Genetic mapping of QTLs for resistance to bacterial leaf streak in hexaploid wheat

Genetic mapping of QTLs for resistance to bacterial leaf streak in hexaploid wheat | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genetic mapping of QTLs for resistance to bacterial leaf streak in hexaploid wheat Krishna Acharya, Zhaohui Liu, Jeffrey Schachterle, Pooja Kumari, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4456913/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Nov, 2024 Read the published version in Theoretical and Applied Genetics → Version 1 posted 5 You are reading this latest preprint version Abstract Bacterial leaf streak (BLS), caused by Xanthomonas translucens pv. undulosa ( Xtu ) poses a significant threat to global wheat production. High levels of BLS resistance are rare in hexaploid wheat. Here, we screened 101 diverse wheat genotypes under greenhouse conditions to identify new sources of BLS resistance. Five lines showed good levels of resistance including the wheat variety Boost and the synthetic hexaploid wheat line W-7984. Recombinant inbred populations derived from the cross of Boost × ND830 (BoostND population) and W-7984 × Opata 85 (ITMI population) were subsequently evaluated in greenhouse and field experiments to investigate the genetic basis of resistance. QTLs on chromosomes 3B, 5A, and 5B were identified in the BoostND population. The 3B and 5A QTLs were significant in all environments, but the 3B QTL was the strongest under greenhouse conditions explaining 38% of the phenotypic variation, and the 5A QTL was the most significant in the field explaining up to 29% of the variation. In the ITMI population, a QTL on chromosome 7D explained as much as 46% of the phenotypic variation in the greenhouse and 18% in the field. BLS severity in both populations was negatively correlated with days to heading, and some QTLs for these traits overlapped, which explained the tendency of later maturing lines to have relatively higher levels of BLS resistance. The findings from this study will contribute to a better understanding of BLS resistance and aid in the development of molecular markers for efficient selection of resistance alleles in wheat breeding programs. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Key Message Robust QTLs conferring resistance to bacterial leaf streak in wheat were mapped on chromosomes 3B and 5A from the variety Boost and on chromosome 7D from the synthetic wheat line W-7984. Introduction Wheat ( Triticum aestivum L., 2 n = 6 x = 42, AABBDD genomes) is one of the major cereal crops in the world. The Northern Plains is a major hard red spring wheat (HRSW) producer in the United States providing about 25 percent of the total U.S. wheat production ( https://www.ers.usda.gov/topics/crops/wheat/wheat-sector-at-a-glance/#classes ); however, its production and seed quality have been constrained by various biotic and abiotic stresses (Pandey et al. 2017 ). Bacterial leaf streak (BLS), caused by Xanthomonas translucens pv. undulosa ( Xtu ) (Smith et al. 1919; Vauterin et al. 1995) has been recognized as an important foliar disease affecting wheat on a global scale (Bamberg 1936 ; Duveiller 1990 ; Friskop et al. 2023 ). This bacterial disease produces symptoms on leaves observed as translucent stripes leading to elongated large brown lesions, and in later stages there is discoloration of the peduncle and alternate black bands referred to as a black chaff on the spike (Duveiller and Bragard 2017 ). BLS has emerged as a threat to spring wheat production in the northern Great Plains causing substantial yield losses and reductions in grain quality (Forster et al. 1988; Tillman et al. 1999 ; McMullen and Adhikari 2011 ; Kandel et al. 2012 ; Sapkota et al. 2020 ; Friskop et al. 2023 ). A recent study conducted in North Dakota demonstrated that losses due to BLS in wheat can reach up to 60% when highly susceptible cultivars are planted (Friskop et al. 2023 ). Infected seeds and crop debris are believed to be the primary sources of inoculum for BLS, therefore clean seeds are recommended to reduce disease incidence (Milus and Mirlohi 1994 ; Malavolta et al. 2000 ). Given that chemical control under field conditions for foliar bacterial diseases is neither economical nor practical, the deployment of genetically resistant cultivars is the most viable option for BLS management (Duveiller et al. 1997 ; McMullen and Adhikari 2011 ; Lux et al. 2023 ). Many wheat accessions, cultivars, breeding lines, wheat relatives, and landraces have been screened for reaction to BLS, but few sources with good levels of genetic resistance have been reported (Hagborg 1974 ; Akhtar and Aslam 1986 ; Duveiller et al. 1993 ; Milus et al. 1994; Tillman et al. 1996 ; Adhikari et al. 2011 ; Kandel et al. 2012 ; Sapkota et al. 2018 ). One study on resistance to BLS by Duveiller et al. ( 1993 ) indicated five major genes ( Bls1, Bls2, Bls3, Bls4 , and Bls5 ) conferring BLS resistance among three partially resistant wheat cultivars (Pavon 76, Mochis T88, and Angostura F88), with Bls1 present in all three wheat cultivars and having the largest effect. Polygenic control of BLS resistance was suggested by Milus and Chalkey (1994) by evaluating 19 wheat cultivars against 81 Xtu strains. Later, Tillman and Harrison ( 1996 ) reported multiple genes controlling BLS resistance in three F 2 populations derived from wheat cultivars Terral 101 (resistant), Coker 9877 (moderately resistant), Pioneer 2548 (susceptible), and Coker 9766 (susceptible), further confirming that BLS resistance was quantitatively controlled. However, Cunfer and Scholari (1982) and Johnson et al. ( 1987 ) suggested that a few triticale lines (Siskiyou, M2A-Beagle, and OK77842) had qualitative and dominant BLS resistance genes, and Wen et al. ( 2018 ) mapped a major gene in ‘Siskiyou’ on chromosome 5R closely linked to the previously described Xct1 locus (Johnson et al. 1987 ). In recent years, QTL mapping and genome-wide association studies (GWAS) have been conducted to identify genomic regions and linked DNA markers associated with BLS resistance. A GWAS conducted using 566 spring wheat landraces with diversity array technology (DArT) markers revealed five genomic regions on chromosomes 1A, 4A, 6B, and 7D conferring BLS resistance (Adhikari et al. 2012b ). Gurung et al. ( 2014 ) utilized the same panel genotyped with single nucleotide polymorphism (SNP) markers and identified a total of five genomic regions on chromosomes 1A, 2B, 3A, 5D, and 6B, including regions on chromosomes 1A and 6B similar to those identified by Adhikari et al. ( 2012b ). Other studies have reported QTLs for BLS resistance on chromosomes 1B, 2A, 2B, ,6D, 7A, and 7B (Kandel et al. 2015 ; Sapkota 2015 ; Ayana 2017 ), and a GWAS conducted by Ramakrishnan et al. ( 2019 ) on a hard red winter wheat panel revealed BLS resistance QTLs on chromosomes 1A, 1B, 3A, 4A, and 7A. The findings from the studies mentioned above revealed only partial resistance QTLs that explained small amounts of the phenotypic variation in BLS. None of them reported the discovery of major genes or QTLs that could be used effectively in breeding programs for BLS resistance. Here, our objectives were to 1) screen a set of diverse wheat accessions for reaction to a highly virulent strain of Xtu to identify sources with good levels of BLS resistance; and 2) identify QTLs associated with BLS resistance in biparental populations derived from two of the best resistant lines. This research led to the identification of QTLs with large effects on BLS resistance that should be useful for the development of BLS-resistant wheat varieties by pyramiding through marker-assisted selection. Materials and methods Plant materials A total of 101 wheat lines (Table S1 ) including hexaploid common (bread) wheat varieties, landraces, and synthetic hexaploid accessions; tetraploid durum ( Triticum turgidum L. ssp. durum (Desf.) Husnot., 2 n = 4 x = 28, AABB genomes) varieties; and accessions of the durum wheat progenitor known as wild emmer wheat ( T. turgidum ssp. dicoccoides (Körn.) Thell (2 n = 4 x = 28, AABB genomes) were screened for reaction to Xtu under greenhouse conditions. Based on these results, two bi-parental mapping populations were selected for identifying genetic loci associated with BLS resistance in wheat. The first mapping population consisted of 190 F 5:6 recombinant inbred lines (RILs) developed by single seed-decent from a cross between the South Dakota HRSW variety ‘Boost’ and the unreleased North Dakota HRSW breeding line ‘ND830’, hereafter referred to as the BoostND population. Boost (SD3900//FN1705-146/SD3851), developed by the spring wheat breeding program at South Dakota State University in 2015, exhibited notable resistance to BLS. In contrast, ND830 (ND744/ND721//Faller'S’) was susceptible to BLS. The second mapping population evaluated was the International Triticeae Mapping Initiative (ITMI) population, which was derived from a cross between the synthetic hexaploid wheat W-7984 (also known as M6) and the International Maize and Wheat Improvement Center (CIMMYT)-bred HRSW variety Opata 85 (PI 591776) (Nelson et al. 1995). This population was developed by single seed-decent by investigators of the ITMI in a collaborative effort to map and characterize numerous traits in hexaploid wheat (Sorrells et al. 2011 and references therein). W-7984, also developed at CIMMYT, was developed by crossing the durum wheat variety Altar 84 and the Aegilops tauschii accession 219. A total of 114 RILs of the ITMI population were evaluated in both greenhouse and field experiments. Greenhouse evaluations for BLS Two seeds of each line were planted in cones (4 cm diameter × 13 cm deep, Stuewe & Sons, Inc., Corvallis, OR USA) filled with Sunshine SB100 soil (Sun Grow Horticulture, Bellevue, WA, USA) and a quarter teaspoon of ‘Osmocote Plus’ 15-19-12 fertilizer (Scotts Sierra Horticultural Product Company, Maysville, OH, USA). After planting, a completely randomized design with two replications was employed by placing the cones in RL98 trays (Stuewe & Sons, Inc., Corvallis, OR USA). To mitigate potential edge effects, the highly susceptible HRSW cultivar RB07 was planted along the outer borders of each RL 98 tray. The plants were grown in the greenhouse until the two- to three-leaf stage (14 days after planting under our greenhouse conditions) before spray inoculation. For the 101 wheat lines, every line was evaluated in a minimum of three and a maximum of six experiments collectively. In each experiment, there were two replications for each entry. For the BoostND population, a total of six experiments were carried out. These experiments were divided into two seasons, with three experiments conducted in the fall of 2022 and three in the spring of 2023. The ITMI population was evaluated in the spring of 2023 in three experiments with each experiment consisting of two replications. In all greenhouse experiments, an experimental unit consisted of two individual plants of a single line planted in a single cone. The assessment of BLS involved spray-inoculating 14-day-old plants following the methodology outlined by El Attari et al. ( 1996 ) and Wen et al. ( 2018 ) with necessary adaptations. The Xtu strain P3 (North Dakota strain; Adhikari et al. 2012a ) was cultured as inoculum as described in Adhikari et al ( 2012b ). The inoculum preparation involved streaking bacterial strains from the stock stored at -80°C onto Wilbrink’s agar plates (WBA: 5 g bactopeptone; 10 g sucrose; 0.5 g dibasic potassium phosphate (K 2 HPO 4 ); 0.25 g magnesium sulfate heptahydrate (MgSO 4 .7H 2 O); 0.05 g sodium sulfate anhydrous (Na 2 SO 3 ); 15 g agar; 75 mg cycloheximide; 1 L deionized water) (Duveiller et al. 1997 ). The plates were then incubated for 48 hours at 28°C to cultivate the Xtu culture. The final inoculum was prepared by suspending the bacterial culture in a 1× phosphate saline buffer, pH 7.4, maintaining the bacterial cell concentration at an optical density (OD600) of 0.5, which approximately corresponds to 1×10 8 colony-forming units per ml in bacteria suspension. Before inoculation, two drops of Tween-20 (polyoxyethylene sorbitan monolaurate, Sigma-Aldrich) per 100 ml were added to the final bacterial suspension to ensure even distribution during the spraying of the inoculum. The bacterial suspension was applied to plants until runoff using a spray gun attached to an air hose with a pressure of approximately 20 psi. The inoculated plants were promptly transferred to misting chambers for 2 days at room temperature under a 16-hour photoperiod and 100% relative humidity (RH). Following the 2-day inoculation period, the plants were moved to the greenhouse and placed in water trays where the room was maintained at 75% RH and 28–30°C. Disease assessment occurred 5–6 days after inoculation based on the symptoms observed on the susceptible check. Each line was scored using an infection type (IT) scale that ranged from 0 to 5 and for percentage of leaf area infected (LAI:0-100%). For IT, a score of 0 = immunity, or no reaction; 1 = small chlorotic spots; 2 = short, thin water-soaked streaks ≤ 2 mm in length; 3 = water-soaked lesions 0.5-1 mm wide x 3–10 mm long; 4 = large water-soaked lesions 1–2 mm wide x 10–30 mm long with no overlap; and 5 = very large water-soaked blotches covering nearly the entire leaf (Fig. 1 ). Disease evaluations in the field The BoostND population was evaluated for BLS at two locations, one in Fargo, ND and the other near Prosper, ND, during the summer of 2023. The ITMI population was assessed for BLS at the location near Prosper, ND. In both locations, each line was evaluated in hill plots consisting of 12–15 seeds per hill with three replicates per line arranged in an optimized row: column design with resistant and susceptible checks arranged throughout the field spatially. The experimental entries were completely randomized. Four hills were arranged in a 1.2 m row spaced 0.30 m apart. Plants at the 4 to 5 Feekes growth stage were inoculated using a Stihl leaf blower with a 12 L tank (STIHL Inc., Virginia Beach, VA) as described in Lux et al. ( 2023 ). The inoculum was prepared as described for the greenhouse experiments where the secondary culture (one Petri plate) was suspended in 3 L of a 0.85% saline solution resulting in an approximate concentration of 1 × 10 8 colony-forming units per milliliter (CFU/ml) of the bacteria. Silicon carbide (grit 320; Alfa Aesar, Heysham, England) at a concentration of 1 g/L was introduced to the inoculum mixture to facilitate leaf surface wounding during inoculation. The inoculation process was repeated once after one week if the disease did not manifest or if environmental conditions were unfavorable. For the Fargo experiment, an artificial misting system was available in the research field. Misting was applied for 5 minutes in 15-minute intervals for 12 hours daily, spanning from 4:00 pm to 4:00 am for 14 days after anthesis of the latest maturing lines. The Prosper location lacked a misting system, allowing the disease to occur in an artificially inoculated natural environment. A disease severity (DS) rating score (0–9) was assigned using a rating scale by Saari and Perscott (1975) with the required modification by estimating the percentage of overall leaf area infected (refer to Table S2 ) at the late milk to early dough development stage of wheat. Evaluation for days to heading An experiment was conducted in the greenhouse to assess the days to heading (DTH) of RILs in the BoostND population in the spring of 2023. A single seed of each RIL and the parents was planted in a randomized complete block design with three replications. Similarly, in the field experiment conducted in Prosper, ND, both the BoostND and ITMI populations were scored for DTH. In the greenhouse, DTH for each line was measured as the time between the planting date and the date when the first head appeared and was exposed from the flag leaf. In the field, DTH for each line was measured as the time between the planting date and the date when 50% of the tillers had headed. Data Analysis SAS 9.4 (SAS Institute, Cary, NC, USA) was used for all data analysis. Bartlett’s Chi-squared or Levene’s test were used to assess homogeneity of variances to determine if replicates within experiments were homogeneous and could be combined for further analysis (Snedecor 1956 ; Levene 1960 ). Homogeneous replicates within experiments were combined and used to calculate average BLS scores, which were used for further analysis. Replicates within experiments were analyzed separately if they were determined not to be homogeneous. Fisher’s least significant difference (LSD) was employed to determine significant differences in the disease scores among RILs at α = 0.05. Pearson correlations between DTH and BLS scores in the greenhouse and field experiments of the BoostND and ITMI populations were examined at α = 0.05. Genotyping and linkage mapping The 190 RILs and parents of the BoostND population were genotyped using the Illumina 90K SNP array (Wang et al. 2014 ). Leaf tissue was collected from each line at the 2–3 leaf stage. DNA extraction followed the ammonium acetate method outlined by Pallota et al. (2003) and was subsequently diluted to a concentration of 40 ng/µl. The genotyping assay was carried out at the USDA small grains genotyping laboratory in Fargo, ND, USA, utilizing a BeadStation and iScan instrument from Illumina. Data clustering analysis was conducted using the software GenomeStudio 2.0.5 from Illumina, Inc. (2020). SNP markers were assigned to chromosomes based on Wang et al. ( 2014 ), and linkage analysis for each chromosome was done independently. The SNPs having minor allele frequency less than 0.01 and more than 50% missing data were removed from the analysis. Genetic linkage maps were constructed using Mapdisto V2.1.8.7 (Lorieux 2012 ). The initial organization of markers into groups was done using the "find groups" command with a minimum LOD of 3.0 and a maximum theta of 0.30. The "order" sequence command established the initial marker order within a linkage group. Subsequent refinement of the sequence involved using the "check inversions," "ripple order," and "drop locus" commands to optimize the map. Map distances were calculated using the Kosambi mapping function (Kosambi 