Disease Of Wheat Research Articles

Identification of Key Modules and Candidate Genes for Powdery Mildew Resistance of Wheat-Agropyron cristatum Translocation Line WAT-2020-17-6 by WGCNA

As one of the serious diseases of wheat, powdery mildew (Blumeria graminis f. sp. tritici) is a long-term threat to wheat production. Therefore, it is of great significance to explore new powdery mildew-resistant genes for breeding. The wild relative species of wheat provide gene resources for resistance to powdery mildew breeding. Agropyron cristatum (2n = 4x = 28, genomes PPPP) is an important wild relative of wheat, carrying excellent genes for high yield, disease resistance, and stress resistance, which can be used for wheat improvement. To understand the molecular mechanism of powdery mildew resistance in the wheat-A. cristatum translocation line WAT2020-17-6, transcriptome sequencing was performed, and the resistance genes were analyzed by weighted gene co-expression network analysis (WGCNA). In the results, 42,845 differentially expressed genes were identified and divided into 18 modules, of which six modules were highly correlated with powdery mildew resistance. Gene ontology (GO) enrichment analysis showed that the six interested modules related to powdery mildew resistance were significantly enriched in N-methyltransferase activity, autophagy, mRNA splicing via spliceosome, chloroplast envelope, and AMP binding. The candidate hub genes of the interested modules were further identified, and their regulatory relationships were analyzed based on co-expression data. The temporal expression pattern of the 12 hub genes was verified within 96 h after powdery mildew inoculation by RT-PCR assay. In this study, we preliminarily explained the resistance mechanism of the wheat-A. cristatum translocation lines and obtained the hub candidate genes, which laid a foundation in the exploration of resistance genes in A. cristatum for powdery mildew-resistant breeding in wheat.

Recognition of glycoside hydrolase 12 proteins by the immune receptor RXEG1 confers Fusarium head blight resistance in wheat.

Fusarium head blight (FHB), caused by Fusarium graminearum, is a devastating disease in wheat (Triticum aestivum) that results in substantial yield losses and mycotoxin contamination. Reliable genetic resources for FHB resistance in wheat are lacking. In this study, we characterized glycoside hydrolase 12 (GH12) family proteins secreted by F. graminearum. We established that two GH12 proteins, Fg05851 and Fg11037, have functionally redundant roles in F. graminearum colonization of wheat. Furthermore, we determined that the GH12 proteins Fg05851 and Fg11037 are recognized by the leucine-rich-repeat receptor-like protein RXEG1 in the dicot Nicotiana benthamiana. Heterologous expression of RXEG1 conferred wheat responsiveness to Fg05851 and Fg11037, enhanced wheat resistance to F. graminearum and reduced levels of the mycotoxin deoxynivalenol in wheat grains in an Fg05851/Fg11037-dependent manner. In the RXEG1 transgenic lines, genes related to pattern-triggered plant immunity, salicylic acid, jasmonic acid, and anti-oxidative homeostasis signalling pathways were upregulated during F. graminearum infection. However, the expression of these genes was not significantly changed during infection by the deletion mutant ΔFg05851/Fg11037, suggesting that the recognition of Fg05851/Fg11037 by RXEG1 triggered plant resistance against FHB. Moreover, introducing RXEG1 into three other different wheat cultivars via crossing also conferred resistance to F. graminearum. Expression of RXEG1 did not have obvious deleterious effects on plant growth and development in wheat. Our study reveals that N. benthamiana RXEG1 remains effective when transferred into wheat, a monocot, which in turn suggests that engineering wheat with interfamily plant immune receptor transgenes is a viable strategy for increasing resistance to FHB.

Alternative scoring methods of fusarium head blight resistance for genomic assisted breeding.

