European Wheat Research Articles

An array of Zymoseptoria tritici effectors suppress plant immune responses.

Zymoseptoria tritici is the most economically significant fungal pathogen of wheat in Europe. However, despite the importance of this pathogen, the molecular interactions between pathogen and host during infection are not well understood. Herein, we describe the use of two libraries of cloned Z. tritici effectors that were screened to identify effector candidates with putative pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI)-suppressing activity. The effectors from each library were transiently expressed in Nicotiana benthamiana, and expressing leaves were treated with bacterial or fungal PAMPs to assess the effectors' ability to suppress reactive oxygen species (ROS) production. From these screens, numerous effectors were identified with PTI-suppressing activity. In addition, some effectors were able to suppress cell death responses induced by other Z. tritici secreted proteins. We used structural prediction tools to predict the putative structures of all of the Z. tritici effectors and used these predictions to examine whether there was enrichment of specific structural signatures among the PTI-suppressing effectors. From among the libraries, multiple members of the killer protein-like 4 (KP4) and killer protein-like 6 (KP6) effector families were identified as PTI suppressors. This observation is intriguing, as these protein families were previously associated with antimicrobial activity rather than virulence or host manipulation. This data provides mechanistic insight into immune suppression by Z. tritici during infection and suggests that, similar to biotrophic pathogens, this fungus relies on a battery of secreted effectors to suppress host immunity during early phases of colonization.

Mapping rust resistance in European winter wheat: many QTLs for yellow rust resistance, but only a few well characterized genes for stem rust resistance

Key messageStem rust resistance was mainly based on a few, already known resistance genes; for yellow rust resistance there was a combination of designated genes and minor QTLs.Yellow rust (YR) caused by Puccinia striiformis f. sp. tritici (Pst) and stem rust (SR) caused by Puccinia graminis f. sp. tritici (Pgt) are among the most damaging wheat diseases. Although, yellow rust has occurred regularly in Europe since the advent of the Warrior race in 2011, damaging stem rust epidemics are still unusual. We analyzed the resistance of seven segregating populations at the adult growth stage with the parents being selected for YR and SR resistances across three to six environments (location–year combinations) following inoculation with defined Pst and Pgt races. In total, 600 progenies were phenotyped and 563 were genotyped with a 25k SNP array. For SR resistance, three major resistance genes (Sr24, Sr31, Sr38/Yr17) were detected in different combinations. Additional QTLs provided much smaller effects except for a gene on chromosome 4B that explained much of the genetic variance. For YR resistance, ten loci with highly varying percentages of explained genetic variance (pG, 6–99%) were mapped. Our results imply that introgression of new SR resistances will be necessary for breeding future rust resistant cultivars, whereas YR resistance can be achieved by genomic selection of many of the detected QTLs.

Genome architecture of the allotetraploid wild grass Aegilops ventricosa reveals its evolutionary history and contributions to wheat improvement

The allotetraploid wild grass Aegilops ventricosa (2n = 4x = 28, genome DvDvNvNv) has been recognized as an important germplasm resource for wheat improvement owing to its ability to tolerate biotic stresses. In particular, the 2NvS segment from Ae. ventricosa, as a stable and effective resistance source, has contributed greatly to wheat improvement. The 2NvS/2AS translocation is a prevalent chromosomal translocation between common wheat and wild relatives, ranking just behind the 1B/1R translocation in importance for modern wheat breeding. Here, we assembled a high-quality chromosome-level reference genome of Ae. ventricosa RM271 with a total length of 8.67 Gb. Phylogenomic analyses revealed that the progenitor of the Dv subgenome of Ae. ventricosa is Ae. tauschii ssp. tauschii (genome DD); by contrast, the progenitor of the D subgenome of bread wheat (Triticum aestivum L.) is Ae. tauschii ssp. strangulata (genome DD). The oldest polyploidization time of Ae. ventricosa occurred ∼0.7 mya. The Dv subgenome of Ae. ventricosa is less conserved than the D subgenome of bread wheat. Construction of a graph-based pangenome of 2AS/6NvL (originally known as 2NvS) segments from Ae. ventricosa and other genomes in the Triticeae enabled us to identify candidate resistance genes sourced from Ae. ventricosa. We identified 12 nonredundant introgressed segments from the Dv and Nv subgenomes using a large winter wheat collection representing the full diversity of the European wheat genetic pool, and 29.40% of European wheat varieties inherit at least one of these segments. The high-quality RM271 reference genome will provide a basis for cloning key genes, including the Yr17-Lr37-Sr38-Cre5 resistance gene cluster in Ae. ventricosa, and facilitate the full use of elite wild genetic resources to accelerate wheat improvement.

