Genetic Diversity, Mycotoxin Profiles, and Population Structure of Fusarium spp. Associated with Fusarium Head Blight in Georgia, U.S.A.

Fusarium head blight (FHB) has become a limiting factor in soft red winter wheat production in the southeast US. Recent epidemics have occurred in Georgia, however genetic information on the Fusarium species responsible for FHB is unknown. This study aimed to assess pathogen population structure and genetic diversity, trichothecene profiles, and representative pathogenicity of 196 Fusarium isolates collected from 44 wheat (n = 85) and 53 corn (n = 111) fields in Georgia. Phylogenetic analysis using the translation elongation factor 1-alpha (635 bp) and RNA polymerase second largest subunit (930 bp) sequence data resolved isolates into 185 haplotypes, representing 12 Fusarium species grouped under five species complexes. F. graminearum with 15-acetyl-deoxynivalenol (15ADON) chemotype (75.6%) and F. incarnatum (57.7%) predominated in wheat and corn, respectively, with a surprisingly higher frequency of NIV F. graminearum (21.8%). Using nine variable number of tandem repeat markers, 82 multilocus genotypes out of 86 F. graminearum isolates were identified and grouped into two genetic clusters, pop1fg (n = 29) and pop2fg (n = 32), as part of the North American populations (NA1 and NA2), but with no chemotype differentiation. F. graminearum populations in Georgia are mostly clonal and might have evolved through at least two introductions from the northeast US and Canada and local adaptation to maintain high genetic diversity. Pathogenicity of F. graminearum isolates from wheat and corn had high FHB severity (>60%) in wheat, depicting the risk they can pose towards future FHB outbreaks. Overall, this baseline study provided important information on Fusarium species diversity including F. graminearum associated with FHB in Georgia that will be useful to formulate integrated disease management incorporating improved host resistance and fungicide spray program.

Quantitative Trait Locus Analysis of Hessian Fly Resistance in Soft Red Winter Wheat.

The Hessian fly (HF) is an invasive insect that has caused millions of dollars in yield losses to southeastern US wheat farms. Genetic resistance is the most sustainable solution to control HF. However, emerging biotypes are quickly overcoming resistance genes in the southeast; therefore, identifying novel sources of resistance is critical. The resistant line "UGA 111729" and susceptible variety "AGS 2038" were crossbred to generate a population of 225 recombinant inbred lines. This population was phenotyped in the growth chamber (GC) during 2019 and 2021 and in field (F) trials in Georgia during the 2021-2022 growing seasons. Visual scoring was utilized in GC studies. The percentage of infested tillers and number of pupae/larvae per tiller, and infested tiller per sample were measured in studies from 2021 to 2022. Averaging across all traits, a major QTL on chromosome 3D explained 42.27% (GC) and 10.43% (F) phenotypic variance within 9.86 centimorgans (cM). SNP marker IWB65911 was associated with the quantitative trait locus (QTL) peak with logarithm of odds (LOD) values of 14.98 (F) and 62.22 (GC). IWB65911 colocalized with resistance gene H32. KASP marker validation verified that UGA 111729 and KS89WGRC06 express H32. IWB65911 may be used for marker-assisted selection.

Bearded or smooth? Awns improve yield when wheat experiences heat stress during grain fill in the southeastern United States.

The presence or absence of awns-whether wheat heads are 'bearded' or 'smooth' - is the most visible phenotype distinguishing wheat cultivars. Previous studies suggest that awns may improve yields in heat or water-stressed environments, but the exact contribution of awns to yield differences remains unclear. Here we leverage historical phenotypic, genotypic, and climate data for wheat (Triticum aestivum) to estimate the yield effects of awns under different environmental conditions over a 12-year period in the southeastern USA. Lines were classified as awned or awnless based on sequence data, and observed heading dates were used to associate grain fill periods of each line in each environment with climatic data and grain yield. In most environments, awn suppression was associated with higher yields, but awns were associated with better performance in heat-stressed environments more common at southern locations. Wheat breeders in environments where awns are only beneficial in some years may consider selection for awned lines to reduce year-to-year yield variability, and with an eye towards future climates.

Utilizing genomics and historical data to optimize gene pools for new breeding programs: A case study in winter wheat.