Fusarium head blight (FHB) is a fungal disease of wheat (Triticum aestivum.L) that causes yield losses and produces mycotoxins which could easily exceed the limits of the EU regulations. Resistance to FHB has a complex genetic architecture and accurate evaluation in breeding programs is key to selecting resistant varieties. The Area Under the Disease Progress Curve (AUDPC) is one of the commonly metric used as a standard methodology to score FHB. Although efficient, AUDPC requires significant costs in phenotyping to cover the entire disease development pattern. Here, we show that there are more efficient alternatives to AUDPC (angle, growing degree days to reach 50% FHB severity, and FHB maximum variance) that reduce the number of field assessments required and allow for fair comparisons between unbalanced evaluations across trials. Furthermore, we found that the evaluation method that captures the maximum variance in FHB severity across plots is the most optimal approach for scoring FHB. In addition, results obtained on experimental data were validated on a simulated experiment where the disease progress curve was modeled as a sigmoid curve with known parameters and assessment protocols were fully controlled. Results show that alternative metrics tested in this study captured key components of quantitative plant resistance. Moreover, the new metrics could be a starting point for more accurate methods for measuring FHB in the field. For example, the optimal interval for FHB evaluation could be predicted using prior knowledge from historical weather data and FHB scores from previous trials. Finally, the evaluation methods presented in this study can reduce the FHB phenotyping burden in plant breeding with minimal losses on signal detection, resulting in a response variable available to use in data-driven analysis such as genome-wide association studies or genomic selection.

Classification of wheat diseases using deep learning networks with field and glasshouse images.

Crop diseases can cause major yield losses, so the ability to detect and identify them in their early stages is important for disease control. Deep learning methods have shown promise in classifying multiple diseases; however, many studies do not use datasets that represent real field conditions, necessitating either further image processing or reducing their applicability. In this paper, we present a dataset of wheat images taken in real growth situations, including both field and glasshouse conditions, with five categories: healthy plants and four foliar diseases, yellow rust, brown rust, powdery mildew and Septoria leaf blotch. This dataset was used to train a deep learning model. The resulting model, named CerealConv, reached a 97.05% classification accuracy. When tested against trained pathologists on a subset of images from the larger dataset, the model delivered an accuracy score 2% higher than the best-performing pathologist. Image masks were used to show that the model was using the correct information to drive its classifications. These results show that deep learning networks are a viable tool for disease detection and classification in the field, and disease quantification is a logical next step.

Microbiome Signature of Endophytes in Wheat Seed Response to Wheat Dwarf Bunt Caused by Tilletia controversa Kühn.

Wheat dwarf bunt leads to the replacement of seeds with fungal galls containing millions of teliospores of the pathogen Tilletia controversa Kühn. As one of the most devastating internationally quarantined wheat diseases, wheat dwarf bunt spreads to cause distant outbreaks by seeds containing teliospores. In this study, based on a combination of amplicon sequencing and isolation approaches, we analyzed the seed microbiome signatures of endophytes between resistant and susceptible cultivars after infection with T. controversa. Among 310 bacterial species obtained only by amplicon sequencing and 51 species obtained only by isolation, we found 14 overlapping species by both methods; we detected 128 fungal species only by amplicon sequencing, 56 only by isolation, and 5 species by both methods. The results indicated that resistant uninfected cultivars hosted endophytic communities that were much more stable and beneficial to plant health than those in susceptible infected cultivars. The susceptible group showed higher diversity than the resistant group, the infected group showed more diversity than the uninfected group, and the microbial communities in seeds were related to infection or resistance to the pathogen. Some antagonistic microbes significantly suppressed the germination rate of the pathogen's teliospores, providing clues for future studies aimed at developing strategies against wheat dwarf bunt. Collectively, this research advances the understanding of the microbial assembly of wheat seeds upon exposure to fungal pathogen (T. controversa) infection. IMPORTANCE This is the first study on the microbiome signature of endophytes in wheat seed response to wheat dwarf bunt caused by Tilletia controversa Kühn. Some antagonistic microbes suppressed the germination of teliospores of the pathogen significantly, which will provide clues for future studies against wheat dwarf bunt. Collectively, this research first advances the understanding of the microbial assembly of wheat seed upon exposure to the fungal pathogen (T. controversa) infection.

