High-resolution mapping of Ryd4Hb, a major resistance gene to Barley yellow dwarf virus from Hordeum bulbosum.

KEY MESSAGE: We mapped Ryd4Hb in a 66.5 kbp interval in barley and dissociated it from a sublethality factor. These results will enable a targeted selection of the resistance in barley breeding. Virus diseases are causing high yield losses in crops worldwide. The Barley yellow dwarf virus (BYDV) complex is responsible for one of the most widespread and economically important viral diseases of cereals. While no gene conferring complete resistance (immunity) has been uncovered in the primary gene pool of barley, sources of resistance were searched and identified in the wild relative Hordeum bulbosum, representing the secondary gene pool of barley. One such locus, Ryd4Hb, has been previously introgressed into barley, and was allocated to chromosome 3H, but is tightly linked to a sublethality factor that prevents the incorporation and utilization of Ryd4Hb in barley varieties. To solve this problem, we fine-mapped Ryd4Hb and separated it from this negative factor. We narrowed the Ryd4Hb locus to a corresponding 66.5 kbp physical interval in the barley 'Morex' reference genome. The region comprises a gene from the nucleotide-binding and leucine-rich repeat immune receptor family, typical of dominant virus resistance genes. The closest homolog to this Ryd4Hb candidate gene is the wheat Sr35 stem rust resistance gene. In addition to the fine mapping, we reduced the interval bearing the sublethality factor to 600 kbp in barley. Aphid feeding experiments demonstrated that Ryd4Hb provides a resistance to BYDV rather than to its vector. The presented results, including the high-throughput molecular markers, will permit a more targeted selection of the resistance in breeding, enabling the use of Ryd4Hb in barley varieties.

Genetic control and prospects of predictive breeding for European winter wheat's Zeleny sedimentation values and Hagberg-Perten falling number.

KEY MESSAGE: Sedimentation values and falling number in the last decades have helped maintain high baking quality despite rigorous selection for grain yield in wheat. Allelic combinations of major loci sustained the bread-making quality while improving grain yield. Glu-D1, Pinb-D1, and non-gluten proteins are associated with sedimentation values and falling number in European wheat. Zeleny sedimentation values (ZSV) and Hagberg-Perten falling number (HFN) are among the most important parameters that help determine the baking quality classes of wheat and, thus, influence the monetary benefits for growers. We used a published data set of 372 European wheat varieties evaluated in replicated field trials in multiple environments. ZSV and HFN traits hold a wide and significant genotypic variation and high broad-sense heritability. The genetic correlations revealed positive and significant associations of ZSV and HFN with each other, grain protein content (GPC) and grain hardness; however, they were all significantly negatively correlated with grain yield. Besides, GPC appeared to be the major predictor for ZSV and HFN. Our genome-wide association analyses based on high-quality SSR, SNP, and candidate gene markers revealed a strong quantitative genetic nature of ZSV and HFN by explaining their total genotypic variance as 41.49% and 38.06%, respectively. The association of known Glutenin (Glu-1) and Puroindoline (Pin-1) with ZSV provided positive analytic proof of our studies. We report novel candidate loci associated with globulins and albumins-the non-gluten monomeric proteins in wheat. In addition, predictive breeding analyses for ZSV and HFN suggest using genomic selection in the early stages of breeding programs with an average prediction accuracy of 81 and 59%, respectively.

Correction: Miedaner et al. Effective Pollen-Fertility Restoration Is the Basis of Hybrid Rye Production and Ergot Mitigation. Plants 2022, 11, 1115.

In the original publication [...].

First genome-scale insights into the virulence of the snow mold causal fungus Microdochium nivale.

