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Powdery mildew, caused by Blumeria graminis f. sp. tritici (Bgt), reduces wheat yields and grain quality, thus posing a significant threat to global food security. Wild relatives of wheat serve as valuable resources for resistance to powdery mildew. Here, the powdery mildew resistance gene Pm6Sl is cloned from the wild wheat species Aegilops longissima. It encodes a nucleotide-binding leucine-rich repeat (NLR) protein featuring a CC-BED module formed by a zinc finger BED (Znf-BED) domain integrated into the coiled-coil (CC) domain. The function of Pm6Sl is validated via mutagenesis, gene silencing, and transgenic assays. In addition, we develop a resistant germplasm harbouring Pm6Sl in a very small segment with no linkage drag along with the diagnostic gene marker pm6sl-1 to facilitate Pm6Sl deployment in wheat breeding programs. The cloning of Pm6Sl, a resistance gene with BED-NLR architecture, will increase our understanding of the molecular mechanisms underlying BED-NLR-mediated resistance to various pathogens., (© 2024. The Author(s).)

Crop wild relatives offer natural variations of disease resistance for crop improvement. Here, we report the isolation of broad-spectrum powdery mildew resistance gene Pm36, originated from wild emmer wheat, that encodes a tandem kinase with a transmembrane domain (WTK7-TM) through the combination of map-based cloning, PacBio SMRT long-read genome sequencing, mutagenesis, and transformation. Mutagenesis assay reveals that the two kinase domains and the transmembrane domain of WTK7-TM are critical for the powdery mildew resistance function. Consistently, in vitro phosphorylation assay shows that two kinase domains are indispensable for the kinase activity of WTK7-TM. Haplotype analysis uncovers that Pm36 is an orphan gene only present in a few wild emmer wheat, indicating its single ancient origin and potential contribution to the current wheat gene pool. Overall, our findings not only provide a powdery mildew resistance gene with great potential in wheat breeding but also sheds light into the mechanism underlying broad-spectrum resistance., (© 2024. The Author(s).)

How plants distribute biomass among organs influences resource acquisition, reproduction and plant-plant interactions, and is essential in understanding plant ecology, evolution, and yield production in agriculture. However, the genetic mechanisms regulating allocation responses to the environment are largely unknown. We studied recombinant lines of wheat (Triticum spp.) grown as single plants under sunlight and simulated canopy shade to investigate genotype-by-environment interactions in biomass allocation to the leaves, stems, spikes, and grains. Size-corrected mass fractions and allometric slopes were employed to dissect allocation responses to light limitation and plant size. Size adjustments revealed light-responsive alleles associated with adaptation to the crop environment. Combined with an allometric approach, we demonstrated that polymorphism in the DELLA protein is associated with the response to shade and size. While a gibberellin-sensitive allelic effect on stem allocation was amplified when plants were shaded, size-dependent effects of this allele drive allocation to reproduction, suggesting that the ontogenetic trajectory of the plant affects the consequences of shade responses for allocation. Our approach provides a basis for exploring the genetic determinants underlying investment strategies in the face of different resource constraints and will be useful in predicting social behaviours of individuals in a crop community., (© 2024 The Authors New Phytologist © 2024 New Phytologist Foundation.)

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., (© 2022. The Author(s).)

