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Fusarium head blight (FHB) or scab is a devastating disease of barley that severely reduces the yield and quality of the grain. Additionally, mycotoxins produced by the causal Fusarium species can contaminate harvested grain, resulting in food safety concerns and further economic losses. In the Upper Midwest region of the United States, Fusarium graminearum is the primary causal agent, and deoxynivalenol (DON) is the main mycotoxin associated with Fusarium infection. Deployment of resistant cultivars is an important component of an integrated strategy to manage this disease. Unfortunately, few good sources of FHB resistance have been identified from the evaluation of large collections of Hordeum germplasm. Over the past 25 years, many barley mapping populations have been developed with selected resistance sources to identify the number, chromosomal position and allelic effect of quantitative trait loci (QTL) contributing to FHB resistance and DON accumulation. To consolidate the genetic data generated from 14 mapping studies that included 22 bi‐ or tri‐parental mapping populations and three genome‐wide association (GWAS) mapping panels, a consensus map was constructed that includes 4145 SNP, SSR, RFLP and AFLP markers. A meta‐analysis based on this consensus map revealed 96 QTL for FHB resistance and 57 for DON accumulation scattered across the barley genome. Many of the QTL explained a low percentage (<10%) of variation for the traits and were often found significant in only one or a few environments in multi‐year/multi‐location field trials. Moreover, many of the FHB/DON QTL mapped to chromosomal positions coincided with various agro‐morphological traits that could influence the level of disease (e.g. heading date, height, spike density, and spike angle), raising the important question of whether the former are true resistance factors or are simply the result of pleiotropy with the latter. Considering the magnitude of effect, consistency of detection across environments and independence from agro‐morphological traits, only three of 96 QTL for FHB and five of 57 QTL for DON were considered priority targets for marker‐assisted selection (MAS). In spite of the challenge for having a limited number of useful QTL for breeding, genomic selection holds promise for increasing the efficiency of developing FHB‐resistant barley cultivars, an essential component of the overall management strategy for the disease. [ABSTRACT FROM AUTHOR]

Barley (Hordeum vulgare L.) seeds are a rich source of β‐glucans that can help reduce blood serum cholesterol content in human diets. However, high levels of this polysaccharide in the seeds causes inefficiencies such as slow lautering during the brewing process due to the higher wort viscosity. Thus, depending on the end use, barley breeding programs have opposing goals with respect to β‐glucan. Discovery of new alleles for β‐glucan content in exotic barley germplasm could have significant commercial importance. We leveraged the Wild Barley Diversity Collection (WBDC; N = 314), grown at University of California‐Davis experiment station, to characterize both the variation in β‐glucan content found in this wild barley subspecies (Hordeum vulgare spp. spontaneum) and to reveal chromosomal regions associated with this trait. Grain β‐glucan content ranged from 2.56 to 11.73% in the WBDC. Within structured subpopulations, geographical coordinates as well as annual precipitation data of original collection sites showed associations with differing levels of β‐glucan content. Genome‐wide association study identified a total of 13 quantitative traits loci (QTL) spread across the seven barley chromosomes that explained most of the variation in β‐glucan content in the WBDC. Previously reported genes associated with β‐glucan content such as β‐glucan synthases and hydrolases (e.g., callose synthase) were identified within these QTL regions. Genes such as Patatin and Nudix hydrolase provide novel targets for manipulating grain β‐glucan in barley. RNA‐seq data from both developing and germinating barley seeds for the annotated genes in the QTL regions provide further corroboration for a functional role for these genes in modulating seed β‐glucan levels. Core Ideas: Wild barley species exhibit extensive variation in seed beta glucan content.Environmental conditions in regions where accessions evolved may affect beta glucan levels.Genes involved in carbohydrate metabolism were in the identified QTL regions. [ABSTRACT FROM AUTHOR]