Using crop modeling to find solutions for wheat diseases: A review

Plant diseases have caused serious challenges in the production of food globally. This has led to hunger and food insecurity. Different solutions for crop diseases have been introduced in the recent past that include cultural control using farm management methods, chemical control, resistant cultivars, and recently, biocontrol. Each of these solutions has varied weaknesses. Faced with the changes in climate and the recurrences of crop diseases, new strategies incorporating preventive measures would be important by reducing risks to crop production from crop diseases, thus alleviating food insecurity. Strategies for the prevention of these diseases and/or forecasting favorable environmental conditions for disease development have not been fully employed as preventive measures. The use of crop modeling has been used to advise farmers on planting procedures that would bring maximum yields using different management procedures at the farm level. Little is known about the use of crop models in crop disease control. In order to increase the use of crop models for these objectives, this review provides the current status quo and will help to stimulate more research in this regard.

A large bioassay identifies Stb resistance genes that provide broad resistance against Septoria tritici blotch disease in the UK.

Septoria tritici blotch (STB) is one of the most damaging fungal diseases of wheat in Europe, largely due to the paucity of effective resistance genes against it in breeding materials. Currently dominant protection methods against this disease, e.g. fungicides and the disease resistance genes already deployed, are losing their effectiveness. Therefore, it is vital that other available disease resistance sources are identified, understood and deployed in a manner that maximises their effectiveness and durability. In this study, we assessed wheat genotypes containing nineteen known major STB resistance genes (Stb1 through to Stb19) or combinations thereof against a broad panel of 93 UK Zymoseptoria tritici isolates. Seedlings were inoculated using a cotton swab and monitored for four weeks. Four infection-related phenotypic traits were visually assessed. These were the days post infection to the development of first symptoms and pycnidia, percentage coverage of the infected leaf area with chlorosis/necrosis and percentage coverage of the infected leaf area with pycnidia. The different Stb genes were found to vary greatly in the levels of protection they provided, with pycnidia coverage at four weeks differing significantly from susceptible controls for every tested genotype. Stb10, Stb11, Stb12, Stb16q, Stb17, and Stb19 were identified as contributing broad spectrum disease resistance, and synthetic hexaploid wheat lines were identified as particularly promising sources of broadly effective STB resistances. No single Z. tritici isolate was found to be virulent against all tested resistance genes. Wheat genotypes carrying multiple Stb genes were found to provide higher levels of resistance than expected given their historical levels of use. Furthermore, it was noted that disease resistance controlled by different Stb genes was associated with different levels of chlorosis, with high levels of early chlorosis in some genotypes correlated with high resistance to fungal pycnidia development, potentially suggesting the presence of multiple resistance mechanisms.The knowledge obtained here will aid UK breeders in prioritising Stb genes for future breeding programmes, in which optimal combinations of resistance genes could be pyramided. In addition, this study identified the most interesting Stb genes for cloning and detailed functional analysis.

A Convolutional Neural Network Model for Wheat Crop Disease Prediction

Wheat is the most important cereal crop, and its low production incurs import pressure on the economy. It fulfills a significant portion of the daily energy requirements of the human body. The wheat disease is one of the major factors that result in low production and negatively affects the national economy. Thus, timely detection of wheat diseases is necessary for improving production. The CNN-based architectures showed tremendous achievement in the image-based classification and prediction of crop diseases. However, these models are computationally expensive and need a large amount of training data. In this research, a light weighted modified CNN architecture is proposed that uses eight layers particularly, three convolutional layers, three SoftMax layers, and two flattened layers, to detect wheat diseases effectively. The high-resolution images were collected from the fields in Azad Kashmir (Pakistan) and manually annotated by three human experts. The convolutional layers use 16, 32, and 64 filters. Every filter uses a 3 × 3 kernel size. The strides for all convolutional layers are set to 1. In this research, three different variants of datasets are used. These variants S1-70%:15%:15%, S2-75%:15%:10%, and S3-80%:10%:10% (train: validation: test) are used to evaluate the performance of the proposed model. The extensive experiments revealed that the S3 performed better than S1 and S2 datasets with 93% accuracy. The experiment also concludes that a more extensive training set with high-resolution images can detect wheat diseases more accurately.