Stomatal penetration: the cornerstone of plant resistance to the fungal pathogen Zymoseptoria tritici

BackgroundSeptoria tritici blotch (STB), caused by the foliar fungus Zymoseptoria tritici, is one of the most damaging disease of wheat in Europe. Genetic resistance against this fungus relies on different types of resistance from non-host resistance (NHR) and host species specific resistance (HSSR) to host resistance mediated by quantitative trait loci (QTLs) or major resistance genes (Stb). Characterizing the diversity of theses resistances is of great importance for breeding wheat cultivars with efficient and durable resistance. While the functional mechanisms underlying these resistance types are not well understood, increasing piece of evidence suggest that fungus stomatal penetration and early establishment in the apoplast are both crucial for the outcome of some interactions between Z. tritici and plants. To validate and extend these previous observations, we conducted quantitative comparative phenotypical and cytological analyses of the infection process corresponding to 22 different interactions between plant species and Z. tritici isolates. These interactions included four major bread wheat Stb genes, four bread wheat accessions with contrasting quantitative resistance, two species resistant to Z. tritici isolates from bread wheat (HSSR) and four plant species resistant to all Z. tritici isolates (NHR).ResultsInfiltration of Z. tritici spores into plant leaves allowed the partial bypass of all bread wheat resistances and durum wheat resistance, but not resistances from other plants species. Quantitative comparative cytological analysis showed that in the non-grass plant Nicotiana benthamiana, Z. tritici was stopped before stomatal penetration. By contrast, in all resistant grass plants, Z. tritici was stopped, at least partly, during stomatal penetration. The intensity of this early plant control process varied depending on resistance types, quantitative resistances being the least effective. These analyses also demonstrated that Stb-mediated resistances, HSSR and NHR, but not quantitative resistances, relied on the strong growth inhibition of the few Z. tritici penetrating hyphae at their entry point in the sub-stomatal cavity.ConclusionsIn addition to furnishing a robust quantitative cytological assessment system, our study uncovered three stopping patterns of Z. tritici by plant resistances. Stomatal resistance was found important for most resistances to Z. tritici, independently of its type (Stb, HSSR, NHR). These results provided a basis for the functional analysis of wheat resistance to Z. tritici and its improvement.

Quantifying albedo impact and radiative forcing of management practices in European wheat cropping systems

Management practices that increase the surface albedo of cultivated land could mitigate climate change, with similar effectiveness to practices that reduce greenhouse gas emissions or favor natural CO2 sequestration. Yet, the efficiency of such practices is barely quantified. In this study, we quantified the impacts of seven different management practices on the surface albedo of winter wheat fields (nitrogen fertilizer, herbicide, fungicide, sowing, harvest, tillage, and crop residues) by analyzing observed daily albedo dynamics from eight European flux-tower sites with interpretable machine learning. We found that management practices have significant influences on surface albedo dynamics compared with climate and soil conditions. The nitrogen fertilizer application has the largest effect among the seven practices as it increases surface albedo by 0.015 ± 0.004 during the first two months after application, corresponding to a radiative forcing of −4.39 ± 1.22 W m−2. Herbicide induces a modest albedo decrease of 0.005 ± 0.002 over 150 d after application by killing weeds in the fallow period only, resulting in a magnitude of radiative forcing of 1.33 ± 1.06 W m−2 which is higher than radiative forcing of other practices in the same period. The substantial temporal evolution of the albedo impacts of management practices increases uncertainties in the estimated albedo-mediated climate impacts of management practices. Although these albedo effects are smaller than published estimates of the greenhouse gas-mediated biogeochemical practices, they are nevertheless significant and should thus be accounted for in climate impact assessments.

