HG0747370.1) (Table S4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe identified loci contribute differently to SFNB susceptibility\u003c/h2\u003e \u003cp\u003eThe QTL analysis identified four distinct loci associated with susceptibility to SFNB, with some fungal isolates showing significant associations with multiple loci within a specific barley population (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Therefore, we assessed the contribution of each QTL to disease, both individually and in combination. Progeny lines were first grouped into genotypic categories based on their parental alleles for each significant QTL, and average disease reaction types were compared across the different genotypic categories in a \u0026ldquo;head-to-head\u0026rdquo; fashion. This comparison was conducted only for fungal isolate-barley population combinations showing an association with two distinct loci.\u003c/p\u003e \u003cp\u003eIn the Hockett \u0026times; PI 67381 population, independent associations were identified at Chr2HS and Chr7HL (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Accordingly, progeny lines were grouped into four categories based on the alleles at Chr2HS and Chr7HL contributed by the parental lines PI 67381 and Hockett. These groups included progeny lines harboring neither susceptibility allele (Chr2HS\u003csup\u003ePI 67381\u003c/sup\u003e-Chr7HL\u003csup\u003ePI 6738\u003c/sup\u003e), one or the other susceptibility allele (Chr2HS\u003csup\u003eHockett\u003c/sup\u003e-Chr7HL\u003csup\u003ePI 67381\u003c/sup\u003e or Chr2HS\u003csup\u003ePI 67381\u003c/sup\u003e-Chr7HL\u003csup\u003eHockett\u003c/sup\u003e), or both susceptibility alleles (Chr2HS\u003csup\u003eHockett\u003c/sup\u003e-Chr7HL\u003csup\u003eHockett\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Upon inoculation of the Hockett \u0026times; PI 67381 population with isolates targeting both Chr2HS and Chr7HL, P-A14, C-A17, FGOB10Ptm-1 and AZB_Ptm20, it was observed that, except for P-A14, Chr2HS\u003csup\u003ePI 67381\u003c/sup\u003e-Chr7HL\u003csup\u003eHockett\u003c/sup\u003e lines, showed no significant difference in disease reaction (average score P-A14\u0026thinsp;=\u0026thinsp;2.83; C-A17\u0026thinsp;=\u0026thinsp;2.51; FGOB10Ptm-1\u0026thinsp;=\u0026thinsp;2.47; AZB_Ptm20\u0026thinsp;=\u0026thinsp;2.54) when compared to Chr2HS\u003csup\u003eHockett\u003c/sup\u003e-Chr7HL\u003csup\u003ePI 67381\u003c/sup\u003e lines (average score P-A14\u0026thinsp;=\u0026thinsp;2.39; C-A17\u0026thinsp;=\u0026thinsp;2.40; FGOB10Ptm-1\u0026thinsp;=\u0026thinsp;2.40; AZB_Ptm20\u0026thinsp;=\u0026thinsp;2.70) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and Table S15). However, disease reactions were significantly higher in progeny lines possessing both susceptibility alleles (Chr2HS\u003csup\u003eHockett\u003c/sup\u003e-Chr7HL\u003csup\u003eHockett\u003c/sup\u003e lines average score P-A14\u0026thinsp;=\u0026thinsp;3.37; C-A17\u0026thinsp;=\u0026thinsp;2.91; FGOB10Ptm-1\u0026thinsp;=\u0026thinsp;2.95; AZB_Ptm20\u0026thinsp;=\u0026thinsp;3.11), compared to lines harboring only one susceptibility allele (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and Table S15).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the TR 326 \u0026times; PI 67381 population, three distinct QTL were identified, although none of the isolates showed a significant association with more than two loci (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Therefore, for the isolates targeting Chr2HS and Chr7HL, P-A14, FGOB10Ptm-1, and AZB_Ptm20, progeny lines were grouped into four categories based on the alleles at Chr2HS and Chr7HL contributed by the parental lines PI 67381 and TR 326. These groups included progeny lines harboring neither susceptibility allele (Chr2HS\u003csup\u003ePI 67381\u003c/sup\u003e-Chr7HL\u003csup\u003ePI 67381\u003c/sup\u003e), one or the other susceptibility allele for Chr2HS and Chr7HL (Chr2HS\u003csup\u003eTR 326\u003c/sup\u003e-Chr7HL\u003csup\u003ePI 67381\u003c/sup\u003e or Chr2HS\u003csup\u003ePI 67381\u003c/sup\u003e-Chr7HL\u003csup\u003eTR 326\u003c/sup\u003e), or both susceptibility alleles (Chr2HS\u003csup\u003eTR 326\u003c/sup\u003e-Chr7HL\u003csup\u003eTR 326\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Upon inoculation of the TR 326 \u0026times; PI 67381 population with isolates targeting both Chr2HS and Chr7HL (P-A14, FGOB10Ptm-1 and AZB_Ptm20) progeny lines carrying the Chr7HL susceptibility allele alone (Chr2HS\u003csup\u003ePI 67381\u003c/sup\u003e-Chr7HL\u003csup\u003eTR 326\u003c/sup\u003e) showed significantly higher disease reaction (average score P-A14\u0026thinsp;=\u0026thinsp;3.01; FGOB10Ptm-1\u0026thinsp;=\u0026thinsp;2.63; AZB_Ptm20\u0026thinsp;=\u0026thinsp;2.68) than lines carrying only the Chr2HS susceptibility allele (Chr2HS\u003csup\u003eTR326\u003c/sup\u003e-Chr7HL\u003csup\u003ePI 67381\u003c/sup\u003e) (average score P-A14\u0026thinsp;=\u0026thinsp;2.37; FGOB10Ptm-1\u0026thinsp;=\u0026thinsp;2.41; AZB_Ptm20\u0026thinsp;=\u0026thinsp;2.20) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Table S16). As observed in the Hockett \u0026times; PI 67381 population, progeny lines possessing both susceptibility alleles (Chr2HS\u003csup\u003eTR 326\u003c/sup\u003e-Chr7HL\u003csup\u003eTR 326\u003c/sup\u003e) exhibited significantly higher disease reactions than lines harboring only one allele (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, and Table S16).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing the same strategy, for the isolates targeting Chr2HS and Chr4HL (Den2.6 and NZKF2), progeny lines were grouped into four categories based on these alleles contributed by the parental lines PI 67381 and TR 326. These groups included progeny lines harboring neither susceptibility allele (Chr2HS\u003csup\u003ePI 67381\u003c/sup\u003e-Chr4HL\u003csup\u003ePI 67381\u003c/sup\u003e), one or the other susceptibility allele for Chr2HS or Chr4HL (Chr2HS\u003csup\u003eTR 326\u003c/sup\u003e-Chr4HL\u003csup\u003ePI 67381\u003c/sup\u003e and Chr2HS\u003csup\u003ePI 67381\u003c/sup\u003e-Chr4HL\u003csup\u003eTR 326\u003c/sup\u003e), or both susceptibility alleles (Chr2HS\u003csup\u003eTR 326\u003c/sup\u003e-Chr4HL\u003csup\u003eTR 326\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, and Table S17). When the isolate Den2.6 was inoculated on the TR326 \u0026times; PI 67381 population, progeny lines carrying the Chr4HL susceptibility allele alone (Chr2HS\u003csup\u003ePI 67381\u003c/sup\u003e-Chr4HL\u003csup\u003eTR 326\u003c/sup\u003e) showed an average disease reaction of 2.08, which was significantly higher compared to an average disease reaction of 1.77 scored for Chr2HS\u003csup\u003eTR 326\u003c/sup\u003e-Chr4HL\u003csup\u003ePI 67381\u003c/sup\u003e lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). By contrast, upon inoculation with NZKF2, no significant difference was observed between lines carrying either single susceptibility allele (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), indicating a comparable contribution of the loci. For both isolates, progeny lines possessing both susceptibility alleles showed higher disease reaction (Chr2HS\u003csup\u003eTR 326\u003c/sup\u003e-Chr4HL\u003csup\u003eTR 326\u003c/sup\u003e lines average score Den2.6\u0026thinsp;=\u0026thinsp;2.31; NZKF2\u0026thinsp;=\u0026thinsp;2.17) compared to lines harboring only one of the susceptibility alleles (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, and Table S17).\u003c/p\u003e \u003cp\u003eCollectively, these observations suggest that the four loci (Chr2HS, Chr4HS, Chr4HL, and Chr7HL) act independently but are synergistic in their contribution to susceptibility when present in the same progeny line. Our results also suggest that, in these populations, the Chr7HL locus contributes more to disease than the Chr2HS, Chr4HS, and Chr4HL loci (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDifferent susceptibility loci contribute differently to leaf symptoms\u003c/h2\u003e \u003cp\u003eOur results suggest that Chr7HS susceptibility provided a quantitatively greater contribution to disease than the loci associated with susceptibility mapping to Chr2HS, Chr4HS, and Chr4HL, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Additionally, in the PI 392501 \u0026times; PI 67381 population, we observed distinct symptom types associated with the different susceptibility loci. When progeny lines harboring Chr4HS susceptibility were inoculated with an isolate targeting Chr4HS (e.g., Den2.6), leaves showed tiny dot-like brown lesions surrounded by an area of yellow, pale chlorotic tissue that expanded over time, covering most of the lamina (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb and Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ed). By contrast, when progeny lines harboring Chr7HL susceptibility were inoculated with an isolate targeting Chr7HL (e.g., P-A14), leaves showed circular to elliptical brown, dry necrotic lesions that expanded with time (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ec and Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ed). This