Identification of Quinone Outside Inhibitor Fungicide-Resistant Isolates of Parastagonospora nodorum from Illinois and Kentucky.

Stagonospora leaf and glume blotch, caused by Parastagonospora nodorum, is a major disease of winter wheat (Triticum aestivum) in the United States capable of significantly reducing grain yield and quality. Pathogens such as P. nodorum that overwinter in crop residue are often an increased concern in cropping systems that utilize no-till farming. In addition, the lack of wheat cultivars with complete resistance to P. nodorum has led to the reliance on foliar fungicides for disease management. Quinone outside inhibitor (QoI) fungicides (Fungicide Resistance Action Committee group 11) are one of the major classes used to manage foliar diseases in wheat. Use of the QoI class of fungicides tends to select isolates of fungal pathogens with resistance due to mutations in the fungal cytochrome b gene. Isolates of P. nodorum were collected from Illinois in 2014 and Kentucky in 2018, 2019, and 2020. Amplification and sequencing of a segment of the cytochrome b gene from these isolates revealed a mutation at codon 143 that confers a change from glycine to alanine in the amino acid sequence (known as the G143A mutation). In vitro plate assays and greenhouse trials were used to confirm and characterize the QoI resistance caused by the G143A mutation. The frequency of the tested isolates with the G143A mutation was 46% (57 of 123 isolates) and 5% (3 of 60 isolates) for Kentucky and Illinois, respectively. This research is the first to identify the G143A mutation in P. nodorum isolates with resistance to QoI fungicides in Illinois and Kentucky.

Differentiation of Wheat Diseases and Pests Based on Hyperspectral Imaging Technology with a Few Specific Bands

Hyperspectral imaging technique is known as a promising non-destructive way for detecting plants diseases and pests. In most previous studies, the utilization of the whole spectrum or a large number of bands as well as the complexity of model structure severely hampers the application of the technique in practice. If a detection system can be established with a few bands and a relatively simple logic, it would be of great significance for application. This study established a method for identifying and discriminating three commonly occurring diseases and pests of wheat, i.e., powdery mildew, yellow rust and aphid with a few specific bands. Through a comprehensive spectral analysis, only three bands at 570, 680 and 750 nm were selected. A novel vegetation index namely Ratio Triangular Vegetation Index (RTVI) was developed for detecting anomalous areas on leaves. Then, the Support Vector Machine (SVM) method was applied to construct the discrimination model based on the spectral ratio analysis. The validating results suggested that the proposed method with only three spectral bands achieved a promising accuracy with the Overall Accuracy (OA) of 83%. With three bands from the hyperspectral imaging data, the three wheat diseases and pests were successfully detected and discriminated. A stepwise strategy including background removal, damage lesions recognition and stresses discrimination was proposed. The present work can provide a basis for the design of low cost and smart instruments for disease and pest detection.

A note on the impact of CYP51 alterations and their combination in the wheat pathogen Zymoseptoria tritici on sensitivity to the azole fungicides epoxiconazole and metconazole

Septoria tritici blotch caused by the fungal pathogen Zymoseptoria tritici is the most economically damaging disease of winter wheat in Ireland. As azoles have been intensively used, Z. tritici has developed different means of resisting their toxic effects with multiple alterations now reported in individual strains. Using previously characterised Irish collections of Z. tritici, the relationships between these different alterations have been examined using conditional inference trees and random forest. The results from this study highlight the importance of specific alterations I381V and S524T, with both contributing most to the reductions in epoxiconazole (EPZ) and metconazole (MTZ) sensitivity. As the azole class of fungicides is an extensive and diverse group, it is possible these alterations do not impact other azoles in a similar manner. Further analysis of these and contemporary collections to additional azoles, including mefentrifluconazole, which continues to provide good field control of Z. tritici, is warranted.