A study of European wheat price series forecasting based on multiple variants of Holt-Winters and LSTM models

Given the importance of wheat in global food security and agricultural economy, accurate price forecasts are of significant practical significance for growers, the food chain, and government macro-control. Four different time series forecasting models were used in this study, including the traditional Holt-Winters three-parameter exponential smoothing method and three improved models based on the Long Short-Term Memory Network (LSTM): the CNN-LSTM, the GRU, and the VMD-LSTM. By comparing the forecasting effects of these models under the same training and test sets, this study aims to select the best models using the best model for predicting European wheat prices from 2010 to 2023 after training. The results show that the CNN-LSTM model performs the best in predicting the European wheat price series, and the future wheat price will show a slow downward trend, and the CNN-LSTM model combines the feature extraction ability of convolutional neural network and the long-term dependency learning ability of LSTM, which can effectively capture the fluctuation characteristics of the price series and the trend characteristics. The GRU model also shows a better prediction ability, especially in dealing with long-term dependencies. in dealing with long-term dependencies. However, the VMD-LSTM model has certain shortcomings in its prediction effect when facing time series data with large fluctuations in the later period. In addition, the Holt-Winters model predicts that after a period of slow decline, wheat prices may rebound, which may be related to the adjustment of market supply and demand and changes in external factors.

Novel PHOTOPERIOD-1 gene variants associate with yield-related and root-angle traits in European bread wheat

Key messagePHOTOPERIOD-1 homoeologous gene copies play a pivotal role in regulation of flowering time in wheat. Here, we show that their influence also extends to spike and shoot architecture and even impacts root development.The sequence diversity of three homoeologous copies of the PHOTOPERIOD-1 gene in European winter wheat was analyzed by Oxford Nanopore amplicon-based multiplex sequencing and molecular markers in a panel of 194 cultivars representing breeding progress over the past 5 decades. A strong, consistent association with an average 8% increase in grain yield was observed for the PpdA1-Hap1 haplotype across multiple environments. This haplotype was found to be linked in 51% of cultivars to the 2NS/2AS translocation, originally introduced from Aegilops ventricosa, which leads to an overestimation of its effect. However, even in cultivars without the 2NS/2AS translocation, PpdA1-Hap1 was significantly associated with increased grain yield, kernel per spike and kernel per m2 under optimal growth conditions, conferring a 4% yield advantage compared to haplotype PpdA1-Hap4. In contrast to Ppd-B1 and Ppd-D1, the Ppd-A1 gene exhibits novel structural variations and a high number of SNPs, highlighting the evolutionary changes that have occurred in this region over the course of wheat breeding history. Additionally, cultivars carrying the photoperiod-insensitive Ppd-D1a allele not only exhibit earlier heading, but also deeper roots compared to those with photoperiod-sensitive alleles under German conditions. PCR and KASP assays have been developed that can be effectively employed in marker-assisted breeding programs to introduce these favorable haplotypes.

Soil and climatic characteristics and farming system shape fungal communities in European wheat fields

Fungi play a pivotal role as highly effective decomposers of plant residues and essential mycorrhizal symbionts, augmenting water and nutrient uptake in plants and contributing to diverse functions within agroecosystems. This study examined soil fungi in 188 wheat fields across nine European pedoclimatic zones under both conventional and organic farming systems, utilizing ITS1 amplicon sequencing. The investigation aimed to quantify changes induced by the farming system in soil fungi and their correlation with soil features and climatic factors across these pedoclimatic zones, spanning from northern to southern Europe. The pedoclimatic zone emerged as a key determinant in shaping the overall composition of the fungal community. Zones characterized by moist and cool climates, along with low levels of available phosphorus and carbonate, exhibited higher fungal richness. However, variations in fungal diversity and relative abundances were observed within zones due to farming system-induced changes. Soil pH and bulk density were identified as major factors, for example, they correlate with an increase in potential pathogenic taxa (Mycosphaerella, Nectriaceae, Alternaria) in two Mediterranean zones and with an increase of potential plant growth promoting taxa (Saitozyma, Solicoccozyma) in the Boreal zone. Organic farming, in general, promoted elevated fungal richness. The Lusitanian and Nemoral zones under organic farming exhibited the highest fungal richness and diversity. In terms of organic farming, both symbiotrophs and potential pathogens increased in the Lusitanian zone, while pathotrophs were more prevalent in the Central Atlantic and South Mediterranean zones under organic farming. These findings propose potential indicators for organic farming, including fungal endophytes in zones characterized by a moist and cool climate, low available phosphorus content, and low soil pH. Organic farming may favor mycorrhizae and potential pathogens in zones with drier and warmer climates, along with higher soil pH, calcium carbonate content, and bulk density. This study provides novel insights and underscores the significance of regional climatic and edaphic conditions in shaping the soil fungal community in different farming systems within European wheat fields.

