Capitalizing genebank core collections for rare and novel disease resistance loci to enhance barley resilience.

In the realm of agricultural sustainability, the utilization of plant genetic resources (PGRs) for enhanced disease resistance is paramount. Preservation efforts in genebanks are justified by their potential contributions to future crop improvement. To capitalize on the potential of PGRs, we focused on a barley core collection from the German ex situ genebank, and contrasted it with a European elite collection. The phenotypic assessment included 812 PGRs and 298 elites with a particular emphasis on four disease traits (Puccinia hordei, Blumeria graminis hordei, Ramularia collo-cygni, and Rhynchosporium commune). An integrated genome-wide association study, employing both Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) and a linear mixed model, was performed to unravel the genetic underpinnings of disease resistance. A total of 932 marker-trait associations were identified and assigned to 49 quantitative trait loci. The accumulation of novel and rare resistance alleles significantly bolstered the overall resistance level in PGRs. Three PGR donors with high counts of novel/rare alleles and exhibited exceptional resistance to leaf rust and powdery mildew were identified, offering promise for targeted pre-breeding goals and enhanced resilience in forthcoming varieties. Our findings underscore the critical contribution of PGRs to strengthening crop resilience and advancing sustainable agricultural practices.

Genomic prediction for root and yield traits of barley under a water availability gradient: a case study comparing different spatial adjustments.

BACKGROUND: In drought periods, water use efficiency depends on the capacity of roots to extract water from deep soil. A semi-field phenotyping facility (RadiMax) was used to investigate above-ground and root traits in spring barley when grown under a water availability gradient. Above-ground traits included grain yield, grain protein concentration, grain nitrogen removal, and thousand kernel weight. Root traits were obtained through digital images measuring the root length at different depths. Two nearest-neighbor adjustments (M1 and M2) to model spatial variation were used for genetic parameter estimation and genomic prediction (GP). M1 and M2 used (co)variance structures and differed in the distance function to calculate between-neighbor correlations. M2 was the most developed adjustment, as accounted by the Euclidean distance between neighbors. RESULTS: The estimated heritabilities ([Formula: see text]) ranged from low to medium for root and above-ground traits. The genetic coefficient of variation ([Formula: see text]) ranged from 3.2 to 7.0% for above-ground and 4.7 to 10.4% for root traits, indicating good breeding potential for the measured traits. The highest [Formula: see text] observed for root traits revealed that significant genetic change in root development can be achieved through selection. We studied the genotype-by-water availability interaction, but no relevant interaction effects were detected. GP was assessed using leave-one-line-out (LOO) cross-validation. The predictive ability (PA) estimated as the correlation between phenotypes corrected by fixed effects and genomic estimated breeding values ranged from 0.33 to 0.49 for above-ground and 0.15 to 0.27 for root traits, and no substantial variance inflation in predicted genetic effects was observed. Significant differences in PA were observed in favor of M2. CONCLUSIONS: The significant [Formula: see text] and the accurate prediction of breeding values for above-ground and root traits revealed that developing genetically superior barley lines with improved root systems is possible. In addition, we found significant spatial variation in the experiment, highlighting the relevance of correctly accounting for spatial effects in statistical models. In this sense, the proposed nearest-neighbor adjustments are flexible approaches in terms of assumptions that can be useful for semi-field or field experiments.

Integration of DNA Methylation and Transcriptome Data Improves Complex Trait Prediction in Hordeum vulgare.

Whole-genome multi-omics profiles contain valuable information for the characterization and prediction of complex traits in plants. In this study, we evaluate multi-omics models to predict four complex traits in barley (Hordeum vulgare); grain yield, thousand kernel weight, protein content, and nitrogen uptake. Genomic, transcriptomic, and DNA methylation data were obtained from 75 spring barley lines tested in the RadiMax semi-field phenomics facility under control and water-scarce treatment. By integrating multi-omics data at genomic, transcriptomic, and DNA methylation regulatory levels, a higher proportion of phenotypic variance was explained (0.72-0.91) than with genomic models alone (0.55-0.86). The correlation between predictions and phenotypes varied from 0.17-0.28 for control plants and 0.23-0.37 for water-scarce plants, and the increase in accuracy was significant for nitrogen uptake and protein content compared to models using genomic information alone. Adding transcriptomic and DNA methylation information to the prediction models explained more of the phenotypic variance attributed to the environment in grain yield and nitrogen uptake. It furthermore explained more of the non-additive genetic effects for thousand kernel weight and protein content. Our results show the feasibility of multi-omics prediction for complex traits in barley.

