Evaluation of select spring barley accessions for resistance to Fusarium head blight and deoxynivalenol accumulation

Evaluation of select spring barley accessions for resistance to Fusarium head blight and deoxynivalenol accumulation | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 22 January 2025 V1 Latest version Share on Evaluation of select spring barley accessions for resistance to Fusarium head blight and deoxynivalenol accumulation Authors : Rae Page 0009-0009-5516-7354 , Ahmad Sallam , Tamas Szinyei , Oadi Matny 0000-0002-8447-2886 , Joseph Wodarek , and Brian Steffenson 0000-0001-7961-5363 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.173758404.41904486/v1 Published Crop Science Version of record Peer review timeline 303 views 164 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Fusarium head blight (FHB), a devastating disease of barley caused primarily by the fungus Fusarium graminearum , causes significant yield losses and grain contamination with mycotoxins. Enhancing resistance to FHB and the resultant accumulation of mycotoxins, such as deoxynivalenol (DON), is an effective and economical method of reducing losses caused by this disease. A diverse panel of 234 barley accessions from world-wide origins was assembled and evaluated in head-to-head comparisons over multi-year and multi-environment field trials to identify those that perform consistently well with respect to FHB resistance and DON accumulation under Upper Midwest conditions. In addition to these two traits, row type (RT), heading date (HD), plant height (HT), kernel density (KD), and node density (ND) assessments were also recorded to investigate the relationship between these agro-morphological traits and both DON concentration and FHB severity. Accessions were genotyped with a barley 50K single nucleotide polymorphism (SNP) microarray in order to assess their population structure and genetic relationships and also investigate patterns of disease resistance. Several accessions originating from diverse backgrounds were identified as having moderately high resistance to FHB and/or DON accumulation. However, most accessions with low disease severity and mycotoxin accumulation also had undesirable agro-morphological traits, a challenge to breeding for FHB resistance in barley. The data generated in this study will serve as a valuable resource for resistance breeding and genetic mapping of resistance to FHB and DON accumulation. Core ideas: • A panel of 234 spring barley accessions were evaluated for FHB severity in field trials over seven years. • Eleven accessions had lower DON values and seven had lower FHB severity values than the resistant control Chevron. • FHB resistance was negatively correlated with plant height and heading date. • DON accumulation was negatively correlated with plant height and heading date. • Breeding for FHB resistance in barley remains difficult due to the influence of agromorphological traits on disease. EVALUATION OF SELECT SPRING BARLEY ACCESSIONS FOR RESISTANCE TO FUSARIUM HEAD BLIGHT AND DEOXYNIVALENOL ACCUMULATION Rae Page 1 , Ahmad H. Sallam 2 , Tamas Szinyei 2 , Oadi Matny 2 , Joseph Wodarek 3 , and Brian Steffenson 2* Affiliations: 1 Department of Plant Pathology, University of Minnesota, St. Paul, MN 55108; current address Syngenta, 317 330 th St., Stanton, MN 55018 2 Department of Plant Pathology, University of Minnesota, St. Paul, MN 55108 3 University of Minnesota, Northwest Research and Outreach Center, Crookston, MN 56716; current address Syngenta, 9497 US 10, Glyndon, MN 56547 *Corresponding author Abbreviations: Please list abbreviations in alphabetical order with the abbreviation first, separated from its definition by a comma. Please use semicolons to distinguish separate abbreviations. AB InBev, Anheuser-Busch InBev; BLUE, best linear unbiased estimation; DON, deoxynivalenol; FHB, Fusarium head blight; HD, heading date; HT, plant height; ICARDA, International Center for Agricultural Research in the Dry Areas; JIC, John Innes Center; KD, kernel density; LD, linkage disequilibrium; NBRP, National BioResource Project; ND, node density; NGB, Nordic Gene Bank; NSGC, USDA-ARS National Small Grains Collection; PCA, principal component analysis; PGRC, Plant Genetic Resources of Canada; RT, row type; SFRSPP, Station Federale de Recherché en Production Vegetale de Changins; VIR, N. I. Vavilov All-Russian Scientific Research Institute of Plant Industry Abstract Fusarium head blight (FHB), a devastating disease of barley caused primarily by the fungus Fusarium graminearum , causes significant yield losses and grain contamination with mycotoxins. Enhancing resistance to FHB and the resultant accumulation of mycotoxins, such as deoxynivalenol (DON), is an effective and economical method of reducing losses caused by this disease. A diverse panel of 234 barley accessions from world-wide origins was assembled and evaluated in head-to-head comparisons over multi-year and multi-environment field trials to identify those that perform consistently well with respect to FHB resistance and DON accumulation under Upper Midwest conditions. In addition to these two traits, row type (RT), heading date (HD), plant height (HT), kernel density (KD), and node density (ND) assessments were also recorded to investigate the relationship between these agro-morphological traits and both DON concentration and FHB severity. Accessions were genotyped with a barley 50K single nucleotide polymorphism (SNP) microarray in order to assess their population structure and genetic relationships and also investigate patterns of disease resistance. Several accessions originating from diverse backgrounds were identified as having moderately high resistance to FHB and/or DON accumulation. However, most accessions with low disease severity and mycotoxin accumulation also had undesirable agro-morphological traits, a challenge to breeding for FHB resistance in barley. The data generated in this study will serve as a valuable resource for resistance breeding and genetic mapping of resistance to FHB and DON accumulation. 1 Introduction Fusarium head blight (FHB), also known as scab, is a destructive disease of barley ( Hordeum vulgare L. ssp. vulgare ) and other small grain cereals. Since its first report in England in 1884, the disease has been reported in many countries including the United States (Stack, 2003). In the Upper Midwest production region, FHB was previously a sporadic disease with few severe outbreaks. Then, in 1993, the first of a series of widespread and severe epidemics hit the region, causing significant economic losses and uncertainty in end-use markets, including malting and animal feed (Goswami and Kistler, 2004; Steffenson, 2003; Windels, 2000). These epidemics devastated the malting barley industry in the Upper Midwest region and reinforced the need for developing improved management practices and resistant cultivars. Other production areas in the western and northeastern United States have more recently been under threat from FHB (Marshall et al., 2014). Control of the disease has proven to be a challenge worldwide as reported in Asia, Europe, South America, and East Africa in recent years (Parry et al., 1995; Olivera, 2023). The ascomycete fungus Fusarium graminearum Schwabe is the main causal agent in North America, although many other Fusarium species are known to cause Fusarium head blight on barley, including F. culmorum (Smith) Sacc. , F. avenaceum (Corda) Sacc. , F. sporotrichioides Sherb. , and F. poae (Peck) Wollenw. (Choo, 2006) . All these species can produce mycotoxins, which act as virulence factors for the fungus when it invades the host. These mycotoxins are retained in the harvested grain, reducing its quality for different end-uses and fueling human and animal health concerns (Foroud et al., 2019). Multiple trichothecene toxins are produced by F. graminearum , with the primary one being deoxynivalenol (DON), also known as vomitoxin. Although yield loss and kernel discoloration are important factors with respect to FHB infections, contamination of grain by DON is of even greater concern. Buyers of malting