Leveraging the potential of big genomic and phenotypic data for genome-wide association mapping in wheat

Genome-wide association mapping studies (GWAS) based on Big Data are a potential approach to improve marker-assisted selection in plant breeding. The number of available phenotypic and genomic data sets in which medium-sized populations of several hundred individuals have been studied is rapidly increasing. Combining these data and using them in GWAS could increase both the power of QTL discovery and the accuracy of estimation of underlying genetic effects, but is hindered by data heterogeneity and lack of interoperability. In this study, we used genomic and phenotypic data sets, focusing on Central European winter wheat populations evaluated for heading date. We explored strategies for integrating these data and subsequently the resulting potential for GWAS. Establishing interoperability between data sets was greatly aided by some overlapping genotypes and a linear relationship between the different phenotyping protocols, resulting in high quality integrated phenotypic data. In this context, genomic prediction proved to be a suitable tool to study relevance of interactions between genotypes and experimental series, which was low in our case. Contrary to expectations, fewer associations between markers and traits were found in the larger combined data than in the individual experimental series. However, the predictive power based on the marker-trait associations of the integrated data set was higher across data sets. Therefore, the results show that the integration of medium-sized to Big Data is an approach to increase the power to detect QTL in GWAS. The results encourage further efforts to standardize and share data in the plant breeding community.

Recombination in the wheat stem rust pathogen mediated by an indigenous barberry species in Spain.

The comeback of wheat stem rust in Europe, caused by Puccinia graminis f. sp. tritici, and the prevalence of the alternate (sexual) host in local areas have recently regained attention as a potential threat to European wheat production. The aim of this study was to investigate a potential epidemiological link between the aecia found on an indigenous barberry species and stem rust infections on nearby cereals and grasses. Aecial infections collected from Berberis vulgaris subsp. seroi were inoculated on a panel of susceptible genotypes of major cereal crop species. In total, 67 stem rust progeny isolates were recovered from wheat (51), barley (7), and rye (9), but none from oat, indicating the potential of barberry derived isolates to infect multiple cereals. Molecular genotyping of the progeny isolates and 20 cereal and grass stem rust samples collected at the same locations and year, revealed a clear genetic relatedness between the progeny isolated from barberry and the stem rust infections found on nearby cereal and grass hosts. Analysis of Molecular Variance indicated that variation between the stem rust populations accounted for only 1%. A Principal Components Analysis using the 62 detected multilocus genotypes also demonstrated a low degree of genetic variation among isolates belonging to the two stem rust populations. Lastly, pairwise comparisons based on fixation index (Fst), Nei's genetic distances and number of effective migrants (Nm) revealed low genetic differentiation and high genetic exchange between the two populations. Our results demonstrated a direct epidemiological link and functionality of an indigenous barberry species as the sexual host of P. graminis in Spain, a factor that should be considered when designing future strategies to prevent stem rust in Europe and beyond.

Multi temporal multispectral UAV remote sensing allows for yield assessment across European wheat varieties already before flowering.