might indicate that, in the PI 392501 \u0026times; PI 67381 population, Chr4HS susceptibility was associated with yellow leaf chlorosis, whereas Chr7HL susceptibility was associated with brown leaf necrosis. Therefore, Chr4HS and Chr7HL susceptibility might be targeted by fungal effectors that trigger distinct physiological host responses upon effector recognition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSFNB poses a significant threat to barley production. Disease management strategies rely on the application of protective chemicals and biocontrol agents (reviewed in Backes et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Abebe, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Helps et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, both strategies have limitations: chemical control can lead to the development of fungicide resistance in the pathogen population, while biocontrol agents can be negatively affected by environmental factors and require lengthy, complex experiments for assessment (Backes et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; El-Saadony et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Effective and durable resistance can be achieved by deploying barley cultivars that harbor SFNB resistance or lack susceptibility genes. However, both the complex nature of this host-pathogen genetic interaction, as well as the genetic diversity in the \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e population at a global level, have been significant obstacles to identifying, characterizing, and maintaining durable sources of genetic resistance (reviewed in Clare et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Clare et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Carlsen et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBecause we previously showed that the SFNB system predominantly conforms to an inverse gene-for-gene interaction where pathogen effectors target specific dominant susceptibility loci (Tamang et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Skiba et al.2022), we generated three barley RIL mapping populations using three distinct susceptible barley lines, each crossed to a single resistant line that lacked susceptibility for use in this study. These segregating mapping populations were evaluated for resistance/susceptibility with a set of 10 globally collected \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates spanning five continents, including two isolates collected near the barley center of diversity (Taliadoros et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We used these isolates to obtain disease reactions across the barley populations, followed by QTL analysis to identify host loci significantly associated with SFNB susceptibility/resistance.\u003c/p\u003e \u003cp\u003eTR 326 and PI 392501 were selected as susceptible parental lines since they represented new differential lines that showed variation in disease reaction upon inoculation with different \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates (Carlsen et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Clare et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), whereas Hockett is a popular North American barley cultivar due to its valuable agronomic traits (Blake \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This experimental design was chosen to determine whether the three susceptible lines possessed the same or different susceptibility genes. \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates collected from geographically diverse barley growing regions were chosen because the effector repertoire of geographically distinct fungal populations evolves to overcome the selection pressure imposed by the host resistance/susceptibility genes present in the locally planted barley cultivars (Karki and Sharp \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Tekauz \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Arabi et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Gupta et al. 2011; McLean et al. 2010b).\u003c/p\u003e \u003cp\u003eWe observed diversity in QTL patterns across fungal isolate-barley population combinations and identified both isolate- and line-specific associations with susceptibility loci, with contributions to disease severity varying quantitatively in some cases. In total, four independent loci associated with SFNB susceptibility were identified, with these quantitative associations mapping to barley Chr2HS, Chr4HS, Chr4HL, and Chr7HL.\u003c/p\u003e \u003cp\u003eAll three RIL populations showed a significant association with Chr7HL at the same genomic position, whereby the most significant markers associated with phenotypic variation were JHI-Hv50k-2016-502956 for the Hockett \u0026times; PI 67381 population (Table S12), JHI-Hv50k-2016-501105 for the TR 326 \u0026times; PI 67381 (Table S13), and JHI-Hv50k-2016-501140 for the PI 392501 \u0026times; PI 67381 (Table S14). This indicates that the same susceptibility gene is likely present in the three susceptible parents, Hockett, TR 326, and PI 392501.\u003c/p\u003e \u003cp\u003eThe long arm of Chr7H has long been known to harbor an important genetic locus involved in the barley-\u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e interaction. The first locus associated with phenotypic variation in SFNB disease reaction, \u003cem\u003eRpt4\u003c/em\u003e, was reported on the long arm of Chr7H and mapped to the same genetic position as our Chr7HL QTL (Williams et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In their research, Williams et al. defined \u003cem\u003eRpt4\u003c/em\u003e as a dominant resistance locus effective against SFNB, based on phenotyping F\u003csub\u003e1\u003c/sub\u003e individuals. The data were not presented, so we do not know how many individuals were tested, nor do we have information on the phenotypic response (Williams et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConversely, more recent studies have shown that, when the F\u003csub\u003e2\u003c/sub\u003e progenies were evaluated on two populations (Tamang et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and an individual population (Skiba et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the Chr7H locus segregated in a 3:1 (susceptible: resistant) ratio, indicating that \u003cem\u003eRpt4\u003c/em\u003e represents a dominant locus associated with SFNB susceptibility, rather than resistance (Tamang et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Skiba et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This observation is in line with the findings reported in the interactions between wheat and the closely related necrotrophic fungal pathogens \u003cem\u003ePyrenophora tritici-repentis\u003c/em\u003e and \u003cem\u003eParastagonospora nodorum\u003c/em\u003e, where it was shown that the genetics of these pathosystems was governed primarily by dominant susceptibility loci in the host (reviewed in Kariyawasam et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Peters-Haugrud et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Friesen and Faris \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Faris and Friesen \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Four additional studies have identified a QTL associated with SFNB susceptibility mapping to Chr7H at the \u003cem\u003eRpt4\u003c/em\u003e locus, including \u003cem\u003eQRpt7\u003c/em\u003e reported by Grewal et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), \u003cem\u003eQRptm7-3\u003c/em\u003e reported by Wang et al. (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), the \u003cem\u003eQRptm-7H-119-137\u003c/em\u003e reported by Tamang et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and the \u003cem\u003eQRptm-7H-96-107\u003c/em\u003e QTL reported by Alhashel et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, the Chr7H QTL reported by Tamang et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) was identified in two RIL populations derived from crosses between the same resistant line used in this study, PI 67381, and two susceptible cultivars, Pinnacle and Tradition, indicating segregation of the same gene associated with SFNB susceptibility. According to Skiba et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Chr7H susceptibility was associated with a virulence that was mapped to \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e chromosome 2. In the current study, except for the Idaho, Danish, and New Zealand isolates, all \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates used in this study showed an association with barley Chr7HL in all three RIL populations, indicating that these isolates harbor the same effector gene located on \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e chromosome 2 as identified by Skiba et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA significant QTL on Chr2HS was identified in the Hockett \u0026times; PI 67381 and TR 326 \u0026times; PI 67381 populations, with each QTL mapping to the same genetic position. The most significant markers associated with phenotypic variation were