barley often adopt a strict standard of less than 0.5 ppm of DON in purchased grain (McMullen et al., 1997). DON accumulation is of greater concern in barley than in wheat because the hull does not separate from the caryopsis in most malting barley cultivars (as it does in wheat), and the hull can contain higher concentrations of DON (Clear et al., 1996; Sallam 2005). Proper management of FHB requires fungicide applications when the threat of an epidemic is high, crop rotation with non-hosts of F. graminearum to reduce inoculum load, and deployment of resistant cultivars. Many research groups around the world have conducted extensive evaluations of barley germplasm in the search for landraces, cultivars, or breeding lines that possess resistance to FHB (Belina et al., 2002.; Buerstmayr et al., 2004; Chen et al., 1991; Choo, 2006; Ma et al., 2009; Mamo and Steffenson, 2015; McCallum et al., 2004; Takeda and Heta, 1989; Zhou et al., 1991). The most extensive evaluation of barley germplasm was reported by Huang et al., (2012) where 23,255 cultivated ( H . vulgare ssp. vulgare ) and wild (H . vulgare ssp. spontaneum ) barley accessions from seven international gene banks were screened for resistance to FHB. From these studies and other screening efforts performed at the University of Minnesota (B.J. Steffenson, unpublished), many moderately resistant sources were identified. In total, over 26,000 Hordeum accessions (i.e., cultivars, breeding lines, landraces, progeny of mapping populations, and collections of two wild relatives, H. vulgare ssp. spontaneum and H. bulbosum [bulbous barley grass] ) have been screened over the past 20 years in the aforementioned studies, mapping population studies (Haas, 2016; Huang et al., 2018a; Schmalenbach et al., 2008), and other trials. However, in no case was any highly resistant accession identified. Resistance to FHB and DON accumulation are highly quantitative traits, strongly influenced by environment, with low to moderate heritabilities (Capettini et al., 2003). Multi-year and multi-environment trials of barley germplasm are required to obtain robust data on the FHB response and accumulation of mycotoxins in a given production region. Additionally, numerous agro-morphological traits are associated with the FHB response in barley (Steffenson, 2003), including heading date, plant height, and spike morphology traits. Earlier heading and shorter plant height are often reported to be correlated with increased FHB severity (de la Peña et al., 1999; Ma et al. 2000; Mesfin et al., 2003; Ogrodowicz et al., 2020; Steffenson et al.,1996). Several studies have also shown that two-rowed barleys tend to exhibit lower levels of disease than six-rowed genotypes. (Chen et al., 1991; Takeda and Heta, 1989; Yoshida et al., 2005; Zhou et al., 1991). This study focuses on a diverse collection of barley accessions (234 six- and two-rowed spring barley accessions) assembled based on their resistance (and some based on their susceptibility) to FHB and/or DON accumulation from previous field trials or the recommendations from collaborators. The objectives of this study were to: (1) evaluate the selected barley accessions in head-to-head comparisons over multi-year and multi-environment field trials in order to identify those that perform consistently well with respect to FHB resistance and DON accumulation under Upper Midwest conditions; (2) genotype the accessions with a large barley 50K single nucleotide polymorphism (SNP) microarray (Bayer et al., 2017); and (3) assess their population structure and genetic relationships. 2 Materials and Methods 2.1 Summary of past field trials Over 21,000 cultivated barley accessions were received from the following gene banks as described by Huang et al (2012): USDA-ARS National Small Grains Collection (NSGC), N. I. Vavilov All-Russian Scientific Research Institute of Plant Industry (VIR), Station Federale de Recherché en Production Vegetale de Changins (SFRSPP), Nordic Gene Bank (NGB), International Center for Agricultural Research in the Dry Areas (ICARDA), the John Innes Center (JIC), the National BioResource Project (NBRP), and Plant Genetic Resources of Canada (PGRC). Huang et al. (2012) described how these accessions were screened for FHB resistance in disease nurseries in the United States and/or China in one or more years (for methods, see Prom et al., 1996), the bulk of which were evaluated from 1999 to 2010. Some accessions were evaluated in only one trial, while others were screened in up to 20 trials. For a detailed breakdown of the screening efforts, field scoring methods, and reactions of 101 select barley accessions, most of which are also included in this study, see Huang et al., (2012). Winter-type and wild barley accessions selected as resistant by Huang et al. (2012) were not included in this study because they would not produce heads without vernalization when they were spring-sown. Five of the spring-type accessions screened by Huang et al. (2012) were removed from this study after observation of inconsistent phenotypes likely due to contaminated seed lots. Of the materials summarized by Huang et al. (2012), 64 accessions were re-evaluated for their reaction to FHB in this study. Additional accessions (n = 170) for resistance screening were assembled based mainly on further evaluation trials at the University of Minnesota, in addition to reports in the literature (Belina et al., 2002), selections from biparental mapping populations involving resistant parents (Ma et al., 2000; Schmalenbach et al., 2008; Haas, 2016), a mutagenized population of the resistant two-rowed accession CIho 4196 (Boyd et al., 2007), and a Composite Cross population (CC XXX-G; Ramage et al., 1976). As maltsters and brewers in the United States have recently shifted their preference from six-rowed to two-rowed cultivars (USDA-NASS and the American Malting Barley Association), we sought out additional resistant two-rowed materials for this investigation. Additional two-rowed germplasm was received from the following barley breeding programs: AB InBev (Fort Collins, CO, USA), North Dakota State University (NDSU; Fargo, ND, USA), University of Minnesota (UMN; St. Paul, MN, USA), Washington State University (WSU; Pullman, WA, USA), USDA-ARS Small Grains Germplasm Research (Aberdeen, ID, USA), Agriculture and Agri-Food Canada (AAFC, Winnipeg, MB, Canada), and the International Maize and Wheat Improvement Center (CIMMYT; El Batán, Mexico). 2.2 Final panel of FHB resistant barley The final panel of barley accessions selected for FHB resistance was assembled based on data from prior screening efforts, notably those summarized in Huang et al. (2012) and also bi-parental mapping studies. In these evaluations, accessions were regarded as resistant if they exhibited low FHB severities and/or DON levels similar to standard controls. Additional breeding lines were selected based on data collected from our trials as well as a few from trials conducted by various collaborators. Twenty accessions classified as susceptible or moderately susceptible (Huang et al., 2012) were retained in the panel for comparison to the resistant accessions or served as controls (Supplemental table S1). The following set of seven standard controls was included in every trial: Chevron (a late-maturing, six-rowed landrace from Switzerland with a moderately high level of resistance), CIho 4196 (a late-maturing, two-rowed landrace from China with a moderately high level of resistance), Quest (a mid-season, six-rowed malting cultivar from Minnesota with moderate resistance), Robust (a mid-season, six-rowed malting cultivar from Minnesota with susceptibility), Stander (a mid-season, six-rowed malting cultivar from Minnesota with susceptibility), ICB 111809 (a mid-season, two-rowed accession from Turkey with FHB susceptibility), and PI 383933 (an early, six-rowed, dwarf accession from Japan with extreme FHB susceptibility). Huang et al. (2012) did not evaluate Quest; however, they classified the four susceptible controls as susceptible or moderately susceptible and Chevron and CIho 4196 as resistant. The entire panel was comprised of 234 spring barley entries and controls with 102 (43.2%) six-rowed types and 133 (56.8%) two-rowed types (Supplemental Table S1). Seventeen accessions (7.3%) were hulless types. The panel consisted of a diverse mix of landraces, older cultivars, modern breeding lines, feed cultivars, malting cultivars, and progeny selected from biparental mapping populations that were typically created for mapping FHB resistance. The accessions originated from 32 countries across six continents (Figure 1). 