High throughput field phenotyping techniques employing multispectral cameras allow extracting a variety of variables and features to predict yield and yield related traits, but little is known about which types of multispectral features are optimal to forecast yield potential in the early growth phase. In this study, we aim to identify multispectral features that are able to accurately predict yield and aid in variety classification at different growth stages throughout the season. Furthermore, we hypothesize that texture features (TFs) are more suitable for variety classification than for yield prediction. Throughout 2021 and 2022, a trial involving 19 and 18 European wheat varieties, respectively, was conducted. Multispectral images, encompassing visible, Red-edge, and near-infrared (NIR) bands, were captured at 19 and 22 time points from tillering to harvest using an unmanned aerial vehicle (UAV) in the first and second year of trial. Subsequently, orthomosaic images were generated, and various features were extracted, including single-band reflectances, vegetation indices (VI), and TFs derived from a gray level correlation matrix (GLCM). The performance of these features in predicting yield and classifying varieties at different growth stages was assessed using random forest models. Measurements during the flowering stage demonstrated superior performance for most features. Specifically, Red reflectance achieved a root mean square error (RMSE) of 52.4g m-2 in the first year and 64.4g m-2 in the second year. The NDRE VI yielded the most accurate predictions with an RMSE of 49.1g m-2 and 60.6g m-2, respectively. Moreover, TFs such as CONTRAST and DISSIMILARITY displayed the best performance in predicting yield, with RMSE values of 55.5g m-2 and 66.3g m-2 across the two years of trial. Combining data from different dates enhanced yield prediction and stabilized predictions across dates. TFs exhibited high accuracy in classifying low and high-yielding varieties. The CORRELATION feature achieved an accuracy of 88% in the first year, while the HOMOGENEITY feature reached 92% accuracy in the second year. This study confirms the hypothesis that TFs are more suitable for variety classification than for yield prediction. The results underscore the potential of TFs derived from multispectral images in early yield prediction and varietal classification, offering insights for HTP and precision agriculture alike.

Low asparagine wheat: Europe's first field trial of genome edited wheat amid rapidly changing regulations on acrylamide in food and genome editing of crops.

We review the undertaking of a field trial of low asparagine wheat lines in which the asparagine synthetase gene, TaASN2, has been knocked out using CRISPR/Cas9. The field trial was undertaken in 2021-2022 and represented the first field release of genome edited wheat in Europe. The year of the field trial and the period since have seen rapid changes in the regulations covering both the field release and commercialisation of genome edited crops in the UK. These historic developments are reviewed in detail. Free asparagine is the precursor for acrylamide formation during high-temperature cooking and processing of grains, tubers, storage roots, beans and other crop products. Consequently, work on reducing the free asparagine concentration of wheat and other cereal grains, as well as the tubers, beans and storage roots of other crops, is driven by the need for food businesses to comply with current and potential future regulations on acrylamide content of foods. The topic illustrates how strategic and applied crop research is driven by regulations and also needs a supportive regulatory environment in which to thrive.

Leveraging trait and QTL covariates to improve genomic prediction of resistance to Fusarium head blight in Central European winter wheat.

Fusarium head blight (FHB) is a devastating disease of wheat, causing yield losses, reduced grain quality, and mycotoxin contamination. Breeding can mitigate the severity of FHB epidemics, especially with genomics-assisted methods. The mechanisms underlying resistance to FHB in wheat have been extensively studied, including phenological traits and genome-wide markers associated with FHB severity. Here, we aimed to improve genomic prediction for FHB resistance across breeding programs by incorporating FHB-correlated traits and FHB-associated loci as model covariates. We combined phenotypic data on FHB severity, anthesis date, and plant height with genome-wide marker data from five Central European winter wheat breeding programs for genome-wide association studies (GWAS) and genomic prediction. Within all populations, FHB was correlated with anthesis date and/or plant height, and a marker linked to the semi-dwarfing locus Rht-D1 was detected with GWAS for FHB. Including the Rht-D1 marker, anthesis date, and/or plant height as covariates in genomic prediction modeling improved prediction accuracy not only within populations but also in cross-population scenarios.

Baking Quality of Organically Grown European Winter Wheat Germplasm in the South-East of Romania