Pink snow mold, caused by a phytopathogenic and psychrotolerant fungus, Microdochium nivale, is a severe disease of winter cereals and grasses that predominantly occurs under snow cover or shortly after its melt. Snow mold has significantly progressed during the past decade, often reaching epiphytotic levels in northern countries and resulting in dramatic yield losses. In addition, M. nivale gradually adapts to a warmer climate, spreading to less snowy territories and causing different types of plant diseases throughout the growing period. Despite its great economic importance, M. nivale is poorly investigated; its genome has not been sequenced and its crucial virulence determinants have not been identified or even predicted. In our study, we applied a hybrid assembly based on Oxford Nanopore and Illumina reads to obtain the first genome sequence of M. nivale. 11,973 genes (including 11,789 protein-encoding genes) have been revealed in the genome assembly. To better understand the genetic potential of M. nivale and to obtain a convenient reference for transcriptomic studies on this species, the identified genes were annotated and split into hierarchical three-level functional categories. A file with functionally classified M. nivale genes is presented in our study for general use. M. nivale gene products that best meet the criteria for virulence factors have been identified. The genetic potential to synthesize human-dangerous mycotoxins (fumonisin, ochratoxin B, aflatoxin, and gliotoxin) has been revealed for M. nivale. The transcriptome analysis combined with the assays for extracellular enzymatic activities (conventional virulence factors of many phytopathogens) was carried out to assess the effect of host plant (rye) metabolites on the M. nivale phenotype. In addition to disclosing plant-metabolite-upregulated M. nivale functional gene groups (including those related to host plant protein destruction and amino acid metabolism, xenobiotic detoxication (including phytoalexins benzoxazinoids), cellulose destruction (cellulose monooxygenases), iron transport, etc.), the performed analysis pointed to a crucial role of host plant lipid destruction and fungal lipid metabolism modulation in plant-M. nivale interactions.

Large-scale genotyping and phenotyping of a worldwide winter wheat genebank for its use in pre-breeding.

Plant genetic resources (PGR) stored at genebanks are humanity's crop diversity savings for the future. Information on PGR contrasted with modern cultivars is key to select PGR parents for pre-breeding. Genotyping-by-sequencing was performed for 7,745 winter wheat PGR samples from the German Federal ex situ genebank at IPK Gatersleben and for 325 modern cultivars. Whole-genome shotgun sequencing was carried out for 446 diverse PGR samples and 322 modern cultivars and lines. In 19 field trials, 7,683 PGR and 232 elite cultivars were characterized for resistance to yellow rust - one of the major threats to wheat worldwide. Yield breeding values of 707 PGR were estimated using hybrid crosses with 36 cultivars - an approach that reduces the lack of agronomic adaptation of PGR and provides better estimates of their contribution to yield breeding. Cross-validations support the interoperability between genomic and phenotypic data. The here presented data are a stepping stone to unlock the functional variation of PGR for European pre-breeding and are the basis for future breeding and research activities.

Genomics-informed prebreeding unlocks the diversity in genebanks for wheat improvement.

The great efforts spent in the maintenance of past diversity in genebanks are rationalized by the potential role of plant genetic resources (PGR) in future crop improvement-a concept whose practical implementation has fallen short of expectations. Here, we implement a genomics-informed prebreeding strategy for wheat improvement that does not discriminate against nonadapted germplasm. We collect and analyze dense genetic profiles for a large winter wheat collection and evaluate grain yield and resistance to yellow rust (YR) in bespoke core sets. Breeders already profit from wild introgressions but PGR still offer useful, yet unused, diversity. Potential donors of resistance sources not yet deployed in breeding were detected, while the prebreeding contribution of PGR to yield was estimated through 'Elite × PGR' F1 crosses. Genomic prediction within and across genebanks identified the best parents to be used in crosses with elite cultivars whose advanced progenies can outyield current wheat varieties in multiple field trials.

Resistance to Snow Mold as a Target Trait for Rye Breeding.

Winter rye is a versatile crop widely used for food and industry. Although rye is resistant to abiotic stressors and many phytopathogens, it is severely damaged by pink snow mold (SM)-a progressive disease caused by the psychrotolerant fungus Microdochium nivale under the snow cover or during prolonged periods of wet and cool conditions. Due to little use of the SM resistance sources in contemporary breeding, varieties with at least moderate resistance to SM are limited. Our study aimed to integrate field assessment under natural conditions and an artificially enriched infection background with laboratory techniques for testing rye accessions and selecting SM resistant sources for applied breeding programs and genetic research. We revealed valuable sources of SM resistance and split rye accessions, according to the level of the genetic divergence of the SM resistance phenotype. This allowed us to select the most distinct donors of the SM resistance, for their use as parental forms, to include novel variability sources in the breeding program for achieving high genetic variability, as well as enhanced and durable SM resistance, in progeny. The rye accessions analyzed here, and the suggested options for their use in breeding, are valuable tools for rye breeding.

The Potential of Genome-Wide Prediction to Support Parental Selection, Evaluated with Data from a Commercial Barley Breeding Program.