Effects of Metconazole + Prothioconazole, Pydiflumetofen + Propiconazole, and Variety Resistance on Fusarium Head Blight and Yield in Hard Red Spring Wheat

Fusarium head blight (FHB), caused primarily by Fusarium graminearum (Schwabe), is one of the most significant diseases of hard red spring wheat (HRSW) in North Dakota. Implementing integrated management practices such as host resistance and fungicides can be used to manage FHB and the associated mycotoxin deoxynivalenol (DON). However, depending on the production year in North Dakota, both management tools might not be needed for FHB. The objective of this research was to evaluate fungicide premixes and varietal resistance on suppression of FHB, suppression of DON, and economic response in diverse FHB environments in dryland HRSW. Seven field experiments were conducted at dryland locations from 2019 to 2021. Two HRSW varieties, WB-Mayville (susceptible) and ND-VitPro (moderately resistant) served as the main plots, and seven fungicide treatments, including a nontreated control, served as the subplots. Based on disease levels and local weather data (relative humidity, rainfall, and temperature), field experiments were categorized into four FHB management environments (high, moderate, low, and no disease) to assess the effectiveness of management tools in field situations faced by HRSW growers. The fungicide premixture treatments of metconazole + prothioconazole and pydiflumetofen + propiconazole provided similar or better suppression of FHB and DON when compared with FHB fungicide standards. The results suggest that the use of fungicides on a moderately resistant variety provided a more favorable economic response when compared with their use on a susceptible variety in moderate and low FHB environments. The results from this study will help guide FHB management decisions in dryland HRSW.

Resistance of QYm.nau-2D to wheat yellow mosaic virus was derived from an alien introgression into common wheat.

The QYm.nau-2D locus conferring wheat yellow mosaic virusresistance is an exotic introgression andwe developed 11 diagnostic markers tightly linked to QYm.nau-2D. Wheat yellow mosaic virus (WYMV) is a serious disease of winter wheat in China. Breeding resistant varieties is the most effectivestrategy for WYMV control. A WYMV resistant locus QYm.nau-2D on the chromosome arm 2DL has been repeatedly reported but the mappedregion is large. In the present study, we screened recombinants using a biparental population and mapped QYm.nau-2D into an 18.8Mbphysical interval.By genome-wide association studies of 372 wheat varieties for WYMV resistance in four environments, we narrowed down QYm.nau-2D into a 16.4Mbinterval. Haplotype analysis indicated QYm.nau-2D were present as six different states due to recombination during hybridization breeding. QYm.nau-2D was finally mapped into a linkage block of 11.2Mb. Chromosome painting using 2D specific probes and collinearity analysis among the published sequences corresponding to QYm.nau-2D region indicated the block was an exotic introgression. The Illumina-sequenced reads of four diploid Aegilops specieswere mapped to the sequence of Fielder, a variety having the introgression. The mapping reads were significantly increased at the putative introgression regions of Fielder. Ae. uniaristata (NN) had the highest mapping reads, suggesting that QYm.nau-2D was possibly an introgressionfrom genome N. We investigated the agronomic performances of different haplotypes and observed no linkage drag of the alien introgression for the 15 tested traits. For marker-assisted selection of QYm.nau-2D, we developed 11 diagnostic markers tightly linked to the locus. This research provided a case study of anexotic introgression, which has been utilized in wheat improvement for WYMV resistance.