Baking quality of organic winter wheat remains a significant challenge, primarily due to the usually lower grain protein concentration realized in organic agriculture. This study examined eighty-four winter wheat genotypes from Europe under organic conditions in the south-east of Romania, during two contrasting seasons. Various quality traits, i.e., grain protein content, bread volume, dough strength, breakdown and extensibility were analyzed. All examined traits exhibited significant differences among cultivars. Molecular assays, utilizing markers for Glu-A1, Glu-D1, Glu-B3g and NAM-A1 loci revealed genetic diversity within the germplasm. The study highlighted sixteen genotypes with bread volume statistically significant different from the average trial. In the organic system the Glu-A1x2* allele had positive effects on bread volume, water absorption, dough strength and extensibility. At Glu-D1, the alleles for Dx5 + Dy10 were detected in over 85% cultivars and showed positive effects on dough development time and extensibility. Another locus with positive effect on baking quality was NAM-A1. In this study the haplotype NAM-A1c had the highest frequency (51%), followed by NAM-A1d (38%) and NAM-A1a (11%). Both NAM-A1a and NAM-A1c haplotypes positively affected bread volume, water absorption, dough strength and extensibility. The Glu-B3g allele had a positive effect on development time. However, these genes could not explain the whole variation of quality parameters, as some of the best cultivars had unfavorable alleles while other cultivars carrying positive alleles had low quality. Obviously, other factors strongly influenced the quality parameters in organic agriculture, and further research is needed to understand their nature. Our results suggest the potential for achieving improved breadmaking quality, under organic conditions, by exploring genetic possibilities. However, combining suitable breadmaking quality with high yield will remain a challenge.

High-Throughput Spike Detection in Greenhouse Cultivated Grain Crops with Attention Mechanisms-Based Deep Learning Models.

Detection of spikes is the first important step toward image-based quantitative assessment of crop yield. However, spikes of grain plants occupy only a tiny fraction of the image area and often emerge in the middle of the mass of plant leaves that exhibit similar colors to spike regions. Consequently, accurate detection of grain spikes renders, in general, a non-trivial task even for advanced, state-of-the-art deep neural networks (DNNs). To improve pattern detection in spikes, we propose architectural changes to Faster-RCNN (FRCNN) by reducing feature extraction layers and introducing a global attention module. The performance of our extended FRCNN-A vs. conventional FRCNN was compared on images of different European wheat cultivars, including "difficult" bushy phenotypes from 2 different phenotyping facilities and optical setups. Our experimental results show that introduced architectural adaptations in FRCNN-A helped to improve spike detection accuracy in inner regions. The mean average precision (mAP) of FRCNN and FRCNN-A on inner spikes is 76.0% and 81.0%, respectively, while on the state-of-the-art detection DNNs, Swin Transformer mAP is 83.0%. As a lightweight network, FRCNN-A is faster than FRCNN and Swin Transformer on both baseline and augmented training datasets. On the FastGAN augmented dataset, FRCNN achieved a mAP of 84.24%, FRCNN-A attained a mAP of 85.0%, and the Swin Transformer achieved a mAP of 89.45%. The increase in mAP of DNNs on the augmented datasets is proportional to the amount of the IPK original and augmented images. Overall, this study indicates a superior performance of attention mechanisms-based deep learning models in detecting small and subtle features of grain spikes.

Two distinct Fusarium graminearum populations colonized European wheat in the past two decades.

Fusarium graminearum is the main causal agent of Fusarium head blight (FHB) disease in wheat in Europe. To reveal population structure and to pinpoint genetic targets of selection we studied genomes of 96 strains of F. graminearum using population genomics. Bayesian and phylogenomic analyses indicated that the F. graminearum emergence in Europe could be linked to two independently evolving populations termed here as East European (EE) and West European (WE) population. The EE strains are primarily prevalent in Eastern Europe, but to a lesser extent also in western and southern areas. In contrast, the WE population appears to be endemic to Western Europe. Both populations evolved in response to population-specific selection forces, resulting in distinct localized adaptations that allowed them to migrate into their environmental niche. The detection of positive selection in genes with protein/zinc ion binding domains, transcription factors and in genes encoding proteins involved in transmembrane transport highlights their important role in driving evolutionary novelty that allow F. graminearum to increase adaptation to the host and/or environment. F. graminearum also maintained distinct sets of accessory genes showing population-specific conservation. Among them, genes involved in host invasion and virulence such as those encoding proteins with high homology to tannase/feruloyl esterase and genes encoding proteins with functions related to oxidation-reduction were mostly found in the WE population. Our findings shed light on genetic features related to microevolutionary divergence of F. graminearum and reveal relevant genes for further functional research aiming at better control of this pathogen.

High-throughput field phenotyping reveals that selection in breeding has affected the phenology and temperature response of wheat in the stem elongation phase.