2.3 Genotyping Leaf tissue was harvested from barley seedlings at the three-leaf stage and dried using silica gel. Genomic DNA extraction and genotyping was performed at the USDA-ARS North Central Small Grains Genotyping Laboratory in Fargo, ND. The 50k Illumina Infinium iSelect genotyping array for barley was used on all samples (containing 44,040 SNP marker assays, Bayer et al., 2017). Genotype calls for single nucleotide polymorphism (SNP) markers were validated manually using Illumina’s GenomeStudio software v2.0.4. The physical locations of markers were aligned to the barley reference genome, cv. Morex v3 (accession GCA_904849725; Mascher et al., 2021). Five individuals with over 15% missing genotype calls were removed from further analysis. Markers with greater than 10% missing data or greater than 5% heterozygosity were also removed. Redundant markers, i.e., those with adjacent marker linkage disequilibrium (LD) of r 2 = 1, were also eliminated from further consideration. 2.4 Population structure of the panel Principal component analysis (PCA) was employed to detect population structure using TASSEL v5 (Bradbury et al., 2007). The first two principal components were plotted to visualize population structure (Figure 2A and 2B). In addition to PCA, characterization of subpopulations was performed with K -means clustering on the genetic distance matrix using the R package factoextra (Kassambara and Mundt, 2020), and the optimal number of subpopulations for analysis was determined using the R package NbClust (Charrad et al., 2014). Hierarchical clustering utilizing Ward’s minimum variance method on the genetic distance matrix was also performed in JMP (JMP, version 13.1.0, SAS Institute Inc., Cary, NC), and a dendrogram using k = 6 clusters was created to further examine relatedness among accessions (Figure 2C, Supplemental Table S1). 2.5 Field evaluations Field evaluations for most accessions in the panel were conducted over seven years (2015-2021) at two locations (Saint Paul, MN and Crookston, MN) for a total of 11 environments. Trials were conducted at the University of Minnesota Northwest Research and Outreach Center (NWROC) in Crookston in 2016 and from 2018-2021 and at the Minnesota Agricultural Experiment Station (MAES) in Saint Paul from 2015-2020. The entire panel (n = 234) was screened from 2019-2021, while a subset of the panel (n = 50 to 175) was screened during the previous years. A randomized complete block design with two replications was used in all experiments, except at Saint Paul in 2017 and 2018 where augmented incomplete block designs were used with one replication of the experimental lines and replicated controls due to space constraints. 2.5.1 Inoculation methods The “grain spawn” method for inoculation (Prom et al., 1997; Steffenson, 2003) was used at the NWROC in Crookston in all years. Briefly, regional isolates (25 to 40, Supplemental Table S3) of F. graminearum were inoculated onto sterilized maize kernels and allowed to colonize this substrate for two weeks. Thereafter, the grain spawn was dried until used for inoculation of the field plots. The inoculum was spread between rows of the test entries two to three times per growing season: once just before the earliest lines in the nursery were heading, again 10-14 days later, and once more selectively near late-heading lines to ensure sufficient inoculum was present at the critical growth stage for infection of all lines. Spray inoculations using macroconidial suspensions were performed in Saint Paul in all years (Steffenson, 2003). A composite of F. graminearum isolates collected from across the state (Supplemental Table S3) was used for inoculum. CO 2 -pressurized backpack sprayers were used to apply approximately 1 ml of inoculum (100,000 macroconidia per milliliter) per 30 cm of row. Plants were inoculated at the heading stage (i.e., when over 50% of the spikes in a row had emerged over 50% out of the flag leaf sheath) and again 3-5 days later. Nurseries in both Crookston and Saint Paul had mist-irrigation systems to provide favorable conditions for infection by the pathogen and subsequent development of the disease. 2.5.2 Disease severity ratings FHB severities were based on diseased kernel counts. Within each plot, 10 spikes were selected arbitrarily, and then the number of symptomatic kernels was counted. Kernels were considered symptomatic if at least a quarter of the surface was discolored. FHB severity (in percent) was calculated by dividing the average number of symptomatic kernels by the average number of kernels within a spike (assessed on three spikes within a row) and multiplying by 100. In Saint Paul, FHB severity assessments were made approximately 20 days after the first inoculation, at the soft- to mid-dough stage of development (Zadoks stage 85 to 86; Zadoks et al., 1974). In Crookston, FHB severity assessments were made once, between the soft- to mid-dough stages of development, although late-heading lines were assessed twice. 2.5.3 Agro-morphological trait assessments In addition to FHB severity, row type (RT), heading date (HD), plant height (HT), kernel density (KD), and node density (ND) assessments were recorded. Row type indicates the number of rows of fertile spikelets in a spike (two- or six-rowed). HT was measured as the distance (in cm) from the base of the plant to the tip of the spike, excluding awns. KD was calculated as the total number of kernels per spike divided by the length of the spike, excluding awns (i.e., the rachis length). ND was calculated by counting the number of rows with fertile spikelets and dividing by the length of the spike, excluding awns (directly related to RT and KD, as ND = KD/RT). 2.6 Deoxynivalenol concentration assay When the accessions were mature, spikes from each plot were harvested by hand for DON assays. Harvested samples were dried at 35°C to 12% moisture, threshed, cleaned, and ground. Ten grams of seed per plot were assayed for DON concentration (ppm) by the Mycotoxin Diagnostic Laboratory at the University of Minnesota using gas chromatography-mass spectrometry (GC-MS) (Dong et al., 2006). 2.7 Statistical analysis of traits Correction for spatial field variability in the disease trials for DON and FHB severity was done using moving grid adjustment (Technow, 2013) after mapping the field plots in rows and columns. This method calculates a moving mean using a surrounding grid of plots of a particular size. The moving mean is then used as a covariate to calculate adjusted phenotypes. The size of the moving average window used was variable depending on the environment. Moving grid adjustment was only used if an increase in reliability (often described as broad-sense heritability, H 2 [Bernardo, 2020]) was observed and if the relative efficiency was > 1 (i.e., the error variance of replicated lines was reduced upon adjustment of the phenotypic values). Then, phenotypes were adjusted for trial effects using the PROC MIXED procedure in SAS v9.4 (SAS Institute 2011; Sallam et al., 2015). The model was y = Xβ + Zu + e , where y is the vector of phenotypes of repeated controls (adjusted for field spatial variability if relevant), β is the vector of fixed trial (year x location) effects, u is the vector of random line effects, and X and Z are incidence matrices to relate the vector of unadjusted phenotypes to β and u, respectively . Best linear unbiased estimations (BLUEs) for accessions in each experiment were estimated with adjusted phenotypes as response variables and accessions as fixed effects. To assess whether accessions with outlier data would affect further analyses and reasonable