You Had Me at "MAGIC"!: Four Barley MAGIC Populations Reveal Novel Resistance QTL for Powdery Mildew.

Blumeria graminis f. sp. hordei (Bgh), the causal agent of barley powdery mildew (PM), is one of the most important barley leaf diseases and is prevalent in most barley growing regions. Infection decreases grain quality and yields on average by 30%. Multi-parent advanced generation inter-cross (MAGIC) populations combine the advantages of bi-parental and association panels and offer the opportunity to incorporate exotic alleles into adapted material. Here, four barley MAGIC populations consisting of six to eight founders were tested for PM resistance in field trials in Denmark. Principle component and STRUCTURE analysis showed the populations were unstructured and genome-wide linkage disequilibrium (LD) decay varied between 14 and 38 Mbp. Genome-wide association studies (GWAS) identified 11 regions associated with PM resistance located on chromosomes 1H, 2H, 3H, 4H, 5H and 7H, of which three regions are putatively novel resistance quantitative trait locus/loci (QTL). For all regions high-confidence candidate genes were identified that are predicted to be involved in pathogen defense. Haplotype analysis of the significant SNPs revealed new allele combinations not present in the founders and associated with high resistance levels.

Genetic Variance of Metabolomic Features and Their Relationship With Malting Quality Traits in Spring Barley.

Barley is the most common source for malt to be used in brewing beer and other alcoholic beverages. This involves converting the starch of barley into fermentable sugars a process that involves malting, that is germinating of the grains, and mashing, which is an enzymatic process. Numerous metabolic processes are involved in germination, where distinct and time-dependent alterations at the metabolite levels happen. In this study, 2,628 plots of 565 spring malting barley lines from Nordic Seed A/S were investigated. Phenotypic records were available for six malting quality (MQ) traits: filtering speed (FS), wort clearness (WCL), extract yield (EY), wort color (WCO), beta glucan (BG), and wort viscosity (WV). Each line had a set of dense genomic markers. In addition, 24,018 metabolomic features (MFs) were obtained for each sample from nuclear magnetic resonance (NMR) spectra for wort samples produced from each experimental plot. The genetic variation in the MFs was investigated using a univariate model, and the relationship between MFs and the MQ traits was studied using a bivariate model. Results showed that a total of 8,604 MFs had heritability estimates significantly larger than 0 and for all MQ traits, there were genetic correlations with up to 86.77% and phenotypic correlations with up to 90.07% of the significant heritable MFs. In conclusion, around one third of all MFs were significantly heritable, among which a considerable proportion had significant additive genetic and/or phenotypic correlations with the MQ traits (WCO, WV, and BG) in spring barley. The results from this study indicate that many of the MFs are heritable and MFs have great potential to be used in breeding barley for high MQ.

Use of multiple traits genomic prediction, genotype by environment interactions and spatial effect to improve prediction accuracy in yield data.

