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.

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.

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.

Sequence-based mapping identifies a candidate transcription repressor underlying awn suppression at the B1 locus in wheat.

Awns are stiff, hair-like structures which grow from the lemmas of wheat (Triticum aestivum) and other grasses that contribute to photosynthesis and play a role in seed dispersal. Variation in awn length in domesticated wheat is controlled primarily by three major genes, most commonly the dominant awn suppressor Tipped1 (B1). This study identifies a transcription repressor responsible for awn inhibition at the B1 locus. Association mapping was combined with analysis in biparental populations to delimit B1 to a distal region of 5AL colocalized with QTL for number of spikelets per spike, kernel weight, kernel length, and test weight. Fine-mapping located B1 to a region containing only two predicted genes, including C2H2 zinc finger transcriptional repressor TraesCS5A02G542800 upregulated in developing spikes of awnless individuals. Deletions encompassing this candidate gene were present in awned mutants of an awnless wheat. Sequence polymorphisms in the B1 coding region were not observed in diverse wheat germplasm whereas a nearby polymorphism was highly predictive of awn suppression. Transcriptional repression by B1 is the major determinant of awn suppression in global wheat germplasm. It is associated with increased number of spikelets per spike and decreased kernel size.

Training population selection and use of fixed effects to optimize genomic predictions in a historical USA winter wheat panel.

KEY MESSAGE: The optimization of training populations and the use of diagnostic markers as fixed effects increase the predictive ability of genomic prediction models in a cooperative wheat breeding panel. Plant breeding programs often have access to a large amount of historical data that is highly unbalanced, particularly across years. This study examined approaches to utilize these data sets as training populations to integrate genomic selection into existing pipelines. We used cross-validation to evaluate predictive ability in an unbalanced data set of 467 winter wheat (Triticum aestivum L.) genotypes evaluated in the Gulf Atlantic Wheat Nursery from 2008 to 2016. We evaluated the impact of different training population sizes and training population selection methods (Random, Clustering, PEVmean and PEVmean1) on predictive ability. We also evaluated inclusion of markers associated with major genes as fixed effects in prediction models for heading date, plant height, and resistance to powdery mildew (caused by Blumeria graminis f. sp. tritici). Increases in predictive ability as the size of the training population increased were more evident for Random and Clustering training population selection methods than for PEVmean and PEVmean1. The selection methods based on minimization of the prediction error variance (PEV) outperformed the Random and Clustering methods across all the population sizes. Major genes added as fixed effects always improved model predictive ability, with the greatest gains coming from combinations of multiple genes. Maximum predictabilities among all prediction methods were 0.64 for grain yield, 0.56 for test weight, 0.71 for heading date, 0.73 for plant height, and 0.60 for powdery mildew resistance. Our results demonstrate the utility of combining unbalanced phenotypic records with genome-wide SNP marker data for predicting the performance of untested genotypes.

High-definition infrared thermography of ice nucleation and propagation in wheat under natural frost conditions and controlled freezing.

MAIN CONCLUSION: An extremely high resolution infrared camera demonstrated various freezing events in wheat under natural conditions. Many of those events shed light on years of misunderstanding regarding freezing in small grains. Infrared thermography has enhanced our knowledge of ice nucleation and propagation in plants through visualization of the freezing process. The majority of infrared analyses have been conducted under controlled conditions and often on individual organs instead of whole plants. In the present study, high-definition (1280 × 720 pixel resolution) infrared thermography was used under natural conditions to visualize the freezing process of wheat plants during freezing events in 2016 and 2017. Plants within plots were found to freeze one at a time throughout the night and in an apparently random manner. Leaves on each plant also froze one at a time in an age-dependent pattern with oldest leaves freezing first. Contrary to a common assumption that freezing begins in the upper parts of leaves; freezing began at the base of the plant and spread upwards. The high resolution camera used was able to verify that a two stage sequence of freezing began within vascular bundles. Neither of the two stages was lethal to leaves, but a third stage was demonstrated at colder temperatures that was lethal and was likely a result of dehydration stress; this stage of freezing was not detectable by infrared. These results underscore the complexity of the freezing process in small grains and indicate that comprehensive observational studies are essential to identifying and selecting freezing tolerance traits in grain crops.

Virulence Differences in Blumeria graminis f. sp. tritici from the Central and Eastern United States.