comparisons, studentized residuals were estimated for phenotype BLUEs, where observations with absolute studentized residuals of 3.0 or greater were considered outliers and removed. Then BLUEs were averaged across trials, thus providing a single phenotypic value for each trait and each accession across all environments for comparison of accession performance. BLUEs were estimated for FHB, DON, HD, and HT. Average BLUE values for all accessions are presented in Supplemental Table S2. Distributions of BLUE values were visualized as histograms (Figure 3). Reliability ( i 2 ) on an entry mean basis was estimated for these four traits using the equation\(i^{2}=\sigma_{g}^{2}/(\sigma_{g}^{2}+\frac{\sigma_{e}^{2}}{n})\), where \(\sigma_{g}^{2}\) is the genetic variance, \(\sigma_{e}^{2}\) is the pooled error variance that includes G x E and residuals, and \(n\)is the number of trials (Table 1) (Bernardo, 2020). Two-sample t-tests (α = 0.05) were performed for all traits to test for differences in mean BLUE values (or simple trait entry means for the traits ND and KD) between two-rowed and six-rowed accessions. Distributions for trait values were visualized as box plots (Supplemental Figure S1). Correlation between traits were analyzed using the R corrplot package (Wei et al., 2017), where the mean BLUE values or simple trait entry means for the traits ND and KD were used as inputs (Figure 4). Entry mean distributions for FHB and DON in each trial year and location were visualized as boxplots (Supplemental Figure S2). Comparison of phenotype means across trials was performed using ANOVA and Tukey-Kramer HSD with α = 0.05 (Supplemental Figure S2). 3 Results 3.1 Genotypic analyses, population structure, and genetic relatedness Five accessions were removed from the genotypic matrix due to over 15% missing data. Upon filtering of the markers generated from the 50k genotyping array and removal of redundant markers in perfect LD ( r 2 = 1), 25,589 SNP markers were utilized in the final analysis of the remaining 229 accessions. Using this genotypic matrix, PCA was used for estimating genetic relatedness of the accessions (Figure 2A and 2B). Cluster analysis using k -means clustering identified two main subpopulations, represented on the PC plot (Figure 2A). PC1 and PC2 explained 17.0 and 9.7% of the variability, respectively. Cluster 1 (n = 93) included all six-rowed accessions, except for 21 two-rowed accessions, all of which are progeny from six-rowed/two-rowed backcross mapping populations. Cluster 2 (n = 136) includes mainly two-rowed accessions (n = 109) and a small subset of six-rowed accessions (n = 28). Hierarchical clustering utilizing Ward’s minimum variance method on the genetic distance matrix was performed in JMP and a dendrogram using k = 6 clusters was created for a more detailed breakdown of relatedness among accessions (Figure 2B and 2C). Cluster A (n = 38) consists of all six-rowed accessions, with worldwide origins from Europe (14), China (3), North America (17), Ethiopia (2), Colombia (1), and Australia (1). This cluster also contains Chevron, the Swiss landrace used as a resistant control and Chevron-derived lines from the Chevron/Stander mapping population (Ma et al., 2000); CIho 11526, noted as a Chevron selection by the NSGC; and the two selections from Composite Cross CC XXX-G (COMP_351 and COMP_355). Cluster B (n = 55) consists of mainly breeding materials, cultivars, and mapping population progeny from barley improvement programs in the United States (UMN, NDSU, ABInBev). This cluster contains three six-rowed control lines (Quest, Robust, and Stander) as well as other notable cultivars (Excel, Lacey, MNBrite, and Rasmusson) from the UMN, NDSU (Foster and Stellar), and ABInBev (Legacy and Tradition) breeding programs. Cluster C (n = 61) consists of all two-rowed accessions except for one six-rowed landrace from Romania. This group was comprised of European landraces and breeding germplasm from AAFC (Winnipeg, MB, Canada), NDSU and the USDA-ARS (Aberdeen, ID). Cluster D (n = 26) contains all two-rowed accessions apart from three six-rowed lines (08_179_1, 08_180_1, and 08_186_1) that were generated by mutagenizing the two-rowed Chinese accession used as a resistant control, CIho 4196. This cluster also contains a group of eight Swiss landraces (those with “HV_XXX” designations) and a few accessions from countries including France, Sweden, Austria, the former Soviet Union, China, and Japan. Cluster E (n = 29) consists of a mix of two-rowed and six-rowed accessions of worldwide origins from Europe, Asia, and the Middle East. The susceptible two-rowed control from Turkey (ICB 111809) and the highly susceptible six-rowed control from Japan (PI 383933) belong to this cluster. Notably, seven accessions in this cluster are hulless barleys. Cluster F (n = 21) consists mostly of Ethiopian landraces (n =15). Five of these Ethiopian accessions have two-rowed deficiens type spikes, in which the sterile lateral spikelets are absent, and seven have either black or purple lemmas. The origins and subpopulation cluster designations for all accessions are given in Supplemental Table S1. 3.2 Phenotypic data Phenotypic data for the six traits under study (DON, FHB, HD, HT, ND, and KD) are given in Supplemental Table S2. For the four key traits of FHB, DON, HT and HD, reliabilities ( i 2 ) ranged from moderately high (FHB with 0.76) to very high (HD with 0.97) (Table 1). Summary statistics for BLUE values of FHB, DON, HD, and HT are presented in Table 1. Each trait showed wide variation in BLUE values. FHB severity had a range of 80.3 percent (skewed due to the highly susceptible control, PI 383933, with a severity of 92.7%), DON had a range of 21.2 ppm, HD had a range of about 30 days, and HT had a range of 58.7 cm. FHB severity and DON accumulation varied greatly among environments (Supplemental Figure S2). The most severe epidemics, and consequently highest FHB levels, occurred at Crookston in 2019 and Saint Paul in 2020. In contrast, the highest mean DON levels were found at Crookston in 2016 and Saint Paul in 2019. DON levels were relatively low in the Crookston trials from 2020-2021, as well as in the Saint Paul trials from 2016-2018. All BLUE values or grand means for all traits and all accessions are presented in Supplemental Table S2. The data for all traits were fairly normally distributed with the exception of KD, which was bimodal and split between two-rowed (lower KD) and six-rowed (higher KD) accessions (Figure 3). Two-sample t-tests (α = 0.05) were performed for all traits to test for differences in mean BLUE values or trait entry means (for ND and KD) between two-rowed and six-rowed accessions. Distributions for trait values were visualized as box plots (Supplemental Figure S1). DON and KD were significantly lower, while HD and ND were significantly higher in the two-rowed accessions versus the six-rowed accessions. There were no significant differences between two-rowed and six-rowed accessions for FHB and HT. 3.3 Trait correlations Pearson correlation coefficients between traits are given in Figure 4A. FHB and DON were positively correlated with each other (r = 0.44), though both were negatively correlated with HD (r = -0.27 and -0.36, respectively) and HT (r = -0.53 and -0.41, respectively). HD and HT were positively correlated (r = 0.30). DON was positively correlated with KD (r = 0.31), but not correlated with ND. FHB was not correlated with KD or ND. Figures 4B and 4C show correlation coefficients between traits in the two- and six-rowed subsets of accessions, respectively. In the two-rowed subset, the correlation between DON and FHB was slightly lower than in the entire panel at r = 0.31. No correlation was found between FHB and HD, while the correlation found between DON and HD was low (r = -0.22). Weaker correlations remained between DON and HT (r = -0.26) and FHB and HT (r = -0.45). No correlations were detected between FHB or DON and KD or ND in either the two-rowed or six-rowed subsets of the panel. In the six-rowed subset, the correlation between DON and FHB was higher than in the entire panel (r = 0.57). This subset also showed stronger negative correlations between FHB and HD (r = -0.45) and DON and HD (r = -0.53), as well as stronger negative correlations between FHB and HT (r = -0.65) and DON and HT (r = -0.69) than in the entire panel. HD and HT were strongly correlated (r = 0.64) in the six-rowed subset and weakly correlated (0.18) in the two-rowed subset. 