1943). The marker data for 114 RILs of the ITMI population were publicly available and retrieved from the GrainGenes database ( http://wheat.pw.usda.gov ). The dataset of almost 2,000 markers consisted mostly of restriction fragment length polymorphism (RFLP) markers (Van Deynze et al. 1995 ; Nelson et al. 1995a , 1995b , 1995c ; Marino et al. 1996 ) and microsatellite markers (Roder et al. 1998 ; Song et al. 2005 ). Markers previously used to construct the linkage map of chromosome 7D were used along with additional KASP markers (see below) to reconstruct the linkage group for chromosome 7D. After linkage maps were assembled, they were anchored to physical maps based on the locations of SNP marker sequences in the Chinese Spring RefSeq v2.1 genome assembly (Zhu et al. 2021 ) to determine the physical locations of the linkage groups along the chromosomes. QTL analysis QTL analysis was conducted using QGENE 4.3.10v (Joehanes and Nelson 2008 ) employing the composite interval mapping (CIM) and single-trait multiple interval mapping (MIM) functions. All the three phenotypic scores, including IT and LAI from the greenhouse and DS from the field, were used in the analysis. A permutation test consisting of 1000 permutations established a LOD significance threshold of 3.0 for CIM or MIM at a significance level of 0.05. The coefficient of determination ( R 2 ) was computed for each QTL, providing an estimate of the phenotypic variation explained for BLS resistance. Markers significantly associated with each QTL were subjected to BLASTn searches against the Chinese Spring RefSeq v2.1 genome assembly (Zhu et al. 2021 ) via the Graingenes website ( https://wheat.pw.usda.gov/GG3/ ) to obtain physical positions for cross-environment QTL comparisons. All the linkage groups were utilized in the initial QTL analysis and the linkage groups containing significant QTLs were reevaluated after removal of redundant and co-segregating markers. The results were confirmed and compared with the initial results from the analysis with the entire SNP dataset. The additive effects of the identified QTLs in the BoostND population were assessed using the genotypic data of the most significant markers for each QTL alongside the average BLS score in each experiment. KASP marker development The SNP markers most closely associated with the peaks of each of the major QTLs were converted to KASP markers using Polymarker (Ramirez-Gonzalez et al. 2015 ), which aligned the SNP sequences with wheat cv. Chinese Spring RefSeq v1.0 and provided two allele-specific primers and one common primer for each assay. Validation of the markers in unique regions in the wheat genome cv. Chinese Spring RefSeq v2.0 assembly was ensured through the physical position and specificity. KASP markers not meeting the standard obtained through Polymarker were manually redesigned using available SNP sequences and the wheat genome cv. Chinese Spring RefSeq v2.0. For the ITMI population, KASP markers for chromosome 7D were designed using polymorphic SNPs for W-9874 and Opata 85 mentioned in Arif et al. ( 2021 and 2022 ) and added to the 7D linkage map. Results Reaction of wheat lines to BLS Greenhouse evaluations of the 101 wheat lines for BLS resistance showed that Boost was the most resistant line with an IT score of 1.15 (Fig. 2 , Table S1 ). Nine lines (8%) were moderately resistant with scores between 1.50 and 2.50, and the remaining lines were moderately to highly susceptible with IT scores greater than 2.5. For LAI, five lines showed less than 10% BLS and were considered highly resistant. Another six lines showed between 10 and 20% BLS and were considered moderately resistant. All remaining lines showed more than 20% BLS and were considered moderately to highly susceptible. The five most resistant lines overall were Boost, W-7984, SW86, SW87, and Mace. As mentioned in the materials and methods, Boost is a HRSW variety and W-7984 is a synthetic hexaploid wheat line. SW86 and SW87 are both synthetic hexaploid wheat lines as well, and they were derived by crossing the durum lines 8155-B1 and 8155-B2 with Ae. tauschii accession CIae 26 (Szabó-Hevér et al. 2018). Mace is an Australian-bred HRSW (Table S1 ). Because Boost and W-7984 were resistant to Xtu (Figs. 3 and 4 ) and RIL populations derived from both lines were available, we pursued the evaluation of the BoostND and ITMI populations to identify QTLs associated with BLS resistance derived from these two highly resistant lines. Evaluation of the BoostND population for BLS In the greenhouse experiments, significant differences ( P < 0.05) were observed among the average disease scores (IT and LAI) for the RILs of the BoostND population in each of the experiments (Fig. 5 , Table S3). In these two experiments, Boost was highly resistant with average IT scores of 1.33 and 1.50 and percentage of LAI scores of 2.33 and 5.80%, whereas ND830 was susceptible with average IT scores of 3.50 and 3.75 and LAI scores of 45.00 of 55.83% in the Fall 2022 and Spring 2023 greenhouse experiments, respectively. The average IT scores of the BoostND RILs ranged from 1.33 to 4.08 and 1.31 to 4.09 in Fall 2022 and Spring 2023, respectively, and the average percentage of LAI scores for the RILs ranged from 1.83 to 57.70% and 2.47 to 68.16% for Fall 2022 and Spring 2023, respectively (Table S3). In the field experiments at Fargo and Prosper, Boost showed strong resistance with an average DS score of 3.00 (ranging from 2 to 5), whereas ND830 was susceptible with average DS scores ranging from 6.00 to 8.00 in the two locations (Fig. 5 ). Significant differences ( P < 0.5) were observed among the RILs of the BoostND population with average DS scores ranging from 2.33 to 8.00 and 2.33 to 9.00 for the field experiments in Fargo and Prosper, respectively (Table S3). Evaluation of the ITMI population for BLS In the greenhouse experiment, significant differences ( P < 0.5) were observed among the ITMI RILs for both IT and LAI (Fig. 6 ). The average scores for W-7984 were 1.42 and 7.83% for IT and LAI, respectively, whereas Opata 85 had IT and LAI scores of 3.92 and 52.50%, respectively (Fig. 6 , Table S4). The average IT scores among the RILs of the ITMI population ranged from 0.58 to 4.67, and the average percentage of LAI was from 1.50–73.33% in the spring 2023 greenhouse experiment. In the field experiment at Prosper, ND, W-7984 showed strong resistance with a DS score of 1.33, whereas Opata 85 had a DS score of 5.00. Significant differences ( P < 0.5) were observed among the RILs of the ITMI population (Fig. 6 , Table S4) where the average DS scores ranged from 1.00 to 6.50. Correlation of BLS with days to heading For the BoostND population, mean values for DTH collected in the greenhouse experiment and the field experiment at Prosper were assessed for correlation with their respective BLS scores. Whereas the BLS and DTH scores in the field experiment were collected from the same plants, the greenhouse data for DTH was collected in an experiment different from the ones used to evaluate BLS. The lines are highly inbred, so this should not affect the ability to analyze data from different experiments. The average DTH was 50 and 49 days for Boost and 41 and 44 days for ND830 in the greenhouse and field, respectively (Table S3). The average DTH among the BoostND population ranged from 38 to 58 and 43 to 53 in the greenhouse and field experiments, respectively. A strong positive correlation was observed between DTH in the greenhouse and DTH in Prosper with coefficient of correlation (r) of 0.70 (Table S5). The BLS scores in the greenhouse experiments showed a positive correlation (r: 0.42 to 0.43) with the BLS scores in the field. A weak negative correlation (r: -0.15 to -0.25) was observed between the BLS scores and DTH in the greenhouse experiments, and a strong negative correlation (r = -0.76) between BLS scores and DTH in the field experiment was observed. Further analysis revealed a negative correlation (r: -0.19 to − 0.25) between the DTH in the field experiment and the BLS scores in the greenhouse. In the ITMI population, DTH data were collected only from the field experiment at Prosper, ND. A strong negative correlation (r = -0.68) was observed between the BLS scores and DTH in that environment (Table S6). Genotyping and linkage mapping A total of 1,275 SNPs were polymorphic between Boost and ND830 (Table S7). After filtering, a total of 518 SNPs remained for use in linkage analysis (Table S8), which resulted in a total of 27 linkage groups corresponding to 20 of the 21 wheat chromosomes covering a total of 1,337 cM of genetic distance. Only chromosome 4D had no polymorphic markers. Chromosomes 2A, 5A, 7A, 1B, 4B, 1D, and 2D were represented by two unlinked linkage groups for each (Fig. S1 ). A total of 21 KASP markers were developed from the SNPs previously mapped in the ITMI population. Of these, 12 showed clear polymorphism between W-7984 and Opata 85 and were subsequently used to genotype the ITMI population and placed on the genetic linkage map for chromosome 7D (Table S9). These KASP markers were given the designations fcp995-fcp1006 . QTL analysis in the BoostND population QTLs associated with DTH were identified on chromosomes 3B, 4A, 5A, and 7B and designated as QHd.fcu-3B.2 , QHd.fcu-4A , QHd.fcu-5A , and QHd.fcu-7B , respectively (Table S10, Fig. S2 ). QHd.fcu-3B.2 and QHd.fcu-7B were detected only in the greenhouse experiment where they explained 7.60 and 17.00% of phenotypic variation, respectively, whereas QHd.fcu-4A and QHd.fcu-5A were detected in both environments with the two QTLs explaining 8.30 and 7.00% of the variation in the greenhouse and 8.40 and 6.60% of the variation in the field experiment at Prosper, respectively. Earliness at QHd.fcu-3B.2 , QHd.fcu-4A , and QHd.fcu-5A was contributed by ND830, whereas earliness at QHd.fcu-7B was contributed by Boost. BLS analysis in the BoostND population evaluated in greenhouse experiments revealed four QTLs located on chromosomes 3B (2), 5A, and 5B designated as QBls.fcu-3B.1, QBls.fcu-3B.2, QBls.fcu-5A , and QBls.fcu-5B.1 (Table 1 , Fig. 7 ). QBls.fcu-3B.2 was associated with BLS resistance in only one of the two greenhouse experiments where it explained 7.00 and 7.60% of the phenotypic variation for IT and LAI, respectively. The other three QTLs were significantly associated with BLS resistance in both greenhouse experiments and for both LAI and IT scores. The phenotypic variation explained by QBls.fcu-3B.1 , QBls.fcu-5A , QBls.fcu-5B.1 for IT was 37.10 and 37.40, 12.70 and 22.60, and 13.30% and 14.50% for the Fall 2022 and Spring 2023 greenhouse seasons, respectively. For LAI, QBls.fcu-3B.1 , QBls.fcu-5A , and QBls.fcu-5B.1 explained 34.50 and 34.70, 11.16 and 23.70, and 11.30 and 16.10% of the phenotypic variation for the Fall 2022 and Spring 2023 greenhouse environments, respectively. In all cases, the alleles conferring BLS resistance at these loci were contributed by Boost. Table 1 QTLs associated with bacterial leaf streak resistance caused by Xanthomonas transulcens pv. undulosa strain P3 identified in the BoostND and ITMI populations. QTL Name Peak markers Greenhouse experiments Field experiments Donor parent for beneficial allele IT_Fall 2022 LAI_Fall 2022 IT_Spring 2023 LAI_Spring 2023 DS_Fargo_2023 DS_Prosper_2023 LOD a ( R 2 × 100) b LOD a ( R 2 × 100) b LOD a ( R 2 × 100) b LOD a ( R 2 × 100) b LOD a ( R 2 × 100) b LOD a ( R 2 × 100) b QBls.fcu-3B.1 IWB51224 IWB12193 19.11 37.10 17.44 34.50 19.32 37.40 17.55 34.70 5.80 13.10 5.60 12.70 Boost QBls.fcu-3B.2 IWB6915 3.01 7.00 3.24 7.60 - - - - - - - - Boost QBls.fcu-4A IWA54 - - - - - - - - - - 4.05 9.40 Boost QBls.fcu-5A IWB47624 IWA2837 5.58 12.70 4.80 11.00 10.59 22.60 11.16 23.70 14.04 28.90 14.38 29.40 Boost QBls.fcu-5B.1 IWB1196 IWA6773 IWB7549 5.88 13.30 4.92 11.30 6.44 14.50 7.25 16.10 - - - - Boost QBls.fcu-5B.2 IWB35252 IWB73479 IWB7342 - - - - - - - - 4.78 10.90 5.54 12.60 ND830 QBls.fcu-7D Xbarc121-7D IWB39179 IWB41457 NA NA NA NA 13.94 43.10 15.36 46.20 NA NA 5.08 18.60 W-7984 a Logarithm of odds. b Proportion of phenotypic variation explained by QTLs, expressed in percentage. - Not significant. NA: Experiment were not conducted. IT: Infection type score (0–5) LAI: Leaf area infected (%) DS: Disease severity score (0–9) In the field experiments, a total of four QTLs located on chromosomes 3B, 4A, 5A, and 5B were associated with BLS resistance (Table 1 , Fig. 7 ). QBls.fcu-3B.1 and QBls.fcu-5A , which were associated with BLS resistance in the greenhouse experiments, were also associated with BLS resistance in both field environments. QBls.fcu-5B.1 , was not significant under field conditions, but a second locus on chromosome 5B specific to BLS in the field experiments was identified and designated QBls.fcu-5B.2. The phenotypic variation explained by QBls.fcu-3B.1 , QBls.fcu-5A , and QBls.fcu-5B.2 was 13.10 and 12.70, 28.90 and 29.40, and 10.90 and 12.65% in Fargo and Prosper, respectively. The QTL on chromosome 4A, designated QBls.fcu-4A , was significantly associated with BLS resistance in the Prosper experiment where it explained 9.40% of the phenotypic variation, but it was not significant in the Fargo field experiment. Resistance alleles for QBls.fcu-3B.1, QBls.fcu-5A , and QBls.fcu-4A were contributed by Boost. However, the resistance allele at QBls.fcu-5B.2 was contributed by the susceptible parent, ND830. Using the most significant SNP marker associated with each environmentally stable QTL, i.e. QTLs that were significant in both field environments and in both greenhouse seasons, we calculated the average BLS scores for different allelic combinations to further assess their relative effects in the field and greenhouse environments. This included the QTLs QBls.fcu-3B.1 and QBls.fcu-5A. In general, lines that had resistance alleles for both QTLs had lower disease scores compared to lines with resistance alleles for one or no QTLs, indicating their effects were largely additive. In the greenhouse experiments, RILs with resistance alleles at QBls.fcu-3B.1 and QBls.fcu-5A showed the strongest BLS resistance reactions and were nearly as resistant as Boost. Lines that had only one of the two resistance QTLs had higher levels of BLS susceptibility, and RILs with ND830 alleles at both QTLs showing similar levels of susceptibility as ND830 (Fig. 4 S). Similar results were observed for the field environments at Fargo and Prosper (Fig. 8 ). QTLs in the ITMI population Data for DTH in the ITMI population was collected in the field experiment at Prosper and subjected to QTL analysis. QTLs identified on chromosomes 2D and 5D were associated with DTH and designated QHd.fcu-2D and QHd.fcu-5D . These two QTLs explained 13.50 and 23.30% of the phenotypic variation, respectively. (Table S10, Fig. S3). The early heading effects at QHd.fcu-2D and QHd.fcu-5D were contributed by Opata 85. For BLS resistance in the ITMI population, a QTL on chromosome 7D designated as QBls.fcu-7D had LOD values of 13.94 and 15.36 and accounted for 43.10 and 46.20% of the phenotypic variation for IT and LAI scores, respectively, in the greenhouse experiment (Table 1 , Fig. 9 ). Resistance effects for QBls.fcu-7D were contributed by the synthetic hexaploid parent, W-9874. The same QTL was identified in the Prosper field experiment where it explained 18% of phenotypic variation. ITMI RILs that possessed the W-7984 allele at QBls.fcu-7D had lower average disease scores compared to the lines with the Opata 85 allele in both the greenhouse and field experiments. In the greenhouse, the average BLS scores among the lines having the 7D resistance allele was 2.09 for IT and 15.33% for LAI compared to 3.51 and 44.40% for IT and LAI, respectively, among RILs lacking the resistance allele. Similar results were observed in the field experiment, where the average BLS score of the lines having the W-7984 allele on 7D was 2.3, whereas lines with the Opata 85 allele had an average score of 3.71 (Fig. 10 ) KASP Markers In addition to the 12 KASP markers developed for chromosome 7D in the ITMI population, of which fcp1001 was the most closely associated with the peak of QBls.fcu-7D , four KASP markers closely associated with the peaks of QBls.fcu-3B.1 , QBls.fcu-5A , QBls.fcu-5B.1 , and QBls.fcu-5B.2 were developed for the BoostND QTLs. These four markers, designated fcp1007-fcp1010 , were evaluated on the parents and the entire BoostND population to verify that they detected their respective original SNP loci and hence, their corresponding QTLs (Table S11, Fig. S5). Discussion Host resistance is currently the only viable approach for managing bacterial leaf streak disease in wheat. In the Northern Great Plains, the identification and utilization of BLS-resistant sources is a top priority for wheat breeding programs. Here, the screening of 101 diverse tetraploid and hexaploid wheat lines indicated that genetic resistance is rare, which agrees with other studies (Duveiller 1990 ; Duveiller et al. 1993 ; Kandel et al. 2012 ). However, we identified five lines, namely Boost, SW87, W-7984, SW86, and Mace, that consistently exhibited resistance against a highly virulent BLS strain in multiple greenhouse experiments. Amont these five lines, SW86, SW87, and W-7984 are synthetic hexaploid wheat lines developed by crossing durum wheat lines with Ae. tauschii , the diploid D-genome donor of modern hexaploid wheat, which suggests that the resistance to BLS among the synthetic lines is most likely coming from the D-genome. However, the Ae. tauschii accession Clae 26 was not only used to create SW86 and SW87, but also other synthetic hexaploids that were susceptible to BLS (Table S1 ). This suggests that the resistance in SW86 and SW87 is coming from the tetraploid parents, 8155-B1 and 8155-B2. Further genetic analysis is needed to confirm this. Boost and Mace are HRSW cultivars developed in the US and Australia, respectively. The BLS resistance in Boost has been reported before, and it was considered the most promising cultivar in terms of BLS resistance among numerous cultivars tested in the United States (Ledman et al. 2023 ). However, despite its recognition as resistance cultivar for some years, no studies have been conducted to investigate the genetics of BLS resistance in Boost. Additionally, although W-7984 has been extensively investigated for various