Key Message: Genomic prediction of genebank accessions benefits from the consideration of additive-by-additive epistasis and subpopulation-specific marker effects. Wheat (Triticum aestivum L.) and other species of the Triticum genus are well represented in genebank collections worldwide. The substantial genetic diversity harbored by more than 850,000 accessions can be explored for their potential use in modern plant breeding. Characterization of these large number of accessions is constrained by the required resources, and this fact limits their use so far. This limitation might be overcome by engaging genomic prediction. The present study compared ten different genomic prediction approaches to the prediction of four traits, namely flowering time, plant height, thousand grain weight, and yellow rust resistance, in a diverse set of 7745 accession samples from Germany's Federal ex situ genebank at the Leibniz Institute of Plant Genetics and Crop Plant Research in Gatersleben. Approaches were evaluated based on prediction ability and robustness to the confounding influence of strong population structure. The authors propose the wide application of extended genomic best linear unbiased prediction due to the observed benefit of incorporating additive-by-additive epistasis. General and subpopulation-specific additive ridge regression best linear unbiased prediction, which accounts for subpopulation-specific marker-effects, was shown to be a good option if contrasting clusters are encountered in the analyzed collection. The presented findings reaffirm that the trait's genetic architecture as well as the composition and relatedness of the training set and test set are major driving factors for the accuracy of genomic prediction., (© 2022. The Author(s).)

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' F 1 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., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Key Message: Heterosis effects for dough quality and baking volume were close to zero. However, hybrids have a higher grain yield at a given level of bread making quality compared to their parental lines. Bread wheat cultivars have been selected according to numerous quality traits to fulfill the requirements of the bread making industry. These include beside protein content and quality also rheological traits and baking volume. We evaluated 35 male and 73 female lines and 119 of their single-cross hybrids at three different locations for grain yield, protein content, sedimentation value, extensograph traits and baking volume. No significant differences (p < 0.05) were found in the mean comparisons of males, females and hybrids, except for higher grain yield and lower protein content in the hybrids. Mid-parent and better-parent heterosis values were close to zero and slightly negative, respectively, for baking volume and extensograph traits. However, the majority of heterosis values resulted in the finding that hybrids had higher grain yield than lines for a given level of baking volume, sedimentation value or energy value of extensograph. Due to the high correlation with the mid-parent values (r > 0.70), an initial prediction of hybrid performance based on line per se performance for protein content, sedimentation value, most traits of the extensograph and baking volume is possible. The low variance due to specific combining ability effects for most quality traits points toward an additive gene action requires quality selection within both heterotic groups. Consequently, hybrid wheat can combine high grain yield with high bread making quality. However, the future use of wheat hybrids strongly depends on the establishment of a cost-efficient and reliable seed production system., (© 2022. The Author(s).)

The accuracy of genomic prediction increases with increasing heritability, and thus the challenge of optimizing the design of multienvironment yield trials under a limited budget arises. With this in mind, we aimed to find the best of several options to sparsely distribute a fixed number of plots across different environments to increase the accuracy of hybrid performance prediction. We used a comprehensive published genomic and phenotypic data set of 1,604 winter wheat (Triticum aestivum L.) hybrids and compared several commonly used biometric models for phenotypic data analysis in a resampling study to identify the one that most accurately estimated the hybrid performance in different imbalanced trials. Our results showed that when using information about genotypic relationships, genotypic values were more strongly associated with the reference values than when this information was ignored. In addition, a balanced environmental sampling resulted in an adequate characterization of each environment and increased the accuracy for estimating the hybrid performance. One promising design involved dividing the genotypes into equally sized subgroups that were tested in a subset of environments, with the constraint that the subgroups overlapped with respect to the environments. This scenario appears to be particularly appropriate, as it provided both high accuracies in the estimates of genotypic values and had low variability resulting from the data sample used. Thus, we were able to clearly demonstrate the utility for optimizing the design of multienvironment hybrid wheat yield trials in times of genomic selection., (© 2021 The Authors. The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America.)