3.4 Best performing accessions None of the tested accessions was immune to FHB, as was expected given the highly quantitative nature of resistance to this disease. Eleven accessions (4.7%) had DON BLUE values lower than that of the resistant control Chevron (< -1.02 ppm; note that negative BLUE values for DON accumulation occur due to adjustment for field spatial variability), while seven accessions (3.0%) had FHB BLUE values lower than that of Chevron (< 14.4% severity) (Table 2, Supplemental Table S2). The ten most resistant accessions in terms of DON accumulation and the ten most resistant accessions in terms of FHB severity are displayed in Table 2. The accession with the lowest DON BLUE value was ICD 117547 (-6.26 ppm), a two-rowed breeding line from AAFC, followed by HV 527 (-2.26 ppm), a two-rowed Swiss landrace. Five BC 3 RILs (S42IL_170, S42IL_117, S42IL_168, S42IL_164, and S42IL_133) were in the top ten accessions with the lowest DON levels and are two-rowed progeny from the wild barley introgression population of Scarlett/ H.v. ssp. spontaneum accession ISR42-8 (Schmalenbach et al., 2008). Other accessions with low DON accumulation included the six-rowed entries of NGB8234 (Finland) and CIho 9056 (Austria) and the two-rowed entry of EEBC_084 (Ethiopia). The accession with the lowest FHB BLUE value was KTYQST_55_1 (12.36% severity), a two-rowed progeny from a mapping population derived from a cross between the six-rowed control Quest and the two-rowed Turkish landrace Kutahya (Haas, 2016). Two additional accessions from this mapping population are in the top ten for FHB resistance (KTYQST_17_3 and KTYQST_17_4), as well as two accessions (BC 2 RILs: W365QST_57_6 and W365QST_37_1) from a Quest by wild barley cross (Quest/( H.v. ssp. spontaneum W-365) (Haas, 2016). A six-rowed accession (CS_9) with the second lowest FHB score (12.71% severity) was derived from the Chevron/Stander mapping population (Ma et al., 2000). Two Swiss landraces (HV 527 and HV 557) also ranked seventh and tenth in terms of FHB resistance. HV 527 was only accession included in the groups having the ten lowest DON values and ten lowest FHB BLUE values. 3.5 Most susceptible accessions The most susceptible accession by far, in terms of FHB severity, was PI 383933, which was used as a highly susceptible control. This short-statured landrace had a FHB BLUE severity value of 92.7% (Table 2, Supplemental Table S2). The next most susceptible accession, PI 452324 (a breeding line from England), had a BLUE value of 46.5% severity. Ten of the 25 most FHB susceptible accessions were reported as susceptible or moderately susceptible by Huang et al. (2012) and included ICB 111809, Lacey, PI 361705, PI 383933, PI 452324, PI 525187, PI 573976, PI 574078, Rasmusson, and Steptoe. Thirty-three accessions (14%) had higher FHB BLUE values (> 31.76% severity) than the susceptible control of Stander. The most susceptible accession in terms of DON accumulation was Rasmusson with a BLUE value of 14.98 ppm, followed closely by the highly susceptible control, PI 383933, with a BLUE value of 14.50 ppm. Stander had the fifth highest DON BLUE value at 12.52 ppm. Twenty out of the 25 most DON susceptible accessions were six-rowed, with 18 accessions from this group being breeding lines or cultivars from North America. 4 Discussion The panel of 234 barley accessions (including controls) utilized in this study was assembled based on decades-long screening efforts of large germplasm collections in addition to collaborator recommendations. From this extensive search for FHB resistance, very few accessions were found to possess desirable levels of resistance that would be suitable for breeding. This global screening effort was summarized in Huang et al. (2012). Of over 23,000 Hordeum accessions evaluated, only 305 (1.3%) possessed potentially useful levels of partial resistance; furthermore, of the 305 accessions selected for further evaluation, only 78 exhibited consistent levels of partial resistance (Supplemental Table S2). Sixty-four of the resistant materials reported by Huang et al. (2012) were reevaluated in this study (Supplemental Table S1). Additionally, breeding lines and progeny from biparental populations were selected with an emphasis on two-rowed germplasm due to the recent shift in preference of maltsters and brewers in the United States from six-rowed to two-rowed cultivars. 4.1 Population structure in the spring barley collection The final panel was highly diverse and highly structured. High diversity was demonstrated in the PCA of the genotypic matrix of the panel, in which the first principal component represented 17% of the variation (Figure 2). However, several groups of accessions were genetically very similar, including progeny from four biparental populations of Scarlett/ISR42-8 ( H. v . ssp. spontaneum ; Israel) BC 3 RILs, Quest/Kutahya BC 2 RILs, Chevron/Stander DHs, and Quest/W-365 ( H.v . ssp. spontaneum ; Iraq) BC 2 RILs. Despite the genetic similarity among these select accessions, several may be carrying different genomic regions contributing to their resistance due to their origin as progeny from genetically distinct parents. Eleven accessions clustered very tightly (in Cluster A) with the six-rowed resistant accession, Chevron, including several older breeding lines or accessions from the United States (CIho 2236, CIho 6613, CIho 7162, and CIho 11526) and two progenies from Composite Cross XXX-G (COMP 351 and COMP 355). CIho 11526 was reported as a selection from Chevron (USDA NSGC), and it is therefore possible that the other older breeding lines were selections from Chevron as well or pure lines very similar to “Chevron.” Chevron is an important founder in the Minnesota breeding program because it was the primary source of resistance against kernel discoloration (de la Peña et al., 1999). The most diverse subpopulation was Cluster E, a group of 29 accessions with worldwide origins. Nine of these accessions were reported as susceptible or moderately susceptible by Huang et al. (2012), and two were the susceptible controls of ICB 111809 and PI 383933. Inflorescence type (two-rowed vs. six-rowed) was found to account for population stratification patterns in previous studies (Huang et al., 2012; Zhang et al., 2009). This was also the case in this study with a few exceptions: two-rowed progeny from six-rowed by two-rowed biparental backcross mapping populations grouped with their six-rowed parents, three six-rowed lines (08_179_1, 08_180_1, and 08_186_1) selected from the mutagenized two-rowed accession CIho 4196 grouped with two-rowed types, three six-rowed accessions from Cluster F grouped with 18 other two-rowed accessions in the cluster, and 23 six-rowed accessions from the highly diverse Cluster E grouped with the rest of the two-rowed accessions. Modern two-rowed malting cultivars and breeding lines were grouped in Cluster C, most of which are related to older European cultivars and landraces. In general, accessions with potentially useful resistance to FHB and DON accumulation were identified from multiple diverse groups, suggesting that the resistant alleles they carry may be different. 