traits for the past 30 + years (Sorrells et al. 2011 and references therein), its resistance to BLS has not been previously reported or explored. Prior to this, the only major QTL for BLS resistance was identified on chromosome 5R of the triticale accession Siskiyou and designated Xct1 (Wen et al. 2018 ). This resistance locus was contributed by the rye genome. In this study, a total of seven BLS resistance QTLs, six in the BoostND population and one in the ITMI population, were identified. Among the six QTLs in the BoostND population, QBls.fcu-5A and QBls.fcu-3B.1 were expressed in all greenhouse and field environments and represent the most stable and robust BLS resistance QTLs with the largest effects. Both QTLs are novel and have not previously been reported to be associated with BLS resistance in wheat. Together, these two QTLs significantly enhanced the level of BLS resistance. Although they did not confer the same level of resistance as observed in Boost itself, having Boost alleles at both QTLs still accounted for around 70 to 80% of the overall resistance relative to Boost. Friskop et al. ( 2023 ) observed that as long as BLS severity scores were less than 5, yield losses in HRSW varieties was not significant. In our study, having QBls.fcu-5A and QBls.fcu-3B.1 together showed average BLS severity scores less than 5 in both field environments suggesting that stacking the two QTL into adapted HRSW varieties would sufficiently mitigate yield losses attributed to BLS. The magnitudes of QBls.fcu-5A and QBls.fcu-3B.1 varied between the field and greenhouse environments, i.e. QBls.fcu-3B.1 had stronger effects in the greenhouse compared to the field and QBls.fcu-5A had stronger effects in the field compared to the greenhouse. It is possible that these differences reflect the differences in how BLS evaluations were conducted where greenhouse experiments involved a single scoring of juvenile plants whereas field plots were scored multiple times throughout the life of the plants as they reached maturity. It may be that QBls.fcu-3B.1 is more critical for conditioning BLS resistance at the juvenile stage and QBls.fcu-5A has stronger resistance effects in older plants. More studies are needed to determine the effects of these QTLs at different growth stages, but a noteworthy result from a practical standpoint is the finding that both QTLs were detected in greenhouse screenings as well as field environments. This indicates that greenhouse evaluations for the effects of these two QTL can be conducted in the greenhouse on juvenile plants, which can save time, labor, and resources when evaluating the deployment of these QTLs into other materials and backgrounds. The two QTLs identified on chromosome 5B in the BoostND population were environment-specific with QBls.fcu-5B.1 being expressed only in the greenhouse and QBls.fcu-5B.2 observed only in field environments. Another difference between the two 5B QTLs is that the resistance effects of QBls.fcu-5B.1 were derived from Boost, whereas the resistance effects of QBls.fcu-5B.2 were derived from ND830. These observations together with the finding that the two QTL map to opposite chromosome arms make it evident that they are distinct and unique loci. Although the results of this study alone would suggest that QBls.fcu-5B.1 may not be worthy to consider for BLS resistance breeding because its effects were not observed in either of our field studies, more field studies are needed to determine if QBls.fcu-5B.1 may be expressed in other environments. QBls.fcu-4A was significant in only one of the two field environments and QBls.fcu-3B.2 was significant in only one of the greenhouse experiments. It is interesting to note that both QTLs coincide with QTLs for DTH, which might suggest they are partially responsible for the correlations observed between heading time and BLS resistance (see more discussion below). But, as with QBls.fcu-5B.1 , both QTLs warrant further studies to more precisely evaluate their effects on BLS resistance in different environments. The QTL QBls.fcu-7D identified on long arm of chromosome 7D in the ITMI population represents another novel QTL identified in this study. Like QBls.fcu-5A and QBls.fcu-3B.1 in the BoostND population, QBls.fcu-7D was identified in greenhouse evaluations and also in the field thereby making it another robust BLS resistance QTL that can be monitored under greenhouse conditions to save time and labor. Because W-7984 is a synthetic hexaploid wheat, resistance alleles at QBls.fcu-7D are derived from the Ae. tauschii accession used to create W-7984, which is accession 219 (Nelson et al. 1995). Ae. tauschii has proved to be a useful source of novel disease resistance genes for wheat, and it is possible that it may possess additional BLS resistance genes that could be mined for wheat improvement. For now, working to combine the resistance alleles for the QTLs identified in the BoostND population together with QBls.fcu-7D seems to be a prudent method of enhancing BLS resistance in wheat. Other studies have reported an association between BLS resistance and DTH or days to maturity (Milus et al. 1994; Tillman et al. 1996 ; Kandel et al. 2012 ). The results of our study also showed negative correlations between DTH and BLS resistance in field and greenhouse environments, and in both the BoostND and ITMI populations. A partial explanation for this observation is that several genomic regions containing DTH QTLs overlapped with those containing QTLs associated with BLS resistance. Most evident was the finding that QHd.fcu-5A and QBls.fcu-5A overlapped on chromosome 5A, and both QTLs were significantly associated with their respective traits in all environments tested. Although QBls.fcu-4A was significant in only one field experiment, it coincided with QHd.fcu-4A for the same experiment, and it is possible that QHd.fcu-3B could contribute to this phenomenon as well given that a minor coinciding BLS resistance QTL was observed in one greenhouse season. The strength of correlation between the BLS scores and DTH may be partially due to other confounding factors in the field such as escapes, differences in phenology at time of inoculation, and other factors as well. More work is needed to dissect the genetics of BLS resistance and plant development, but breeders should be aware of this association as they work to incorporate BLS resistance into their materials. The BLS resistance QTLs identified in this study should prove useful for the improvement of BLS resistance in common wheat varieties, and the KASP markers that we developed, although not diagnostic, can be used to assist in the monitoring of QTL introgression. Genotypic analysis together with phenotypic screenings for BLS resistance in greenhouse settings should together prove useful for immediate development of BLS-resistant germplasm. Moving forward, it is important to focus on fine-mapping and cloning of the large-effect QTLs to develop diagnostic markers for marker-assisted selection in breeding and to gain better understanding of the genetic mechanisms governing BLS resistance. The QTLs identified in the BoostND population exhibited largely additive effects on BLS resistance, indicating that combining these QTLs would confer greater BLS resistance compared to their individual effects. Therefore, the pyramiding of these QTLs along with the 7D QTL derived from Ae. tauschii may prove to be an effective means of producing wheat varieties with good levels of BLS resistance. Additionally, the search for novel sources of BLS resistance among Ae. tauschii and other wheat relatives may prove useful for the identification of additional alleles and genetic loci to further combat BLS in wheat growing regions. Declarations Competing interests The authors have no relevant financial or non-financial interests to disclose. Author contribution Statement KA, AJG, ZL, JS, and JDF conceived and designed the experiments. KA, PK, and FM performed the experiments. JDF, ZL, SSX, and AJG contributed the materials. KA analyzed the data. The manuscript was drafted by KA, JDF, and AJG and all authors commented on previous version of the manuscript. All the authors read and approved the final version of the manuscript. Ethics approval NA Consent to participate NA Consent to publish NA Funding This research was supported by USDA-ARS CRIS Project: 3060-21000-046-000D. Acknowledgements We would like to thank Fazal Manan, Gongjun Shi, Andre Miranda, and Gabriel Dusek for their help with planting and disease evaluation in greenhouse and fields. We would like to thank Katherine Running, Sudeshi Seneviratne, Zengcui Zhang, Gurminder Singh, Heaven James, and Ava Wiitamaki for their help with greenhouse and field harvesting. 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. USDA is an equal opportunity provider and employer. Data availability The datasets generated during and/or analyzed during the current study are available as supplementary materials and/or from the corresponding author on reasonable request. References Adhikari TB, Gurung S, Hansen JM, Bonman JM (2012a) Pathogenic and genetic diversity of X anthomonas translucens pv. undulosa in North Dakota. Phytopathology 102:390–402 Adhikari TB, Gurung S, Hansen JM, Jackson EW, Bonman JM (2012b) Association mapping of quantitative trait loci in spring wheat landraces conferring resistance to bacterial leaf streak and spot blotch. Plant Genome 5:1–16 Adhikari TB, Hansen JM, Gurung S, Bonman JM (2011) Identification of new sources of resistance in winter wheat to multiple strains of Xanthomonas translucens pv. undulosa . Plant Dis 95:582–588 Akhtar MA, Aslam M (1986) Xanthomonas campestris pv. undulosa on wheat. RACHIS, Barley Wheat Newsl 5:34–37 Arif MAR, Agacka-Mołdoch M, Qualset CO, Börner A (2022) Mapping of additive and epistatic QTLs linked to seed longevity in bread wheat ( Triticum aestivum L). Cereal Res Commun 1–7 Arif MAR, Komyshev EG, Genaev MA, Koval VS, Shmakov NA, Börner A, Afonnikov DA (2022) QTL analysis for bread wheat seed size, shape, and color characteristics estimated by digital image processing. Plants 11:2105. https://doi.org/10.3390/plants11162105 Arif MAR, Shokat S, Plieske J, Ganal M, Lohwasser U, Chesnokov YV, Börner A (2021) A SNP-based genetic dissection of versatile traits in bread wheat ( Triticum aestivum L). Plant J 108:960–976 Ayana G (2017) Molecular characterization of spot blotch and bacterial leaf streak resistance in bread wheat. Electronic Theses and Dissertations. 2260. https://openprairie.sdstate.edu/etd/2260 Bamberg RH (1936) Black chaff disease of wheat. J Agri Res 52:397–417 Cunfer BM, Scolari BL (1982) Xanthomonas campestris pv. translucens on triticale and other small grains. Phytopathology 72:683–686 Duveiller E (1990) Screening criteria for bacterial leaf streak in bread wheat, durum wheat and triticale in CIMMYT. Pages 1011–1016 in: Proc. 7th Int. Conf. Plant Pathogenic Bacteria, Vol. 2. Z. Klement, ed. Akademiai Kiado, Budapest Duveiller E (1990) Seed detection of Xanthomonas campestris pv. undulosa using a modification of Wilbrink’s agar medium. Parastica 40:3–17 Duveiller E, Fucikovsky L, Rudolph K (eds) (1997) The bacterial diseases of wheat: Concepts and methods of disease management. CIMMYT) Duveiller E, van Ginkel M, Thijssen M (1993) Genetic analysis of resistance to bacterial leaf streak caused by Xanthomonas campestris pv. undulosa in bread wheat. Euphytica 66:35–43 Duveiller E, Bragard C, Maraite H (1997) Bacterial leaf streak and black chaf caused by Xanthomonas translucens . In: Duveiller E, Fucikovsky L, Rudolph K (eds) The bacterial diseases of wheat: concepts and methods of disease management. CIMMYT, Mexico, pp 25–47 Duveiller E, Bragard C (2017) CHAPTER 5: Detection of Xanthomonas translucens in Wheat Seeds. In Detection of Plant-Pathogenic Bacteria in Seed and Other Planting Material, Second Edition (pp. 27–32). American Phytopathological Society, St. Paul, MN El Attari H, Sarraf A, Alizadeh A, Dechamp-Guillaume G, Barrault G (1996) Genetic analysis of partial resistance to bacterial leaf streak ( Xanthomonas campestris pv. cerealis ) in wheat. Plant Pathol 45:736–741 Forster RL, Schaad NW (1988) Control of black chaff of wheat with seed treatment and a foundation seed health program. Plant Dis 72:935–938 Friskop A, Green A, Ransom J, Liu Z, Knodel J, Hansen B, Halvorson J, Lux L (2020) Bacterial leaf streak and black chaff of wheat. North Dakota State University Extension Publication PP1566 Friskop A, Green A, Ransom J, Liu Z, Knodel J, Hansen B, Halvorson J, Lux L, Phytopathology (2023) https://doi.org/10.1094/PHYTO-08-22-0282-SA Gurung S, Mamidi S, Bonman JM, Xiong M, Brown-Guedira G, Adhikari TB (2014) Genome-wide association study reveals novel quantitative trait loci associated with resistance to multiple leaf spot diseases of spring wheat. PLoS ONE 9:e108179 Hagborg WAF (1974) Notes on bacterial diseases of cereals and some other crop plants. Can Plant Dis Surv 54:129–151 Huang BE, Verbyla KL, Verbyla AP, Raghavan C, Singh VK, Gaur P, Cavanagh CR (2015) MAGIC populations in crops: Current status and future prospects. Theor Appl Genet 128:999–1017 Joehanes R, Nelson JC (2008) QGene 4.0, an extensible Java QTLanalysis platform. Bioinformatics 24:2788–2727 Johnson JW, Cunfer BM, Morey DD (1987) Inheritance of resistance to Xanthomonas campestris pv. translucens in triticale. Euphytica 36:603–607 Kandel YR, Glover KD, Tande CA, Osborne LE (2012) Evaluation of spring wheat germplasm for resistance to bacterial leaf streak caused by Xanthomonas campestris pv. translucens . Plant Dis 96:1743–1748 Kandel YR, Glover KD, Osborne LE, Gonzalez-Hernandez JL (2015) Mapping quantitative resistance loci for bacterial leaf streak disease in hard red spring wheat using an identity by descent mapping approach. Euphytica 201:53–65 Kosambi DD (1944) The Estimation of Map Distances from Recombination Values. Ann Eugen 12:172–175. https://doi.org/10.1111/j.1469-1809.1943.tb02321.x Ledman KE, Osdaghi E, Curland RD, Liu Z, Dill-Macky R (2023) Epidemiology, host resistance, and genomics of the small grain cereals pathogen Xanthomonas translucens : new advances and future prospects. https://doi.org/10.1094/PHYTO-11-22-0403-SA . Phytopathology Levene H (1960) Robust tests for equality of variances. In: Olkin I, Hotelling H (eds) Contributions to Probability and Statistics: essays in Honor of Harold Hoteling. Stanford University Press, Stanford, CA, pp 278–292 Lorieux M (2012) MapDisto: fast and efficient computation of genetic linkage maps. Mol Breed 30(2):1231–1235 Lux L, Friskop A, Liu Z (2020) Bacterial leaf streak and black chaff of wheat. Mol Plant Pathol 21:291–302 Lux L, Halvorson J, Hansen B, Meyer S, Shi G, Liu Z, Friskop A (2023) Effect of copper sulfate pentahydrate and cultivar resistance on reducing bacterial leaf streak in hard red spring wheat in North Dakota. Plant Health Prog. (In press) Malavolta VA Jr, de Oliveira MAR, de Oliveira AR (2000) Identification and survival of Xanthomonas translucens pv. cerealis in seeds and crop debris of wheat. Summa Phytopathologica 26:20–23 Marino CL, Nelson JC, Lu YH, Sorrells ME, Leroy P, Lopes CR, Hart GE (1996) Molecular genetic maps of the group 6 chromosomes of hexaploid wheat ( Triticum aestivum L. em. Thell) Genome 39:359–366 McMullen M, Adhikari T (2011) Bacterial leaf streak and black chaff of wheat. Plant Disease Management. Fargo, ND, USA: North Dakota State University Extension Publication, PP1566 Milus EA, Duveiller E, Kirkpatrick TL, Chalkey DB (1996) Relationships between disease reactions under controlled conditions and se-verity of wheat bacterial streak in the field. Plant Dis 80:726–730 Milus EA, Mirlohi AF (1994) Use of disease reactions to identify resistance in wheat to bacterial streak. Plant Dis 78:157–161 Nelson JC, Andreescu C, Breseghello F, Finney PL, Gualberto DG, Bergman CJ, Pena RJ, Perretant MR, Leroy P, Qualset CO, Sorrells ME (2006) Quantitative trait locus analysis of wheat quality traits. Euphytica 149:145–159 Nelson JC, Sorrells ME, Van Deynze AE, Lu YH, Atkinson M, Bernard M, Leroy P, Faris JD, Anderson JA (1995a) Molecular mapping of wheat: major genes and rearrangements in homoeologous groups 4, 5, and 7. Genetics 141:721–731 Nelson JC, Van Deynze AE, Autrique E, Sorrells ME, Lu YH, Merlino M, Atkinson M, Leroy P (1995b) Molecular mapping of wheat. Homoeologous group 2. Genome 38:516–524 Nelson JC, Van Deynze AE, Autrique E, Sorrells ME, Lu YH, Negre S, Bernard M, Leroy P (1995c) Molecular mapping of wheat. Homoeologous group 3. Genome 38:525–533 Pandey P, Irulappan V, Bagavathiannan MV, Senthil-Kumar M (2017) Impact of combined abiotic and biotic stresses on plant growth and avenues for crop improvement by exploiting physio-morphological traits. Front Plant Sci 8:537. 10.3389/fpls.2017.00537 Ramakrishnan SM, Sidhu JS, Ali S, Kaur N, Wu J, Sehgal SK (2019) Molecular characterization of bacterial leaf streak resistance in hard winter wheat. PeerJ 7:e7276. http://doi.org/10.7717/peerj.7276 Ramirez-Gonzalez RH, Uauy C, Caccamo M (2015) PolyMarker: a fast polyploid primer design pipeline. Bioinformatics 31:2038–2039 Roder MS, Korzun V, Wendehake K, Plaschke J, Tixier MH, Leroy P, Ganal MW (1998) A microsatellite map of wheat. Genetics 149:2007–2023 Saari EE, Prescott JM (1975) A scale for appraising the foliar intensity of wheat diseases. Plant Dis Rep 59:377–380 Sapkota S (2015) Identification and genomic mapping of resistance to bacterial leaf streak in wheat. MS thesis. North Dakota State University, Fargo, ND Sapkota S, Mergoum M, Liu Z (2020) The translucens group of Xanthomonas translucens : Complicated and important pathogens causing bacterial leaf streak on cereals. Mol Plant Pathol 21:291–302 Sapkota S, Zhang Q, Chittem K, Mergoum M, Xu SS, Liu Z (2018) Evaluation of triticale accessions for resistance to wheat bacterial leaf streak caused by Xanthomonas translucens pv. undulosa . Plant Pathol 67: 595–602 Snedecor GW (1956) Statistical methods: applied to experiments in agriculture and biology. The Iowa state college Song QJ, Shi JR, Singh S, Fickus EW, Costa JM, Lewis J, Gill BS, Ward R, Cregan PB (2005) Development and mapping of microsatellite (SSR) markers in wheat. Theor Appl Genet 110:550–560. 