Wheat powdery mildew is a disease of global importance that occurs across a wide geographic area in the United States. A virulence survey of Blumeria graminis f. sp. tritici, the causal agent, was conducted by sampling 36 wheat fields in 15 U.S. states in the years 2013 and 2014. Using a hierarchical sampling protocol, isolates were derived from three separated plants at each of five separated sites within each field in order to assess the spatial distribution of pathotypes. In total, 1,017 isolates from those fields were tested individually on single-gene differential cultivars containing a total of 21 powdery mildew resistance (Pm) genes. Several recently introgressed mildew resistance genes from wild wheat relatives (Pm37, Pm53, MlAG12, NCAG13, and MlUM15) exhibited complete or nearly complete resistance to all local B. graminis f. sp. tritici populations from across the sampled area. One older gene, Pm4b, also retained at least some efficacy across the sampled area. The B. graminis f. sp. tritici population sampled from Arkansas and Missouri, on the western edge of the eastern soft red winter wheat region, had virulence profiles more similar to other soft wheat mildew populations than to the geographically closer population from hard wheat fields in the Plains states of Oklahoma, Nebraska, and Kansas. The Plains population differed in that it was avirulent to several Pm genes long defeated in the soft-wheat-growing areas. Virulence complexity was greatest east of the Mississippi River, and diminished toward the west. Several recently introgressed Pm genes (Pm25, Pm34, Pm35, and NCA6) that are highly effective against mildew in the field in North Carolina were unexpectedly susceptible to eastern-U.S. B. graminis f. sp. tritici populations in detached-leaf tests. Sampled fields displayed a wide range of pathotype diversity and spatial distribution, suggesting that epidemics are caused by varying numbers of pathotypes in all regions. The research confirmed that most long-used Pm genes are defeated in the eastern United States, and the U.S. B. graminis f. sp. tritici population has different virulence profiles in the hard- and soft-wheat regions, which are likely maintained by host selection, isolation by distance, and west-to-east gene flow.

Mapping of powdery mildew resistance gene Pm53 introgressed from Aegilops speltoides into soft red winter wheat.

KEY MESSAGE: A powdery mildew resistance gene was introgressed from Aegilops speltoides into winter wheat and mapped to chromosome 5BL. Closely linked markers will permit marker-assisted selection for the resistance gene. Powdery mildew of wheat (Triticum aestivum L.) is a major fungal disease in many areas of the world, caused by Blumeria graminis f. sp. tritici (Bgt). Host plant resistance is the preferred form of disease prevention because it is both economical and environmentally sound. Identification of new resistance sources and closely linked markers enable breeders to utilize these new sources in marker-assisted selection as well as in gene pyramiding. Aegilops speltoides (2n = 2x = 14, genome SS), has been a valuable disease resistance donor. The powdery mildew resistant wheat germplasm line NC09BGTS16 (NC-S16) was developed by backcrossing an Ae. speltoides accession, TAU829, to the susceptible soft red winter wheat cultivar 'Saluda'. NC-S16 was crossed to the susceptible cultivar 'Coker 68-15' to develop F2:3 families for gene mapping. Greenhouse and field evaluations of these F2:3 families indicated that a single gene, designated Pm53, conferred resistance to powdery mildew. Bulked segregant analysis showed that multiple simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers specific to chromosome 5BL segregated with the resistance gene. The gene was flanked by markers Xgwm499, Xwmc759, IWA6024 (0.7 cM proximal) and IWA2454 (1.8 cM distal). Pm36, derived from a different wild wheat relative (T. turgidum var. dicoccoides), had previously been mapped to chromosome 5BL in a durum wheat line. Detached leaf tests revealed that NC-S16 and a genotype carrying Pm36 differed in their responses to each of three Bgt isolates. Pm53 therefore appears to be a new source of powdery mildew resistance.

Molecular characterization of a new powdery mildew resistance gene Pm54 in soft red winter wheat.

KEY MESSAGE: A new powdery mildew resistance gene Pm54 was identified on chromosome 6BL in soft red winter wheat. Powdery mildew is causing increasing damage to wheat production in the southeastern USA. To combat the disease, a continuing need exists to discover new genes for powdery mildew resistance and to incorporate those genes into breeding programs. Pioneer(®) variety 26R61 (shortened as 26R61) and AGS 2000 have been used as checks in the Uniform Southern Soft Red Winter Wheat Nursery for a decade, and both have provided good resistance across regions during that time. In the present study, a genetic analysis of mildew resistance was conducted on a RIL population developed from a cross of 26R61 and AGS 2000. Phenotypic evaluation was conducted in the field at Plains, GA, and Raleigh, NC, in 2012 and 2013, a total of four environments. Three quantitative trait loci (QTL) with major effect were consistently detected on wheat chromosomes 2BL, 4A and 6BL. The 2BL QTL contributed by 26R61 was different from Pm6, a widely used gene in the southeastern USA. The other two QTL were identified from AGS 2000. The 6BL QTL was subsequently characterized as a simple Mendelian factor when the population was inoculated with a single Blumeria graminis f. sp. tritici (Bgt) isolate in controlled environments. Since there is no known powdery mildew resistance gene (Pm) on this particular location of common wheat, the gene was designated Pm54. The closely linked marker Xbarc134 was highly polymorphic in a set of mildew differentials, indicating that the marker should be useful for pyramiding Pm54 with other Pm genes by marker-assisted selection.

Genes Conferring Sensitivity to Stagonospora nodorum Necrotrophic Effectors in Stagonospora Nodorum Blotch-Susceptible U.S. Wheat Cultivars.