Parental selection is at the beginning and contributes significantly to the success of any breeding work. The value of a cross is reflected in the potential of its progeny population. Breeders invest substantial resources in evaluating progeny to select the best performing genotypes as candidates for variety development. Several proposals have been made to use genomics to support parental selection. These have mostly been evaluated using theoretical considerations or simulation studies. However, evaluations using experimental data have rarely been conducted. In this study, we tested the potential of genomic prediction for predicting the progeny mean, variance, and usefulness criterion using data from an applied breeding population for winter barley. For three traits with genetic architectures at varying levels of complexity, ear emergence, plant height, and grain yield, progeny mean, variance, and usefulness criterion were predicted and validated in scenarios resembling situations in which the described tools shall be used in plant breeding. While the population mean could be predicted with moderate to high prediction abilities amounting to 0.64, 0.21, and 0.39 in ear emergence, plant height, and grain yield, respectively, the prediction of family variance appeared difficult, as reflected in low prediction abilities of 0.41, 0.11, and 0.14, for ear emergence, plant height, and grain yield, respectively. We have shown that identifying superior crosses remains a challenging task and suggest that the success of predicting the usefulness criterion depends strongly on the complexity of the underlying trait.

Capturing Wheat Phenotypes at the Genome Level.

Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world's most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public-private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.

Effective Pollen-Fertility Restoration Is the Basis of Hybrid Rye Production and Ergot Mitigation.

Hybrid rye breeding leads to considerably higher grain yield and a higher revenue to the farmer. The basis of hybrid seed production is the CMS-inducing Pampa (P) cytoplasm derived from an Argentinean landrace and restorer-to-fertility (Rf) genes. European sources show an oligogenic inheritance, with major and minor Rf genes, and mostly result in low-to-moderate pollen-fertility levels. This results in higher susceptibility to ergot (Claviceps purpurea) because rye pollen and ergot spores are in strong competition for the unfertilized stigma. Rf genes from non-adapted Iranian primitive rye and old Argentinean cultivars proved to be most effective. The major Rf gene in these sources was localized on chromosome 4RL, which is also a hotspot of restoration in other Triticeae. Marker-based introgression into elite rye materials led to a yield penalty and taller progenies. The Rfp1 gene of IRAN IX was fine-mapped, and two linked genes of equal effects were detected. Commercial hybrids with this gene showed a similar low ergot infection when compared with population cultivars. The task of the future is to co-adapt these exotic Rfp genes to European elite gene pools by genomic-assisted breeding.

Alterations in the Transcriptome of Rye Plants following the Microdochium nivale Infection: Identification of Resistance/Susceptibility-Related Reactions Based on RNA-Seq Analysis.

Microdochium nivale is a progressive and devastating phytopathogen that causes different types of cereal crop and grass diseases that are poorly characterized at the molecular level. Although rye (Secale cereale L.) is one of the most resistant crops to most of the phytopathogens, it is severely damaged by M. nivale. The recent high-quality chromosome-scale assembly of rye genome has improved whole-genome studies of this crop. In the present work, the first transcriptome study of the M. nivale-infected crop plant (rye) with the detailed functional gene classification was carried out, along with the physiological verification of the RNA-Seq data. The results revealed plant reactions that contributed to their resistance or susceptibility to M. nivale. Phytohormone abscisic acid was shown to promote plant tolerance to M. nivale. Flavonoids were proposed to contribute to plant resistance to this pathogen. The upregulation of plant lipase encoding genes and the induction of lipase activity in M. nivale-infected plants revealed in our study were presumed to play an important role in plant susceptibility to the studied phytopathogen. Our work disclosed important aspects of plant-M. nivale interactions, outlined the directions for future studies on poorly characterized plant diseases caused by this phytopathogen, and provided new opportunities to improve cereals breeding and food security strategies.

Reciprocal Recurrent Genomic Selection Is Impacted by Genotype-by-Environment Interactions.

Reciprocal recurrent genomic selection is a breeding strategy aimed at improving the hybrid performance of two base populations. It promises to significantly advance hybrid breeding in wheat. Against this backdrop, the main objective of this study was to empirically investigate the potential and limitations of reciprocal recurrent genomic selection. Genome-wide predictive equations were developed using genomic and phenotypic data from a comprehensive population of 1,604 single crosses between 120 female and 15 male wheat lines. Twenty superior female lines were selected for initiation of the reciprocal recurrent genomic selection program. Focusing on the female pool, one cycle was performed with genomic selection steps at the F2 (60 out of 629 plants) and the F5 stage (49 out of 382 plants). Selection gain for grain yield was evaluated at six locations. Analyses of the phenotypic data showed pronounced genotype-by-environment interactions with two environments that formed an outgroup compared to the environments used for the genome-wide prediction equations. Removing these two environments for further analysis resulted in a selection gain of 1.0 dt ha-1 compared to the hybrids of the original 20 parental lines. This underscores the potential of reciprocal recurrent genomic selection to promote hybrid wheat breeding, but also highlights the need to develop robust genome-wide predictive equations.