10.1007/s00122-004-1871-x Sorrells ME, Gustafson JP, Somers D, Chao S, Benscher D, Brown-Guedira G, Huttner E, Kilian A, McGuire PE, Ross K, Tanaka J, Wenzl P, Williams K, Qualset CO (2011) Reconstruction of the synthetic W7984 x Opata M85 wheat reference population. Genome 54:875–882 Tillman BL, Harrison SA (1996) Heritability of resistance to bacterial streak in winter wheat. Crop Sci 36:412–418 Tillman BL, Harrison SA, Clark CA, Milus EA, Russin JS (1996) Evaluation of bread wheat germplasm for resistance to bacterial streak. Crop Sci 36:1063–1068 Tillman BL, Kursell WS, Harrison SA, Russin JS (1999) Yield loss caused by bacterial streak in winter wheat. Plant Dis 83:609–614 Van Deynze AE, Dubcovsky J, Gill KS, Nelson JC, Sorrels ME, Dvorak J, Gill BS, Lagudah ES, McCouch SR, Appels R (1995) Molecular-genetic maps for group 1 chromosomes of Triticeae species and their relation to chromosomes in rice and oat. Genome 38:45–59 Wang S, Wong D, Forrest K, Allen A, Chao S, Huang BE, Maccaferri M, Salvi S, Milner SG, Cattivelli L (2014) Characterization of polyploid wheat genomic diversity using a high-density 90,000 single nucleotide polymorphism array. J Plant Biotechnol 12:787–796 Wen A, Jayawardana M, Fiedler J, Sapkota S, Shi G, Peng Z (2018) Genetic mapping of a major gene in triticale conferring resistance to bacterial leaf streak. Theor Appl Genet 13:649–658 Zhu T, Wang L, Rimbert H, Rodriguez JC, Deal KR, De Oliveira R, Choulet F, Keeble-Gagnère G, Tibbits J, Rogers J, Eversole K (2021) Optical maps refine the bread wheat Triticum aestivum cv. Chinese Spring genome assembly. Plant J 107:303–314 Supplementary Files SupplementaryFigures15.docx SupplementaryTables111.xlsx Cite Share Download PDF Status: Published Journal Publication published 13 Nov, 2024 Read the published version in Theoretical and Applied Genetics → Version 1 posted Editorial decision: Major revisions 10 Sep, 2024 Reviewers agreed at journal 13 Jun, 2024 Reviewers invited by journal 11 Jun, 2024 Editor assigned by journal 22 May, 2024 First submitted to journal 21 May, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4456913","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":313334143,"identity":"cf5dd293-71d3-48aa-a087-3c0cc7514bb8","order_by":0,"name":"Krishna Acharya","email":"","orcid":"","institution":"North Dakota State University","correspondingAuthor":false,"prefix":"","firstName":"Krishna","middleName":"","lastName":"Acharya","suffix":""},{"id":313334144,"identity":"82460583-15ac-4963-a7a5-e349dddccca0","order_by":1,"name":"Zhaohui Liu","email":"","orcid":"","institution":"North Dakota State University","correspondingAuthor":false,"prefix":"","firstName":"Zhaohui","middleName":"","lastName":"Liu","suffix":""},{"id":313334145,"identity":"44e212e4-a870-4ce8-8a59-23c12047cfad","order_by":2,"name":"Jeffrey Schachterle","email":"","orcid":"","institution":"Brigham Young University-Provo: Brigham Young University","correspondingAuthor":false,"prefix":"","firstName":"Jeffrey","middleName":"","lastName":"Schachterle","suffix":""},{"id":313334146,"identity":"7661364c-fbfe-4faa-9e90-a2ef9924d594","order_by":3,"name":"Pooja Kumari","email":"","orcid":"","institution":"North Dakota State University","correspondingAuthor":false,"prefix":"","firstName":"Pooja","middleName":"","lastName":"Kumari","suffix":""},{"id":313334147,"identity":"3005a31d-0dce-42c0-8ac9-1cf9a12cda86","order_by":4,"name":"Fazal Manan","email":"","orcid":"","institution":"North Dakota State University","correspondingAuthor":false,"prefix":"","firstName":"Fazal","middleName":"","lastName":"Manan","suffix":""},{"id":313334148,"identity":"90e46cde-c248-4282-9014-898c9895f257","order_by":5,"name":"Steven Xu","email":"","orcid":"","institution":"USDA-ARS Western Regional Research Center","correspondingAuthor":false,"prefix":"","firstName":"Steven","middleName":"","lastName":"Xu","suffix":""},{"id":313334149,"identity":"909a3880-7509-4e0d-b769-375249f5f026","order_by":6,"name":"Andrew Green","email":"","orcid":"","institution":"North Dakota State University","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Green","suffix":""},{"id":313334150,"identity":"36870817-a211-4512-897b-900930a2c3b6","order_by":7,"name":"Justin Faris","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIiWNgGAWjYBACxgYgkcDAwA9mfoCKShDQwtgA1CIJohlnEKMFZpEkiGDmIUYL84z05w8e7mCQ4J99uPmzTc09ewaJ9Ic3fjDUyhkcwGHFjBzDhsQzDBIS5xLbpHOOFSc2SOQYW/YwHDfGo4WxIbGNoY7hDGMbcw5bQgKDRA6bBA/DscSZDbi0pD8EaZGQP8PY/NniXwLIYc8k/+DVkmAI1mJwhrFBmrEtgbFBIsFMmoehJrEfh/cZe94Yzkhsk5AwBDpMsrcvIbGN542xtYzBAWN+HFoM29MffPzZZiMhd4b98Ycf3xLs+dnTH958U1Enx4ZLC8TBSBEBUWlwGIcGBgZ5XBJ1OLWMglEwCkbBiAMAZ7lW/Ks+e8QAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-9653-3287","institution":"USDA-ARS: USDA Agricultural Research Service","correspondingAuthor":true,"prefix":"","firstName":"Justin","middleName":"","lastName":"Faris","suffix":""}],"badges":[],"createdAt":"2024-05-21 20:06:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4456913/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4456913/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00122-024-04767-x","type":"published","date":"2024-11-13T15:58:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":59179329,"identity":"a93008c1-c291-4507-a850-be12cf592662","added_by":"auto","created_at":"2024-06-27 10:17:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":725873,"visible":true,"origin":"","legend":"\u003cp\u003eInfection type-based rating scale for wheat seedlings artificially infected with bacterial leaf streak pathogen, Xanthomonas translucens pv. undulosa in the greenhouse where a score of 0 = immunity, or no reaction; 1 = small chlorotic spots; 2 = short, thin water-soaked streaks ≤ 2 mm in length; 3 = water-soaked lesions 0.5-1 mm wide x 3-10 mm long; 4 = large water-soaked lesions 1-2 mm wide x 10-30 mm long with no overlap; and 5 = very large water-soaked blotches covering nearly the entire leaf.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4456913/v1/444effaf2f11d02e66f81cf9.png"},{"id":59179327,"identity":"1db5d0c4-db44-4f63-be47-dc9682169ac8","added_by":"auto","created_at":"2024-06-27 10:17:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":9061,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the reactions of the 101 wheat genotypes to bacterial leaf streak caused by Xanthomonas transulcens pv. undulosa (P3: North Dakota strain) in multiple greenhouse experiments. Two disease scoring parameters (infection type: 0-5 and leaf area infected: 0-100 %) were used for disease evaluation.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4456913/v1/4fbe7164c89a2990502798df.png"},{"id":59180042,"identity":"3129bdbd-0ba4-492d-b4ba-a5f50200cffe","added_by":"auto","created_at":"2024-06-27 10:25:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2340874,"visible":true,"origin":"","legend":"\u003cp\u003eBLS reaction on Boost, ND830, W-7984, and Opata 85, the parents of the two recombinant inbred line populations (BoostND and ITMI) to bacterial leaf streak caused by Xanthomonas transulcens pv.undulosa (P3: North Dakota strain) 6 days after inoculation in the greenhouse experiment.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4456913/v1/c856ab0613ee263b0d0b61cf.png"},{"id":59179331,"identity":"793c6877-bd33-4ada-a018-ac123f5da309","added_by":"auto","created_at":"2024-06-27 10:17:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1109060,"visible":true,"origin":"","legend":"\u003cp\u003eParental lines of the BoostND and ITMI populations (a: Boost, b: ND830, c: W-7984, and d: Opata 85) after artificial inoculation with the bacterial leaf streak pathogen, Xanthomonas transulcens pv. undulosa in field experiment at Prosper, ND.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4456913/v1/83c60dbbdf9143be6b57cd19.png"},{"id":59179333,"identity":"ef15846c-c250-4e09-a502-5a4a70ee37ed","added_by":"auto","created_at":"2024-06-27 10:17:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":25603,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the reactions of the BoostND population to bacterial leaf streak caused by Xanthomonas transulcens pv. undulosa (P3: North Dakota strain) in the greenhouse\u003cstrong\u003e (a)\u003c/strong\u003e and field (\u003cstrong\u003eb)\u003c/strong\u003e experiments (Fargo and Prosper, North Dakota). In the greenhouse, two disease scoring parameters (infection type: 0-5, and leaf area infected: 0-100 %) were used, while disease severity (DS) based disease score (0-9) was used for disease evaluation in the field experiments.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4456913/v1/8564e501ca5a80e8d20777da.png"},{"id":59180521,"identity":"c1c01aea-f332-47d5-a270-250f401a44ba","added_by":"auto","created_at":"2024-06-27 10:33:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":8258,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of reactions to bacterial leaf streak caused by Xanthomonas translucens pv. undulosa (P3: North Dakota strain) among the recombinant inbred lines of the ITMI population in the greenhouse and field experiments. In the greenhouse, two disease scoring parameters (Infection type: 0-5, and leaf area infected: 0-100 %) were used, while disease severity (DS) based disease score (0-9) was used for the field evaluation.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4456913/v1/e1b253ebccf49ad915cd321d.png"},{"id":59180520,"identity":"e19a4c94-e255-4769-921e-1ff9222c08ee","added_by":"auto","created_at":"2024-06-27 10:33:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":210352,"visible":true,"origin":"","legend":"\u003cp\u003eQTLs identified for resistance to bacterial leaf streak (BLS) on chromosomes 3B, 4A, 5A, and 5B in the BoostND population in different greenhouse and field experiments. The marker loci and genetic position are shown to the left and right, respectively, along the genetic linkage maps. The physical maps with black solid box as the position of centromeres indicate the relative physical positions of the markers along the chromosomes based on the Chinese Spring reference genome. A dashed line indicates a LOD cutoff of 3.0. In legends, IT stands for infection type score (0-5) and LAI stands for leaf area infected in percentage (0-100 %), DTH stand for days to heading, and disease severity (DS) based disease score (0-9) was used for field evaluation in Fargo and Prosper, ND.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4456913/v1/38c6661d77f9f8a3c168e7fc.png"},{"id":59180040,"identity":"d567af9a-a0b0-4682-8c53-f8de6a59243d","added_by":"auto","created_at":"2024-06-27 10:25:51","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":14921,"visible":true,"origin":"","legend":"\u003cp\u003eAdditive effects of the major QTLs for resistance to bacterial leaf streak (BLS) on chromosomes 3B (\u003cem\u003eQBls.fcu-3B.1\u003c/em\u003e) and 5A (\u003cem\u003eQBls.fcu-5A\u003c/em\u003e)\u003cem\u003e \u003c/em\u003eof the BoostND population in field experiments at Fargo and Prosper, ND. Resistant and susceptible allele of the peak marker of each QTL and the average BLS scores of the RILs were used for analysis. Disease severity (DS) based disease score (0-9) was used for field evaluation. Means with the same small letters are not significantly different at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4456913/v1/4272e42e12973fc0ec98011b.png"},{"id":59179336,"identity":"eabd8070-40dd-446a-ab2d-7c6ebff287c2","added_by":"auto","created_at":"2024-06-27 10:17:51","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":29409,"visible":true,"origin":"","legend":"\u003cp\u003eQTL for resistance to bacterial leaf streak (BLS) identified on chromosome 7D in the ITMI population in a greenhouse experiment and a field experiment in Prosper, ND in 2023. The marker loci and genetic position are shown to the left and right, respectively, along the linkage map. The physical map with black solid box as the position of centromeres indicates the relative physical positions of the markers along the chromosome based on the Chinese Spring reference genome. A dashed line indicates a LOD cutoff of 3.0 for single trait interval mapping. In legends, IT stands for infection type score (0-5) and LAI stands for leaf area infected in percentage (0-100 %), DTH stand for days to heading, and disease severity (DS) based disease score (0-9) was used for field evaluation in Prosper, ND.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4456913/v1/c16e89c76cf959d3365154fb.png"},{"id":59179339,"identity":"1041252f-fb4b-4f46-b110-9f3466e44322","added_by":"auto","created_at":"2024-06-27 10:17:51","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":16256,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of the major QTL for resistance to bacterial leaf streak (BLS) on chromosomes 7D (QBls.fcu-7D) of the ITMI population in the greenhouse and field (Prosper) experiments. Resistant and susceptible allele of the peak marker of the QTL and the average BLS scores of the RILs were used for analysis. Infection type score (0-5) and leaf area infected (0-100 %) were used for disease evaluation in the greenhouse and disease severity (DS) based disease score (0-9) was used for field evaluation. Means with the same small letters are not significantly different at P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4456913/v1/c02b938de0816f86d088b60c.png"},{"id":69285218,"identity":"53315c13-1300-4bbc-830c-28d57f4609ac","added_by":"auto","created_at":"2024-11-18 19:24:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4915536,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4456913/v1/843634e5-e65d-4c3b-9f33-f80121a44b7e.pdf"},{"id":59179341,"identity":"dcdfb7a7-9cd1-486a-a3e3-7cc85868a1b3","added_by":"auto","created_at":"2024-06-27 10:17:52","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":4208922,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures15.docx","url":"https://assets-eu.researchsquare.com/files/rs-4456913/v1/4f3f525b18708496f55be853.docx"},{"id":59179340,"identity":"9f8afd80-330e-4ff6-8fa9-42df29439821","added_by":"auto","created_at":"2024-06-27 10:17:51","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":459489,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables111.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4456913/v1/1f6f2f5cff78b8c55ab78597.xlsx"}],"financialInterests":"","formattedTitle":"Genetic mapping of QTLs for resistance to bacterial leaf streak in hexaploid wheat","fulltext":[{"header":"Key Message","content":"\u003cp\u003eRobust QTLs conferring resistance to bacterial leaf streak in wheat were mapped on chromosomes 3B and 5A from the variety Boost and on chromosome 7D from the synthetic wheat line W-7984.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eWheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L., 2\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6\u003cem\u003ex\u003c/em\u003e\u0026thinsp;=\u0026thinsp;42, AABBDD genomes) is one of the major cereal crops in the world. The Northern Plains is a major hard red spring wheat (HRSW) producer in the United States providing about 25 percent of the total U.S. wheat production (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ers.usda.gov/topics/crops/wheat/wheat-sector-at-a-glance/#classes\u003c/span\u003e\u003cspan address=\"https://www.ers.usda.gov/topics/crops/wheat/wheat-sector-at-a-glance/#classes\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e); however, its production and seed quality have been constrained by various biotic and abiotic stresses (Pandey et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Bacterial leaf streak (BLS), caused by \u003cem\u003eXanthomonas translucens\u003c/em\u003e pv. \u003cem\u003eundulosa\u003c/em\u003e (\u003cem\u003eXtu\u003c/em\u003e) (Smith et al. 1919; Vauterin et al. 1995) has been recognized as an important foliar disease affecting wheat on a global scale (Bamberg \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1936\u003c/span\u003e; Duveiller \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Friskop et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This bacterial disease produces symptoms on leaves observed as translucent stripes leading to elongated large brown lesions, and in later stages there is discoloration of the peduncle and alternate black bands referred to as a black chaff on the spike (Duveiller and Bragard \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBLS has emerged as a threat to spring wheat production in the northern Great Plains causing substantial yield losses and reductions in grain quality (Forster et al. 1988; Tillman et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; McMullen and Adhikari \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kandel et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sapkota et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Friskop et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A recent study conducted in North Dakota demonstrated that losses due to BLS in wheat can reach up to 60% when highly susceptible cultivars are planted (Friskop et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Infected seeds and crop debris are believed to be the primary sources of inoculum for BLS, therefore clean seeds are recommended to reduce disease incidence (Milus and Mirlohi \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Malavolta et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Given that chemical control under field conditions for foliar bacterial diseases is neither economical nor practical, the deployment of genetically resistant cultivars is the most viable option for BLS management (Duveiller et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; McMullen and Adhikari \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lux et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany wheat accessions, cultivars, breeding lines, wheat relatives, and landraces have been screened for reaction to BLS, but few sources with good levels of genetic resistance have been reported (Hagborg \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1974\u003c/span\u003e; Akhtar and Aslam \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Duveiller et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Milus et al. 1994; Tillman et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Adhikari et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kandel et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sapkota