THE INFLUENCE OF THE PREDECESSOR AND SOWING DATES ON THE DEVELOPMENT OF SEED INFECTION OF WINTER WHEAT VARIETIES IN THE CONDITIONS OF THE FOREST-STEPPE OF UKRAINE

THE INFLUENCE OF THE PREDECESSOR AND SOWING DATES ON THE DEVELOPMENT OF SEED INFECTION OF WINTER WHEAT VARIETIES IN THE CONDITIONS OF THE FOREST-STEPPE OF UKRAINE

Biosurfactants produced by the novel strain Bacillus subtilis H1 as an efficient tool to biocontrol fungal diseases of tomato and wheat

Plant fungal diseases cause up to 15-20% and, in extreme cases, up to 60% yield loss globally. The use of chemical pesticides for the suppression of fungal plant diseases has many negative consequences for the environment. Therefore, new alternatives to suppress fungal pathogens are actively sought. In this present study, biosurfactants produced by the novel strain of Bacillus subtilis H1 were monitored for their ability to inhibit the growth of phytopathogens Fusarium oxysporum f. sp. lycopersici and Alternaria spp in in vitro and in vivo experiments. In the in vitro experiment, the treatment of tomato leaves with a biosurfactant fully inhibited the growth of F. oxysporum, and reduced the growth of Alternaria spp mycelium by 11.5 times at a concentration of 1000 mg/l. In the in vivo experiment, the use of a biosurfactant reduced the degree of damage to tomato and wheat plants, but less than in the in vitro experiment. In the case of tomato leaves infected with F. oxysporum and Alternaria spp and treated with 1000 mg/l biosurfactants, a decrease of 2.6 and 2.1 times was determined relative to infected but intreated leaves. For wheat leaves, the decrease was by 1.6 and 2.0 times, respectively. It can be concluded that biosurfactants produced by B. subtilis H1 are promising to be used for fungal pathogens biocontrol.

The Status of Wheat Stem Rust in Syria During the Period 2018-2021

Kassem, M. 2023. The Status of Wheat Stem Rust in Syria During the Period 2018-2021. Arab Journal of Plant Protection, 41(4): 339-347. https://doi.org/10.22268/AJPP-41.4.339347 Wheat stem rust caused by Puccinia graminis f. sp. tritici (Pgt) is an important disease of wheat in the world. This disease is also observed in Syria and repeatedly increased from one year to another during the last decade. This study aimed to investigate the status of the distribution of the disease in the Syrian wheat fields, in addition to its virulence by evaluating the reaction type of differential lines and local varieties. The results of the field survey showed that the spread of wheat stem rust disease in all studied regions in Syria (northern, central and coastal regions) during the period 2018-2021. The prevalence of infected fields reached 59, 75, 67, 59%, respectively, with some variability among regions based on the moisture level (rainfall/irrigation) and average temperature. In general, the infection with Pgt appeared in wheat fields earlier than usual in Syria (mid-May), with costal wheat field infection observed at the beginning of April, whereas from mid to late April in the central and northern regions. The dominant bulk of Pgt characterized by increased virulence, year after year in Syria. The percentage of susceptible genes reached 77% in 2021, whereas it was 47.7% in 2018. One major gene Sr13 was still effective against Pgt population. Reaction type of resistance genes varied, some of them maintained the reaction type during the study period, but most of them was susceptible and moderately susceptible, and the others lost its resistant. For example, genes 9e, 24 and 31 that were resistant in 2018 began to lose resistance in 2019. The reaction type of Syrian local cultivars was affected by the virulence of the Pgt bulk. The percentage of susceptible cultivars was 66.6%, the moderately susceptible was 20.8%, and the moderately resistant was 12.5% and restricted to three varieties (Doma 6, cham 9 and Bohouth9), whereas resistant varieties were absent. Keywords: Differential lines, Cham 9, Bohouth 9, wheat stem rust, Sr2, Sr13, Syria

Diagnostic Guide: Fusarium Head Blight of Cereal Grains

Fusarium head blight (FHB) is a devastating disease of wheat ( Triticum aestivum) worldwide. This disease is caused by Fusarium species including Fusarium graminearum Schwabe sensu stricto, F. culmorum (Wm. G. Sm.) Sacc., and F. pseudograminearum O'Donnell & T. Aoki . This diagnostic guide provides information on the pathogen, signs and symptoms of disease, host range, and geographic distribution. A discussion of the mycotoxins produced by the pathogens, as well as management strategies, is also presented. Proper diagnosis will allow practitioners greater opportunity to adequately control FHB disease. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