Crop growth and phenology are driven by seasonal changes in environmental variables, with temperature as one important factor. However, knowledge about genotype-specific temperature response and its influence on phenology is limited. Such information is fundamental to improve crop models and adapt selection strategies. We measured the increase in height of 352 European winter wheat varieties in 4 years to quantify phenology, and fitted an asymptotic temperature response model. The model used hourly fluctuations in temperature to parameterize the base temperature (Tmin), the temperature optimum (rmax), and the steepness (lrc) of growth responses. Our results show that higher Tmin and lrc relate to an earlier start and end of stem elongation. A higher rmax relates to an increased final height. Both final height and rmax decreased for varieties originating from the continental east of Europe towards the maritime west. A genome-wide association study (GWAS) indicated a quantitative inheritance and a large degree of independence among loci. Nevertheless, genomic prediction accuracies (GBLUPs) for Tmin and lrc were low (r≤0.32) compared with other traits (r≥0.59). As well as known, major genes related to vernalization, photoperiod, or dwarfing, the GWAS indicated additional, as yet unknown loci that dominate the temperature response.

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

Key messageSedimentation 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.

Genetic regions determine tolerance to nitrogen deficiency in European elite bread wheats grown under contrasting nitrogen stress scenarios.

Clustering 24 environments in four contrasting nitrogen stress scenarios enabled the detection of genetic regions determining tolerance to nitrogen deficiency in European elite bread wheats. Increasing the nitrogen use efficiency of wheat varieties is an important goal for breeding. However, most genetic studies of wheat grown at different nitrogen levels in the field report significant interactions with the genotype. The chromosomal regions possibly involved in these interactions are largely unknown. The objective of this study was to quantify the response of elite bread wheat cultivars to different nitrogen field stress scenarios and identify genomic regions involved in this response. For this purpose, 212 elite bread wheat varieties were grown in a multi-environment trial at different nitrogen levels. Genomic regions associated with grain yield, protein concentration and grain protein deviation responses to nitrogen deficiency were identified. Environments were clustered according to adjusted means for grain yield, yield components and grain protein concentration. Four nitrogen availability scenarios were identified: optimal condition, moderate early deficiency, severe late deficiency, and severe continuous deficiency. A large range of tolerance to nitrogen deficiency was observed among varieties, which were ranked differently in different nitrogen deficiency scenarios. The well-known negative correlation between grain yield and grain protein concentration also existed between their respective tolerance indices. Interestingly, the tolerance indices for grain yield and grain protein deviation were either null or weakly positive meaning that breeding for the two traits should be less difficult than expected. Twenty-two QTL regions were identified for the tolerance indices. By selecting associated markers, these regions may be selected separately or combined to improve the tolerance to N deficiency within a breeding programme.

Sequence Characterization of Extra-Chromosomal Circular DNA Content in Multiple Blackgrass (Alopecurus myosuroides) Populations.

Alopecurus myosuroides (blackgrass) is a problematic weed of Western European winter wheat, and its success is largely due to widespread multiple-herbicide resistance. Previous analysis of F2 seed families derived from two distinct blackgrass populations exhibiting equivalent non-target site resistance (NTSR) phenotypes shows resistance is polygenic and evolves from standing genetic variation. Using a CIDER-seq pipeline, we show that herbicide-resistant (HR) and herbicide-sensitive (HS) F3 plants from these F2 seed families as well as the parent populations they were derived from carry extra-chromosomal circular DNA (eccDNA). We identify the similarities and differences in the coding structures within and between resistant and sensitive populations. Although the numbers and size of detected eccDNAs varied between the populations, comparisons between the HR and HS blackgrass populations identified shared and unique coding content, predicted genes, and functional protein domains. These include genes related to herbicide detoxification such as Cytochrome P450s, ATP-binding cassette transporters, and glutathione transferases including AmGSTF1. eccDNA content was mapped to the A. myosuroides reference genome, revealing genomic regions at the distal end of chromosome 5 and the near center of chromosomes 1 and 7 as regions with a high number of mapped eccDNA gene density. Mapping to 15 known herbicide-resistant QTL regions showed that the eccDNA coding sequences matched twelve, with four QTL matching HS coding sequences; only one region contained HR coding sequences. These findings establish that, like other pernicious weeds, blackgrass has eccDNAs that contain homologs of chromosomal genes, and these may contribute genetic heterogeneity and evolutionary innovation to rapidly adapt to abiotic stresses, including herbicide treatment.