4.2 Correlation between traits DON accumulation was positively correlated with FHB symptoms, but only moderately so (Figure 4), as reported in other studies (Buerstmayer et al, 2004; Choo et al., 2004; He et al. 2015; Huang et al., 2018; Tucker et al. 2019; Tucker et al., 2022). Moreover, trials that produced the highest average FHB severities did not necessarily produce the highest average DON levels. Symptom development and DON production are both highly influenced by environmental conditions at different developmental stages. FHB infections on kernels may not always result in obvious visual symptoms, yet these same kernels may still contain significant amounts of DON, which can be due to many different factors. In some years, favorable conditions for fungal growth and toxin accumulation can occur during the period of plant senescence, at which time visible FHB symptoms will not increase, reducing correlations between visually scored FHB and final DON concentration. It is possible that certain barley genotypes produce more striking visual symptoms than others with lesser kernel discoloration, but which may still have significant fungal proliferation. Production of DON and other mycotoxins has been shown to be an element of the general stress response by F. graminearum (Ponts, 2015) to both biotic and abiotic stressors. Varying levels of oxidative stress (produced by host defense responses), heat, pH, and/or UV stress could induce varying levels of DON production by the invading pathogen (Ponts, 2015) that would not necessarily be correlated with visible manifestation of the disease. Additionally, precipitation during the grain fill period may also leach DON (a water-soluble mycotoxin) from the seed, further obscuring the relationship between FHB and DON (Pun et al., 2013). Late heading and tall barley accessions generally sustain lower FHB severities as reported in several previous studies (Huang et al., 2018; Parry et al., 1995, Rudd et al., 2001; Steffenson et al., 1996). Additionally, a recent meta-analysis consolidating results from 22 mapping populations using a consensus map revealed that many previously reported FHB/DON QTL mapped to chromosomal positions coincident with agro-morphological traits, including heading date and height (Sallam et al., 2024). In this study, moderate negative correlations (HD, r = -0.27 and -0.36; HT, r = -0.53 and -0.41, respectively; Figure 3) were found between these two traits and both FHB and DON. Late heading and tall stature may serve as escape mechanisms from infection. Although every effort is made to provide an environment favorable for infection through overhead irrigation of the FHB nurseries during the season, the late heading of some barleys may coincide with periods of hot and dry weather, conditions that are not conducive to FHB infection. Moreover, some barley accessions with late maturity exhibit a shorter period between the heading stage and senescence, a trait that could ultimately reduce the time available for fungal colonization and DON accumulation. A possible example of this is with Chevron, a resistant accession that heads late and exhibits a short maturation period (K. Smith, personal communication). Unfortunately, late heading and tall stature are undesirable traits for spring barley grown in the Upper Midwest. It should also be noted that in this study lodging was prevented by supporting plants with stakes and netting. Tall barley tends to be more prone to lodging, which can contribute to higher disease and DON levels due to the spikes laying close to the inoculum source and being subject to higher moisture conditions near the soil surface. KD and DON were moderately correlated (Figure 3) in the entire barley panel, but not in the two- or six-rowed subsets. This was because DON was significantly lower in the two-rowed subset of the panel (Supplemental Figure S1), and KD is directly related to row type. No correlation was observed between ND and FHB/DON in the panel, a result in contrast to previously reported observations that dense spikes showed higher disease severity (Steffenson et al., 1996; Ma et al., 2000). Dense spike architecture, providing a more favorable micro-environment for infection and disease spread throughout the spike, has been hypothesized to contribute to higher FHB levels in genotypes with high ND. However, Yoshida et al. (2005) showed that spike density in a set of near-isogenic lines (NILs) had little or no effect on FHB severity, as was observed in the diverse germplasm of this study. The germplasm in this study was curated mainly because accessions displayed resistance to FHB, which may partially account for the lack of correlation between ND and FHB/DON. 4.3 Two-rowed versus six-rowed germplasm Two-rowed spike morphology has been reported to be correlated with lower FHB severities (Huang et al., 2018; Parry et al., 1995; Rudd et al., 2001; Steffenson et al., 1996). It has been speculated by authors of previous studies that two-rowed spikes may serve as less favorable niches for FHB infection and progression because a lower density inflorescence reduces moisture retention. Additionally, the denser six-rowed spikes allow for infection to spread more easily across kernels at the same node, as is it common to see triplets of infected kernels in six-rowed spikes (Steffenson, 2003). In this panel, no significant difference in FHB was observed between the means of the two-rowed and six-rowed subsets (Supplemental Figure S1). Yoshida et al. (2005) showed that although six-rowed NILs were more diseased than their two-rowed counterparts, the difference in FHB severity between two-/six-rowed pairs was relatively low and not always detected. They concluded that it should be possible to develop six-rowed cultivars with comparable levels of resistance to two-rowed cultivars. The inclusion within this panel of both six- and two-rowed types reported to have FHB resistance may also explain the lack of difference in means between the subsets. The two-rowed subset of the panel had a significantly lower mean DON level than the six-rowed subset. While FHB severities may be comparable between two- and six-rowed accessions, the lower density spike architecture in two-rowed accessions may contribute to lower DON accumulation during senescence and after disease symptoms are rated. Interestingly, the six-rowed subset of the panel showed correlations between FHB and HD, while the two-rowed subset did not (Figure 4). Also, the two-rowed subset showed only a weak correlation (-0.22) between DON and HD, while the six-rowed subset showed a much higher correlation between these two traits (-0.53). The range of HD was similar between the subsets (Supplemental Figure S1); however, the six-rowed subset had a significantly earlier HD. Thirteen of the twenty earliest heading accessions in the panel were six-rowed. Similarly, the two-rowed subset showed a weaker correlation between HT and FHB/DON (-0.45/-0.26) than the six-rowed subset (-0.65/-0.69), even though distributions of HT were similar between the two subsets. 4.4 Resistance to FHB and DON in the panel Huang et al. (2012) identified 78 accessions that exhibited a consistent level of partial resistance, which included 27 wild, 5 winter, and 5 spring barley accessions not represented in this study. Of the other 41 accessions reported as resistant by Huang et al. (2012; defined as ≤ 75% mean relative FHB severity of Stander), we found that 40 of them also exhibited FHB severities below that of Stander, although 14 were greater than 75% of the FHB severity of Stander (> 23.8% severity) and one, HOR211, was higher in severity than Stander. Huang et al. (2012) reported that Atahualpa and CIho 6611 were the two accessions with the lowest relative disease severities. In this study, we found that Atahualpa was not strikingly resistant, as it had a BLUE value for FHB of 24.42% and ranked 124 th out of all accessions tested for FHB resistance. CIho 6611 was more resistant, with a BLUE value of 19.86% (57 th most resistant), although 19 of the other accessions evaluated by Huang et al. were found to be more resistant in this study. Three resistant accessions (VIR 28797, HV 527, and Chevron) reported by Huang et al. (2012) were among the top ten most resistant accessions found in our panel. Notably, only seven and 11 accessions performed better than the standard resistant control of Chevron for FHB and DON, respectively. Some of the accessions with the lowest FHB severities originated from the Quest/Kutahya BC 2 RIL population (Haas, 2016). Three accessions in the top ten and an additional five in the top 30 originated from this cross (Table 2 and Supplemental Table S2). These lines had lower FHB than either of the two parents