et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). One study on resistance to BLS by Duveiller et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) indicated five major genes (\u003cem\u003eBls1, Bls2, Bls3, Bls4\u003c/em\u003e, and \u003cem\u003eBls5\u003c/em\u003e) conferring BLS resistance among three partially resistant wheat cultivars (Pavon 76, Mochis T88, and Angostura F88), with \u003cem\u003eBls1\u003c/em\u003e present in all three wheat cultivars and having the largest effect. Polygenic control of BLS resistance was suggested by Milus and Chalkey (1994) by evaluating 19 wheat cultivars against 81 \u003cem\u003eXtu\u003c/em\u003e strains. Later, Tillman and Harrison (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) reported multiple genes controlling BLS resistance in three F\u003csub\u003e2\u003c/sub\u003e populations derived from wheat cultivars Terral 101 (resistant), Coker 9877 (moderately resistant), Pioneer 2548 (susceptible), and Coker 9766 (susceptible), further confirming that BLS resistance was quantitatively controlled. However, Cunfer and Scholari (1982) and Johnson et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1987\u003c/span\u003e) suggested that a few triticale lines (Siskiyou, M2A-Beagle, and OK77842) had qualitative and dominant BLS resistance genes, and Wen et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) mapped a major gene in \u0026lsquo;Siskiyou\u0026rsquo; on chromosome 5R closely linked to the previously described \u003cem\u003eXct1\u003c/em\u003e locus (Johnson et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, QTL mapping and genome-wide association studies (GWAS) have been conducted to identify genomic regions and linked DNA markers associated with BLS resistance. A GWAS conducted using 566 spring wheat landraces with diversity array technology (DArT) markers revealed five genomic regions on chromosomes 1A, 4A, 6B, and 7D conferring BLS resistance (Adhikari et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e). Gurung et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) utilized the same panel genotyped with single nucleotide polymorphism (SNP) markers and identified a total of five genomic regions on chromosomes 1A, 2B, 3A, 5D, and 6B, including regions on chromosomes 1A and 6B similar to those identified by Adhikari et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e). Other studies have reported QTLs for BLS resistance on chromosomes 1B, 2A, 2B, ,6D, 7A, and 7B (Kandel et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sapkota \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ayana \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and a GWAS conducted by Ramakrishnan et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) on a hard red winter wheat panel revealed BLS resistance QTLs on chromosomes 1A, 1B, 3A, 4A, and 7A.\u003c/p\u003e \u003cp\u003eThe findings from the studies mentioned above revealed only partial resistance QTLs that explained small amounts of the phenotypic variation in BLS. None of them reported the discovery of major genes or QTLs that could be used effectively in breeding programs for BLS resistance. Here, our objectives were to 1) screen a set of diverse wheat accessions for reaction to a highly virulent strain of \u003cem\u003eXtu\u003c/em\u003e to identify sources with good levels of BLS resistance; and 2) identify QTLs associated with BLS resistance in biparental populations derived from two of the best resistant lines. This research led to the identification of QTLs with large effects on BLS resistance that should be useful for the development of BLS-resistant wheat varieties by pyramiding through marker-assisted selection.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials\u003c/h2\u003e \u003cp\u003eA total of 101 wheat lines (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) including hexaploid common (bread) wheat varieties, landraces, and synthetic hexaploid accessions; tetraploid durum (\u003cem\u003eTriticum turgidum\u003c/em\u003e L. ssp. \u003cem\u003edurum\u003c/em\u003e (Desf.) Husnot., 2\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4\u003cem\u003ex\u003c/em\u003e\u0026thinsp;=\u0026thinsp;28, AABB genomes) varieties; and accessions of the durum wheat progenitor known as wild emmer wheat (\u003cem\u003eT. turgidum\u003c/em\u003e ssp. \u003cem\u003edicoccoides\u003c/em\u003e (K\u0026ouml;rn.) Thell (2\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4\u003cem\u003ex\u003c/em\u003e\u0026thinsp;=\u0026thinsp;28, AABB genomes) were screened for reaction to \u003cem\u003eXtu\u003c/em\u003e under greenhouse conditions. Based on these results, two bi-parental mapping populations were selected for identifying genetic loci associated with BLS resistance in wheat.\u003c/p\u003e \u003cp\u003eThe first mapping population consisted of 190 F\u003csub\u003e5:6\u003c/sub\u003e recombinant inbred lines (RILs) developed by single seed-decent from a cross between the South Dakota HRSW variety \u0026lsquo;Boost\u0026rsquo; and the unreleased North Dakota HRSW breeding line \u0026lsquo;ND830\u0026rsquo;, hereafter referred to as the BoostND population. Boost (SD3900//FN1705-146/SD3851), developed by the spring wheat breeding program at South Dakota State University in 2015, exhibited notable resistance to BLS. In contrast, ND830 (ND744/ND721//Faller'S\u0026rsquo;) was susceptible to BLS.\u003c/p\u003e \u003cp\u003eThe second mapping population evaluated was the International Triticeae Mapping Initiative (ITMI) population, which was derived from a cross between the synthetic hexaploid wheat W-7984 (also known as M6) and the International Maize and Wheat Improvement Center (CIMMYT)-bred HRSW variety Opata 85 (PI 591776) (Nelson et al. 1995). This population was developed by single seed-decent by investigators of the ITMI in a collaborative effort to map and characterize numerous traits in hexaploid wheat (Sorrells et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2011\u003c/span\u003e and references therein). W-7984, also developed at CIMMYT, was developed by crossing the durum wheat variety Altar 84 and the \u003cem\u003eAegilops tauschii\u003c/em\u003e accession 219. A total of 114 RILs of the ITMI population were evaluated in both greenhouse and field experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGreenhouse evaluations for BLS\u003c/h2\u003e \u003cp\u003eTwo seeds of each line were planted in cones (4 cm diameter \u0026times; 13 cm deep, Stuewe \u0026amp; Sons, Inc., Corvallis, OR USA) filled with Sunshine SB100 soil (Sun Grow Horticulture, Bellevue, WA, USA) and a quarter teaspoon of \u0026lsquo;Osmocote Plus\u0026rsquo; 15-19-12 fertilizer (Scotts Sierra Horticultural Product Company, Maysville, OH, USA). After planting, a completely randomized design with two replications was employed by placing the cones in RL98 trays (Stuewe \u0026amp; Sons, Inc., Corvallis, OR USA). To mitigate potential edge effects, the highly susceptible HRSW cultivar RB07 was planted along the outer borders of each RL 98 tray. The plants were grown in the greenhouse until the two- to three-leaf stage (14 days after planting under our greenhouse conditions) before spray inoculation. For the 101 wheat lines, every line was evaluated in a minimum of three and a maximum of six experiments collectively. In each experiment, there were two replications for each entry. For the BoostND population, a total of six experiments were carried out. These experiments were divided into two seasons, with three experiments conducted in the fall of 2022 and three in the spring of 2023. The ITMI population was evaluated in the spring of 2023 in three experiments with each experiment consisting of two replications. In all greenhouse experiments, an experimental unit consisted of two individual plants of a single line planted in a single cone.\u003c/p\u003e \u003cp\u003eThe assessment of BLS involved spray-inoculating 14-day-old plants following the methodology outlined by El Attari et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) and Wen et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) with necessary adaptations. The \u003cem\u003eXtu\u003c/em\u003e strain P3 (North Dakota strain; Adhikari et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012a\u003c/span\u003e) was cultured as inoculum as described in Adhikari et al (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e). The inoculum preparation involved streaking bacterial strains from the stock stored at -80\u0026deg;C onto Wilbrink\u0026rsquo;s agar plates (WBA: 5 g bactopeptone; 10 g sucrose; 0.5 g dibasic potassium phosphate (K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e); 0.25 g magnesium sulfate heptahydrate (MgSO\u003csub\u003e4\u003c/sub\u003e.7H\u003csub\u003e2\u003c/sub\u003eO); 0.05 g sodium sulfate anhydrous (Na\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e3\u003c/sub\u003e); 15 g agar; 75 mg cycloheximide; 1 L deionized water) (Duveiller et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The plates were then incubated for 48 hours at 28\u0026deg;C to cultivate the \u003cem\u003eXtu\u003c/em\u003e culture. The final inoculum was prepared by suspending the bacterial culture in a 1\u0026times; phosphate saline buffer, pH 7.4, maintaining the bacterial cell concentration at an optical density (OD600) of 0.5, which approximately corresponds to 1\u0026times;10\u003csup\u003e8\u003c/sup\u003e colony-forming units per ml in bacteria suspension. Before inoculation, two drops of Tween-20 (polyoxyethylene sorbitan monolaurate, Sigma-Aldrich) per 100 ml were added to the final bacterial suspension to ensure even distribution during the spraying of the inoculum. The bacterial suspension was applied to plants until runoff using a spray gun attached to an air hose with a pressure of approximately 20 psi. The inoculated plants were promptly transferred to misting chambers for 2 days at room temperature under a 16-hour photoperiod and 100% relative humidity (RH). Following the 2-day inoculation period, the plants were moved to the greenhouse and placed in water trays where the room was maintained at 75% RH and 28\u0026ndash;30\u0026deg;C. Disease assessment occurred 5\u0026ndash;6 days after inoculation based on the symptoms observed on the susceptible check. Each line was scored using an infection type (IT) scale that ranged from 0 to 5 and for percentage of leaf area infected (LAI:0-100%). For IT, a score of 0\u0026thinsp;=\u0026thinsp;immunity, or no reaction; 1\u0026thinsp;=\u0026thinsp;small chlorotic spots; 2\u0026thinsp;=\u0026thinsp;short, thin water-soaked streaks\u0026thinsp;\u0026le;\u0026thinsp;2 mm in length; 3\u0026thinsp;=\u0026thinsp;water-soaked lesions 0.5-1 mm wide x 3\u0026ndash;10 mm long; 4\u0026thinsp;=\u0026thinsp;large water-soaked lesions 1\u0026ndash;2 mm wide x 10\u0026ndash;30 mm long with no overlap; and 5\u0026thinsp;=\u0026thinsp;very large water-soaked blotches covering nearly the entire leaf (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDisease evaluations in the field\u003c/h2\u003e \u003cp\u003eThe BoostND population was evaluated for BLS at two locations, one in Fargo, ND and the other near Prosper, ND, during the summer of 2023. The ITMI population was assessed for BLS at the location near Prosper, ND. In both locations, each line was evaluated in hill plots consisting of 12\u0026ndash;15 seeds per hill with three replicates per line arranged in an optimized row: column design with resistant and susceptible checks arranged throughout the field spatially. The experimental entries were completely randomized. Four hills were arranged in a 1.2 m row spaced 0.30 m apart. Plants at the 4 to 5 Feekes growth stage were inoculated using a Stihl leaf blower with a 12 L tank (STIHL Inc., Virginia Beach, VA) as described in Lux et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The inoculum was prepared as described for the greenhouse experiments where the secondary culture (one Petri plate) was suspended in 3 L of a 0.85% saline solution resulting in an approximate concentration of 1 \u0026times; 10\u003csup\u003e8\u003c/sup\u003e colony-forming units per milliliter (CFU/ml) of the bacteria. Silicon carbide (grit 320; Alfa Aesar, Heysham, England) at a concentration of 1 g/L was introduced to the inoculum mixture to facilitate leaf surface wounding during inoculation. The inoculation process was repeated once after one week if the disease did not manifest or if environmental conditions were unfavorable.\u003c/p\u003e \u003cp\u003eFor the Fargo experiment, an artificial misting system was available in the research field. Misting was applied for 5 minutes in 15-minute intervals for 12 hours daily, spanning from 4:00 pm to 4:00 am for 14 days after anthesis of the latest maturing lines. The Prosper location lacked a misting system, allowing the disease to occur in an artificially inoculated natural environment. A disease severity (DS) rating score (0\u0026ndash;9) was assigned using a rating scale by Saari and Perscott (1975) with the required modification by estimating the percentage of overall leaf area infected (refer to Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e) at the late milk to early dough development stage of wheat.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEvaluation for days to heading\u003c/h3\u003e\n\u003cp\u003eAn experiment was conducted in the greenhouse to assess the days to heading (DTH) of RILs in the BoostND population in the spring of 2023. A single seed of each RIL and the parents was planted in a randomized complete block design with three replications. Similarly, in the field experiment conducted in Prosper, ND, both the BoostND and ITMI populations were scored for DTH. In the greenhouse, DTH for each line was measured as the time between the planting date and the date when the first head appeared and was exposed from the flag leaf. In the field, DTH for each line was measured as the time between the planting date and the date when 50% of the tillers had headed.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eSAS 9.4 (SAS Institute, Cary, NC, USA) was used for all data analysis. Bartlett\u0026rsquo;s Chi-squared or Levene\u0026rsquo;s test were used to assess homogeneity of variances to determine if replicates within experiments were homogeneous and could be combined for further analysis (Snedecor \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1956\u003c/span\u003e; Levene \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1960\u003c/span\u003e). Homogeneous replicates within experiments were combined and used to calculate average BLS scores, which were used for further analysis. Replicates within experiments were analyzed separately if they were determined not to be homogeneous. Fisher\u0026rsquo;s least significant difference (LSD) was employed to determine significant differences in the disease scores among RILs at α\u0026thinsp;=\u0026thinsp;0.05. Pearson correlations between DTH and BLS scores in the greenhouse and field experiments of the BoostND and ITMI populations were examined at α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGenotyping and linkage mapping\u003c/h2\u003e \u003cp\u003eThe 190 RILs and parents of the BoostND population were genotyped using the Illumina 90K SNP array (Wang et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Leaf tissue was collected from each line at the 2\u0026ndash;3 leaf stage. DNA extraction followed the ammonium acetate method outlined by Pallota et al. (2003) and was subsequently diluted to a concentration of 40 ng/\u0026micro;l. The genotyping assay was carried out at the USDA small grains genotyping laboratory in Fargo, ND, USA, utilizing a BeadStation and iScan instrument from Illumina. Data clustering analysis was conducted using the software GenomeStudio 2.0.5 from Illumina, Inc. (2020). SNP markers were assigned to chromosomes based on Wang et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and linkage analysis for each chromosome was done independently. The SNPs having minor allele frequency less than 0.01 and more than 50% missing data were removed from the analysis. Genetic linkage maps were constructed using Mapdisto V2.1.8.7 (Lorieux \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The initial organization of markers into groups was done using the \"find groups\" command with a minimum LOD of 3.0 and a maximum theta of 0.30. The \"order\" sequence command established the initial marker order within a linkage group. Subsequent refinement of the sequence involved using the \"check inversions,\" \"ripple order,\" and \"drop locus\" commands to optimize the map. Map distances were calculated using the Kosambi mapping function (Kosambi 1943).\u003c/p\u003e \u003cp\u003eThe marker data for 114 RILs of the ITMI population were publicly available and retrieved from the GrainGenes database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://wheat.pw.usda.gov\u003c/span\u003e\u003cspan address=\"http://wheat.pw.usda.