Climate changes leading to higher summer temperatures can adversely affect cool season crops like spring barley. In the Upper Midwest region of the United States, one option for escaping this stress factor is to plant winter or facultative type cultivars in the autumn and then harvest in early summer before the onset of high-temperature stress. However, the major challenge in breeding such cultivars is incorporating sufficient winter hardiness to survive the extremely low temperatures that commonly occur in this production region. To broaden the genetic base for winter hardiness in the University of Minnesota breeding program, 2,214 accessions from the N. I. Vavilov Institute of Plant Industry (VIR) were evaluated for winter survival (WS) in St. Paul, Minnesota. From this field trial, 267 (>12%) accessions survived [designated as the VIR-low-temperature tolerant (LTT) panel] and were subsequently evaluated for WS across six northern and central Great Plains states. The VIR-LTT panel was genotyped with the Illumina 9K SNP chip, and then a genome-wide association study was performed on seven WS datasets. Twelve significant associations for WS were identified, including the previously reported frost resistance gene FR-H2 as well as several novel ones. Multi-allelic haplotype analysis revealed the most favorable alleles for WS in the VIR-LTT panel as well as another recently studied panel (CAP-LTT). Seventy-eight accessions from the VIR-LTT panel exhibited a high and consistent level of WS and select ones are being used in winter barley breeding programs in the United States and in a multiparent population. [ABSTRACT FROM AUTHOR]

Key message: Fine mapping of barley 6H pericentromeric region identified FHB QTL with opposite effects, and high grain protein content was associated with increased FHB severity. Resistance to Fusarium head blight (FHB), kernel discoloration (KD), deoxynivalenol (DON) accumulation and grain protein content (GPC) are important traits for breeding malting barley varieties. Previous work mapped a Chevron-derived FHB QTL to the pericentromeric region of 6H, coinciding with QTL for KD resistance and GPC. The Chevron allele reduced FHB and KD, but unfavorably increased GPC. To determine whether the correlations are caused by linkage or pleiotropy, a fine mapping approach was used to dissect the QTL underlying these quality and disease traits. Two populations, referred to as Gen10 and Gen10/Lacey, derived from a recombinant near-isogenic line (rNIL) were developed. Recombinants were phenotyped for FHB, KD, DON, GPC and other agronomic traits. Three FHB, two DON and two KD QTLs were identified. One of the three FHB QTLs, one DON QTL and one KD QTL were coincident with the GPC QTL, which contains the Hv-NAM1 locus affecting grain protein accumulation. The Chevron allele at the GPC QTL increased GPC and FHB and decreased DON and KD. The other two FHB QTL and the other DON and KD QTL were identified in the regions flanking the Hv-NAM1 locus, and the Chevron alleles decreased FHB, DON and KD. Our results suggested that the QTL associated with FHB, KD, DON and GPC in the pericentromeric region of 6H was controlled by both pleiotropy and tightly linked loci. The rNILs identified in this study with low FHB severity and moderate GPC may be used for breeding malting barley cultivars. [ABSTRACT FROM AUTHOR]

Improving grain protein content and flour quality is an important goal for wheat (Triticum aestivum L.) breeding programs. Identification of genomic regions and markers associated with quality traits can help enrich wheat breeding germplasm with favorable alleles. This study was carried out to identify genetic loci controlling bread‐making quality traits in a nested association mapping population of 230 lines. Lines were evaluated for grain protein content and three gluten aggregation traits from the Brabender GlutoPeak instrument: peak maximum time (PMT), maximum torque (MT), and aggregation energy (AE). The strongest positive correlation was observed between AE and MT (r =.87), whereas the strongest negative correlation was between PMT and MT (r = −.46). We identified 25 quantitative trait loci (QTL) associated with these traits on 14 chromosomes, of which three QTL were common between MT and AE. Quantitative trait loci explaining the largest variations were observed for grain protein content and AE, with QTL that explained 13% of the observed variation for each trait. Nearly 62% of the discovered QTL were of medium effect, as the median percentage of observed phenotypic variation explained by the detected QTL was 7% (range of 4–13%). Based on QTL analysis and marker screening results, the populations discussed in this study are segregating for the known alleles of the high‐molecular‐weight glutenin gene Glu‐D1. As some lines have better phenotypic performance relative to the elite common parent RB07, crossing these lines with elite breeding lines can help increase the frequency of alleles contributing to improve flour quality traits. [ABSTRACT FROM AUTHOR]