With the rapid generation and preservation of both genomic and phenotypic information for many genotypes within crops and across locations, emerging breeding programs have a valuable opportunity to leverage these resources to 1) establish the most appropriate genetic foundation at program inception and 2) implement robust genomic prediction platforms that can effectively select future breeding lines. Integrating genomics-enabled breeding into cultivar development can save costs and allow resources to be reallocated towards advanced (i.e., later) stages of field evaluation, which can facilitate an increased number of testing locations and replicates within locations. In this context, a reestablished winter wheat breeding program was used as a case study to understand best practices to leverage and tailor existing genomic and phenotypic resources to determine optimal genetics for a specific target population of environments. First, historical multi-environment phenotype data, representing 1,285 advanced breeding lines, were compiled from multi-institutional testing as part of the SunGrains cooperative and used to produce GGE biplots and PCA for yield. Locations were clustered based on highly correlated line performance among the target population of environments into 22 subsets. For each of the subsets generated, EMMs and BLUPs were calculated using linear models with the 'lme4' R package. Second, for each subset, TPs representative of the new SC breeding lines were determined based on genetic relatedness using the 'STPGA' R package. Third, for each TP, phenotypic values and SNP data were incorporated into the 'rrBLUP' mixed models for generation of GEBVs of YLD, TW, HD and PH. Using a five-fold cross-validation strategy, an average accuracy of r = 0.42 was obtained for yield between all TPs. The validation performed with 58 SC elite breeding lines resulted in an accuracy of r = 0.62 when the TP included complete historical data. Lastly, QTL-by-environment interaction for 18 major effect genes across three geographic regions was examined. Lines harboring major QTL in the absence of disease could potentially underperform (e.g., Fhb1 R-gene), whereas it is advantageous to express a major QTL under biotic pressure (e.g., stripe rust R-gene). This study highlights the importance of genomics-enabled breeding and multi-institutional partnerships to accelerate cultivar development.

Profiling of Fusarium head blight resistance QTL haplotypes through molecular markers, genotyping-by-sequencing, and machine learning.

KEY MESSAGE: Marker-assisted selection is important for cultivar development. We propose a system where a training population genotyped for QTL and genome-wide markers may predict QTL haplotypes in early development germplasm. Breeders screen germplasm with molecular markers to identify and select individuals that have desirable haplotypes. The objective of this research was to investigate whether QTL haplotypes can be accurately predicted using SNPs derived by genotyping-by-sequencing (GBS). In the SunGrains program during 2020 (SG20) and 2021 (SG21), 1,536 and 2,352 lines submitted for GBS were genotyped with markers linked to the Fusarium head blight QTL: Qfhb.nc-1A, Qfhb.vt-1B, Fhb1, and Qfhb.nc-4A. In parallel, data were compiled from the 2011-2020 Southern Uniform Winter Wheat Scab Nursery (SUWWSN), which had been screened for the same QTL, sequenced via GBS, and phenotyped for: visual Fusarium severity rating (SEV), percent Fusarium damaged kernels (FDK), deoxynivalenol content (DON), plant height, and heading date. Three machine learning models were evaluated: random forest, k-nearest neighbors, and gradient boosting machine. Data were randomly partitioned into training-testing splits. The QTL haplotype and 100 most correlated GBS SNPs were used for training and tuning of each model. Trained machine learning models were used to predict QTL haplotypes in the testing partition of SG20, SG21, and the total SUWWSN. Mean disease ratings for the observed and predicted QTL haplotypes were compared in the SUWWSN. For all models trained using the SG20 and SG21, the observed Fhb1 haplotype estimated group means for SEV, FDK, DON, plant height, and heading date in the SUWWSN were not significantly different from any of the predicted Fhb1 calls. This indicated that machine learning may be utilized in breeding programs to accurately predict QTL haplotypes in earlier generations.

Genetics of Fusarium head blight resistance in soft red winter wheat using a genome-wide association study.