OMICs, Epigenetics, and Genome Editing Techniques for Food and Nutritional Security.

The incredible success of crop breeding and agricultural innovation in the last century greatly contributed to the Green Revolution, which significantly increased yields and ensures food security, despite the population explosion. However, new challenges such as rapid climate change, deteriorating soil, and the accumulation of pollutants require much faster responses and more effective solutions that cannot be achieved through traditional breeding. Further prospects for increasing the efficiency of agriculture are undoubtedly associated with the inclusion in the breeding strategy of new knowledge obtained using high-throughput technologies and new tools in the future to ensure the design of new plant genomes and predict the desired phenotype. This article provides an overview of the current state of research in these areas, as well as the study of soil and plant microbiomes, and the prospective use of their potential in a new field of microbiome engineering. In terms of genomic and phenomic predictions, we also propose an integrated approach that combines high-density genotyping and high-throughput phenotyping techniques, which can improve the prediction accuracy of quantitative traits in crop species.

Unlocking big data doubled the accuracy in predicting the grain yield in hybrid wheat.

The potential of big data to support businesses has been demonstrated in financial services, manufacturing, and telecommunications. Here, we report on efforts to enter a new data era in plant breeding by collecting genomic and phenotypic information from 12,858 wheat genotypes representing 6575 single-cross hybrids and 6283 inbred lines that were evaluated in six experimental series for yield in field trials encompassing ~125,000 plots. Integrating data resulted in twofold higher prediction ability compared with cases in which hybrid performance was predicted across individual experimental series. Our results suggest that combining data across breeding programs is a particularly appropriate strategy to exploit the potential of big data for predictive plant breeding. This paradigm shift can contribute to increasing yield and resilience, which is needed to feed the growing world population.

Addressing Research Bottlenecks to Crop Productivity.

Asymmetry of investment in crop research leads to knowledge gaps and lost opportunities to accelerate genetic gain through identifying new sources and combinations of traits and alleles. On the basis of consultation with scientists from most major seed companies, we identified several research areas with three common features: (i) relatively underrepresented in the literature; (ii) high probability of boosting productivity in a wide range of crops and environments; and (iii) could be researched in 'precompetitive' space, leveraging previous knowledge, and thereby improving models that guide crop breeding and management decisions. Areas identified included research into hormones, recombination, respiration, roots, and source-sink, which, along with new opportunities in phenomics, genomics, and bioinformatics, make it more feasible to explore crop genetic resources and improve breeding strategies.

Snow mold of winter cereals: a complex disease and a challenge for resistance breeding.

KEY MESSAGE: Snow mold resistance is a complex quantitative trait highly affected by environmental conditions during winter that must be addressed by resistance breeding. Snow mold resistance in winter cereals is an important trait for many countries in the Northern Hemisphere. The disease is caused by at least four complexes of soilborne fungi and oomycetes of which Microdochium nivale and M. majus are among the most common pathogens. They have a broad host range covering all winter and spring cereals and can basically affect all plant growth stages and organs. Their attack leads to a low germination rate, and/or pre- and post-emergence death of seedlings after winter and, depending on largely unknown environmental conditions, also to foot rot, leaf blight, and head blight. Resistance in winter wheat and triticale is governed by a multitude of quantitative trait loci (QTL) with mainly additive effects highly affected by genotype × environment interaction. Snow mold resistance interacts with winter hardiness in a complex way leading to a co-localization of resistance QTLs with QTLs/genes for freezing tolerance. In practical breeding, a multistep procedure is necessary with (1) freezing tolerance tests, (2) climate chamber tests for snow mold resistance, and (3) field tests in locations with and without regularly occurring snow cover. In the future, resistance sources should be genetically characterized also in rye by QTL mapping or genome-wide association studies. The development of genomic selection procedures should be prioritized in breeding research.