In many production areas, barley (Hordeum vulgare L.) is attacked by the leaf rust pathogen (Puccinia hordei Otth), a basidiomycetous fungus that reduces both its yield and quality. Many leaf rust resistance genes, known as reaction to P. hordei (Rph) genes, have been described in barley. To differentiate genetic variants for virulence in pathogen populations, plant pathologists use differentials (i.e., sets of host lines carrying different resistance genes). The sources of Rph1–15 were derived from cultivars, landraces, and wild barley (H. vulgare ssp. spontaneum K. Koch) accessions with diverse geographic origins and agromorphological traits. Ideal differential sets comprise single‐gene lines backcrossed to a single adapted accession that is susceptible to all known races of a pathogen. In this study, sources of Rph1–15 and other Rph gene donors were backcrossed to the susceptible barley cultivar Bowman and then genotyped to characterize the chromosomal positions and sizes of introgressions. Overall, 95 Bowman introgression lines for leaf rust resistance were developed and characterized for their rust phenotypes and genotypes. A single line was selected to represent each of the 15 Rph genes for use as the new barley leaf rust differential set. The existence of possible new resistance genes in the studied germplasm was postulated. The new Bowman Rph1–15 differential lines will facilitate the efficient virulence phenotyping of P. hordei and serve as valuable genetic stocks for Rph gene stacking and cloning in barley. [ABSTRACT FROM AUTHOR]

Genomic selection uses marker-based predictions to improve and accelerate the breeding process. Numerous studies have investigated the accuracy of genomic predictions; however, few studies have directly compared genomic and phenotypic selection. We compared genomic and phenotypic selection in five sets of selection candidates from a barley breeding program. In each set, about 96 breeding lines were genotyped with 1536 single nucleotide polymorphism (SNP) markers and phenotyped for yield, Fusarium head blight (FHB) severity, and deoxynivalenol (DON) concentration. A set of 168 lines and the same set of SNP markers were used to train a prediction model and predict the performance of the selection candidates using ridge regression best linear unbiased prediction. The best-performing 10% of the breeding lines in each selection candidate set were selected using both methods and revaluated in several trials. We found similar significant response to selection using genomic and phenotypic selection for FHB severity and DON concentration, and no significant response for yield using either method. For all traits, genomic selection significantly increased genetic similarity compared with the selection candidates. In addition, genomic selection, compared with phenotypic selection, resulted in an increase in the frequency of favorable alleles. Three indirect selection methods for DON concentration, (predicted FHB severity, empirical FHB severity, and predicted DON concentration) performed similarly to direct phenotypic selection for DON, but differed considerably in cost. We conclude that the use of genomic selection for yield and FHB breeding in barley should result in gains from response to selection that are similar to the gains obtained using phenotypic selection, but with a shorter breeding cycle time and lower cost. [ABSTRACT FROM AUTHOR]

Genomic selection combines phenotypic and molecular marker data from a training population to predict the genotypic values of untested lines. It can improve breeding efficiency as large pools of untested lines can be evaluated for selection. Training population (TP) composition is one of the most important factors affecting the accuracy of genomic prediction. The University of Minnesota wheat breeding program implements genomic selection at the F5 stage for Fusarium head blight (FHB) resistance. This study used field data for FHB resistance in wheat (Triticum aestivum L.) to investigate the use of small-size TPs designed with and without stratified sampling for three FHB traits in three different F5 populations (TP17, TP18, and TP19). We also compared the accuracies of these two TP design methods with the accuracy obtained from a large size TP. Lastly, we evaluated the impact on trait predictions when the parents of F5 lines were included in the TP. We found that the small size TP selected randomly, without stratification, had the lowest predictive ability across the three F5 populations and across the three traits. This trend was statistically significant (p = 0.05) for all three traits in TP17 and two traits in TP18. Designing a small-size TP by stratified sampling led to a higher accuracy than a large-size TP in most traits across TP18 and TP19; this is because stratified sampling allowed the selection of a small set of closely related lines. We also observed that the addition of parental lines to the TP and evaluating the TP in two replications led to an increase in predictive abilities in most cases. [ABSTRACT FROM AUTHOR]