Resistance breeding is crucial for sustainable control of wheat leaf rust and single nucleotide polymorphism (SNP)-based genome-wide association studies (GWAS) are widely used to dissect leaf rust resistance. Unfortunately, GWAS based on SNPs often explained only a small proportion of the genetic variation. We compared SNP-based GWAS with a method based on functional haplotypes (FH) considering epistasis in a comprehensive hybrid wheat mapping population composed of 133 parents plus their 1574 hybrids and characterized with 626 245 high-quality SNPs. In total, 2408 and 1 139 828 significant associations were detected in the mapping population by using SNP-based and FH-based GWAS, respectively. These associations mapped to 25 and 69 candidate regions, correspondingly. SNP-based GWAS highlighted two already-known resistance genes, Lr22a and Lr34-B, while FH-based GWAS detected associations not only on these genes but also on two additional genes, Lr10 and Lr1. As revealed by a second hybrid wheat population for independent validation, the use of detected associations from SNP-based and FH-based GWAS reached predictabilities of 11.72% and 22.86%, respectively. Therefore, FH-based GWAS is not only more powerful for detecting associations, but also improves the accuracy of marker-assisted selection compared with the SNP-based approach., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Key Message: Hybrid wheat breeding is a promising strategy to improve the level of leaf rust and stripe rust resistance in wheat. Leaf rust and stripe rust belong to the most important fungal diseases in wheat production. Due to a dynamic development of new virulent races, epidemics appear in high frequency and causes significant losses in grain yield and quality. Therefore, research is needed to develop strategies to breed wheat varieties carrying highly efficient resistances. Stacking of dominant resistance genes through hybrid breeding is such an approach. Within this study, we investigated the genetic architecture of leaf rust and stripe rust resistance of 1750 wheat hybrids and their 230 parental lines using a genome-wide association study. We observed on average a lower rust susceptibility for hybrids in comparison to their parental inbred lines and some hybrids outperformed their better parent with up to 56%. Marker-trait associations were identified on chromosome 3D and 4A for leaf rust and on chromosome 2A, 2B, and 6A for stripe rust resistance by using a genome-wide association study with a Bonferroni-corrected threshold of P < 0.10. Detected loci on chromosomes 4A and 2A were located within previously reported genomic regions affecting leaf rust and stripe rust resistance, respectively. The degree of dominance was for most associations favorable in the direction of improved resistance. Thus, resistance can be increased in hybrid wheat breeding by fixing complementary leaf rust and stripe rust resistance genes with desired dominance effects in opposite parental pools.

Resistance breeding is crucial for a sustainable control of leaf rust (Puccinia triticina) in wheat (Triticum aestivum L.) while directly targeting functional variants is the Holy Grail for efficient marker-assisted selection and map-based cloning. We assessed the limits and prospects of exome association analysis for severity of leaf rust in a large hybrid wheat population of 1574 single-crosses plus their 133 parents. After imputation and quality control, exome sequencing revealed 202 875 single-nucleotide polymorphisms (SNPs) covering 19.7% of the high-confidence annotated gene space. We performed intensive data mining and found significant associations for 2171 SNPs corresponding to 50 different loci. Some of these associations mapped in the proximity of the already known resistance genes Lr21, Lr34-B, Lr1 and Lr10, while other associated genomic regions, such as those on chromosomes 1A and 3D, harboured several annotated genes putatively involved in resistance. Validation with an independent population helped to narrow down the list of putative resistance genes that should be targeted by fine-mapping. We expect that the proposed strategy of intensive data mining coupled with validation will significantly influence research in plant genetics and breeding., (© 2019 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.)

Genebanks are valuable sources of genetic diversity, which can help to cope with future problems of global food security caused by a continuously growing population, stagnating yields and climate change. However, the scarcity of phenotypic and genotypic characterization of genebank accessions severely restricts their use in plant breeding. To warrant the seed integrity of individual accessions during periodical regeneration cycles in the field phenotypic characterizations are performed. This study provides non-orthogonal historical data of 12,754 spring and winter wheat accessions characterized for flowering time, plant height, and thousand grain weight during 70 years of seed regeneration at the German genebank. Supported by historical weather observations outliers were removed following a previously described quality assessment pipeline. In this way, ready-to-use processed phenotypic data across regeneration years were generated and further validated. We encourage international and national genebanks to increase their efforts to transform into bio-digital resource centers. A first important step could consist in unlocking their historical data treasures that allows an educated choice of accessions by scientists and breeders.