Host resistance is an effective and sustainable approach to manage the negative impact of Fusarium head blight (FHB) on wheat (Triticum aestivum L.) grain yield and quality. The objective of this study was to characterize the phenotypic responses and identify quantitative trait loci (QTL) conditioning different FHB resistance types using a panel of 236 elite soft red winter wheat (SRWW) lines in a genome-wide association study (GWAS). The panel was phenotyped for five FHB and three morphological traits under two field and two greenhouse environments in 2018-2019 and 2019-2020. We identified 160 significant marker-trait associations (MTAs) for FHB traits and 11 MTAs for plant height. Eleven QTL showed major effects and explained >10% phenotypic variation (PV) for FHB resistance. Among these major loci, three QTL were stable and five QTL exhibited a pleiotropic effect. The QTL QFhb-3BL, QFhb-5AS, QFhb-5BL, QFhb-7AS.1, QFhb-7AS.2, and QFhb-7BS are presumed to be novel. Pyramiding multiple resistance alleles from all the major-effect QTL resulted in a significant reduction in FHB incidence, severity, index, deoxynivalenol (DON), and Fusarium-damaged kernel (FDK) by 17, 43, 45, 55, and 25%, respectively. Further validation of these QTL could potentially facilitate successful introgression of these resistance loci in new cultivars for improved FHB resistance in breeding programs.

A network modeling approach provides insights into the environment-specific yield architecture of wheat.

Wheat (Triticum aestivum) yield is impacted by a diversity of developmental processes which interact with the environment during plant growth. This complex genetic architecture complicates identifying quantitative trait loci that can be used to improve yield. Trait data collected on individual processes or components of yield have simpler genetic bases and can be used to model how quantitative trait loci generate yield variation. The objectives of this experiment were to identify quantitative trait loci affecting spike yield, evaluate how their effects on spike yield proceed from effects on component phenotypes, and to understand how the genetic basis of spike yield variation changes between environments. A 358 F5:6 recombinant inbred line population developed from the cross of LA-95135 and SS-MPV-57 was evaluated in 2 replications at 5 locations over the 2018 and 2019 seasons. The parents were 2 soft red winter wheat cultivars differing in flowering, plant height, and yield component characters. Data on yield components and plant growth were used to assemble a structural equation model to characterize the relationships between quantitative trait loci, yield components, and overall spike yield. The effects of major quantitative trait loci on spike yield varied by environment, and their effects on total spike yield were proportionally smaller than their effects on component traits. This typically resulted from contrasting effects on component traits, where an increase in traits associated with kernel number was generally associated with a decrease in traits related to kernel size. In all, the complete set of identified quantitative trait loci was sufficient to explain most of the spike yield variation observed within each environment. Still, the relative importance of individual quantitative trait loci varied dramatically. Path analysis based on coefficients estimated through structural equation model demonstrated that these variations in effects resulted from both different effects of quantitative trait loci on phenotypes and environment-by-environment differences in the effects of phenotypes on one another, providing a conceptual model for yield genotype-by-environment interactions in wheat.

Accounting for heading date gene effects allows detection of small-effect QTL associated with resistance to Septoria nodorum blotch in wheat.

In humid and temperate areas, Septoria nodorum blotch (SNB) is a major fungal disease of common wheat (Triticum aestivum L.) in which grain yield is reduced when the pathogen, Parastagonospora nodorum, infects leaves and glumes during grain filling. Foliar SNB susceptibility may be associated with sensitivity to P. nodorum necrotrophic effectors (NEs). Both foliar and glume susceptibility are quantitative, and the underlying genetics are not understood in detail. We genetically mapped resistance quantitative trait loci (QTL) to leaf and glume blotch using a double haploid (DH) population derived from the cross between the moderately susceptible cultivar AGS2033 and the resistant breeding line GA03185-12LE29. The population was evaluated for SNB resistance in the field in four successive years (2018-2021). We identified major heading date (HD) and plant height (PH) variants on chromosomes 2A and 2D, co-located with SNB escape mechanisms. Five QTL with small effects associated with adult plant resistance to SNB leaf and glume blotch were detected on 1A, 1B, and 6B linkage groups. These QTL explained a relatively small proportion of the total phenotypic variation, ranging from 5.6 to 11.8%. The small-effect QTL detected in this study did not overlap with QTL associated with morphological and developmental traits, and thus are sources of resistance to SNB.

Wheat end-use quality: State of art, genetics, genomics-assisted improvement, future challenges, and opportunities.