Genomic selection has been extensively implemented in plant breeding schemes. Genomic selection incorporates dense genome-wide markers to predict the breeding values for important traits based on information from genotype and phenotype records on traits of interest in a reference population. To date, most relevant investigations have been performed using single trait genomic prediction models (STGP). However, records for several traits at once are usually documented for breeding lines in commercial breeding programs. By incorporating benefits from genetic characterizations of correlated phenotypes, multiple trait genomic prediction (MTGP) may be a useful tool for improving prediction accuracy in genetic evaluations. The objective of this study was to test whether the use of MTGP and including proper modeling of spatial effects can improve the prediction accuracy of breeding values in commercial barley and wheat breeding lines. We genotyped 1,317 spring barley and 1,325 winter wheat lines from a commercial breeding program with the Illumina 9K barley and 15K wheat SNP-chip (respectively) and phenotyped them across multiple years and locations. Results showed that the MTGP approach increased correlations between future performance and estimated breeding value of yields by 7% in barley and by 57% in wheat relative to using the STGP approach for each trait individually. Analyses combining genomic data, pedigree information, and proper modeling of spatial effects further increased the prediction accuracy by 4% in barley and 3% in wheat relative to the model using genomic relationships only. The prediction accuracy for yield in wheat and barley yield trait breeding, were improved by combining MTGP and spatial effects in the model.

Genomic Selection Using Pedigree and Marker-by-Environment Interaction for Barley Seed Quality Traits From Two Commercial Breeding Programs.

With the current advances in the development of low-cost high-density array-based DNA marker technologies, cereal breeding programs are increasingly relying on genomic selection as a tool to accelerate the rate of genetic gain in seed quality traits. Different sources of genetic information are being explored, with the most prevalent being combined additive information from marker and pedigree-based data, and their interaction with the environment. In this, there has been mixed evidence on the performance of use of these data. This study undertook an extensive analysis of 907 elite winter barley (Hordeum vulgare L.) lines across multiple environments from two breeding companies. Six genomic prediction models were evaluated to demonstrate the effect of using pedigree and marker information individually and in combination, as well their interactions with the environment. Each model was evaluated using three cross-validation schemes that allows the prediction of newly developed lines (lines that have not been evaluated in any environment), prediction of new or unobserved years, and prediction of newly developed lines in unobserved years. The results showed that the best prediction model depends on the cross-validation scheme employed. In predicting newly developed lines in known environments, marker information had no advantage to pedigree information. Predictions in this scenario also benefited from including genotype-by-environment interaction in the models. However, when predicting lines and years not observed previously, marker information was superior to pedigree data. Nonetheless, such scenarios did not benefit from the addition of genotype-by-environment interaction. A combination of pedigree-based and marker-based information produced a similar or only marginal improvement in prediction ability. It was also discovered that combining populations from the different breeding programs to increase training population size had no advantage in prediction.

Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines.

Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines) are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families.

Silicon-induced changes in antifungal phenolic acids, flavonoids, and key phenylpropanoid pathway genes during the interaction between miniature roses and the biotrophic pathogen Podosphaera pannosa.

Application of 3.6 mm silicon (Si+) to the rose (Rosa hybrida) cultivar Smart increased the concentration of antimicrobial phenolic acids and flavonoids in response to infection by rose powdery mildew (Podosphaera pannosa). Simultaneously, the expression of genes coding for key enzymes in the phenylpropanoid pathway (phenylalanine ammonia lyase, cinnamyl alcohol dehydrogenase, and chalcone synthase) was up-regulated. The increase in phenolic compounds correlated with a 46% reduction in disease severity compared with inoculated leaves without Si application (Si-). Furthermore, Si application without pathogen inoculation induced gene expression and primed the accumulation of several phenolics compared with the uninoculated Si- control. Chlorogenic acid was the phenolic acid detected in the highest concentration, with an increase of more than 80% in Si+ inoculated compared with Si- uninoculated plants. Among the quantified flavonoids, rutin and quercitrin were detected in the highest concentrations, and the rutin concentration increased more than 20-fold in Si+ inoculated compared with Si- uninoculated plants. Both rutin and chlorogenic acid had antimicrobial effects on P. pannosa, evidenced by reduced conidial germination and appressorium formation of the pathogen, both after spray application and infiltration into leaves. The application of rutin and chlorogenic acid reduced powdery mildew severity by 40% to 50%, and observation of an effect after leaf infiltration indicated that these two phenolics can be transported to the epidermal surface. In conclusion, we provide evidence that Si plays an active role in disease reduction in rose by inducing the production of antifungal phenolic metabolites as a response to powdery mildew infection.