Rye Snow Mold-Associated Microdochium nivale Strains Inhabiting a Common Area: Variability in Genetics, Morphotype, Extracellular Enzymatic Activities, and Virulence.

Snow mold is a severe plant disease caused by psychrophilic or psychrotolerant fungi, of which Microdochium species are the most harmful. A clear understanding of Microdochium biology has many gaps; the pathocomplex and its dynamic are poorly characterized, virulence factors are unknown, genome sequences are not available, and the criteria of plant snow mold resistance are not elucidated. Our study aimed to identify comprehensive characteristics of a local community of snow mold-causing Microdochium species colonizing a particular crop culture. By using the next-generation sequencing (NGS) technique, we characterized fungal and bacterial communities of pink snow mold-affected winter rye (Secale cereale) plants within a given geographical location shortly after snowmelt. Twenty-one strains of M. nivale were isolated, classified on the basis of internal transcribed spacer 2 (ITS2) region, and characterized by morphology, synthesis of extracellular enzymes, and virulence. Several types of extracellular enzymatic activities, the level of which had no correlations with the degree of virulence, were revealed for Microdochium species for the first time. Our study shows that genetically and phenotypically diverse M. nivale strains simultaneously colonize winter rye plants within a common area, and each strain is likely to utilize its own, unique strategy to cause the disease using "a personal" pattern of extracellular enzymes.

Correction to: Genetic mapping of the labile (lab) gene: a recessive locus causing irregular spikelet fertility in labile-barley (Hordeum vulgare convar. labile).

While continuing our quest towards the identification of the labile (lab) locus in barley, we discovered that the previously assigned map location on the long arm of chromosome 5H was wrong.

Prospects of GWAS and predictive breeding for European winter wheat's grain protein content, grain starch content, and grain hardness.

Grain quality traits determine the classification of registered wheat (Triticum aestivum L.) varieties. Although environmental factors and crop management practices exert a considerable influence on wheat quality traits, a significant proportion of the variance is attributed to the genetic factors. To identify the underlying genetic factors of wheat quality parameters viz., grain protein content (GPC), grain starch content (GSC), and grain hardness (GH), we evaluated 372 diverse European wheat varieties in replicated field trials in up to eight environments. We observed that all of the investigated traits hold a wide and significant genetic variation, and a significant negative correlation exists between GPC and GSC plus grain yield. Our association analyses based on 26,694 high-quality single nucleotide polymorphic markers revealed a strong quantitative genetic nature of GPC and GSC with associations on groups 2, 3, and 6 chromosomes. The identification of known Puroindoline-b gene for GH provided a positive analytic proof for our studies. We report that a locus QGpc.ipk-6A controls both GPC and GSC with opposite allelic effects. Based on wheat's reference and pan-genome sequences, the physical characterization of two loci viz., QGpc.ipk-2B and QGpc.ipk-6A facilitated the identification of the candidate genes for GPC. Furthermore, by exploiting additive and epistatic interactions of loci, we evaluated the prospects of predictive breeding for the investigated traits that suggested its efficient use in the breeding programs.

Mapping Stem Rust (Puccinia graminis f. sp. secalis) Resistance in Self-Fertile Winter Rye Populations.

Rye stem rust caused by Puccinia graminis f. sp. secalis can be found in all European rye growing regions. When the summers are warm and dry, the disease can cause severe yield losses over large areas. To date only little research was done in Europe to trigger resistance breeding. To our knowledge, all varieties currently registered in Germany are susceptible. In this study, three biparental populations of inbred lines and one testcross population developed for mapping resistance were investigated. Over 2 years, 68-70 genotypes per population were tested, each in three locations. Combining the phenotypic data with genotyping results of a custom 10k Infinium iSelect single nucleotide polymorphism (SNP) array, we identified both quantitatively inherited adult plant resistance and monogenic all-stage resistance. A single resistance gene, tentatively named Pgs1, located at the distal end of chromosome 7R, could be identified in two independently developed populations. With high probability, it is closely linked to a nucleotide-binding leucine-rich repeat (NB-LRR) resistance gene homolog. A marker for a competitive allele-specific polymerase chain reaction (KASP) genotyping assay was designed that could explain 73 and 97% of the genetic variance in each of both populations, respectively. Additional investigation of naturally occurring rye leaf rust (caused by Puccinia recondita ROEBERGE) revealed a gene complex on chromosome 7R. The gene Pgs1 and further identified quantitative trait loci (QTL) have high potential to be used for breeding stem rust resistant rye.