and are therefore promising transgressive segregants for use in breeding. However, many of these lines were also taller and later heading than either parent. Two accessions in the top ten originated from the Quest/W-365 ( H.v. ssp. spontaneum ; Iraq) BC 2 RIL population (Haas, 2016) and had much lower FHB severity than Quest, indicating the resistance alleles were likely contributed by the wild parent. One of these lines (W365QST_57_6) was taller than Quest; however, the other (W365QST_37_1) was not, and neither was markedly later in heading. Thus, these lines may be good candidates for FHB resistance in a six-rowed background. The lines that are closely related to Chevron (CIho 2236, CIho 6613, CIho 7162, CIho 11526, COMP 351, and COMP 355) performed well with respect to FHB, with all having lower than 18.1% severity, which is lower than that of the two-rowed control, CIho 4196 (18.2%). The two landraces from Switzerland, HV 527 and HV 557, which also ranked in the top ten most FHB resistant were both very tall (over 90 cm) and late heading (over 15 days later than typical Midwestern varieties), which make them less desirable for resistance breeding. Chevron remained remarkably resistant compared to all other accessions. The high level of FHB resistance in Chevron was recognized and used for breeding Midwestern barley as early as the 1930s (Immer and Christensen, 1943; Shands, 1939). Aside from its FHB resistance, Chevron also has very bright kernels, a trait valued in the malting/brewing industries and is a source of the durable stem rust resistance gene Rpg1 . The top performing accessions with respect to FHB were not the same as the top performing accessions with respect to DON accumulation, except for HV 527 (two-rowed, Switzerland), NGB8243 (six-rowed, Finland), and CIho 9056 (six-rowed, Austria), which had markedly low levels of both FHB and DON. Seven of the top ten accessions with respect to DON had FHB severities at least 7.4% higher than Chevron. Other accessions that had low DON and low FHB included KTYQST_53_4, Chevron, and accessions genetically related to Chevron, including derivatives COMP 355, CIho 6613, and CIho 7162. A two-rowed breeding line from Canada (AAFC), ICD 117547, had by far the lowest BLUE DON value, almost 3 ppm lower than the next best accession, but was also taller and later heading than desirable. Five of the ten accessions with the lowest DON concentrations were two-rowed progeny from the Scarlett/ H.v. ssp. spontaneum accession ISR42-8 BC 3 RIL population. Scarlett, a German malting cultivar, was not originally identified as a line with FHB resistance and was not phenotyped in this study. Scarlett was reported to be resistant to F. graminearum (Martínez et al., 2020) in comparison to four other malting cultivars, but was also classified as a susceptible cultivar by Yang et al. (2010). Additional field trials are underway to confirm the reaction of Scarlett to FHB. Regardless of the source of resistance in these progenies, whether originating from Scarlett, the wild parent, or both, their low level of DON is promising. These lines are also much shorter than many other accessions with low DON and/or FHB (all are shorter than 66 cm, over 5 cm shorter than the control Quest), a trait that is desired in typical Midwestern barley cultivars. Some barley breeders have begun prioritizing DON measurements over in-field FHB phenotyping data (Kevin Smith, personal communication) because they tend to have higher heritability and require less labor to obtain. Additionally, harvested barley with high levels of DON are discounted more severely at the grain elevator that visibly infected kernels. Applying a hypothetical selection index (prioritizing DON content) of DON lower than that of the resistant control Quest (6.14 ppm), FHB severity lower than that of the susceptible control Robust (28%), heading dates no later than 60 days after planting, and plant height no higher than 80 cm, only 46 (19.7%) accessions in the panel make the cut. These parameters are quite broad for heading date and height: if HD is limited to a maximum of 56 days after planting, the list of accessions that meets these criteria drops to 26 (11%). This example illustrates the difficulty of identifying sources of resistance that do not also possess undesirable agro-morphological characteristics. Cluster E contained a majority of the most FHB susceptible accessions. Many of these accessions corresponded to those reported as susceptible in Huang et al. (2012). Five of the ten most FHB susceptible accessions had some of the earliest heading dates, all below 50 days after planting. Cluster B contained a majority of accessions with the highest DON levels, many of which are North American six-rowed breeding lines or cultivars. As barley production in the United States has shifted from 70% (of total malt) six-rowed cultivars in 1986 to over 93% two-rowed varieties in 2020 (USDA-NASS and the American Malting Barley Association), the problem of high DON susceptibility in major cultivars has likely been reduced. For example, most of the modern two-rowed North American germplasm (Cluster C) included in this study (i.e., AC Minoa, AAC Goldman, AC Oxbow, AAC Connect, Island, TR15152, TR16629, 2ND29827, 2ND27921, 2ND28065, 95RS316A, 2AB04_X01084_27, and 2AB09_X06F058HL_68) had BLUE DON values below 4.3 ppm–over 1.8 ppm lower than that of the six-rowed control Quest. 4.5 Conclusions The data generated in this study serve as an extensive and comprehensive summary of worldwide spring barley sources of FHB resistance and their performance in the Upper Midwest production region. Several accessions originating from diverse backgrounds were identified as having moderately high resistance to FHB and/or DON accumulation. These accessions are promising candidates for resistance breeding and may be carrying different underlying resistance alleles which could be combined to develop progeny with even higher levels of resistance. This study highlighted a major challenge that has been acknowledged previously: identifying accessions with low disease severity and low mycotoxin accumulation that do not have undesirable agro-morphological traits. The highly quantitative nature of resistance and its dependence on plant height, heading date, and spike morphology traits contribute to the difficulty in breeding for resistance. Breeding progress will continue to be slow and incremental, as no major effect QTL have been discovered and are unlikely to exist in the primary barley genepool. Acknowledgments Funding for this research was supported in part by the United States Wheat and Barley Scab Initiative (agreements 59-0206-0-182, “Evaluation and Genetic Characterization of Hordeum Germplasm for Resistance to FHB” and 59-0206-2-147, “Genetic Enhancement of Fusarium Head Blight (FHB) Resistance in Barley”), the American Malting Barley Association, and the Lieberman-Okinow Endowment at the University of Minnesota. RP was supported by a University of Minnesota Doctoral Dissertation Fellowship. We thank all of the cooperators who kindly contributed germplasm for this study. We thank Michael Miller and Michelle Jugovich for their excellent technical assistance in data collection. Conflict of Interest The authors declare no conflict of interest. ORCIDs Rae Page, 0009-0009-5516-7354; Ahmad H. Sallam, 0000-0003-3212-9231; Oadi Matny, 0000-0002-8447-2886; Brian Steffenson, 0000-0001-7961-5363 Supplemental Material Supplemental Table S1. Information on 234 spring barley accessions evaluated for Fusarium head blight resistance and deoxynivalenol concentration in Minnesota. Supplemental Table S2. List of 234 spring barley accessions with trait values expressed as average best linear unbiased estimates (BLUEs) for deoxynivalenol concentration (DON), Fusarium head blight percent severity (FHB), heading date (HD), and plant height (HT), and as grand means for node density (ND), and kernel density (KD). Supplemental Table S3. Information on isolates of Fusarium graminearum used in all trial years for the Fusarium head blight nurseries. Supplemental Figure S1. Boxplots for distributions of