Wheat is the most important source of food, feed, and nutrition for humans and livestock around the world. The expanding population has increasing demands for various wheat products with different quality attributes requiring the development of wheat cultivars that fulfills specific demands of end-users including millers and bakers in the international market. Therefore, wheat breeding programs continually strive to meet these quality standards by screening their improved breeding lines every year. However, the direct measurement of various end-use quality traits such as milling and baking qualities requires a large quantity of grain, traits-specific expensive instruments, time, and an expert workforce which limits the screening process. With the advancement of sequencing technologies, the study of the entire plant genome is possible, and genetic mapping techniques such as quantitative trait locus mapping and genome-wide association studies have enabled researchers to identify loci/genes associated with various end-use quality traits in wheat. Modern breeding techniques such as marker-assisted selection and genomic selection allow the utilization of these genomic resources for the prediction of quality attributes with high accuracy and efficiency which speeds up crop improvement and cultivar development endeavors. In addition, the candidate gene approach through functional as well as comparative genomics has facilitated the translation of the genomic information from several crop species including wild relatives to wheat. This review discusses the various end-use quality traits of wheat, their genetic control mechanisms, the use of genetics and genomics approaches for their improvement, and future challenges and opportunities for wheat breeding.

Identification of Main-Effect and Environmental Interaction QTL and Their Candidate Genes for Drought Tolerance in a Wheat RIL Population Between Two Elite Spring Cultivars.

Understanding the genetics of drought tolerance can expedite the development of drought-tolerant cultivars in wheat. In this study, we dissected the genetics of drought tolerance in spring wheat using a recombinant inbred line (RIL) population derived from a cross between a drought-tolerant cultivar, 'Reeder' (PI613586), and a high-yielding but drought-susceptible cultivar, 'Albany.' The RIL population was evaluated for grain yield (YLD), grain volume weight (GVW), thousand kernel weight (TKW), plant height (PH), and days to heading (DH) at nine different environments. The Infinium 90 k-based high-density genetic map was generated using 10,657 polymorphic SNP markers representing 2,057 unique loci. Quantitative trait loci (QTL) analysis detected a total of 11 consistent QTL for drought tolerance-related traits. Of these, six QTL were exclusively identified in drought-prone environments, and five were constitutive QTL (identified under both drought and normal conditions). One major QTL on chromosome 7B was identified exclusively under drought environments and explained 13.6% of the phenotypic variation (PV) for YLD. Two other major QTL were detected, one each on chromosomes 7B and 2B under drought-prone environments, and explained 14.86 and 13.94% of phenotypic variation for GVW and YLD, respectively. One novel QTL for drought tolerance was identified on chromosome 2D. In silico expression analysis of candidate genes underlaying the exclusive QTLs associated with drought stress identified the enrichment of ribosomal and chloroplast photosynthesis-associated proteins showing the most expression variability, thus possibly contributing to stress response by modulating the glycosyltransferase (TraesCS6A01G116400) and hexosyltransferase (TraesCS7B01G013300) unique genes present in QTL 21 and 24, respectively. While both parents contributed favorable alleles to these QTL, unexpectedly, the high-yielding and less drought-tolerant parent contributed desirable alleles for drought tolerance at four out of six loci. Regardless of the origin, all QTL with significant drought tolerance could assist significantly in the development of drought-tolerant wheat cultivars, using genomics-assisted breeding approaches.

Genome-Wide Association Mapping for Yield and Related Traits Under Drought Stressed and Non-stressed Environments in Wheat.

Understanding the genetics of drought tolerance in hard red spring wheat (HRSW) in northern USA is a prerequisite for developing drought-tolerant cultivars for this region. An association mapping (AM) study for drought tolerance in spring wheat in northern USA was undertaken using 361 wheat genotypes and Infinium 90K single-nucleotide polymorphism (SNP) assay. The genotypes were evaluated in nine different locations of North Dakota (ND) for plant height (PH), days to heading (DH), yield (YLD), test weight (TW), and thousand kernel weight (TKW) under rain-fed conditions. Rainfall data and soil type of the locations were used to assess drought conditions. A mixed linear model (MLM), which accounts for population structure and kinship (PC+K), was used for marker-trait association. A total of 69 consistent QTL involved with drought tolerance-related traits were identified, with p ≤ 0.001. Chromosomes 1A, 3A, 3B, 4B, 4D, 5B, 6A, and 6B were identified to harbor major QTL for drought tolerance. Six potential novel QTL were identified on chromosomes 3D, 4A, 5B, 7A, and 7B. The novel QTL were identified for DH, PH, and TKW. The findings of this study can be used in marker-assisted selection (MAS) for drought-tolerance breeding in spring wheat.

Characterizing the oligogenic architecture of plant growth phenotypes informs genomic selection approaches in a common wheat population.