Key Message: Using a two-part breeding strategy based on a population improvement and a product development component can leverage hybrid wheat breeding. Despite the technological advance of methods to facilitate hybrid breeding in self-pollinating crops, line breeding is still the dominating breeding strategy. This is likely due to a higher long-term selection gain in line compared to hybrid breeding. In this respect, recent studies on two-part strategies splitting the breeding program into a population improvement and a product development component could mark a trend reversal. Here, an overview of experimental and simulation-based studies exploring the possibilities to integrate genome-wide prediction into recurrent selection is given. Furthermore, possibilities to make use of recurrent selection for inter-population improvement are discussed. Current findings of simulation studies and quantitative genetic considerations suggest that long-term selection gain of hybrid breeding can be increased by implementing a two-part selection strategy based on reciprocal recurrent genomic selection. This would strengthen the competitiveness of hybrid versus line breeding facilitating to develop outstanding hybrid varieties also for self-pollinating plants such as wheat.

In wheat (Triticum spp.), modifying inflorescence (spike) morphology can increase grain number and size and thus improve yield. Here, we demonstrated the potential for manipulating and predicting spike morphology, based on 44 traits. In 12 wheat cultivars, we observed that detillering (removal of branches), which alters photosynthate distribution, changed spike morphology. Our genome-wide association study detected close associations between carbon partitioning (e.g. tiller number, main shoot dry weight) and spike morphology (e.g. spike length, spikelet density) traits in 210 cultivars. Most carbon-partitioning traits (e.g. tiller dry weight, harvest index) demonstrated high prediction abilities (>0.5). For spike morphology, some traits (e.g. total and fertile spikelet number, spike length) displayed high prediction abilities (0.3-0.5), but others (e.g. spikelet fertility, spikelet density) exhibited low prediction abilities (<0.2). Grain size traits were closely correlated in field and greenhouse experiments. Stepwise regression analysis suggests that significantly associated traits in the greenhouse explain 35.35% of the variation in grain yield and 67.63% of the variation in thousand-kernel weight in the field. Therefore, the traits identified in this study affect spike morphology; these traits can be used to predict and improve plant architecture and thus increase yield.

Key Message: The portfolio of available Reduced height loci (Rht-B1, Rht-D1, and Rht24) can be exploited for hybrid wheat breeding to achieve the desired heights in the female and male parents, as well as in the hybrids, without adverse effects on other traits relevant for hybrid seed production. Plant height is an important trait in wheat line breeding, but is of even greater importance in hybrid wheat breeding. Here, the height of the female and male parental lines must be controlled and adjusted relative to each other to maximize hybrid seed production. In addition, the height of the resulting hybrids must be fine-tuned to meet the specific requirements of the farmers in the target regions. Moreover, this must be achieved without adversely impacting traits relevant for hybrid seed production. In this study, we explored Reduced height (Rht) loci effective in elite wheat and exploited their utilization for hybrid wheat breeding. We performed association mapping in a panel of 1705 wheat hybrids and their 225 parental lines, which besides the Rht-B1 and Rht-D1 loci revealed Rht24 as a major QTL for plant height. Furthermore, we found that the Rht-1 loci also reduce anther extrusion and thus cross-pollination ability, whereas Rht24 appeared to have no adverse effect on this trait. Our results suggest different haplotypes of the three Rht loci to be used in the female or male pool of a hybrid breeding program, but also show that in general, plant height is a quantitative trait controlled by numerous small-effect QTL. Consequently, marker-assisted selection for the major Rht loci must be complemented by phenotypic selection to achieve the desired height in the female and male parents as well as in the wheat hybrids.