JHI-Hv50k-2016-65583 for the Hockett \u0026times; PI 67381 population (Table S12) and JHI-Hv50k-2016-67492 for the TR 326 \u0026times; PI 67381 (Table S13). The Chr2HS QTL localized to the same region as the \u003cem\u003eSFNB-2H-8-10\u003c/em\u003e QTL reported in an association mapping study that included breeding lines from the Upper Midwestern US breeding programs (Burlakoti et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In addition, our Chr2HS QTL mapped to the same position as the \u003cem\u003eQRptm-2H-1-31\u003c/em\u003e QTL identified by Tamang et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in three different RIL populations and as the Chr2H QTL detected by Skiba et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in the QTL analysis on Hockett \u0026times; PI 67381. Consequently, despite their diverse geographic origins, all isolates targeting Chr2HS in Hockett \u0026times; PI 67381 and TR 326 \u0026times; PI 67381, namely P-A14, FGOB10Ptm-1, AZB_Ptm20, and C-A17, only in the Hockett \u0026times; PI 67381 population, likely harbor the same effector gene on \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e chromosome 1 as reported by Skiba et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe noticed that the Chr2HS QTL identified in the Hockett \u0026times; PI 67381 population accounted for a larger phenotypic variation compared to the same locus identified in the TR 326 \u0026times; PI 67381 population when the RIL populations were inoculated with isolates P-A14, ID220, and AZB_Ptm20 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This indicates that the barley lines Hockett and TR 326 likely possess the same gene but distinct alleles conferring Chr2HS susceptibility, with the Hockett allele providing a more substantial contribution to disease development, possibly due to a stronger pathogen effector-host target association. Alternatively, the effects of the Chr2HS gene may be influenced by differences in genetic background across populations. Uniquely, the Azerbaijan isolate AZB_Ptm20 was the only isolate to induce a more significant QTL associated with the Chr2HS locus than the Chr7HL locus, suggesting that the \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e effector alleles at Chr1 and Chr2 may differ in this isolate compared to the other isolates used here. Cloning and validation of these genes would allow us to characterize the variability at these effector gene loci as well as provide insight on the evolution at these loci.\u003c/p\u003e \u003cp\u003eIn the Hockett \u0026times; PI 67381 population, both isolates, P-A14 and C-A17, showed a significant association with Chr2HS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the TR 326 \u0026times; PI 67381 population P-A14 also showed a significant association with the same Chr2HS locus; however, no association with Chr2HS was observed when C-A17 was inoculated on this population (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This might suggest that, although both isolates originated in Montana (USA), the virulence alleles at the chromosome 1 locus likely differ between C-A17 and P-A14, with the P-A14 allele contributing more to virulence than the C-A17 allele. This situation has been shown previously in a study on the allelic diversity of the \u003cem\u003eP. nodorum\u003c/em\u003e necrotrophic effector gene \u003cem\u003eSnTox5\u003c/em\u003e, in which isolates harboring different \u003cem\u003eSnTox5\u003c/em\u003e alleles showed significantly different levels of virulence on wheat lines carrying the corresponding susceptibility locus \u003cem\u003eSnn5\u003c/em\u003e (Kariyawasam et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Validation of this hypothesis will require the cloning and functional characterization of this gene and its protein.\u003c/p\u003e \u003cp\u003eTwo significant associations with Chr4H were identified in this study. One of them, mapping to the short arm of Chr4H, was detected only in the PI 392501 \u0026times; PI 67381 population after inoculation with the \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates Den2.6, NZKF2, and ID220, with BOPA1_3644\u0026thinsp;\u0026minus;\u0026thinsp;1483 as the most closely associated marker (Table S14). Given the same genetic position on Chr4HS and the use of the same isolates Den2.6 and NZKF2, this locus likely corresponds to the \u003cem\u003eQRptm-4H-120-125\u003c/em\u003e QTL identified on Chr4H in the RIL population Tradition \u0026times; PI 67381 (Tamang et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moreover, the Chr4HS QTL was mapped to a similar position as the \u003cem\u003eQRpts4\u003c/em\u003e locus identified by Grewal et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), which was associated with resistance against both forms of \u003cem\u003eP. teres\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eDen2.6, NZKF2 and ID220 were collected in Denmark, New Zealand, and Idaho, USA, respectively. This indicates that virulence targeting 4HS occurs in pathogen populations across three continents and could become widespread if barley cultivars with the corresponding 4HS susceptibility were broadly grown.\u003c/p\u003e \u003cp\u003eAnalogously, the QTL detected on the long arm of Chr4H in the TR 326 \u0026times; PI 67381 population, with JHI-Hv50k-2016-272241 as the most significant marker (Table S13), likely corresponds to the QTL \u003cem\u003eQRptm-4H-120-125\u003c/em\u003e identified by Tamang et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in the Pinnacle \u0026times; PI 67381 population that was detected when the population was inoculated with the \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates Den2.6 and NZKF2. This indicates that Den2.6, NZKF2, and ID220 secrete an effector that targets the same susceptibility present on the short arm of Chr4H in the barley lines PI 392501 and Tradition, whereas a different effector produced by Den2.6 and NZKF2 targets a different susceptibility gene on the long arm of Chr4H in the barley lines TR 326 and Pinnacle.\u003c/p\u003e \u003cp\u003eOur results showed that each of the four identified QTL represented an independent association with SFNB susceptibility, but they did not contribute equally to symptom development. \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates targeting Chr7HL, including P-A14, C-A17, FGOB10Ptm-1, G76S, SG1, Mor4-2, and AZB_Ptm20, were more virulent on all three susceptible parental lines than isolates showing no association with Chr7HL (ID220, Den2.6, and NZKF2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In addition, for the TR 326 \u0026times; PI 67381 populations, when average disease reaction was compared between the different genotypic categories, progeny lines harboring only the Chr7HL susceptible allele showed significantly higher average disease reaction compared to progeny lines harboring only the Chr2HS susceptible allele (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Therefore, these data indicate that the association with Chr7HL susceptibility made the greatest contribution to SFNB symptom development relative to any of the other associations and that the most virulent \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates harbor a conserved effector that targets Chr7HL susceptibility.\u003c/p\u003e \u003cp\u003eAnalysis of Tradition \u0026times; PI 67381 (Tamang et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Hockett \u0026times; PI 67381 (Skiba et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) F\u003csub\u003e2\u003c/sub\u003e populations previously showed that, at the barley Chr7HL and Chr2HS loci, progeny lines segregated in a 3:1 (susceptible to resistant) ratio at both the Chr7H and Chr2H loci following inoculation with \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates P-A14 and FGOB10-Ptm1, indicating that the Chr7HL and Chr2HS genes are dominant for susceptibility. In addition, when analyzing phenotypic data of RIL populations showing multiple loci associated with susceptibility, we observed that the different QTL acted synergistically, where progeny lines harboring both susceptibility alleles showed significantly higher disease reaction scores than lines harboring only one susceptibility allele (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These observations support the hypothesis that the \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e -barley pathosystem is governed by multiple inverse gene-for-gene interactions analogous to the \u003cem\u003eP. nodorum-\u003c/em\u003ewheat and \u003cem\u003eP. tritici-repentis-\u003c/em\u003ewheat interactions, whereby multiple necrotrophic effectors target a corresponding dominant susceptibility gene product to promote disease development in an additive manner (reviewed in Kariyawasam et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Peters-Haugrud et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Friesen and Faris \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Faris and Friesen \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurther analysis of disease phenotypes revealed locus-specific