Comparison of linear and semi-parametric models incorporating genomic, pedigree, and associated loci information for the prediction of resistance to stripe rust in an Austrian winter wheat breeding program

Key messageWe used a historical dataset on stripe rust resistance across 11 years in an Austrian winter wheat breeding program to evaluate genomic and pedigree-based linear and semi-parametric prediction methods.Stripe rust (yellow rust) is an economically important foliar disease of wheat (Triticum aestivum L.) caused by the fungus Puccinia striiformis f. sp. tritici. Resistance to stripe rust is controlled by both qualitative (R-genes) and quantitative (small- to medium-effect quantitative trait loci, QTL) mechanisms. Genomic and pedigree-based prediction methods can accelerate selection for quantitative traits such as stripe rust resistance. Here we tested linear and semi-parametric models incorporating genomic, pedigree, and QTL information for cross-validated, forward, and pairwise prediction of adult plant resistance to stripe rust across 11 years (2008–2018) in an Austrian winter wheat breeding program. Semi-parametric genomic modeling had the greatest predictive ability and genetic variance overall, but differences between models were small. Including QTL as covariates improved predictive ability in some years where highly significant QTL had been detected via genome-wide association analysis. Predictive ability was moderate within years (cross-validated) but poor in cross-year frameworks.

Effect of Nozzle Configuration and Ground Sprayer Speed on Fusarium Head Blight Management in Wheat

Fusarium head blight (FHB), caused primarily by Fusarium graminearum in the United States, is the most important disease of wheat in Kentucky and the surrounding region. In addition to reduced grain yields, grain quality also can be affected due to reduced test weights and contamination of harvested grain by associated mycotoxins, such as deoxynivalenol (DON). Although application of a foliar fungicide during the beginning anthesis growth stage is one of the primary practices used to manage FHB and DON, a major limiting factor in achieving greater efficacy has been fungicide coverage of wheat heads. This research evaluated different nozzle configurations and ground sprayer speeds for their effect on coverage of simulated wheat heads and management of FHB and DON in field trials conducted in 2017 to 2019 at the University of Kentucky Research and Education Center in Princeton, KY. Based on the results from this research, only minor differences in simulated wheat head coverage were observed across nozzle configurations and sprayer speeds. Depending on the nozzle configuration being used, applicators may need to adjust their sprayer speed to achieve maximum control of FHB.

Mapping and characterization of a novel adult-plant leaf rust resistance gene LrYang16G216 via bulked segregant analysis and conventional linkage method.

A novel adult-plant leaf rust resistance gene LrYang16G216 on wheat chromosome 6BL was identified and mapped to a 0.59cM genetic interval by BSA and conventional linkage method. Leaf rust (Puccinia triticina) is one of the most devastating fungal diseases of wheat (Triticum aestivum L.). Discovery and identification of new resistance genes is essential to develop disease-resistant cultivars. An advanced breeding line Yang16G216 was previously identified to confer adult-plant resistance (APR) to leaf rust. In this research, a recombinant inbred line (RIL) population was constructed from the cross between Yang16G216 and a highly susceptible line Yang16M6393, and genotyped with exome capture sequencing and 55K SNP array. Through bulked segregant analysis (BSA) and genetic linkage mapping, a stable APR gene, designated as LrYang16G216, was detected and mapped to the distal region of chromosome arm 6BL with a genetic interval of 2.8cM. For further verification, another RIL population derived from the cross between Yang16G216 and a susceptible wheat variety Yangmai 29 was analyzed using the enriched markers in the target interval, and LrYang16G216 was further narrowed to a 0.59cM genetic interval flanked by the KASP markers Ax109403980 and Ax95083494, corresponding to the physical position 712.34-713.94Mb in the Chinese Spring reference genome, in which twenty-six disease resistance-related genes were annotated. Based on leaf rust resistance spectrum, mapping data and physical location, LrYang16G216 was identified to be a novel and effective APR gene. The LrYang16G216 with linked markers will be useful for marker-assisted selection in wheat resistance breeding.