Key Message: Predictabilities for wheat hybrids less related to the estimation set were improved by shifting from single- to multiple-trait genomic prediction of Fusarium head blight severity. Breeding for improved Fusarium head blight resistance (FHBr) of wheat is a very laborious and expensive task. FHBr complexity is mainly due to its highly polygenic nature and because FHB severity (FHBs) is greatly influenced by the environment. Associated traits plant height and heading date may provide additional information related to FHBr, but this is ignored in single-trait genomic prediction (STGP). The aim of our study was to explore the benefits in predictabilities of multiple-trait genomic prediction (MTGP) over STGP of target trait FHBs in a population of 1604 wheat hybrids using information on 17,372 single nucleotide polymorphism markers along with indicator traits plant height and heading date. The additive inheritance of FHBs allowed accurate hybrid performance predictions using information on general combining abilities or average performance of both parents without the need of markers. Information on molecular markers and indicator trait(s) improved FHBs predictabilities for hybrids less related to the estimation set. Indicator traits must be observed on the predicted individuals to benefit from MTGP. Magnitudes of genetic and phenotypic correlations along with improvements in predictabilities made plant height a better indicator trait for FHBs than heading date. Thus, MTGP having only plant height as indicator trait already maximized FHBs predictabilities. Provided a good indicator trait was available, MTGP could reduce the impacts of genotype environment [Formula: see text] interaction on STGP for hybrids less related to the estimation set.

Increasing wheat yield is a key global challenge to producing sufficient food for a growing human population. Wheat grain yield can be boosted by exploiting heterosis, the superior performance of hybrids compared with midparents. Here we present a tailored quantitative genetic framework to study the genetic basis of midparent heterosis in hybrid populations derived from crosses among diverse parents. We applied this framework to an extensive data set assembled for winter wheat. Grain yield was assessed for 1,604 hybrids and their 135 parental elite breeding lines in 11 environments. The hybrids outperformed the midparents by 10% on average, representing approximately 15 years of breeding progress in wheat, thus further substantiating the remarkable potential of hybrid-wheat breeding. Genome-wide prediction and association mapping implemented through the developed quantitative genetic framework showed that dominance effects played a less prominent role than epistatic effects in grain-yield heterosis in wheat.

Key Message: Selecting contrasting environments allows decreasing phenotyping intensity but still maintaining high accuracy to assess yield stability. Improving yield stability of wheat varieties is important to cope with enhanced abiotic stresses caused by climate change. The objective of our study was to (1) develop and implement an improved heritability estimate to examine the required scale of phenotyping for assessing yield stability in wheat, (2) compare yield performance and yield stability of wheat hybrids and inbred lines, (3) investigate the association of agronomic traits with yield stability, and (4) explore the possibility of selecting subsets of environments allowing to portray large proportion of the variation of yield stability. Our study is based on phenotypic data from five series of official winter wheat registration trials in Germany each including 119-132 genotypes evaluated in up to 50 environments. Our findings suggested that phenotyping in at least 40 environments is required to reliably estimate yield stability to guarantee heritability estimates above 0.7. Contrasting the yield stability of hybrids versus lines revealed no significant differences. Absence of stable associations between yield stability and further agronomic traits suggested low potential of indirect selection to improve yield stability. Selecting posteriori contrasting environments based on the genotype-by-environment interaction effects allowed decreasing phenotyping intensity, but still maintaining high accuracy to assess yield stability. The huge potential of the developed strategy to select contrasting and informative environments has to be validated as a next step in an a priori scenario based on genotype-by-location interaction effects.

We investigated associations between the metabolic phenotype, consisting of quantitative data of 76 metabolites from 135 contrasting winter wheat (Triticum aestivum) lines, and 17 372 single nucleotide polymorphism (SNP) markers. Metabolite profiles were generated from flag leaves of plants from three different environments, with average repeatabilities of 0.5-0.6. The average heritability of 0.25 was unaffected by the heading date. Correlations among metabolites reflected their functional grouping, highlighting the strict coordination of various routes of the citric acid cycle. Genome-wide association studies identified significant associations for six metabolic traits, namely oxalic acid, ornithine, L-arginine, pentose alcohol III, L-tyrosine, and a sugar oligomer (oligo II), with between one and 17 associated SNPs. Notable associations with genes regulating transcription or translation explained between 2.8% and 32.5% of the genotypic variance (pG). Further candidate genes comprised metabolite carriers (pG 32.5-38.1%), regulatory proteins (pG 0.3-11.1%), and metabolic enzymes (pG 2.5-32.5%). The combinatorial use of genomic and metabolic data to construct partially directed networks revealed causal inferences in the correlated metabolite traits and associated SNPs. The evaluated causal relationships will provide a basis for predicting the effects of genetic interferences on groups of correlated metabolic traits, and thus on specific metabolic phenotypes., (© The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