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The dataset of almost 2,000 markers consisted mostly of restriction fragment length polymorphism (RFLP) markers (Van Deynze et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Nelson et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1995a\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1995b\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1995c\u003c/span\u003e; Marino et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) and microsatellite markers (Roder et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Song et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Markers previously used to construct the linkage map of chromosome 7D were used along with additional KASP markers (see below) to reconstruct the linkage group for chromosome 7D. After linkage maps were assembled, they were anchored to physical maps based on the locations of SNP marker sequences in the Chinese Spring RefSeq v2.1 genome assembly (Zhu et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to determine the physical locations of the linkage groups along the chromosomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eQTL analysis\u003c/h2\u003e \u003cp\u003eQTL analysis was conducted using QGENE 4.3.10v (Joehanes and Nelson \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) employing the composite interval mapping (CIM) and single-trait multiple interval mapping (MIM) functions. All the three phenotypic scores, including IT and LAI from the greenhouse and DS from the field, were used in the analysis. A permutation test consisting of 1000 permutations established a LOD significance threshold of 3.0 for CIM or MIM at a significance level of 0.05. The coefficient of determination (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) was computed for each QTL, providing an estimate of the phenotypic variation explained for BLS resistance. Markers significantly associated with each QTL were subjected to BLASTn searches against the Chinese Spring RefSeq v2.1 genome assembly (Zhu et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) via the Graingenes website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wheat.pw.usda.gov/GG3/\u003c/span\u003e\u003cspan address=\"https://wheat.pw.usda.gov/GG3/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to obtain physical positions for cross-environment QTL comparisons. All the linkage groups were utilized in the initial QTL analysis and the linkage groups containing significant QTLs were reevaluated after removal of redundant and co-segregating markers. The results were confirmed and compared with the initial results from the analysis with the entire SNP dataset. The additive effects of the identified QTLs in the BoostND population were assessed using the genotypic data of the most significant markers for each QTL alongside the average BLS score in each experiment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eKASP marker development\u003c/h2\u003e \u003cp\u003eThe SNP markers most closely associated with the peaks of each of the major QTLs were converted to KASP markers using Polymarker (Ramirez-Gonzalez et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which aligned the SNP sequences with wheat cv. Chinese Spring RefSeq v1.0 and provided two allele-specific primers and one common primer for each assay. Validation of the markers in unique regions in the wheat genome cv. Chinese Spring RefSeq v2.0 assembly was ensured through the physical position and specificity. KASP markers not meeting the standard obtained through Polymarker were manually redesigned using available SNP sequences and the wheat genome cv. Chinese Spring RefSeq v2.0. For the ITMI population, KASP markers for chromosome 7D were designed using polymorphic SNPs for W-9874 and Opata 85 mentioned in Arif et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e and \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and added to the 7D linkage map.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eReaction of wheat lines to BLS\u003c/h2\u003e \u003cp\u003eGreenhouse evaluations of the 101 wheat lines for BLS resistance showed that Boost was the most resistant line with an IT score of 1.15 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Nine lines (8%) were moderately resistant with scores between 1.50 and 2.50, and the remaining lines were moderately to highly susceptible with IT scores greater than 2.5. For LAI, five lines showed less than 10% BLS and were considered highly resistant. Another six lines showed between 10 and 20% BLS and were considered moderately resistant. All remaining lines showed more than 20% BLS and were considered moderately to highly susceptible.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe five most resistant lines overall were Boost, W-7984, SW86, SW87, and Mace. As mentioned in the materials and methods, Boost is a HRSW variety and W-7984 is a synthetic hexaploid wheat line. SW86 and SW87 are both synthetic hexaploid wheat lines as well, and they were derived by crossing the durum lines 8155-B1 and 8155-B2 with \u003cem\u003eAe. tauschii\u003c/em\u003e accession CIae 26 (Szab\u0026oacute;-Hev\u0026eacute;r et al. 2018). Mace is an Australian-bred HRSW (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Because Boost and W-7984 were resistant to \u003cem\u003eXtu\u003c/em\u003e (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and RIL populations derived from both lines were available, we pursued the evaluation of the BoostND and ITMI populations to identify QTLs associated with BLS resistance derived from these two highly resistant lines.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of the BoostND population for BLS\u003c/h2\u003e \u003cp\u003eIn the greenhouse experiments, significant differences (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05) were observed among the average disease scores (IT and LAI) for the RILs of the BoostND population in each of the experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Table S3). In these two experiments, Boost was highly resistant with average IT scores of 1.33 and 1.50 and percentage of LAI scores of 2.33 and 5.80%, whereas ND830 was susceptible with average IT scores of 3.50 and 3.75 and LAI scores of 45.00 of 55.83% in the Fall 2022 and Spring 2023 greenhouse experiments, respectively. The average IT scores of the BoostND RILs ranged from 1.33 to 4.08 and 1.31 to 4.09 in Fall 2022 and Spring 2023, respectively, and the average percentage of LAI scores for the RILs ranged from 1.83 to 57.70% and 2.47 to 68.16% for Fall 2022 and Spring 2023, respectively (Table S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the field experiments at Fargo and Prosper, Boost showed strong resistance with an average DS score of 3.00 (ranging from 2 to 5), whereas ND830 was susceptible with average DS scores ranging from 6.00 to 8.00 in the two locations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Significant differences (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.5) were observed among the RILs of the BoostND population with average DS scores ranging from 2.33 to 8.00 and 2.33 to 9.00 for the field experiments in Fargo and Prosper, respectively (Table S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of the ITMI population for BLS\u003c/h2\u003e \u003cp\u003eIn the greenhouse experiment, significant differences (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.5) were observed among the ITMI RILs for both IT and LAI (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The average scores for W-7984 were 1.42 and 7.83% for IT and LAI, respectively, whereas Opata 85 had IT and LAI scores of 3.92 and 52.50%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Table S4). The average IT scores among the RILs of the ITMI population ranged from 0.58 to 4.67, and the average percentage of LAI was from 1.50\u0026ndash;73.33% in the spring 2023 greenhouse experiment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the field experiment at Prosper, ND, W-7984 showed strong resistance with a DS score of 1.33, whereas Opata 85 had a DS score of 5.00. Significant differences (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.5) were observed among the RILs of the ITMI population (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Table S4) where the average DS scores ranged from 1.00 to 6.50.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of BLS with days to heading\u003c/h2\u003e \u003cp\u003eFor the BoostND population, mean values for DTH collected in the greenhouse experiment and the field experiment at Prosper were assessed for correlation with their respective BLS scores. Whereas the BLS and DTH scores in the field experiment were collected from the same plants, the greenhouse data for DTH was collected in an experiment different from the ones used to evaluate BLS. The lines are highly inbred, so this should not affect the ability to analyze data from different experiments. The average DTH was 50 and 49 days for Boost and 41 and 44 days for ND830 in the greenhouse and field, respectively (Table S3). The average DTH among the BoostND population ranged from 38 to 58 and 43 to 53 in the greenhouse and field experiments, respectively.\u003c/p\u003e \u003cp\u003eA strong positive correlation was observed between DTH in the greenhouse and DTH in Prosper with coefficient of correlation (r) of 0.70 (Table S5). The BLS scores in the greenhouse experiments showed a positive correlation (r: 0.42 to 0.43) with the BLS scores in the field. A weak negative correlation (r: -0.15 to -0.25) was observed between the BLS scores and DTH in the greenhouse experiments, and a strong negative correlation (r = -0.76) between BLS scores and DTH in the field experiment was observed. Further analysis revealed a negative correlation (r: -0.19 to \u0026minus;\u0026thinsp;0.25) between the DTH in the field experiment and the BLS scores in the greenhouse.\u003c/p\u003e \u003cp\u003eIn the ITMI population, DTH data were collected only from the field experiment at Prosper, ND. A strong negative correlation (r = -0.68) was observed between the BLS scores and DTH in that environment (Table S6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGenotyping and linkage mapping\u003c/h2\u003e \u003cp\u003eA total of 1,275 SNPs were polymorphic between Boost and ND830 (Table S7). After filtering, a total of 518 SNPs remained for use in linkage analysis (Table S8), which resulted in a total of 27 linkage groups corresponding to 20 of the 21 wheat chromosomes covering a total of 1,337 cM of genetic distance. Only chromosome 4D had no polymorphic markers. Chromosomes 2A, 5A, 7A, 1B, 4B, 1D, and 2D were represented by two unlinked linkage groups for each (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA total of 21 KASP markers were developed from the SNPs previously mapped in the ITMI population. Of these, 12 showed clear polymorphism between W-7984 and Opata 85 and were subsequently used to genotype the ITMI population and placed on the genetic linkage map for chromosome 7D (Table S9). These KASP markers were given the designations \u003cem\u003efcp995-fcp1006\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eQTL analysis in the BoostND population\u003c/h2\u003e \u003cp\u003eQTLs associated with DTH were identified on chromosomes 3B, 4A, 5A, and 7B and designated as \u003cem\u003eQHd.fcu-3B.2\u003c/em\u003e, \u003cem\u003eQHd.fcu-4A\u003c/em\u003e, \u003cem\u003eQHd.fcu-5A\u003c/em\u003e, and \u003cem\u003eQHd.fcu-7B\u003c/em\u003e, respectively (Table S10, Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). \u003cem\u003eQHd.fcu-3B.2\u003c/em\u003e and \u003cem\u003eQHd.fcu-7B\u003c/em\u003e were detected only in the greenhouse experiment where they explained 7.60 and 17.00% of phenotypic variation, respectively, whereas \u003cem\u003eQHd.fcu-4A\u003c/em\u003e and \u003cem\u003eQHd.fcu-5A\u003c/em\u003e were detected in both environments with the two QTLs explaining 8.30 and 7.00% of the variation in the greenhouse and 8.40 and 6.60% of the variation in the field experiment at Prosper, respectively. Earliness at \u003cem\u003eQHd.fcu-3B.2\u003c/em\u003e, \u003cem\u003eQHd.fcu-4A\u003c/em\u003e, and \u003cem\u003eQHd.fcu-5A\u003c/em\u003e was contributed by ND830, whereas earliness at \u003cem\u003eQHd.fcu-7B\u003c/em\u003e was contributed by Boost.\u003c/p\u003e \u003cp\u003eBLS analysis in the BoostND population evaluated in greenhouse experiments revealed four QTLs located on chromosomes 3B (2), 5A, and 5B designated as \u003cem\u003eQBls.fcu-3B.1, QBls.fcu-3B.2, QBls.fcu-5A\u003c/em\u003e, and \u003cem\u003eQBls.fcu-5B.1\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). \u003cem\u003eQBls.fcu-3B.2\u003c/em\u003e was associated with BLS resistance in only one of the two greenhouse experiments where it explained 7.00 and 7.60% of the phenotypic variation for IT and LAI, respectively. The other three QTLs were significantly associated with BLS resistance in both greenhouse experiments and for both LAI and IT scores. The phenotypic variation explained by \u003cem\u003eQBls.fcu-3B.1\u003c/em\u003e, \u003cem\u003eQBls.fcu-5A\u003c/em\u003e, \u003cem\u003eQBls.fcu-5B.1\u003c/em\u003e for IT was 37.10 and 37.40, 12.70 and 22.60, and 13.30% and 14.50% for the Fall 2022 and Spring 2023 greenhouse seasons, respectively. For LAI, \u003cem\u003eQBls.fcu-3B.1\u003c/em\u003e, \u003cem\u003eQBls.fcu-5A\u003c/em\u003e, and \u003cem\u003eQBls.fcu-5B.1\u003c/em\u003e explained 34.50 and 34.70, 11.16 and 23.70, and 11.30 and 16.10% of the phenotypic variation for the Fall 2022 and Spring 2023 greenhouse environments, respectively. In all cases, the alleles conferring BLS resistance at these loci were contributed by Boost.\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\u003eQTLs associated with bacterial leaf streak resistance caused by \u003cem\u003eXanthomonas transulcens\u003c/em\u003e pv. \u003cem\u003eundulosa\u003c/em\u003e strain P3 identified in the BoostND and ITMI populations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eQTL Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePeak markers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c10\" namest=\"c3\"\u003e \u003cp\u003eGreenhouse experiments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c14\" namest=\"c11\"\u003e \u003cp\u003eField experiments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eDonor parent for beneficial allele\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eIT_Fall 2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eLAI_Fall 2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eIT_Spring 2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eLAI_Spring 2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eDS_Fargo_2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eDS_Prosper_2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLOD\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e \u0026times; 100) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLOD\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e \u0026times; 100) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLOD\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e \u0026times; 100) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLOD\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e \u0026times; 100) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLOD\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e \u0026times; 100) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eLOD\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e(\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e \u0026times; 100) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQBls.fcu-3B.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIWB51224\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eIWB12193\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e34.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e13.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e12.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eBoost\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQBls.fcu-3B.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIWB6915\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e 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\u003cp\u003e\u003cem\u003eQBls.fcu-4A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIWA54\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e9.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eBoost\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQBls.fcu-5A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIWB47624\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eIWA2837\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e28.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e14.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e29.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eBoost\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQBls.fcu-5B.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIWB1196\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eIWA6773\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eIWB7549\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eBoost\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQBls.fcu-5B.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIWB35252 IWB73479\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eIWB7342\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e10.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e12.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eND830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQBls.fcu-7D\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eXbarc121-7D IWB39179 IWB41457\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e18.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eW-7984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003e\u003csup\u003ea\u003c/sup\u003e Logarithm of odds.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003e\u003csup\u003eb\u003c/sup\u003e Proportion of phenotypic variation explained by QTLs, expressed in percentage.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003e- Not significant.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003eNA: Experiment were not conducted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003eIT: Infection type score (0\u0026ndash;5)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003eLAI: Leaf area infected (%)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003eDS: Disease severity score (0\u0026ndash;9)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the field experiments, a total of four QTLs located on chromosomes 3B, 4A, 5A, and 5B were associated with BLS resistance (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). \u003cem\u003eQBls.fcu-3B.1\u003c/em\u003e and \u003cem\u003eQBls.fcu-5A\u003c/em\u003e, which were associated with BLS resistance in the greenhouse experiments, were also associated with BLS resistance in both field environments. \u003cem\u003eQBls.fcu-5B.1\u003c/em\u003e, was not significant under field conditions, but a second locus on chromosome 5B specific to BLS in the field experiments was identified and designated \u003cem\u003eQBls.fcu-5B.2.