BACKGROUND: Genetic variation in growth over the course of the season is a major source of grain yield variation in wheat, and for this reason variants controlling heading date and plant height are among the best-characterized in wheat genetics. While the major variants for these traits have been cloned, the importance of these variants in contributing to genetic variation for plant growth over time is not fully understood. Here we develop a biparental population segregating for major variants for both plant height and flowering time to characterize the genetic architecture of the traits and identify additional novel QTL. RESULTS: We find that additive genetic variation for both traits is almost entirely associated with major and moderate-effect QTL, including four novel heading date QTL and four novel plant height QTL. FT2 and Vrn-A3 are proposed as candidate genes underlying QTL on chromosomes 3A and 7A, while Rht8 is mapped to chromosome 2D. These mapped QTL also underlie genetic variation in a longitudinal analysis of plant growth over time. The oligogenic architecture of these traits is further demonstrated by the superior trait prediction accuracy of QTL-based prediction models compared to polygenic genomic selection models. CONCLUSIONS: In a population constructed from two modern wheat cultivars adapted to the southeast U.S., almost all additive genetic variation in plant growth traits is associated with known major variants or novel moderate-effect QTL. Major transgressive segregation was observed in this population despite the similar plant height and heading date characters of the parental lines. This segregation is being driven primarily by a small number of mapped QTL, instead of by many small-effect, undetected QTL. As most breeding populations in the southeast U.S. segregate for known QTL for these traits, genetic variation in plant height and heading date in these populations likely emerges from similar combinations of major and moderate effect QTL. We can make more accurate and cost-effective prediction models by targeted genotyping of key SNPs.

Analysis of the primary sources of quantitative adult plant resistance to stripe rust in U.S. soft red winter wheat germplasm.

Stripe rust, or yellow rust (Puccinia striiformis Westend. f. sp. tritic), is a disease of wheat (Triticum aestivum L.) historically causing significant economic losses in cooler growing regions. Novel isolates of stripe rust with increased tolerance for high temperatures were detected in the United States circa 2000. This increased heat tolerance puts geographic regions, such as the soft red winter wheat (SRWW) growing region of the southeastern United States, at greater risk of stripe rust induced losses. In order to identify sources of stripe rust resistance in contemporary germplasm, we conducted genome-wide association (GWA) studies on stripe rust severity measured in two panels. The first consisted of 273 older varieties, landraces, and some modern elite breeding lines and was evaluated in environments in the U.S. Pacific Northwest and the southeastern United States. The second panel consisted of 588 modern, elite SRWW breeding lines and was evaluated in four environments in Arkansas and Georgia. The analyses identified three major resistance loci on chromosomes: 2AS (presumably the 2NS:2AS alien introgression from Aegilops ventricosa Tausch; syn. Ae. caudata L.), 3BS, and 4BL. The 4BL locus explained a greater portion of variance in resistance than either the 2AS or 3BS loci in southeastern environments. However, its effects were unstable across different environments and sets of germplasm, possibly a result of its involvement in epistatic interactions. Relatively few lines carry resistance alleles at all three loci, suggesting that there is a pre-existing reservoir of enhanced stripe rust resistance that may be further exploited by regional breeding programs.

A novel adult plant leaf rust resistance gene Lr2K38 mapped on wheat chromosome 1AL.

Soft red winter wheat (SRWW) cultivar AGS 2038 has a high level of seedling and adult plant leaf rust (LR) resistance. To map and characterize LR resistance in AGS 2038, a recombinant inbred line (RIL) population consisting of 225 lines was developed from a cross between AGS 2038 and moderately resistant line UGA 111729. The parents and RIL population were phenotyped for LR response in three field environments at Plains and Griffin, GA, in the 2017-2018 and 2018-2019 growing seasons, one greenhouse environment at the adult-plant stage, and at seedling stage. The RIL population was genotyped with the Illumina iSelect 90K SNP marker array, and a total of 7667 polymorphic markers representing 1513 unique loci were used to construct a linkage map. Quantitative trait loci (QTL) analysis detected six QTL, QLr.ags-1AL, QLr.ags-2AS, QLr.ags-2BS1, QLr.ags-2BS2, QLr.ags-2BS3, and QLr.ags-2DS, for seedling and adult plant LR resistance. Of these, the major adult plant leaf rust resistance QTL, QLr.ags-1AL, was detected on all field and greenhouse adult plant tests and explained up to 34.45% of the phenotypic variation. QLr.ags-1AL, tightly flanked by IWB20487 and IWA4022 markers, was contributed by AGS 2038. Molecular marker analysis using a diagnostic marker linked to Lr59 showed that QLr.ags-1AL was different from Lr59, the only known LR resistance gene on 1AL. Therefore, the QTL was temporarily designated as Lr2K38. Lr2K38-linked marker IWB20487 was highly polymorphic among 30 SRWW lines and should be useful for selecting the Lr2K38 in wheat breeding programs.

Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes.

The performance of genomic prediction (GP) on genetically correlated traits can be improved through an interdependence multi-trait model under a multi-environment context. In this study, a panel of 237 soft facultative wheat (Triticumaestivum L.) lines was evaluated to compare single- and multi-trait models for predicting grain yield (GY), harvest index (HI), spike fertility (SF), and thousand grain weight (TGW). The panel was phenotyped in two locations and two years in Florida under drought and moderately drought stress conditions, while the genotyping was performed using 27,957 genotyping-by-sequencing (GBS) single nucleotide polymorphism (SNP) makers. Five predictive models including Multi-environment Genomic Best Linear Unbiased Predictor (MGBLUP), Bayesian Multi-trait Multi-environment (BMTME), Bayesian Multi-output Regressor Stacking (BMORS), Single-trait Multi-environment Deep Learning (SMDL), and Multi-trait Multi-environment Deep Learning (MMDL) were compared. Across environments, the multi-trait statistical model (BMTME) was superior to the multi-trait DL model for prediction accuracy in most scenarios, but the DL models were comparable to the statistical models for response to selection. The multi-trait model also showed 5 to 22% more genetic gain compared to the single-trait model across environment reflected by the response to selection. Overall, these results suggest that multi-trait genomic prediction can be an efficient strategy for economically important yield component related traits in soft wheat.

First Report of Fusarium poae Causing Fusarium Head Blight of Wheat in Georgia, USA.

Fusarium head blight (FHB) is one of the most troublesome fungal diseases challenging US wheat (Triticum aestivum L.) production (Savary et al. 2019). Harmful mycotoxin contamination, primarily due to deoxynivalenol (DON) in the Fusarium-damaged kernels (FDK), can negatively impact human and livestock health (McMullen et al. 1997). Although Fusarium graminearum is the primary causal agent of FHB, several other species including F. poae could pose a risk by producing dangerous mycotoxins such as nivalenol, DON, HT-2, and T-2 (Stenglein 2009). Severe FHB epidemics on wheat have occurred in recent years along with increased corn acreage across the southeast US specifically in Georgia (Ghimire et al. 2020). Five symptomatic wheat heads displaying bleaching symptoms were randomly collected from 19 different fields across 13 counties of Georgia in late spring of 2018. Infected kernels were dipped in 6% sodium hypochlorite for 10 min and rinsed three times with sterilized water. Blot dried kernels were placed on potato dextrose agar (PDA) and incubated for 7 days at 25°C under 12-h photoperiod. Three isolates (GA18W-2.1.6, GA18W-6.1.4, and GA18W-10.2.3) from Terrell, Peach, and Sumter counties exhibited dense, whitish mycelium colony typical of F. poae (Leslie and Summerell 2006). When grown in carboxymethylcellulose broth, isolates produced globose to piriform microconidia (5.1 to 12.4 µm by 4.4 to 11.2 µm) that were aseptate or had a single septation. The morphological identification was further confirmed by DNA sequencing. Single hyphal tip isolates were grown on cellophane overlain on PDA for 10 days. Fungal DNA was extracted using a Qiagen DNeasy Plant Mini Kit. Genomic DNA was sequenced using TEF1 and TEF2 primer pairs that target the translation elongation factor 1-α (EF1-α) locus (O'Donnell et al. 1998). BLASTn query of the obtained sequences of GA18W-2.1.6 (accession no. MT856907) and GA18W-10.2.3 (accession no. MT856909) were identified as F. poae with a 99% sequence homology with GenBank reference accession MK629641, while GA18W-6.1.4 (accession no. MT856908) displayed 100% similarity with F. poae accession KJ947343. Koch's postulates were performed under greenhouse conditions. Three seeds of the FHB susceptible wheat cultivar 'SS8641' were planted in individual cone-tainers with three replications (two cone-tainers/replicate). Wheat plants were vernalized for six weeks and then moved back to the greenhouse. Each F. poae isolate was spray inoculated (50,000 spores/ml) at the flowering stage onto 18-24 wheat heads. A field isolate of F. graminearum was included as a positive control whereas heads mock-inoculated with water were used as a negative control. Inoculated wheat heads were incubated in black plastic bags for 48 hours. Disease severity and FDK were recorded three weeks post inoculation. Disease severities were 6.7% (GA18W-2.1.6), 8.3% (GA18W-10.2.3), and 15.2% (GA18W-6.1.4) compared to 90.0% in the positive control similar to Arrúa et al (2019). No symptoms were observed in the negative control. FDK was 18% (GA18W-2.1.6), 28% (GA18W-10.2.3) and 44% (GA18W-6.1.4). F. poae was re-isolated from the infected heads and found to be morphologically identical to the isolates used for inoculation. To our knowledge, this is the first report of F. poae associated with FHB of wheat in the state of Georgia, USA. F. poae isolates from Georgia might produce mycotoxins in addition to reducing grain yield which needs further study.