Plants producing antisense or double-stranded RNA molecules that target specific genes of eukaryotic pests or pathogens can become protected from their attack. This beneficial effect was also reported for plant-fungus interactions and is believed to reflect uptake of the RNAs by the fungus via an as yet unknown mechanism, followed by target gene silencing. Here we report that wheat plants pre-infected with Barley stripe mosaic virus (BSMV) strains containing antisense sequences against target genes of the Fusarium head blight (FHB) fungus F. culmorum caused a reduction of corresponding transcript levels in the pathogen and reduced disease symptoms. Stable transgenic wheat plants carrying an RNAi hairpin construct against the β-1, 3-glucan synthase gene FcGls1 of F. culmorum or a triple combination of FcGls1 with two additional, pre-tested target genes also showed enhanced FHB resistance in leaf and spike inoculation assays under greenhouse and near-field conditions, respectively. Microscopic evaluation of F. culmorum development in plants transiently or stably expressing FcGls1 silencing constructs revealed aberrant, swollen fungal hyphae, indicating severe hyphal cell wall defects. The results lead us to propose host-induced gene silencing (HIGS) as a plant protection approach that may also be applicable to highly FHB-susceptible wheat genotypes., (© The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

Bread-making quality traits are central targets for wheat breeding. The objectives of our study were to (1) examine the presence of major effect QTLs for quality traits in a Central European elite wheat population, (2) explore the optimal strategy for predicting the hybrid performance for wheat quality traits, and (3) investigate the effects of marker density and the composition and size of the training population on the accuracy of prediction of hybrid performance. In total 135 inbred lines of Central European bread wheat (Triticum aestivum L.) and 1,604 hybrids derived from them were evaluated for seven quality traits in up to six environments. The 135 parental lines were genotyped using a 90k single-nucleotide polymorphism array. Genome-wide association mapping initially suggested presence of several quantitative trait loci (QTLs), but cross-validation rather indicated the absence of major effect QTLs for all quality traits except of 1000-kernel weight. Genomic selection substantially outperformed marker-assisted selection in predicting hybrid performance. A resampling study revealed that increasing the effective population size in the estimation set of hybrids is relevant to boost the accuracy of prediction for an unrelated test population.

Key Message: Genomic selection models can be trained using historical data and filtering genotypes based on phenotyping intensity and reliability criterion are able to increase the prediction ability. We implemented genomic selection based on a large commercial population incorporating 2325 European winter wheat lines. Our objectives were (1) to study whether modeling epistasis besides additive genetic effects results in enhancement on prediction ability of genomic selection, (2) to assess prediction ability when training population comprised historical or less-intensively phenotyped lines, and (3) to explore the prediction ability in subpopulations selected based on the reliability criterion. We found a 5 % increase in prediction ability when shifting from additive to additive plus epistatic effects models. In addition, only a marginal loss from 0.65 to 0.50 in accuracy was observed using the data collected from 1 year to predict genotypes of the following year, revealing that stable genomic selection models can be accurately calibrated to predict subsequent breeding stages. Moreover, prediction ability was maximized when the genotypes evaluated in a single location were excluded from the training set but subsequently decreased again when the phenotyping intensity was increased above two locations, suggesting that the update of the training population should be performed considering all the selected genotypes but excluding those evaluated in a single location. The genomic prediction ability was substantially higher in subpopulations selected based on the reliability criterion, indicating that phenotypic selection for highly reliable individuals could be directly replaced by applying genomic selection to them. We empirically conclude that there is a high potential to assist commercial wheat breeding programs employing genomic selection approaches.