\u003c/em\u003e The phenotypic variation explained by \u003cem\u003eQBls.fcu-3B.1\u003c/em\u003e, \u003cem\u003eQBls.fcu-5A\u003c/em\u003e, and \u003cem\u003eQBls.fcu-5B.2\u003c/em\u003e was 13.10 and 12.70, 28.90 and 29.40, and 10.90 and 12.65% in Fargo and Prosper, respectively. The QTL on chromosome 4A, designated \u003cem\u003eQBls.fcu-4A\u003c/em\u003e, was significantly associated with BLS resistance in the Prosper experiment where it explained 9.40% of the phenotypic variation, but it was not significant in the Fargo field experiment. Resistance alleles for \u003cem\u003eQBls.fcu-3B.1, QBls.fcu-5A\u003c/em\u003e, and \u003cem\u003eQBls.fcu-4A\u003c/em\u003e were contributed by Boost. However, the resistance allele at \u003cem\u003eQBls.fcu-5B.2\u003c/em\u003e was contributed by the susceptible parent, ND830.\u003c/p\u003e \u003cp\u003eUsing the most significant SNP marker associated with each environmentally stable QTL, i.e. QTLs that were significant in both field environments and in both greenhouse seasons, we calculated the average BLS scores for different allelic combinations to further assess their relative effects in the field and greenhouse environments. This included the QTLs \u003cem\u003eQBls.fcu-3B.1\u003c/em\u003e and \u003cem\u003eQBls.fcu-5A.\u003c/em\u003e In general, lines that had resistance alleles for both QTLs had lower disease scores compared to lines with resistance alleles for one or no QTLs, indicating their effects were largely additive. In the greenhouse experiments, RILs with resistance alleles at \u003cem\u003eQBls.fcu-3B.1\u003c/em\u003e and \u003cem\u003eQBls.fcu-5A\u003c/em\u003e showed the strongest BLS resistance reactions and were nearly as resistant as Boost. Lines that had only one of the two resistance QTLs had higher levels of BLS susceptibility, and RILs with ND830 alleles at both QTLs showing similar levels of susceptibility as ND830 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eS). Similar results were observed for the field environments at Fargo and Prosper (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eQTLs in the ITMI population\u003c/h2\u003e \u003cp\u003eData for DTH in the ITMI population was collected in the field experiment at Prosper and subjected to QTL analysis. QTLs identified on chromosomes 2D and 5D were associated with DTH and designated \u003cem\u003eQHd.fcu-2D\u003c/em\u003e and \u003cem\u003eQHd.fcu-5D\u003c/em\u003e. These two QTLs explained 13.50 and 23.30% of the phenotypic variation, respectively. (Table S10, Fig. S3). The early heading effects at \u003cem\u003eQHd.fcu-2D\u003c/em\u003e and \u003cem\u003eQHd.fcu-5D\u003c/em\u003e were contributed by Opata 85.\u003c/p\u003e \u003cp\u003eFor BLS resistance in the ITMI population, a QTL on chromosome 7D designated as \u003cem\u003eQBls.fcu-7D\u003c/em\u003e had LOD values of 13.94 and 15.36 and accounted for 43.10 and 46.20% of the phenotypic variation for IT and LAI scores, respectively, in the greenhouse experiment (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Resistance effects for \u003cem\u003eQBls.fcu-7D\u003c/em\u003e were contributed by the synthetic hexaploid parent, W-9874. The same QTL was identified in the Prosper field experiment where it explained 18% of phenotypic variation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eITMI RILs that possessed the W-7984 allele at \u003cem\u003eQBls.fcu-7D\u003c/em\u003e had lower average disease scores compared to the lines with the Opata 85 allele in both the greenhouse and field experiments. In the greenhouse, the average BLS scores among the lines having the 7D resistance allele was 2.09 for IT and 15.33% for LAI compared to 3.51 and 44.40% for IT and LAI, respectively, among RILs lacking the resistance allele. Similar results were observed in the field experiment, where the average BLS score of the lines having the W-7984 allele on 7D was 2.3, whereas lines with the Opata 85 allele had an average score of 3.71 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eKASP Markers\u003c/h2\u003e \u003cp\u003eIn addition to the 12 KASP markers developed for chromosome 7D in the ITMI population, of which \u003cem\u003efcp1001\u003c/em\u003e was the most closely associated with the peak of \u003cem\u003eQBls.fcu-7D\u003c/em\u003e, four KASP markers closely associated with the peaks of \u003cem\u003eQBls.fcu-3B.1\u003c/em\u003e, \u003cem\u003eQBls.fcu-5A\u003c/em\u003e, \u003cem\u003eQBls.fcu-5B.1\u003c/em\u003e, and \u003cem\u003eQBls.fcu-5B.2\u003c/em\u003e were developed for the BoostND QTLs. These four markers, designated \u003cem\u003efcp1007-fcp1010\u003c/em\u003e, were evaluated on the parents and the entire BoostND population to verify that they detected their respective original SNP loci and hence, their corresponding QTLs (Table S11, Fig. S5).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHost resistance is currently the only viable approach for managing bacterial leaf streak disease in wheat. In the Northern Great Plains, the identification and utilization of BLS-resistant sources is a top priority for wheat breeding programs. Here, the screening of 101 diverse tetraploid and hexaploid wheat lines indicated that genetic resistance is rare, which agrees with other studies (Duveiller \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Duveiller et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Kandel et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, we identified five lines, namely Boost, SW87, W-7984, SW86, and Mace, that consistently exhibited resistance against a highly virulent BLS strain in multiple greenhouse experiments. Amont these five lines, SW86, SW87, and W-7984 are synthetic hexaploid wheat lines developed by crossing durum wheat lines with \u003cem\u003eAe. tauschii\u003c/em\u003e, the diploid D-genome donor of modern hexaploid wheat, which suggests that the resistance to BLS among the synthetic lines is most likely coming from the D-genome. However, the \u003cem\u003eAe. tauschii\u003c/em\u003e accession Clae 26 was not only used to create SW86 and SW87, but also other synthetic hexaploids that were susceptible to BLS (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This suggests that the resistance in SW86 and SW87 is coming from the tetraploid parents, 8155-B1 and 8155-B2. Further genetic analysis is needed to confirm this.\u003c/p\u003e \u003cp\u003eBoost and Mace are HRSW cultivars developed in the US and Australia, respectively. The BLS resistance in Boost has been reported before, and it was considered the most promising cultivar in terms of BLS resistance among numerous cultivars tested in the United States (Ledman et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, despite its recognition as resistance cultivar for some years, no studies have been conducted to investigate the genetics of BLS resistance in Boost. Additionally, although W-7984 has been extensively investigated for various traits for the past 30\u0026thinsp;+\u0026thinsp;years (Sorrells et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2011\u003c/span\u003e and references therein), its resistance to BLS has not been previously reported or explored. Prior to this, the only major QTL for BLS resistance was identified on chromosome 5R of the triticale accession Siskiyou and designated \u003cem\u003eXct1\u003c/em\u003e (Wen et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This resistance locus was contributed by the rye genome.\u003c/p\u003e \u003cp\u003eIn this study, a total of seven BLS resistance QTLs, six in the BoostND population and one in the ITMI population, were identified. Among the six QTLs in the BoostND population, \u003cem\u003eQBls.fcu-5A\u003c/em\u003e and \u003cem\u003eQBls.fcu-3B.1\u003c/em\u003e were expressed in all greenhouse and field environments and represent the most stable and robust BLS resistance QTLs with the largest effects. Both QTLs are novel and have not previously been reported to be associated with BLS resistance in wheat. Together, these two QTLs significantly enhanced the level of BLS resistance. Although they did not confer the same level of resistance as observed in Boost itself, having Boost alleles at both QTLs still accounted for around 70 to 80% of the overall resistance relative to Boost. Friskop et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) observed that as long as BLS severity scores were less than 5, yield losses in HRSW varieties was not significant. In our study, having \u003cem\u003eQBls.fcu-5A\u003c/em\u003e and \u003cem\u003eQBls.fcu-3B.1\u003c/em\u003e together showed average BLS severity scores less than 5 in both field environments suggesting that stacking the two QTL into adapted HRSW varieties would sufficiently mitigate yield losses attributed to BLS.\u003c/p\u003e \u003cp\u003eThe magnitudes of \u003cem\u003eQBls.fcu-5A\u003c/em\u003e and \u003cem\u003eQBls.fcu-3B.1\u003c/em\u003e varied between the field and greenhouse environments, i.e. \u003cem\u003eQBls.fcu-3B.1\u003c/em\u003e had stronger effects in the greenhouse compared to the field and \u003cem\u003eQBls.fcu-5A\u003c/em\u003e had stronger effects in the field compared to the greenhouse. It is possible that these differences reflect the differences in how BLS evaluations were conducted where greenhouse experiments involved a single scoring of juvenile plants whereas field plots were scored multiple times throughout the life of the plants as they reached maturity. It may be that \u003cem\u003eQBls.fcu-3B.1\u003c/em\u003e is more critical for conditioning BLS resistance at the juvenile stage and \u003cem\u003eQBls.fcu-5A\u003c/em\u003e has stronger resistance effects in older plants. More studies are needed to determine the effects of these QTLs at different growth stages, but a noteworthy result from a practical standpoint is the finding that both QTLs were detected in greenhouse screenings as well as field environments. This indicates that greenhouse evaluations for the effects of these two QTL can be conducted in the greenhouse on juvenile plants, which can save time, labor, and resources when evaluating the deployment of these QTLs into other materials and backgrounds.\u003c/p\u003e \u003cp\u003eThe two QTLs identified on chromosome 5B in the BoostND population were environment-specific with \u003cem\u003eQBls.fcu-5B.1\u003c/em\u003e being expressed only in the greenhouse and \u003cem\u003eQBls.fcu-5B.2\u003c/em\u003e observed only in field environments. Another difference between the two 5B QTLs is that the resistance effects of \u003cem\u003eQBls.fcu-5B.1\u003c/em\u003e were derived from Boost, whereas the resistance effects of \u003cem\u003eQBls.fcu-5B.2\u003c/em\u003e were derived from ND830. These observations together with the finding that the two QTL map to opposite chromosome arms make it evident that they are distinct and unique loci. Although the results of this study alone would suggest that \u003cem\u003eQBls.fcu-5B.1\u003c/em\u003e may not be worthy to consider for BLS resistance breeding because its effects were not observed in either of our field studies, more field studies are needed to determine if \u003cem\u003eQBls.fcu-5B.1\u003c/em\u003e may be expressed in other environments.\u003c/p\u003e \u003cp\u003e \u003cem\u003eQBls.fcu-4A\u003c/em\u003e was significant in only one of the two field environments and \u003cem\u003eQBls.fcu-3B.2\u003c/em\u003e was significant in only one of the greenhouse experiments. It is interesting to note that both QTLs coincide with QTLs for DTH, which might suggest they are partially responsible for the correlations observed between heading time and BLS resistance (see more discussion below). But, as with \u003cem\u003eQBls.fcu-5B.1\u003c/em\u003e, both QTLs warrant further studies to more precisely evaluate their effects on BLS resistance in different environments.\u003c/p\u003e \u003cp\u003eThe QTL \u003cem\u003eQBls.fcu-7D\u003c/em\u003e identified on long arm of chromosome 7D in the ITMI population represents another novel QTL identified in this study. Like \u003cem\u003eQBls.fcu-5A\u003c/em\u003e and \u003cem\u003eQBls.fcu-3B.1\u003c/em\u003e in the BoostND population, \u003cem\u003eQBls.fcu-7D\u003c/em\u003e was identified in greenhouse evaluations and also in the field thereby making it another robust BLS resistance QTL that can be monitored under greenhouse conditions to save time and labor. Because W-7984 is a synthetic hexaploid wheat, resistance alleles at \u003cem\u003eQBls.fcu-7D\u003c/em\u003e are derived from the \u003cem\u003eAe. tauschii\u003c/em\u003e accession used to create W-7984, which is accession 219 (Nelson et al. 1995). \u003cem\u003eAe. tauschii\u003c/em\u003e has proved to be a useful source of novel disease resistance genes for wheat, and it is possible that it may possess additional BLS resistance genes that could be mined for wheat improvement. For now, working to combine the resistance alleles for the QTLs identified in the BoostND population together with \u003cem\u003eQBls.fcu-7D\u003c/em\u003e seems to be a prudent method of enhancing BLS resistance in wheat.\u003c/p\u003e \u003cp\u003eOther studies have reported an association between BLS resistance and DTH or days to maturity (Milus et al. 1994; Tillman et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Kandel et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The results of our study also showed negative correlations between DTH and BLS resistance in field and greenhouse environments, and in both the BoostND and ITMI populations. A partial explanation for this observation is that several genomic regions containing DTH QTLs overlapped with those containing QTLs associated with BLS resistance. Most evident was the finding that \u003cem\u003eQHd.fcu-5A\u003c/em\u003e and \u003cem\u003eQBls.fcu-5A\u003c/em\u003e overlapped on chromosome 5A, and both QTLs were significantly associated with their respective traits in all environments tested. Although \u003cem\u003eQBls.fcu-4A\u003c/em\u003e was significant in only one field experiment, it coincided with \u003cem\u003eQHd.fcu-4A\u003c/em\u003e for the same experiment, and it is possible that \u003cem\u003eQHd.fcu-3B\u003c/em\u003e could contribute to this phenomenon as well given that a minor coinciding BLS resistance QTL was observed in one greenhouse season. The strength of correlation between the BLS scores and DTH may be partially due to other confounding factors in the field such as escapes, differences in phenology at time of inoculation, and other factors as well. More work is needed to dissect the genetics of BLS resistance and plant development, but breeders should be aware of this association as they work to incorporate BLS resistance into their materials.\u003c/p\u003e \u003cp\u003eThe BLS resistance QTLs identified in this study should prove useful for the improvement of BLS resistance in common wheat varieties, and the KASP markers that we developed, although not diagnostic, can be used to assist in the monitoring of QTL introgression. Genotypic analysis together with phenotypic screenings for BLS resistance in greenhouse settings should together prove useful for immediate development of BLS-resistant germplasm. Moving forward, it is important to focus on fine-mapping and cloning of the large-effect QTLs to develop diagnostic markers for marker-assisted selection in breeding and to gain better understanding of the genetic mechanisms governing BLS resistance. The QTLs identified in the BoostND population exhibited largely additive effects on BLS resistance, indicating that combining these QTLs would confer greater BLS resistance compared to their individual effects. Therefore, the pyramiding of these QTLs along with the 7D QTL derived from \u003cem\u003eAe. tauschii\u003c/em\u003e may prove to be an effective means of producing wheat varieties with good levels of BLS resistance. Additionally, the search for novel sources of BLS resistance among \u003cem\u003eAe. tauschii\u003c/em\u003e and other wheat relatives may prove useful for the identification of additional alleles and genetic loci to further combat BLS in wheat growing regions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthor contribution Statement\u003c/h2\u003e \u003cp\u003eKA, AJG, ZL, JS, and JDF conceived and designed the experiments. KA, PK, and FM performed the experiments. JDF, ZL, SSX, and AJG contributed the materials. KA analyzed the data. The manuscript was drafted by KA, JDF, and AJG and all authors commented on previous version of the manuscript. All the authors read and approved the final version of the manuscript.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics approval\u003c/h2\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to publish\u003c/strong\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was supported by USDA-ARS CRIS Project: 3060-21000-046-000D.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe would like to thank Fazal Manan, Gongjun Shi, Andre Miranda, and Gabriel Dusek for their help with planting and disease evaluation in greenhouse and fields. We would like to thank Katherine Running, Sudeshi Seneviratne, Zengcui Zhang, Gurminder Singh, Heaven James, and Ava Wiitamaki for their help with greenhouse and field harvesting. 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. USDA is an equal opportunity provider and employer.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets generated during and/or analyzed during the current study are available as supplementary materials and/or from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdhikari TB, Gurung S, Hansen JM, Bonman JM (2012a) Pathogenic and genetic diversity of X\u003cem\u003eanthomonas translucens\u003c/em\u003e pv. \u003cem\u003eundulosa\u003c/em\u003e in North Dakota. Phytopathology 102:390\u0026ndash;402\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdhikari TB, Gurung S, Hansen JM, Jackson EW, Bonman JM (2012b) Association mapping of quantitative trait loci in spring wheat landraces conferring resistance to bacterial leaf streak and spot blotch. Plant Genome 5:1\u0026ndash;16\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdhikari TB, Hansen JM, Gurung S, Bonman JM (2011) Identification of new sources of resistance in winter wheat to multiple strains of \u003cem\u003eXanthomonas translucens\u003c/em\u003e pv. \u003cem\u003eundulosa\u003c/em\u003e. Plant Dis 95:582\u0026ndash;588\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkhtar MA, Aslam M (1986) \u003cem\u003eXanthomonas campestris\u003c/em\u003e pv. \u003cem\u003eundulosa\u003c/em\u003e on wheat. RACHIS, Barley Wheat Newsl 5:34\u0026ndash;37\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArif MAR, Agacka-Mołdoch M, Qualset CO, B\u0026ouml;rner A (2022) Mapping of additive and epistatic QTLs linked to seed longevity in bread wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L). Cereal Res Commun 1\u0026ndash;7\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArif MAR, Komyshev EG, Genaev MA, Koval VS, Shmakov NA, B\u0026ouml;rner A, Afonnikov DA (2022) QTL analysis for bread wheat seed size, shape, and color characteristics estimated by digital image processing. Plants 11:2105. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/plants11162105\u003c/span\u003e\u003cspan address=\"10.3390/plants11162105\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArif MAR, Shokat S, Plieske J, Ganal M, Lohwasser U, Chesnokov YV, B\u0026ouml;rner A (2021) A SNP-based genetic dissection of versatile traits in bread wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L). Plant J 108:960\u0026ndash;976\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyana G (2017) Molecular characterization of spot blotch and bacterial leaf streak resistance in bread wheat. Electronic Theses and Dissertations. 2260. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://openprairie.sdstate.edu/etd/2260\u003c/span\u003e\u003cspan address=\"https://openprairie.sdstate.edu/etd/2260\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBamberg RH (1936) Black chaff disease of wheat. J Agri Res 52:397\u0026ndash;417\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCunfer BM, Scolari BL (1982) \u003cem\u003eXanthomonas campestris\u003c/em\u003e pv. \u003cem\u003etranslucens\u003c/em\u003e on triticale and other small grains. Phytopathology 72:683\u0026ndash;686\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuveiller E (1990) Screening criteria for bacterial leaf streak in bread wheat, durum wheat and triticale in CIMMYT. Pages 1011\u0026ndash;1016 in: Proc. 7th Int. Conf. Plant Pathogenic Bacteria, Vol. 2. Z. Klement, ed. Akademiai Kiado, Budapest\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuveiller E (1990) Seed detection of \u003cem\u003eXanthomonas campestris\u003c/em\u003e pv. \u003cem\u003eundulosa\u003c/em\u003e using a modification of Wilbrink\u0026rsquo;s agar medium. Parastica 40:3\u0026ndash;17\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuveiller E, Fucikovsky L, Rudolph K (eds) (1997) The bacterial diseases of wheat: Concepts and methods of disease management. CIMMYT)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuveiller E, van Ginkel M, Thijssen M (1993) Genetic analysis of resistance to bacterial leaf streak caused by \u003cem\u003eXanthomonas campestris\u003c/em\u003e pv. \u003cem\u003eundulosa\u003c/em\u003e in bread wheat. Euphytica 66:35\u0026ndash;43\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuveiller E, Bragard C, Maraite H (1997) Bacterial leaf streak and black chaf caused by \u003cem\u003eXanthomonas translucens\u003c/em\u003e. In: Duveiller E, Fucikovsky L, Rudolph K (eds) The bacterial diseases of wheat: concepts and methods of disease management. CIMMYT, Mexico, pp 25\u0026ndash;47\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuveiller E, Bragard C (2017) CHAPTER 5: Detection of \u003cem\u003eXanthomonas translucens\u003c/em\u003e in Wheat Seeds. In Detection of Plant-Pathogenic Bacteria in Seed and Other Planting Material, Second Edition (pp. 27\u0026ndash;32). American Phytopathological Society, St. Paul, MN\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl Attari H, Sarraf A, Alizadeh A, Dechamp-Guillaume G, Barrault G (1996) Genetic analysis of partial resistance to bacterial leaf streak (\u003cem\u003eXanthomonas campestris\u003c/em\u003e pv. \u003cem\u003ecerealis\u003c/em\u003e) in wheat. Plant Pathol 45:736\u0026ndash;741\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForster RL, Schaad NW (1988) Control of black chaff of wheat with seed treatment and a foundation seed health program. Plant Dis 72:935\u0026ndash;938\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriskop A, Green A, Ransom J, Liu Z, Knodel J, Hansen B, Halvorson J, Lux L (2020) Bacterial leaf streak and black chaff of wheat. North Dakota State University Extension Publication PP1566\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriskop A, Green A, Ransom J, Liu Z, Knodel J, Hansen B, Halvorson J, Lux L, Phytopathology (2023) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1094/PHYTO-08-22-0282-SA\u003c/span\u003e\u003cspan address=\"10.1094/PHYTO-08-22-0282-SA\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGurung S, Mamidi S, Bonman JM, Xiong M, Brown-Guedira G, Adhikari TB (2014) Genome-wide association study reveals novel quantitative trait loci associated with resistance to multiple leaf spot diseases of spring wheat. PLoS ONE 9:e108179\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHagborg WAF (1974) Notes on bacterial diseases of cereals and some other crop plants. Can Plant Dis Surv 54:129\u0026ndash;151\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang BE, Verbyla KL, Verbyla AP, Raghavan C, Singh VK, Gaur P, Cavanagh CR (2015) MAGIC populations in crops: Current status and future prospects. Theor Appl Genet 128:999\u0026ndash;1017\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoehanes R, Nelson JC (2008) QGene 4.0, an extensible Java QTLanalysis platform. Bioinformatics 24:2788\u0026ndash;2727\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson JW, Cunfer BM, Morey DD (1987) Inheritance of resistance to \u003cem\u003eXanthomonas campestris\u003c/em\u003e pv. \u003cem\u003etranslucens\u003c/em\u003e in triticale. Euphytica 36:603\u0026ndash;607\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKandel YR, Glover KD, Tande CA, Osborne LE (2012) Evaluation of spring wheat germplasm for resistance to bacterial leaf streak caused by \u003cem\u003eXanthomonas campestris\u003c/em\u003e pv. \u003cem\u003etranslucens\u003c/em\u003e. Plant Dis 96:1743\u0026ndash;1748\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKandel YR, Glover KD, Osborne LE, Gonzalez-Hernandez JL (2015) Mapping quantitative resistance loci for bacterial leaf streak disease in hard red spring wheat using an identity by descent mapping approach. Euphytica 201:53\u0026ndash;65\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKosambi DD (1944) The Estimation of Map Distances from Recombination Values. Ann Eugen 12:172\u0026ndash;175. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1469-1809.1943.tb02321.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1469-1809.1943.tb02321.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLedman KE, Osdaghi E, Curland RD, Liu Z, Dill-Macky R (2023) Epidemiology, host resistance, and genomics of the small grain cereals pathogen \u003cem\u003eXanthomonas translucens\u003c/em\u003e: new advances and future prospects. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1094/PHYTO-11-22-0403-SA\u003c/span\u003e\u003cspan address=\"10.1094/PHYTO-11-22-0403-SA\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Phytopathology\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevene H (1960) Robust tests for equality of variances. In: Olkin I, Hotelling H (eds) Contributions to Probability and Statistics: essays in Honor of Harold Hoteling. Stanford University Press, Stanford, CA, pp 278\u0026ndash;292\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLorieux M (2012) MapDisto: fast and efficient computation of genetic linkage maps. Mol Breed 30(2):1231\u0026ndash;1235\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLux L, Friskop A, Liu Z (2020) Bacterial leaf streak and black chaff of wheat. Mol Plant Pathol 21:291\u0026ndash;302\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLux L, Halvorson J, Hansen B, Meyer S, Shi G, Liu Z, Friskop A (2023) Effect of copper sulfate pentahydrate and cultivar resistance on reducing bacterial leaf streak in hard red spring wheat in North Dakota. Plant Health Prog. (In press)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalavolta VA Jr, de Oliveira MAR, de Oliveira AR (2000) Identification and survival of \u003cem\u003eXanthomonas translucens\u003c/em\u003e pv. \u003cem\u003ecerealis\u003c/em\u003e in seeds and crop debris of wheat. Summa Phytopathologica 26:20\u0026ndash;23\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarino CL, Nelson JC, Lu YH, Sorrells ME, Leroy P, Lopes CR, Hart GE (1996) Molecular genetic maps of the group 6 chromosomes of hexaploid wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L. em. Thell) Genome 39:359\u0026ndash;366\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcMullen M, Adhikari T (2011) Bacterial leaf streak and black chaff of wheat. Plant Disease Management. Fargo, ND, USA: North Dakota State University Extension Publication, PP1566\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilus EA, Duveiller E, Kirkpatrick TL, Chalkey DB (1996) Relationships between disease reactions under controlled conditions and se-verity of wheat bacterial streak in the field. Plant Dis 80:726\u0026ndash;730\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilus EA, Mirlohi AF (1994) Use of disease reactions to identify resistance in wheat to bacterial streak. Plant Dis 78:157\u0026ndash;161\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNelson JC, Andreescu C, Breseghello F, Finney PL, Gualberto DG, Bergman CJ, Pena RJ, Perretant MR, Leroy P, Qualset CO, Sorrells ME (2006) Quantitative trait locus analysis of wheat quality traits. Euphytica 149:145\u0026ndash;159\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNelson JC, Sorrells ME, Van Deynze AE, Lu YH, Atkinson M, Bernard M, Leroy P, Faris JD, Anderson JA (1995a) Molecular mapping of wheat: major genes and rearrangements in homoeologous groups 4, 5, and 7. Genetics 141:721\u0026ndash;731\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNelson JC, Van Deynze AE, Autrique E, Sorrells ME, Lu YH, Merlino M, Atkinson M, Leroy P (1995b) Molecular mapping of wheat. Homoeologous group 2. Genome 38:516\u0026ndash;524\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNelson JC, Van Deynze AE, Autrique E, Sorrells ME, Lu YH, Negre S, Bernard M, Leroy P (1995c) Molecular mapping of wheat. Homoeologous group 3. Genome 38:525\u0026ndash;533\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandey P, Irulappan V, Bagavathiannan MV, Senthil-Kumar M (2017) Impact of combined abiotic and biotic stresses on plant growth and avenues for crop improvement by exploiting physio-morphological traits. Front Plant Sci 8:537. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpls.2017.00537\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2017.00537\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamakrishnan SM, Sidhu JS, Ali S, Kaur N, Wu J, Sehgal SK (2019) Molecular characterization of bacterial leaf streak resistance in hard winter wheat. PeerJ 7:e7276. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.7717/peerj.7276\u003c/span\u003e\u003cspan address=\"10.7717/peerj.7276\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamirez-Gonzalez RH, Uauy C, Caccamo M (2015) PolyMarker: a fast polyploid primer design pipeline. Bioinformatics 31:2038\u0026ndash;2039\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoder MS, Korzun V, Wendehake K, Plaschke J, Tixier MH, Leroy P, Ganal MW (1998) A microsatellite map of wheat. Genetics 149:2007\u0026ndash;2023\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaari EE, Prescott JM (1975) A scale for appraising the foliar intensity of wheat diseases. Plant Dis Rep 59:377\u0026ndash;380\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSapkota S (2015) Identification and genomic mapping of resistance to bacterial leaf streak in wheat. MS thesis. North Dakota State University, Fargo, ND\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSapkota S, Mergoum M, Liu Z (2020) The translucens group of \u003cem\u003eXanthomonas translucens\u003c/em\u003e: Complicated and important pathogens causing bacterial leaf streak on cereals. Mol Plant Pathol 21:291\u0026ndash;302\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSapkota S, Zhang Q, Chittem K, Mergoum M, Xu SS, Liu Z (2018) Evaluation of triticale accessions for resistance to wheat bacterial leaf streak caused by \u003cem\u003eXanthomonas translucens\u003c/em\u003e pv. \u003cem\u003eundulosa\u003c/em\u003e. Plant Pathol 67: 595\u0026ndash;602\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnedecor GW (1956) Statistical methods: applied to experiments in agriculture and biology. The Iowa state college\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong QJ, Shi JR, Singh S, Fickus EW, Costa JM, Lewis J, Gill BS, Ward R, Cregan PB (2005) Development and mapping of microsatellite (SSR) markers in wheat. Theor Appl Genet 110:550\u0026ndash;560. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00122-004-1871-x\u003c/span\u003e\u003cspan address=\"10.1007/s00122-004-1871-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSorrells ME, Gustafson JP, Somers D, Chao S, Benscher D, Brown-Guedira G, Huttner E, Kilian A, McGuire PE, Ross K, Tanaka J, Wenzl P, Williams K, Qualset CO (2011) Reconstruction of the synthetic W7984 x Opata M85 wheat reference population. Genome 54:875\u0026ndash;882\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTillman BL, Harrison SA (1996) Heritability of resistance to bacterial streak in winter wheat. Crop Sci 36:412\u0026ndash;418\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTillman BL, Harrison SA, Clark CA, Milus EA, Russin JS (1996) Evaluation of bread wheat germplasm for resistance to bacterial streak. Crop Sci 36:1063\u0026ndash;1068\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTillman BL, Kursell WS, Harrison SA, Russin JS (1999) Yield loss caused by bacterial streak in winter wheat. Plant Dis 83:609\u0026ndash;614\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Deynze AE, Dubcovsky J, Gill KS, Nelson JC, Sorrels ME, Dvorak J, Gill BS, Lagudah ES, McCouch SR, Appels R (1995) Molecular-genetic maps for group 1 chromosomes of Triticeae species and their relation to chromosomes in rice and oat. Genome 38:45\u0026ndash;59\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang S, Wong D, Forrest K, Allen A, Chao S, Huang BE, Maccaferri M, Salvi S, Milner SG, Cattivelli L (2014) Characterization of polyploid wheat genomic diversity using a high-density 90,000 single nucleotide polymorphism array. J Plant Biotechnol 12:787\u0026ndash;796\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen A, Jayawardana M, Fiedler J, Sapkota S, Shi G, Peng Z (2018) Genetic mapping of a major gene in triticale conferring resistance to bacterial leaf streak. Theor Appl Genet 13:649\u0026ndash;658\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu T, Wang L, Rimbert H, Rodriguez JC, Deal KR, De Oliveira R, Choulet F, Keeble-Gagn\u0026egrave;re G, Tibbits J, Rogers J, Eversole K (2021) Optical maps refine the bread wheat \u003cem\u003eTriticum aestivum\u003c/em\u003e cv. Chinese Spring genome assembly. Plant J 107:303\u0026ndash;314\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":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-4456913/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4456913/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBacterial leaf streak (BLS), caused by \u003cem\u003eXanthomonas translucens\u003c/em\u003epv. \u003cem\u003eundulosa\u003c/em\u003e (\u003cem\u003eXtu\u003c/em\u003e) poses a significant threat to global wheat production. High levels of BLS resistance are rare in hexaploid wheat. Here, we screened 101 diverse wheat genotypes under greenhouse conditions to identify new sources of BLS resistance. Five lines showed good levels of resistance including the wheat variety Boost and the synthetic hexaploid wheat line W-7984. Recombinant inbred populations derived from the cross of Boost × ND830 (BoostND population) and W-7984 × Opata 85 (ITMI population) were subsequently evaluated in greenhouse and field experiments to investigate the genetic basis of resistance. QTLs on chromosomes 3B, 5A, and 5B were identified in the BoostND population. The 3B and 5A QTLs were significant in all environments, but the 3B QTL was the strongest under greenhouse conditions explaining 38% of the phenotypic variation, and the 5A QTL was the most significant in the field explaining up to 29% of the variation. In the ITMI population, a QTL on chromosome 7D explained as much as 46% of the phenotypic variation in the greenhouse and 18% in the field. BLS severity in both populations was negatively correlated with days to heading, and some QTLs for these traits overlapped, which explained the tendency of later maturing lines to have relatively higher levels of BLS resistance. The findings from this study will contribute to a better understanding of BLS resistance and aid in the development of molecular markers for efficient selection of resistance alleles in wheat breeding programs.\u003c/p\u003e","manuscriptTitle":"Genetic mapping of QTLs for resistance to bacterial leaf streak in hexaploid wheat","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-27 10:17:46","doi":"10.21203/rs.3.rs-4456913/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2024-09-10T15:26:48+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-06-13T14:50:40+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-11T23:40:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-22T14:20:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2024-05-21T16:05:51+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":"85d4d273-b0a5-4aff-8b1d-2e55abfd10e7","owner":[],"postedDate":"June 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-18T19:17:32+00:00","versionOfRecord":{"articleIdentity":"rs-4456913","link":"https://doi.org/10.1007/s00122-024-04767-x","journal":{"identity":"theoretical-and-applied-genetics","isVorOnly":false,"title":"Theoretical and Applied Genetics"},"publishedOn":"2024-11-13 15:58:19","publishedOnDateReadable":"November 13th, 2024"},"versionCreatedAt":"2024-06-27 10:17:46","video":"","vorDoi":"10.1007/s00122-024-04767-x","vorDoiUrl":"https://doi.org/10.1007/s00122-024-04767-x","workflowStages":[]},"version":"v1","identity":"rs-4456913","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4456913","identity":"rs-4456913","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}