best linear unbiased estimate (BLUE) values combined across all environments for the following traits: Deoxynivalenol concentration (DON), Fusarium head blight percent severity (FHB), heading date (HD), and plant height (HT). Kernel density (KD) and node density (ND) values represent grand means for all environments on an entry-mean basis. Traits are split between the two-rowed (n = 133) and six-rowed (n = 102) accessions in the panel. The top and bottom box edges show the 25th and 75th percentiles of total data. Asterisks above the plots indicate a significant difference between subpopulation means ( p < 0.05, two-sample t-test). Supplemental Figure S2. Boxplots for distributions of deoxynivalenol concentration (DON) and Fusarium head blight percent severity (FHB) for the barley panel in all trials. Solid horizontal lines show medians. The top and bottom box edges show the 25th and 75th percentiles of total data. Different letters above box plots indicate significant differences between trial means (p < 0.05, Tukey HSD). The number of accessions with observations in each trial is indicated at the top right corner of each plot. References Bayer, M. M., Rapazote-Flores, P., Ganal, M., Hedley, P. E., Macaulay, M., Plieske, J., Ramsay, L., Russell, J., Shaw, P. D., Thomas, W., & Waugh, R. (2017). 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Countries of origin for the panel of barley accessions selected for study of FHB resistance colored by number of accessions. The numbers of accessions are superimposed on the countries. Note that the number of accessions from the United States also includes ones derived from biparental populations (n = 38) developed for mapping FHB resistance. Figure 2. (A) Principal component analysis (PCA) of 229 barley accessions using 25,589 SNP markers. Two subpopulations were inferred by K -means clustering. Two-rowed and six-rowed accessions are indicated using circles and triangles, respectively. (B) PCA of 229 barley accessions colored according to k = 6 subpopulations determined via hierarchical clustering utilizing Ward’s minimum variance method on the genetic distance matrix. Colors correspond to the clusters in Figure 2.C. (C) A dendrogram of all accessions colored according to k = 6 subpopulations determined via hierarchical clustering utilizing Ward’s minimum variance method on the genetic distance matrix. Figure 3. Histograms for the distribution of best linear unbiased estimate (BLUE) values combined across all environments for the following traits: Fusarium head blight percent severity (FHB), deoxynivalenol concentration (DON), heading date (HD), and plant height (HT). Kernel density (KD) and node density (ND) values represent grand means for all environments on an entry-mean basis. Values for control lines are displayed. The X -axis represents the unit of measurement for each trait, and the Y -axis represents the count of accessions. Figure 4. (A) Correlation coefficients between best linear unbiased estimate (BLUE) values combined across all environments for the following traits (or grand means on an entry-mean basis for KD and ND): Fusarium head blight percent severity (FHB), deoxynivalenol concentration (DON), heading date (HD), and plant height (HT), kernel density (KD), and node density (ND). The darker blue color indicates a higher positive correlation, while a darker red color indicates a more negative correlation. Correlations with no colored circle present are not significant ( p > 0.05). (B) Correlation coefficients between traits in the two-rowed (n = 133) subset of the panel of accessions. (C) Correlation coefficients between traits in the six-rowed (n = 102) subset of the panel of accessions. Table 1 . Reliability ( i 2 ) on an entry mean basis and descriptive statistics for best linear unbiased estimates (BLUEs) for Fusarium head blight (FHB) severity, deoxynivalenol concentration (DON), heading date (HD), and plant height (HT). DON, ppm 11 0.82 3.8 ± 3.4 3.5 -6.3 - 15.0 21.2 FHB, % severity 11 0.76 25.0 ± 7.9 23.7 12.4 - 92.7 80.3 HD, days after planting 11 0.97 56.3 ± 5.4 56.6 41.2 - 71.1 30.0 HT, cm 7 0.92 76.0 ± 10.0 77.1 40.4 - 99.1 58.7 Table 2. Ten accessions, ranked for lowest deoxynivalenol concentration (DON), and ten accessions, ranked for lowest Fusarium head blight severity (FHB), from a panel of 234 diverse spring barley accessions screened at two locations in Minnesota. n obs BLUE value a n obs BLUE value a n obs BLUE value a n obs BLUE value a unit ppm % severity days after planting cm Lowest DON ICD_117547 2 Breeding line Canada 6 -6.26 4 21.75 4 65.3 1 82.3 HV_527 2 Landrace Switzerland 9 -2.26 10 14.37 10 68.3 7 92.3 S42IL_170 2 Mapping pop b Germany 9 -2.07 10 22.30 10 59.7 6 60.5 NGB8234 6 Landrace Finland 10 -1.92 10 14.53 10 58.8 6 92.6 S42IL_117 2 Mapping pop b Germany 9 -1.88 10 25.43 10 58.3 6 62.1 S42IL_168 2 Mapping pop b Germany 10 -1.75 9 23.94 10 58.5 6 60.1 S42IL_164 2 Mapping pop b Germany 10 -1.60 10 23.65 9 58.1 6 62.5 EEBC_084 2 Landrace Ethiopia Deficiens 10 -1.55 7 34.57 9 65.7 6 90.3 CIho 9056 6 Cultivar Austria 11 -1.27 11 14.82 11 57.6 7 88.4 S42IL_133 2 Mapping pop b Germany 10 -1.07 10 27.63 10 61.7 6 66.0 Lowest FHB KTYQST_55_1 2 Mapping pop c USA 5 1.50 5 12.36 5 61.5 4 85.9 CS_9 6 Mapping pop d USA 7 2.52 7 12.71 7 65.2 6 87.5 KTYQST_17_3 2 Mapping pop c USA 5 2.42 5 13.02 5 58.8 4 87.0 W365QST_57_6 2 Mapping pop e USA 6 1.04 6 13.90 6 54.3 4 79.6 VIR_28797 6 Landrace Austria 7 0.65 7 14.11 7 61.4 6 87.1 KTYQST_17_4 2 Mapping pop c USA 5 3.03 5 14.21 5 57.9 4 83.7 HV_527 2 Landrace Switzerland 9 -2.26 10 14.37 10 68.3 7 92.3 Chevron f 6 Landrace Switzerland 11 -1.02 11 14.40 11 60.1 7 88.7 W365QST_37_1 2 Mapping pop e USA 5 6.14 5 14.42 5 52.4 4 66.2 HV_557 2 Landrace Switzerland 6 0.19 6 14.52 6 66.8 4 93.1 Controls (sorted by DON) Chevron 6 Landrace Switzerland 11 -1.02 11 14.40 11 60.1 7 88.7 CIho 4196 2 Landrace China 10 1.84 10 18.15 11 63.0 6 77.2 Quest 6 Malting USA 10 6.14 10 21.25 10 51.1 7 76.6 ICB_111809 g 2 Landrace Turkey 11 6.32 11 34.57 11 51.8 7 71.4 Robust 6 Malting USA 10 9.88 10 28.24 10 51.1 7 77.2 Stander 6 Malting USA 10 12.52 11 31.76 11 52.6 7 71.9 PI_383933 h 6 Landrace Japan Dwarf 9 14.50 11 92.69 10 41.2 7 40.4 a Data are expressed as average best linear unbiased estimates (BLUEs) after adjustment for field spatial variability (using moving grid adjustment); thus, negative values are possible for a trait such as DON, where raw trait values are often small fractions. b Mapping population pedigree: Scarlett/ISR42-8 ( H.v . ssp. spontaneum ; Israel) BC 3 recombinant inbred lines (RIL) c Mapping population pedigree: Quest/Kutahya BC 2 RIL d Mapping population pedigree: Chevron/Stander doubled haploids e Mapping population pedigree: Quest/W-365 ( H.v . ssp. spontaneum ; Iraq) BC 2 RIL f Moderately resistant controls highlighted green g Susceptible controls highlighted orange h Highly susceptible control highlighted red Information & Authors Information Version history V1 Version 1 22 January 2025 Peer review timeline Published Crop Science Version of Record 15 Aug 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords barley fusarium head blight plant disease plant genetic resources Authors Affiliations Rae Page 0009-0009-5516-7354 University of Minnesota Twin Cities View all articles by this author Ahmad Sallam University of Minnesota Twin Cities View all articles by this author Tamas Szinyei University of Minnesota Twin Cities View all articles by this author Oadi Matny 0000-0002-8447-2886 University of Minnesota Twin Cities View all articles by this author Joseph Wodarek University of Minnesota View all articles by this author Brian Steffenson 0000-0001-7961-5363 [email protected] University of Minnesota Twin Cities View all articles by this author Metrics & Citations Metrics Article Usage 303 views 164 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Rae Page, Ahmad Sallam, Tamas Szinyei, et al. Evaluation of select spring barley accessions for resistance to Fusarium head blight and deoxynivalenol accumulation. Authorea . 22 January 2025. 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