Hybrid breeding promises to boost yield and stability. The single most important element in implementing hybrid breeding is the recognition of a high-yielding heterotic pattern. We have developed a three-step strategy for identifying heterotic patterns for hybrid breeding comprising the following elements. First, the full hybrid performance matrix is compiled using genomic prediction. Second, a high-yielding heterotic pattern is searched based on a developed simulated annealing algorithm. Third, the long-term success of the identified heterotic pattern is assessed by estimating the usefulness, selection limit, and representativeness of the heterotic pattern with respect to a defined base population. This three-step approach was successfully implemented and evaluated using a phenotypic and genomic wheat dataset comprising 1,604 hybrids and their 135 parents. Integration of metabolomic-based prediction was not as powerful as genomic prediction. We show that hybrid wheat breeding based on the identified heterotic pattern can boost grain yield through the exploitation of heterosis and enhance recurrent selection gain. Our strategy represents a key step forward in hybrid breeding and is relevant for self-pollinating crops, which are currently shifting from pure-line to high-yielding and resilient hybrid varieties.

Key Message: Fusarium head blight and Septoria tritici blotch resistances are complex traits and can be improved efficiently by genomic selection modeling main and epistatic effects. Enhancing the resistance against Fusarium head blight (FHB) and Septoria tritici blotch (STB) is of central importance for a sustainable wheat production. Our study is based on a large experimental data set of 2325 inbred lines genotyped with 12,642 SNP markers and phenotyped in multi-environmental trials for FHB and STB resistance as well as for plant height. Our objectives were to (1) investigate the impact of plant height on FHB and STB severity, (2) examine the potential of marker-assisted selection, and (3) study the prediction ability of genomic selection modeling main and epistatic effects. We observed low correlations between plant height and FHB (r = -0.15; P < 0.05) as well as STB severity (r = -0.17; P < 0.05) suggesting negligible morphological resistances. Cross-validation in combination with association mapping revealed absence of large effect QTL impeding an efficient pyramiding of different resistance loci through marker-assisted selection. The prediction ability of genomic selection was high amounting to 0.6 for FHB and 0.5 for STB resistance. Therefore, genomic selection is a promising tool to improve FHB and STB resistance in wheat.

Background: Fusarium head blight (FHB) and Septoria tritici blotch (STB) severely impair wheat production. With the aim to further elucidate the genetic architecture underlying FHB and STB resistance, we phenotyped 1604 European wheat hybrids and their 135 parental lines for FHB and STB disease severities and determined genotypes at 17,372 single-nucleotide polymorphic loci., Results: Cross-validated association mapping revealed the absence of large effect QTL for both traits. Genomic selection showed a three times higher prediction accuracy for FHB than STB disease severity for test sets largely unrelated to the training sets., Conclusions: Our findings suggest that the genetic architecture is less complex and, hence, can be more properly tackled to perform accurate prediction for FHB than STB disease severity. Consequently, FHB disease severity is an interesting model trait to fine-tune genomic selection models exploiting beyond relatedness also knowledge of the genetic architecture.

Background: The main goal of our study was to investigate the implementation, prospects, and limits of marker imputation for quantitative genetic studies contrasting map-independent and map-dependent algorithms. We used a diversity panel consisting of 372 European elite wheat (Triticum aestivum L.) varieties, which had been genotyped with SNP arrays, and performed intensive simulation studies., Results: Our results clearly showed that imputation accuracy was substantially higher for map-dependent compared to map-independent methods. The accuracy of marker imputation depended strongly on the linkage disequilibrium between the markers in the reference panel and the markers to be imputed. For the decay of linkage disequilibrium present in European wheat, we concluded that around 45,000 markers are needed for low cost, low-density marker profiling. This will facilitate high imputation accuracy, also for rare alleles. Genomic selection and diversity studies profited only marginally from imputing missing values. In contrast, the power of association mapping increased substantially when missing values were imputed., Conclusions: Imputing missing values is especially of interest for an economic implementation of association mapping in breeding populations.