susceptibility responses. Association with Chr2HS and Chr7HL each resulted in expanding brown necrotic spots in susceptible progeny lines across all three RIL populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). A possible explanation for this phenotype can be inferred from the observation that barley leaf cells accumulate polyphenolic compounds upon fungal infection (Ishihara et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ube et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2019\u003c/span\u003e and \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). During PCD, polyphenolic compounds are released from the vacuole and oxidized into brown melanin pigments through phenoloxidase activity (Boeckx et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tilley et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; reviewed in Hatsugai et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Mayer \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Therefore, based on this information, we could hypothesize that the necrotic phenotype associated with Chr2HS and Chr7HL susceptibility relies on a physiological response that initiates with the induction of PCD, followed by the release and oxidation of phenolic compounds, leading to the formation of melanin that becomes visible as tissue browning. Further work is needed to validate this hypothesis.\u003c/p\u003e \u003cp\u003eBoth the Chr2HS and Chr7HL QTL contain barley genes encoding proteins related to disease resistance, such as Toll/interleukin-1/Nucleotide-binding site-leucine-rich repeat (TIR/NBS-LRR) receptor-like proteins, which represent interesting candidate genes capable of triggering host PCD induction upon recognition of the cognate fungal effector (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Table S4). Ongoing work includes fine-scale genetic mapping of susceptibility genes on Chr2HS and Chr7HL and functional analyses to determine the roles of these candidate genes (Alhashel et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast to Chr2HS and Chr7HL susceptibility, the association with Chr4HS observed in progeny lines of the PI 392501 \u0026times; PI 67381 population resulted in the development of tiny brown necrotic spots surrounded by an expanding area of yellow chlorotic tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This phenotype is reminiscent of the light-dependent chlorotic response (Strelkov et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) caused by the interaction between the host susceptibility gene \u003cem\u003eTsc2\u003c/em\u003e and the effector Ptr ToxB produced by the wheat fungal pathogen \u003cem\u003eP. tritici-repentis\u003c/em\u003e (Friesen and Faris, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). However, to the best of our knowledge, no Ptr ToxB orthologs are present in \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e, and no \u003cem\u003eTsc2\u003c/em\u003e orthologs have been annotated in barley. Intriguingly, our results indicate that the most significant marker associated with the Chr4HS QTL mapped to a barley gene annotated as chorismate synthase (accession number HORVU.MOREX.r3.4HG0391140.1; Table S2). Chorismate synthases are involved in the biosynthesis of photoactive pigments and defense-related secondary metabolites (reviewed in Hubrich et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Kretschmer et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). If indeed this is the targeted gene in barley, it could be hypothesized that the \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e effector targeting chorismate synthase on Chr4HS could perturb physiological processes involved in early host defense responses, thereby promoting fungal colonization during the initial phase of infection and leading to the formation of chlorotic lesions. Functional analysis of the barley chorismate synthase will help to better understand the mechanism of the host response.\u003c/p\u003e \u003cp\u003eIn conclusion, our study showed that the genetic architecture of SFNB susceptibility is governed by the independent action of multiple dominant host genes. Even though some of these genes might be conserved at specific loci across different barley lines, they do not contribute equally to disease development, and allelic variants of the same gene may have evolved because of diversifying selection during coevolution with the pathogen. Among the loci identified in this study, we showed that the Chr7HL locus is generally the largest contributor to SFNB susceptibility and is targeted by seven of ten globally collected \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates spanning four continents. The identification and elimination of susceptibility genes in host germplasm represents the most straightforward strategy for developing barley cultivars that harbor genetic resistance to necrotrophic pathogens (Oliver et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2012\u003c/span\u003e and \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Downie et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, the elimination of Chr7HL susceptibility should be prioritized in barley breeding programs. Although the Chr7HL susceptibility had the largest effects across these three populations, the other three susceptibility loci on Chr2HS, Chr4HS, and Chr4HL also contributed significantly to disease and should also be eliminated. Even if the local \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e populations do not currently harbor the corresponding effectors, unknowingly introgressing the susceptibility targets on Chr2HS, Chr4HS, and Chr4HL would put selection pressure on the pathogen population to adapt to these susceptibilities. As examples of potential evolution in barley-growing regions of the world, the Chr2HS susceptibility was targeted by seven of the 10 isolates, covering four continents; Chr4HS was targeted by three isolates spanning three continents; and Chr4HL was targeted by two isolates spanning two continents. It appears that the effector genes targeting the four barley susceptibility genes presented here are distributed globally, facilitating easy adaptation to locally planted barley cultivars. Therefore, monitoring for these barley susceptibility genes as well as the local \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e populations is critical to managing this global disease.\u003c/p\u003e \u003cp\u003eThis study provides barley breeders with valuable tools, including germplasm and potential markers, to develop new resistant cultivars via marker-assisted selection. Additionally, the barley research community can use the knowledge presented here to validate genes and conduct functional studies to characterize the molecular mechanisms underlying SFNB susceptibility.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eAuthor contribution statement\u003c/h2\u003e \u003cp\u003eMCM and TLF designed the study. RS and JDF generated sequencing data for the barley markers. MCM, RS and SA performed mapping, phenotyping and QTL analysis. MCM, RS and TLF analyzed the data. MCM and TLF wrote the manuscript. SY and ZL contributed to review the manuscript. All authors edited and approved the manuscript.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eDisclaimer\u003c/h2\u003e \u003cp\u003eThe mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the U.S. Department of Agriculture, Agricultural Research Service, through CRIS project 3060-22000-051-000D.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMCM and TLF designed the study. RS and JDF generated sequencing data for the barley markers. MCM, RS and SA performed mapping, phenotyping and QTL analysis. MCM, RS and TLF analyzed the data. MCM and TLF wrote the manuscript. SY and ZL contributed to review the manuscript. All authors edited and approved the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors are grateful to Danielle Holmes, Dr. Alyssa Flobinus, Zoie Gilpin, and Garret Kuhn for their assistance with plant care and phenotype data collection, to Mary Osenga\u0026rsquo;s technical work on generating the barley population markers. We would also like to thank Drs. Robert Brueggeman, Karl Effertz, Sajid Rehman, and Eva Stukenbrock for contributing diseased leaf samples and/or P. teres f maculata isolates for use in this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data used in this study is available in the supplementary files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbaba G, Hailu W, Shiferaw T, Fekadu W, Alamerew S (2024) Adult-plant resistance to leaf scald and net form net blotch in food barley genotypes at a hot spot location in Ethiopia. Heliyon, 10(22). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.heliyon.2024.e40529\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2024.e40529\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbebe W (2021) Barley net blotch disease management: A review. Int J Environ Agric Res 7(9), 69\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdhikari A, Steffenson BJ, Smith KP, Smith M, Dill-Macky R (2020) Identification of quantitative trait loci for net form net blotch resistance in contemporary barley breeding germplasm from the USA using genome-wide association mapping. Theor Appl Genet 133, 1019\u0026ndash;1037. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-019-03528-5\u003c/span\u003e\u003cspan address=\"10.1007/s00122-019-03528-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkhavan A, Turkington TK, Kebede B, Tekauz A, Kutcher HR, Kirkham C, Xi K, Kumar K, Tucker JR, Strelkov SE (2015) Prevalence of mating type idiomorphs in \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003eteres\u003c/em\u003e and \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e populations from the Canadian prairies. Can J Plant Pathol 37:52\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/07060661.2014.995710\u003c/span\u003e\u003cspan address=\"10.1080/07060661.2014.995710\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkhavan A, Turkington TK, Askarian H, Tekauz A, Xi K, Tucker JR, Kutcher HR, Strelkov SE (2016). Virulence of \u003cem\u003ePyrenophora teres\u003c/em\u003e populations in western Canada. Can J Plant Pathol 38, 183\u0026ndash;196. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/07060661.2016.1159617\u003c/span\u003e\u003cspan address=\"10.1080/07060661.2016.1159617\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkhavan A, Turkington TK, Askarian H, Tekauz A, Legge WG, Xi K, Kutcher R, Tucker JR, Kirkham C, Kumar K, Strelkov S (2017) Assessing the genetic and pathogenic variability of \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e (spot form net blotch of barley) and its ability to overcome currently-used sources of resistance on the Canadian prairies. Soils and Crops Workshop 2017 Mar 6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlhashel AF, Sharma Poudel R, Fiedler J, Carlson CH, Rasmussen J, Baldwin T, Friesen TL, Brueggeman RS, Yang S (2021) Genetic mapping of host resistance to the \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolate 13IM8. 3. G3, 11(12), jkab341. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/g3journal/jkab341\u003c/span\u003e\u003cspan address=\"10.1093/g3journal/jkab341\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlhashel AF, Fiedler JD, Nandety RS, Skiba RM, Bruggeman RS, Baldwin T, Friesen TL, Yang S (2023) Genetic and physical localization of a major susceptibility gene to \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e in barley. Theor Appl Genet 136(5), 118. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-023-04367-1\u003c/span\u003e\u003cspan address=\"10.1007/s00122-023-04367-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArabi M, Al-Safadi IE, Charbaji BT (2003) Pathogenic variation among isolates of \u003cem\u003ePyrenophora teres\u003c/em\u003e the causal agent of barley net blotch. J Phytopathol 151:376\u0026ndash;382. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1439-0434.2003.00734.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1439-0434.2003.00734.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBackes A, Guerriero G, Ait Barka E, Jacquard C (2021) \u003cem\u003ePyrenophora teres\u003c/em\u003e: taxonomy, morphology, interaction with barley, and mode of control. Front Plant Sci 12, 614951. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2021.614951\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2021.614951\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlake T (2008) Hockett: Release and Recommendation Notices, Planning Conference January 2008. Montana State University, Barley Breeding Program.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoeckx T, Winters AL, Webb KJ, Kingston-Smith AH (2015) Polyphenol oxidase in leaves: is there any significance to the chloroplastic localization? J Exp Bot 66(12), 3571\u0026ndash;3579. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jxb/erv141\u003c/span\u003e\u003cspan address=\"10.1093/jxb/erv141\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurlakoti RR, Gyawali S, Chao S, Smith KP, Horsley RD, Cooper B, Muehlbauer GJ, Neate SM (2017) Genome-wide association study of spot form of net blotch resistance in the Upper Midwest barley breeding programs. Phytopathology 107(1), 100\u0026ndash;108. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1094/PHYTO-03-16-0136-R\u003c/span\u003e\u003cspan address=\"10.1094/PHYTO-03-16-0136-R\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCantalapiedra CP, Boudiar R, Casas AM, Igartua E, Contreras-Moreira B (2015) BARLEYMAP: physical and genetic mapping of nucleotide sequences and annotation of surrounding loci in barley. Mol Breeding 35:13 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1007/s11032-015-0253-1\u003c/span\u003e\u003cspan address=\"10.1007/s11032-015-0253-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarlsen SA, Neupane A, Wyatt NA, Richards JK, Faris JD, Xu SS, Brueggeman RS, Friesen TL (2017) Characterizing the \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e\u0026ndash;barley interaction using pathogen genetics. G3 7(8), 2615\u0026ndash;2626. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1534/g3.117.043265\u003c/span\u003e\u003cspan address=\"10.1534/g3.117.043265\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClare SJ, Duellman KM, Richards JK, Poudel RS, Merrick LF, Friesen TL, Brueggeman RS (2022) Association mapping reveals a reciprocal virulence/avirulence locus within diverse US \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e isolates. BMC Genom 23(1), 285. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12864-022-08529-1\u003c/span\u003e\u003cspan address=\"10.1186/s12864-022-08529-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClare SJ, Wyatt NA, Brueggeman RS, Friesen TL (2020) Research advances in the \u003cem\u003ePyrenophora teres\u003c/em\u003e\u0026ndash;barley interaction. Mol Plant Path 21(2), 272\u0026ndash;288. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/mpp.12896\u003c/span\u003e\u003cspan address=\"10.1111/mpp.12896\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClare SJ, Oğuz A\u0026Ccedil;, Effertz K, Sharma R, Deven See P, Karakaya A, Brueggeman RS (2021) Genome-wide association mapping of \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e and \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003eteres\u003c/em\u003e resistance loci utilizing natural Turkish wild and landrace barley populations. G3 11(11), 269. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/g3journal/jkab269\u003c/span\u003e\u003cspan address=\"10.1093/g3journal/jkab269\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDokhanchi H, Babai-Ahari A, Arzanlou M, Abed-Ashtiani F (2022) Characterization of \u003cem\u003ePyrenophora\u003c/em\u003e, \u003cem\u003eBipolaris\u003c/em\u003e, \u003cem\u003eCurvularia\u003c/em\u003e and \u003cem\u003eExserohilum\u003c/em\u003e species associated with leaf spot diseases on barley in East Azerbaijan province, using morphological and molecular data. J Appl Res Plant Protect 11(1), 1\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDownie RC, Lin M, Corsi B, Ficke A, Lillemo M, Oliver RP, Phan HT, Tan KC, Cockram J (2021) Septoria nodorum blotch of wheat: disease management and resistance breeding in the face of shifting disease dynamics and a changing environment. Phytopathology 111(6), 906\u0026ndash;920. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1094/PHYTO-07-20-0280-RVW\u003c/span\u003e\u003cspan address=\"10.1094/PHYTO-07-20-0280-RVW\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl-Saadony MT, Saad AM, Soliman SM, Salem HM, Ahmed AI, Mahmood M, El-Tahan AM, Ebrahim AA, Abd El-Mageed TA, Negm SH, Selim S (2022) Plant growth-promoting microorganisms as biocontrol agents of plant diseases: Mechanisms, challenges and future perspectives. Front Plant Sci 13: 923880. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2022.923880\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2022.923880\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl Yousfi B, Brahim E (2001) Net blotch in semi arid regions of Morocco. I Epidemiology. Field Crops Research 73.1: 35\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0378-4290(01)00180-0\u003c/span\u003e\u003cspan address=\"10.1016/S0378-4290(01)00180-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllwood SR, Wallwork H (2018) Diseases affecting barley: net blotches. In: Integrated disease management of wheat and barley (pp. 191\u0026ndash;202). Burleigh Dodds Science Publishing.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaris JD, Friesen TL (2020) Plant genes hijacked by necrotrophic fungal pathogens. Curr Opin Plant Biol 56, 74\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pbi.2020.04.003\u003c/span\u003e\u003cspan address=\"10.1016/j.pbi.2020.04.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaris JD, Zhang Z, Lu H, Lu S, Reddy L, Cloutier S, Fellers JP, Meinhardt SW, Rasmussen JB, Xu SS, Friesen TL (2010) A unique wheat disease resistance-like gene governs effector-triggered susceptibility to necrotrophic pathogens. PNAS 107: 13544\u0026ndash;13549. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1004090107\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1004090107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFranckowiak JD, Platz GJ (2013) International database for barley genes and barley genetic stocks. Barley Genet Newsl 43, 48\u0026ndash;223.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriesen TL, Faris JD (2004) Molecular mapping of resistance to \u003cem\u003ePyrenophora tritici-repentis\u003c/em\u003e race 5 and sensitivity to Ptr ToxB in wheat. Theor Appl genet 109(3), 464\u0026ndash;471. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pbi.2020.04.003\u003c/span\u003e\u003cspan address=\"10.1016/j.pbi.2020.04.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriesen TL, Faris JD, Lai Z, Steffenson BJ (2006) Identification and chromosomal location of major genes for resistance to \u003cem\u003ePyrenophora teres\u003c/em\u003e in a doubled-haploid barley population. Genome 49, 855\u0026ndash;859. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1139/g06-024\u003c/span\u003e\u003cspan address=\"10.1139/g06-024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriesen TL, Meinhardt SW, Faris JD (2007) The \u003cem\u003eStagonospora nodorum-\u003c/em\u003ewheat pathosystem involves multiple proteinaceous host‐selective toxins and corresponding host sensitivity genes that interact in an inverse gene‐for‐gene manner. Plant J 51(4), 681\u0026ndash;692. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-313X.2007.03166.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-313X.2007.03166.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriesen TL, Faris JD (2021) Characterization of effector\u0026ndash;target interactions in necrotrophic pathosystems reveals trends and variation in host manipulation. Annu Rev Phytopathol 59(1), 77\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-phyto-120320-012807\u003c/span\u003e\u003cspan address=\"10.1146/annurev-phyto-120320-012807\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrewal TS, Rossnagel BG, Pozniak CJ, Scoles GJ (2008) Mapping quantitative trait loci associated with barley net blotch resistance. Theor Appl Genet 116, 529\u0026ndash;539. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-007-0688-9\u003c/span\u003e\u003cspan address=\"10.1007/s00122-007-0688-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta S, Loughman R (2001) Current virulence of \u003cem\u003ePyrenophora teres\u003c/em\u003e on barley in Western Australia. Plant Dis 85:960\u0026ndash;966. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1094/PDIS.2001.85.9.960\u003c/span\u003e\u003cspan address=\"10.1094/PDIS.2001.85.9.960\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHatsugai N, Yamada K, Goto-Yamada S, Hara-Nishimura I (2015) Vacuolar processing enzyme in plant programmed cell death. Front Plant Sci 6, 234. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2015.00234\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2015.00234\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelps J, Lopez-Ruiz F, Zerihun A, van den Bosch F (2024) Do growers using solo fungicides affect the durability of disease control of growers using mixtures and alternations? The case of spot-form net blotch in western Australia. Phytopathology 114(3), 590\u0026ndash;602. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1094/PHYTO-02-23-0050-R\u003c/span\u003e\u003cspan address=\"10.1094/PHYTO-02-23-0050-R\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu P, Meng Y, Wise RP (2009) Functional contribution of chorismate synthase, anthranilate synthase, and chorismate mutase to penetration resistance in barley\u0026ndash;powdery mildew interactions. MPMI 22(3), 311\u0026ndash;320. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1094/MPMI-22-3-0311\u003c/span\u003e\u003cspan address=\"10.1094/MPMI-22-3-0311\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHubrich F, M\u0026uuml;ller M, Andexer JN (2021) Chorismate-and isochorismate converting enzymes: versatile catalysts acting on an important metabolic node. Chem Commun 57(20), 2441\u0026ndash;2463. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1039/D0CC08078K\u003c/span\u003e\u003cspan address=\"10.1039/D0CC08078K\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIshihara A, Kumeda R, Hayashi N, Yagi Y, Sakaguchi N, Kokubo Y, Ueno K (2017). Induced accumulation of tyramine, serotonin, and related amines in response to \u003cem\u003eBipolaris sorokiniana\u003c/em\u003e infection in barley. Biosci Biotechnol Biochem 81(6), 1090\u0026ndash;1098. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09168451.2017.1290520\u003c/span\u003e\u003cspan address=\"10.1080/09168451.2017.1290520\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJayakodi M, Lu Q, Pidon H, Rabanus-Wallace MT, Bayer M, Lux T, Guo Y, Jaegle B, Badea A, Bekele W, Brar GS (2024). Structural variation in the pangenome of wild and domesticated barley. Nature 636(8043), 654\u0026ndash;662. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41586-024-08187-1\u003c/span\u003e\u003cspan address=\"10.1038/s41586-024-08187-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ\u0026oslash;rgensen JH, Bech C, Jensen J (2000) Reaction of European spring barley varieties to a population of the net blotch fungus. Plant Breed 119(1), 43\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1439-0523.2000.00455.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1439-0523.2000.00455.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarki CB, Sharp EL (1986) Pathogenic variation in some isolates of \u003cem\u003ePyrenophora teres\u003c/em\u003e f. sp. \u003cem\u003emaculata\u003c/em\u003e on barley. Plant Dis 70: 684. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1094/PD-70-684\u003c/span\u003e\u003cspan address=\"10.1094/PD-70-684\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKariyawasam GK, Richards JK, Wyatt NA, Running KL, Xu SS, Liu Z, Borowicz P, Faris JD, Friesen TL (2022) The \u003cem\u003eParastagonospora nodorum\u003c/em\u003e necrotrophic effector SnTox5 targets the wheat gene \u003cem\u003eSnn5\u003c/em\u003e and facilitates entry into the leaf mesophyll. New Phytol 233, 1: 409\u0026ndash;426. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nph.17602\u003c/span\u003e\u003cspan address=\"10.1111/nph.17602\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKariyawasam, GK, Nelson, AC, Williams, SJ, Solomon, PS, Faris, JD, Friesen, TL (2023) The necrotrophic pathogen \u003cem\u003eParastagonospora nodorum\u003c/em\u003e is a master manipulator of wheat defense. MPMI \u003cem\u003e36\u003c/em\u003e(12), 764\u0026ndash;773. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1094/MPMI-05-23-0067-IRW\u003c/span\u003e\u003cspan address=\"10.1094/MPMI-05-23-0067-IRW\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKretschmer M, Croll D, Kronstad JW (2017) Chloroplast-associated metabolic functions influence the susceptibility of maize to \u003cem\u003eUstilago maydis\u003c/em\u003e. MPP 18(9), 1210\u0026ndash;1221. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/mpp.12485\u003c/span\u003e\u003cspan address=\"10.1111/mpp.12485\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLammari HI, Rehfus A, Stammler G, Fellahi ZEA, Benbelkacem A, Benslimane H (2020) Occurrence and frequency of spot form and net form of net blotch disease of barley in Algeria. J Plant Dis Prot 127, 35\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s41348-019-00278-w\u003c/span\u003e\u003cspan address=\"10.1007/s41348-019-00278-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLightfoot DJ, Able AJ (2010) Growth of \u003cem\u003ePyrenophora teres\u003c/em\u003e in planta during barley net blotch disease. Australas Plant Pathol 39: 499\u0026ndash;507. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1071/AP10121\u003c/span\u003e\u003cspan address=\"10.1071/AP10121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Z, Faris JD, Oliver RP, Tan KC, Solomon PS, McDonald MC, McDonald BA, Nunez A, Lu S, Rasmussen JB, Friesen TL (2009) SnTox3 acts in effector triggered susceptibility to induce disease on wheat carrying the \u003cem\u003eSnn3\u003c/em\u003e gene. PLoS Pathog 5 (9), e1000581.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Z, Friesen TL (2010) Identification of \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e, causal agent of spot type net blotch of barley in North Dakota. Plant Dis 94, 4: 480\u0026ndash;480.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Z, Ellwood, SR, Oliver, RP, Friesen TL (2011) \u003cem\u003ePyrenophora teres\u003c/em\u003e: profile of an increasingly damaging barley pathogen. MPP 12, 1\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1364-3703.2010.00649.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1364-3703.2010.00649.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Z, Zhang Z, Faris JD, Oliver RP, Syme R, McDonald MC, and Friesen TL (2012) The cysteine rich necrotrophic effector SnTox1 produced by \u003cem\u003eStagonospora nodorum\u003c/em\u003e triggers susceptibility of wheat lines harboring \u003cem\u003eSnn1\u003c/em\u003e. PLoS Pathog 8 (1), e1002467.