Fusarium Head Blight and Rust Diseases in Soft Red Winter Wheat in the Southeast United States: State of the Art, Challenges and Future Perspective for Breeding.

Among the biotic constraints to wheat (Triticum aestivum L.) production, fusarium head blight (FHB), caused by Fusarium graminearum, leaf rust (LR), caused by Puccinia triticina, and stripe rust (SR) caused by Puccinia striiformis are problematic fungal diseases worldwide. Each can significantly reduce grain yield while FHB causes additional food and feed safety concerns due to mycotoxin contamination of grain. Genetic resistance is the most effective and sustainable approach for managing wheat diseases. In the past 20 years, over 500 quantitative trait loci (QTLs) conferring small to moderate effects for the different FHB resistance types have been reported in wheat. Similarly, 79 Lr-genes and more than 200 QTLs and 82 Yr-genes and 140 QTLs have been reported for seedling and adult plant LR and SR resistance, respectively. Most QTLs conferring rust resistance are race-specific generally conforming to a classical gene-for-gene interaction while resistance to FHB exhibits complex polygenic inheritance with several genetic loci contributing to one resistance type. Identification and deployment of additional genes/QTLs associated with FHB and rust resistance can expedite wheat breeding through marker-assisted and/or genomic selection to combine small-effect QTL in the gene pool. LR disease has been present in the southeast United States for decades while SR and FHB have become increasingly problematic in the past 20 years, with FHB arguably due to increased corn acreage in the region. Currently, QTLs on chromosome 1B from Jamestown, 1A, 1B, 2A, 2B, 2D, 4A, 5A, and 6A from W14, Ning7840, Ernie, Bess, Massey, NC-Neuse, and Truman, and 3B (Fhb1) from Sumai 3 for FHB resistance, Lr9, Lr10, Lr18, Lr24, Lr37, LrA2K, and Lr2K38 genes for LR resistance, and Yr17 and YrR61 for SR resistance have been extensively deployed in southeast wheat breeding programs. This review aims to disclose the current status of FHB, LR, and SR diseases, summarize the genetics of resistance and breeding efforts for the deployment of FHB and rust resistance QTL on soft red winter wheat cultivars, and present breeding strategies to achieve sustainable management of these diseases in the southeast US.

QTL analysis of farinograph and mixograph related traits in spring wheat under heat stress conditions.

Farinograph and mixograph-related parameters are key elements in wheat end-products quality. Understanding the genetic control of these traits and the influence of environmental factors such as heat stress, and their interaction are critical for developing cultivars with improved for those traits. To identify QTL for six farinograph and three mixograph traits, two double haploid (DH) populations (Yecora Rojo × Ksu106 and Klasic × Ksu105) were used in experiments conducted at Riyadh and Al Qassim locations under heat stress. Single nucleotide polymorphism (SNP) markers were used to determine the number of QTLs controlling these parameters. The genetic analysis of farinograph and mixograph-related traits showed considerable variation with transgressive segregation regardless of heat stress conditions in both locations. A total of 108 additive QTLs were detected for the six farinograph and three mixograph traits in the Yecora Rojo × Ksu106 population in both locations under heat treatments. These QTLs were distributed over all 21 wheat chromosomes except 3A. Similarly, in Klassic × Ksu105 population, there were an additional 68 QTLs identified over the two locations and were allocated on all chromosomes except 1D, 2A, 6A, and 6D. In population (Yecora Rojo × Ksu106), the QTL on chromosome 7A (Excalibur_c62415_288) showed significant effects for farinograph and mixograph traits (FDDT, FDST, FBD, M × h8, and M × t) under normal and heat stress condition at both locations. Interestingly, several QTLs that are related to farinograph and mixograph traits, which showed stable expression under both locations, were detected on chromosome 7A in population (Klassic × Ksu105). Results from this study show the quantitative nature of the genetic control of the studied traits and constitute a step toward identifying major QTLs that can be sued molecular-marker assisted breeding to develop new improved quality wheat cultivars.