Background: Septoria tritici blotch is an important leaf disease of European winter wheat. In our survey, we analyzed Septoria tritici blotch resistance in field trials with a large population of 1,055 elite hybrids and their 87 parental lines. Entries were fingerprinted with the 9 k SNP array. The accuracy of prediction of Septoria tritici blotch resistance achieved with different genome-wide mapping approaches was evaluated based on robust cross validation scenarios., Results: Septoria tritici blotch disease severities were normally distributed, with genotypic variation being significantly (P < 0.01) larger than zero. The cross validation study revealed an absence of large effect QTL for additive and dominance effects. Application of genomic selection approaches particularly designed to tackle complex agronomic traits allowed to double the accuracy of prediction of Septoria tritici blotch resistance compared to calculation methods suited to detect QTL with large effects., Conclusions: Our study revealed that Septoria tritici blotch resistance in European winter wheat is controlled by multiple loci with small effect size. This suggests that the currently achieved level of resistance in this collection is likely to be durable, as involvement of a high number of genes in a resistance trait reduces the risk of the resistance to be overcome by specific pathogen isolates or races.

Key Message: Commercial heterosis for grain yield is present in hybrid wheat but long-term competiveness of hybrid versus line breeding depends on the development of heterotic groups to improve hybrid prediction. Detailed knowledge of the amount of heterosis and quantitative genetic parameters are of paramount importance to assess the potential of hybrid breeding. Our objectives were to (1) examine the extent of midparent, better-parent and commercial heterosis in a vast population of 1,604 wheat (Triticum aestivum L.) hybrids and their parental elite inbred lines and (2) discuss the consequences of relevant quantitative parameters for the design of hybrid wheat breeding programs. Fifteen male lines were crossed in a factorial mating design with 120 female lines, resulting in 1,604 of the 1,800 potential single-cross hybrid combinations. The hybrids, their parents, and ten commercial wheat varieties were evaluated in multi-location field experiments for grain yield, plant height, heading time and susceptibility to frost, lodging, septoria tritici blotch, yellow rust, leaf rust, and powdery mildew at up to five locations. We observed that hybrids were superior to the mean of their parents for grain yield (10.7 %) and susceptibility to frost (-7.2 %), leaf rust (-8.4 %) and septoria tritici blotch (-9.3 %). Moreover, 69 hybrids significantly (P < 0.05) outyielded the best commercial inbred line variety underlining the potential of hybrid wheat breeding. The estimated quantitative genetic parameters suggest that the establishment of reciprocal recurrent selection programs is pivotal for a successful long-term hybrid wheat breeding.

Abiotic stress tolerance in plants is pivotal to increase yield stability, but its genetic basis is still poorly understood. To gain insight into the genetic architecture of frost tolerance, this work evaluated a large mapping population of 1739 wheat (Triticum aestivum L.) lines and hybrids adapted to Central Europe in field trials in Germany and fingerprinted the lines with a 9000 single-nucleotide polymorphism array. Additive effects prevailed over dominance effects. A two-dimensional genome scan revealed the presence of epistatic effects. Genome-wide association mapping in combination with a robust cross-validation strategy identified one frost tolerance locus with a major effect located on chromosome 5B. This locus was not in linkage disequilibrium with the known frost loci Fr-B1 and Fr-B2. The use of the detected diagnostic markers on chromosome 5B, however, does not allow prediction of frost tolerance with high accuracy. Application of genome-wide selection approaches that take into account also loci with small effect sizes considerably improved prediction of the genetic variation of frost tolerance in wheat. The developed prediction model is valuable for improving frost tolerance because this trait displays a wide variation in occurrence across years and is therefore a difficult target for conventional phenotypic selection.