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Z, Holmes DJ, Faris JD, Chao S, Brueggeman RS, Edwards MC, Friesen TL (2015) Necrotrophic effector-triggered susceptibility (NETS) underlies the barley\u0026ndash;\u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003eteres\u003c/em\u003e interaction specific to chromosome 6H. MPP 16(2), 188\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLorang JM, Sweat TA, Wolpert TJ (2007) Plant disease susceptibility conferred by a \u0026ldquo;resistance\u0026rdquo; gene. PNAS 104(37), 14861\u0026ndash;14866.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLorang J, Kidarsa T, Bradford CS, Gilbert B, Curtis M, Tzeng SC, Wolpert TJ (2012) Tricking the guard: exploiting plant defense for disease susceptibility. Science 338 (6107), 659\u0026ndash;662.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManninen OM, Jalli M, Kalendar R, Schulman A, Afanasenko O and Robinson J (2006) Mapping of major spot-type and net-type net blotch resistance genes in the Ethiopian barley line CI 9819. Genome 49, 1564\u0026ndash;1571.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarshall JM, Kinzer K, Brueggeman RS (2015) First report of \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e the cause of spot form net blotch of barley in Idaho. Plant Dis 99(12), 1860.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMayer AM (2006) Polyphenol oxidases in plants and fungi: going places? A review. Phytochemistry 67(21), 2318\u0026ndash;2331.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMclean MS, Howlett BJ, Hollaway GJ (2009) Epidemiology and control of spot form of net blotch (\u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e) of barley: a review. Crop Past Sci 60, 303\u0026ndash;315. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1071/CP08173\u003c/span\u003e\u003cspan address=\"10.1071/CP08173\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMclean MS, Howlett BJ, Hollaway GJ (2010a) Spot form of net blotch, caused by \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e, is the most prevalent foliar disease of barley in Victoria, Australia. Australas. Plant Pathol 39, 46\u0026ndash;49 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1071/AP09054\u003c/span\u003e\u003cspan address=\"10.1071/AP09054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcLean MS, FJ Keiper, GJ Hollaway (2010b) Genetic and pathogenic diversity in \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e in barley crops of Victoria, Australia. Australas Plant Pathol 39: 319\u0026ndash;325. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1071/AP09097\u003c/span\u003e\u003cspan address=\"10.1071/AP09097\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcLean MS, Martin A, Gupta S, Sutherland MW, Hollaway GJ, Platz GJ (2014) Validation of a new spot form of net blotch differential set and evidence for hybridisation between the spot and net forms of net blotch in Australia. Australas Plant Pathol 43, 223\u0026ndash;233. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13313-014-0285-8\u003c/span\u003e\u003cspan address=\"10.1007/s13313-014-0285-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMittal S (2022) Wheat and Barley Production Trends and Research Priorities: A Global Perspective. In: Kashyap, P.L. New Horizons in Wheat and Barley Research. Springer Singapore. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-981-16-4449-8_1\u003c/span\u003e\u003cspan address=\"10.1007/978-981-16-4449-8_1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNavr\u0026aacute;tilov\u0026aacute; P, Toegelov\u0026aacute; H, Tulpov\u0026aacute; Z, Kuo YT, Stein N, Doležel J, Houben A, Šimkov\u0026aacute; H, Mascher M (2022) Prospects of telomere-to‐telomere assembly in barley: Analysis of sequence gaps in the MorexV3 reference genome. Plant Biotechnol J 20(7), 1373\u0026ndash;1386. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/pbi.13816\u003c/span\u003e\u003cspan address=\"10.1111/pbi.13816\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeupane A, Tamang P, Brueggeman RS, Friesen TL (2015) Evaluation of a barley core collection for spot form net blotch reaction reveals distinct genotype-specific pathogen virulence and host susceptibility. Phytopathology 105 (4), 509\u0026ndash;517. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1094/PHYTO-04-14-0107-R\u003c/span\u003e\u003cspan address=\"10.1094/PHYTO-04-14-0107-R\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOğuz A\u0026Ccedil;, \u0026Ouml;lmez F, Karakaya A (2019) Genetic diversity of net blotch pathogens of barley in Turkey. Int J Agric Biol 21:1089\u0026ndash;1096. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17957/IJAB/15.0998\u003c/span\u003e\u003cspan address=\"10.17957/IJAB/15.0998\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliver RP, Solomon PS (2010) New developments in pathogenicity and virulence of necrotrophs. Curr Opin Plant Biol 13(4), 415\u0026ndash;419. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pbi.2010.05.003\u003c/span\u003e\u003cspan address=\"10.1016/j.pbi.2010.05.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliver RP, Friesen TL, Faris JD, Solomon PS (2012) \u003cem\u003eStagonospora nodorum\u003c/em\u003e: from pathology to genomics and host resistance. Annu Rev Phytopathol 50(1), 23\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-phyto-081211-173019\u003c/span\u003e\u003cspan address=\"10.1146/annurev-phyto-081211-173019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliver R, Lichtenzveig J, Tan KC, Waters O, Rybak K, Lawrence J, Friesen T, Burgess P (2014) Absence of detectable yield penalty associated with insensitivity to Pleosporales necrotrophic effectors in wheat grown in the West Australian wheat belt. Plant Pathology, 63(5), 1027\u0026ndash;1032. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ppa.12191\u003c/span\u003e\u003cspan address=\"10.1111/ppa.12191\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeters-Haugrud AR, Zhang Z, Friesen TL, Faris JD (2022) Genetics of resistance to septoria nodorum blotch in wheat. Theor Appl Genet 135, 11: 3685\u0026ndash;3707. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-022-04036-9\u003c/span\u003e\u003cspan address=\"10.1007/s00122-022-04036-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichards JK, Kariyawasam GK, Seneviratne S, Wyatt NA, Xu SS, Liu Z, Friesen TL (2022) A triple threat: the \u003cem\u003eParastagonospora nodorum\u003c/em\u003e SnTox267 effector exploits three distinct host genetic factors to cause disease in wheat. New Phytol 233 (1), 427\u0026ndash;442. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nph.17601\u003c/span\u003e\u003cspan address=\"10.1111/nph.17601\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi G, Zhang Z, Friesen TL, Raats D, Fahima T, Brueggeman RS, Lu S, Trick HN, Liu Z, Chao W, Faris JD (2016) The hijacking of a receptor kinase-driven pathway by a wheat fungal pathogen leads to disease. Sci Adv 2 (10). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/sciadv.1600822\u003c/span\u003e\u003cspan address=\"10.1126/sciadv.1600822\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkiba RM, Wyatt NA, Kariyawasam GK, Fiedler JD, Yang S, Brueggeman RS, Friesen TL (2022) Host and pathogen genetics reveal an inverse gene-for-gene association in the P. \u003cem\u003eteres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e\u0026ndash;barley pathosystem. Theor Appl Genet 135(10), 3597\u0026ndash;3609. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-022-04204-x\u003c/span\u003e\u003cspan address=\"10.1007/s00122-022-04204-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmedegard-Petersen V (1971) \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003eteres\u003c/em\u003e and \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e on barley in Denmark. Arskrift den kgle Vet-Landbohojsk 971, 124\u0026ndash;144.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrelkov SE, Lamari L, Ballance GM (1998) Induced chlorophyll degradation by a chlorosis toxin from \u003cem\u003ePyrenophora tritici-repentis\u003c/em\u003e. Can J plant pathol 20: 428\u0026ndash;435. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/07060669809500417\u003c/span\u003e\u003cspan address=\"10.1080/07060669809500417\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaliadoros D, Feurtey A, Wyatt N, Barr\u0026egrave;s B, Gladieux P, Friesen TL, Stukenbrock EH (2024) Emergence and spread of the barley net blotch pathogen coincided with crop domestication and cultivation history. PLoS Genet 20(1), e1010884. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pgen.1010884\u003c/span\u003e\u003cspan address=\"10.1371/journal.pgen.1010884\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTamang P, Neupane A, Mamidi S, Friesen TL, Brueggeman RS (2015) Association mapping of seedling resistance to spot form net blotch in a worldwide collection of barley. Phytopathology 105(4), 500\u0026ndash;508. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1094/PHYTO-04-14-0106-R\u003c/span\u003e\u003cspan address=\"10.1094/PHYTO-04-14-0106-R\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTamang P, Richards JK, Alhashal A, Sharma Poudel R, Horsley RD, Friesen TL, Brueggeman RS (2019) Mapping of barley susceptibility/resistance QTL against spot form net blotch caused by \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e using RIL populations. Theor Appl Genet 132, 1953\u0026ndash;1963. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-019-03328-x\u003c/span\u003e\u003cspan address=\"10.1007/s00122-019-03328-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTekauz A (1990) Characterization and distribution of pathogenic variation in \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003eteres\u003c/em\u003e and \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e from western Canada. Can J Plant Pathol 12(2), 141\u0026ndash;148. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/07060669009501017\u003c/span\u003e\u003cspan address=\"10.1080/07060669009501017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTilley A, McHenry MP, McHenry JA, Solah V, Bayliss K (2023) Enzymatic browning: The role of substrates in polyphenol oxidase mediated browning. Curr Res Food Sci 7, 100623. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.crfs.2023.100623\u003c/span\u003e\u003cspan address=\"10.1016/j.crfs.2023.100623\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTini F, Covarelli L, Ricci G, Balducci E, Orfei M, Beccari G (2022) Management of \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003eteres\u003c/em\u003e, the causal agent of net form net blotch of barley, in a two-year field experiment in central Italy. Pathogens 11(3), 291 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/pathogens11030291\u003c/span\u003e\u003cspan address=\"10.3390/pathogens11030291\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTomić A, Trkulja V, Matić S, Trkulja N, Iličić R, Scortichini M, Popović Milovanović T (2024) Net blotch (\u003cem\u003ePyrenophora teres\u003c/em\u003e Drechsler): An increasingly significant threat to barley production. Plant Prot Sci 60(1), 1\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17221/122/2023-PPS\u003c/span\u003e\u003cspan address=\"10.17221/122/2023-PPS\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUbe N, Yabuta Y, Tohnooka T, Ueno K, Taketa S, Ishihara A (2019) Biosynthesis of phenylamide phytoalexins in pathogen-infected barley. Int J Mol Sci 20(22), 5541. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms20225541\u003c/span\u003e\u003cspan address=\"10.3390/ijms20225541\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUbe N, Katsuyama Y, Kariya K, Tebayashi SI, Sue M, Tohnooka T, Ueno K, Taketa S, Ishihara A (2021) Identification of methoxylchalcones produced in response to CuCl2 treatment and pathogen infection in barley. Phytochemistry 184, 112650. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.phytochem.2020.112650\u003c/span\u003e\u003cspan address=\"10.1016/j.phytochem.2020.112650\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVasighzadeh A, Sharifnabi B, Javan-Nikkhah M, Seifollahi E, Landermann‐Habetha D, Feurtey A, Holtgrewe‐Stukenbrock E (2021) Population genetic structure of four regional populations of the barley pathogen \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e in Iran is characterized by high genetic diversity and sexual recombination. Plant Pathol 70(3), 735\u0026ndash;744. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ppa.13326\u003c/span\u003e\u003cspan address=\"10.1111/ppa.13326\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWyatt NA, Friesen TL (2021) Four reference quality genome assemblies of \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e: a resource for studying the barley spot form net blotch interaction. MPMI 34(1), 135\u0026ndash;139. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1094/MPMI-08-20-0228-A\u003c/span\u003e\u003cspan address=\"10.1094/MPMI-08-20-0228-A\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Mace ES, Platz GJ, Hunt CH, Hickey LT, Franckowiak JD, Jordan DR (2015) Spot form of net blotch resistance in barley is under complex genetic control. Theor Appl Genet 128, 489\u0026ndash;499. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-014-2447-z\u003c/span\u003e\u003cspan address=\"10.1007/s00122-014-2447-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams KJ, Lichon A, Gianquitto P, Kretschmer JM, Karakousis A, Manning S, Langridge P, Wallwork H (1999) Identification and mapping of a gene conferring resistance to the spot form of net blotch (\u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e) in barley. Theor Appl Genet 99:323\u0026ndash;327. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s001220051239\u003c/span\u003e\u003cspan address=\"10.1007/s001220051239\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams KJ, Platz GJ, Barr AR, Cheong J, Willsmore K, Cakir M, Wallwork H (2003). A comparison of the genetics of seedling and adult plant resistance to the spot form of net blotch (\u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e). Aust J Agric Res 54(12), 1387\u0026ndash;1394. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1071/AR03028\u003c/span\u003e\u003cspan address=\"10.1071/AR03028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":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":"Barley, Pyrenophora teres f. maculata, Spot form net blotch, QTL mapping, Susceptibility loci","lastPublishedDoi":"10.21203/rs.3.rs-8961109/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8961109/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe necrotrophic fungal pathogen \u003cem\u003ePyrenophora teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e causes spot form net blotch (SFNB), a global disease of barley. This fungus uses effector proteins to promote infection, which act in an inverse gene-for-gene manner by targeting dominant host susceptibility genes. Currently, there is a general understanding of the genetics of resistance/susceptibility in the host; however, there are still gaps in our understanding of global pathogen virulence and how it has evolved to target the host and cause disease. Because the \u003cem\u003eP. teres\u003c/em\u003e f. \u003cem\u003emaculata\u003c/em\u003e-barley interaction conforms to an inverse gene-for-gene model, we crossed three different susceptible barley lines (Hockett, TR 326, and PI 392501) with the resistant line PI 67381 and developed and mapped recombinant inbred populations to characterize the susceptibility in these lines based on their response to ten pathogen isolates collected from globally diverse barley growing regions on five continents. Four independent quantitative trait loci (QTL) showed associations with susceptibility and mapped to barley chromosomes (Chr) 2HS, 4HS, 4HL, and 7HL. In all three populations, the same genomic position on Chr7H was associated with the highest susceptibility levels and was targeted by seven of the ten fungal isolates. The QTL identified on Chr2HS mapped to the same position in two populations and was also targeted by seven of the ten isolates. However, the Chr4HS and Chr4HL susceptibilities were targeted by only three and two of the global isolates, respectively. This work shows that pathogen populations under different host selection pressures can evolve to target different barley susceptibilities.\u003c/p\u003e","manuscriptTitle":"Global perspective on the genetic architecture of susceptibility to spot form net blotch in barley","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-06 20:28:27","doi":"10.21203/rs.3.rs-8961109/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-31T18:26:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-31T04:38:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-18T22:58:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69642563769387969107424969584673514131","date":"2026-03-08T23:40:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264356107477534747955210838814798853811","date":"2026-03-04T15:35:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-01T22:56:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-28T13:45:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-25T14:28:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2026-02-24T21:07:34+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":"cdf16509-e6a8-4d51-91f4-232e0b152510","owner":[],"postedDate":"March 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T16:02:57+00:00","versionOfRecord":{"articleIdentity":"rs-8961109","link":"https://doi.org/10.1007/s00122-026-05250-5","journal":{"identity":"theoretical-and-applied-genetics","isVorOnly":false,"title":"Theoretical and Applied Genetics"},"publishedOn":"2026-04-28 15:58:23","publishedOnDateReadable":"April 28th, 2026"},"versionCreatedAt":"2026-03-06 20:28:27","video":"","vorDoi":"10.1007/s00122-026-05250-5","vorDoiUrl":"https://doi.org/10.1007/s00122-026-05250-5","workflowStages":[]},"version":"v1","identity":"rs-8961109","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8961109","identity":"rs-8961109","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}