The translucens group of Xanthomonas translucens: Complicated and important pathogens causing bacterial leaf streak on cereals.

Xanthomonas translucens is a group of gram-negative bacteria that can cause important diseases in cereal crops and forage grasses. Different pathovars have been defined according to their host ranges, and molecular and biochemical characteristics. Pathovars have been placed into two major groups: translucens and graminis. The translucens group contains the pathovars causing bacterial leaf streak (BLS) on cereal crops such as wheat, barley, triticale, rye, and oat. In recent years, BLS has re-emerged as a major problem for many wheat- and barley-producing areas worldwide. The biology of the pathogens and the host-pathogen interactions in cereal BLS diseases were poorly understood. However, recent genome sequence data have provided an insight into the bacterial phylogeny and identification and pathogenicity/virulence. Furthermore, identification of sources of resistance to BLS and mapping of the resistance genes have been initiated. TAXONOMY: Kingdom Bacteria; Phylum Proteobacteria; Class Gammaproteobacteria; Order Xanthomonadales; Family Xanthomonadaceae; Genus Xanthomonas; Species X. translucens; translucens group pathovars: undulosa, translucens, cerealis, hordei, and secalis; graminis group pathovars: arrhenatheri, graminis, poae, phlei; newly established pathovar: pistaciae. HOST RANGE: X. translucens mainly infects plant species in the Poaceae with the translucens group on cereal crop species and the graminis group on forage grass species. However, some strains have been isolated from, and are able to infect, ornamental asparagus and pistachio trees. Most pathovars have a narrow host range, while a few can infect a broad range of hosts. GENOME: The complete genome sequence is available for two X. translucens pv. undulosa strains and one pv. translucens strain. A draft genome sequence is also available for at least one strain from each pathovar. The X. translucens pv. undulosa strain Xt4699 was the first to have its complete genome sequenced, which consists of 4,561,137 bp with total GC content approximately at 68% and 3,528 predicted genes. VIRULENCE MECHANISMS: Like most xanthomonads, X. translucens utilizes a type III secretion system (T3SS) to deliver a suite of T3SS effectors (T3Es) inside plant cells. Transcription activator-like effectors, a special group of T3Es, have been identified in most of the X. translucens genomes, some of which have been implicated in virulence. Genetic factors determining host range virulence have also been identified.

Understanding the Genetic Basis of Spike Fertility to Improve Grain Number, Harvest Index, and Grain Yield in Wheat Under High Temperature Stress Environments.

Moderate heat stress accompanied by short episodes of extreme heat during the post-anthesis stage is common in most US wheat growing areas and causes substantial yield losses. Sink strength (grain number) is a key yield limiting factor in modern wheat varieties. Increasing spike fertility (SF) and improving the partitioning of assimilates can optimize sink strength which is essential to improve wheat yield potential under a hot and humid environment. A genome-wide association study (GWAS) allows identification of novel quantitative trait loci (QTLs) associated with SF and other partitioning traits that can assist in marker assisted breeding. In this study, GWAS was performed on a soft wheat association mapping panel (SWAMP) comprised of 236 elite lines using 27,466 single nucleotide polymorphisms (SNPs). The panel was phenotyped in two heat stress locations over 3 years. GWAS identified 109 significant marker-trait associations (MTAs) (p ≤ 9.99 x 10-5) related to eight phenotypic traits including SF (a major component of grain number) and spike harvest index (SHI, a major component of grain weight). MTAs detected on chromosomes 1B, 3A, 3B, and 5A were associated with multiple traits and are potentially important targets for selection. More than half of the significant MTAs (60 out of 109) were found in genes encoding different types of proteins related to metabolism, disease, and abiotic stress including heat stress. These MTAs could be potential targets for further validation study and may be used in marker-assisted breeding for improving wheat grain yield under post-anthesis heat stress conditions. This is the first study to identify novel QTLs associated with SF and SHI which represent the major components of grain number and grain weight, respectively, in wheat.