Stagonospora nodorum is a necrotrophic fungal pathogen that causes Stagonospora nodorum blotch (SNB), a yield- and quality-reducing disease of wheat. S. nodorum produces a set of necrotrophic effectors (NEs) that interact with the products of host sensitivity genes to cause cell death and increased susceptibility to disease. The focus of this study was determination of NE sensitivity among 25 winter wheat cultivars, many of them from the southeastern United States, that are susceptible to SNB, as well as the moderately resistant 'NC-Neuse'. Thirty-three isolates of S. nodorum previously collected from seven southeastern U.S. states were cultured for NE production, and the culture filtrates were used in an infiltration bioassay. Control strains of Pichia pastoris that expressed SnToxA, SnTox1, or SnTox3 were also used. All SNB-susceptible cultivars were sensitive to at least one NE, while NC-Neuse was insensitive to all NEs tested. Among the sensitive lines, 32% contained sensitivity gene Tsn1 and 64% contained sensitivity gene Snn3. None were sensitive to SnTox1. Additionally, 10 molecular markers for sensitivity genes Tsn1, Snn1, Snn2, and Snn3 were evaluated for diagnostic potential. Only the marker Xfcp623 for Tsn1 was diagnostic, and it was in perfect agreement with the results of the infiltration bioassays. The results illuminate which NE sensitivity genes may be of concern in breeding for resistance to SNB in the southeastern United States.

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.

Increased Prediction Accuracy Using Combined Genomic Information and Physiological Traits in A Soft Wheat Panel Evaluated in Multi-Environments.

An integration of field-based phenotypic and genomic data can potentially increase the genetic gain in wheat breeding for complex traits such as grain and biomass yield. To validate this hypothesis in empirical field experiments, we compared the prediction accuracy between multi-kernel physiological and genomic best linear unbiased prediction (BLUP) model to a single-kernel physiological or genomic BLUP model for grain yield (GY) using a soft wheat population that was evaluated in four environments. The physiological data including canopy temperature (CT), SPAD chlorophyll content (SPAD), membrane thermostability (MT), rate of senescence (RS), stay green trait (SGT), and NDVI values were collected at four environments (2016, 2017, and 2018 at Citra, FL; 2017 at Quincy, FL). Using a genotyping-by-sequencing (GBS) approach, a total of 19,353 SNPs were generated and used to estimate prediction model accuracy. Prediction accuracies of grain yield evaluated in four environments improved when physiological traits and/or interaction effects (genotype × environment or physiology × environment) were included in the model compared to models with only genomic data. The proposed multi-kernel models that combined physiological and genomic data showed 35 to 169% increase in prediction accuracy compared to models with only genomic data included when heading date was used as a covariate. In general, higher response to selection was captured by the model combing effects of physiological and genotype × environment interaction compared to other models. The results of this study support the integration of field-based physiological data into GY prediction to improve genetic gain from selection in soft wheat under a multi-environment context.

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.

MlAG12: a Triticum timopheevii-derived powdery mildew resistance gene in common wheat on chromosome 7AL.

Wheat powdery mildew is an economically important disease in cool and humid environments. Powdery mildew causes yield losses as high as 48% through a reduction in tiller survival, kernels per head, and kernel size. Race-specific host resistance is the most consistent, environmentally friendly and, economical method of control. The wheat (Triticum aestivum L.) germplasm line NC06BGTAG12 possesses genetic resistance to powdery mildew introgressed from the AAGG tetraploid genome Triticum timopheevii subsp. armeniacum. Phenotypic evaluation of F(3) families derived from the cross NC06BGTAG12/'Jagger' and phenotypic evaluation of an F(2) population from the cross NC06BGTAG12/'Saluda' indicated that resistance to the 'Yuma' isolate of powdery mildew was controlled by a single dominant gene in NC06BGTAG12. Bulk segregant analysis (BSA) revealed simple sequence repeat (SSR) markers specific for chromosome 7AL segregating with the resistance gene. The SSR markers Xwmc273 and Xwmc346 mapped 8.3 cM distal and 6.6 cM proximal, respectively, in NC06BGTAG12/Jagger. The multiallelic Pm1 locus maps to this region of chromosome 7AL. No susceptible phenotypes were observed in an evaluation of 967 F(2) individuals in the cross NC06BGTAG12/'Axminster' (Pm1a) which indicated that the NC06BGTAG12 resistance gene was allelic or in close linkage with the Pm1 locus. A detached leaf test with ten differential powdery mildew isolates indicated the resistance in NC06BGTAG12 was different from all designated alleles at the Pm1 locus. Further linkage and allelism tests with five other temporarily designated genes in this very complex region will be required before giving a permanent designation to this gene. At this time the gene is given the temporary gene designation MlAG12.

Population genetic analysis of an Eastern U.S. wheat powdery mildew population reveals geographic subdivision and recent common ancestry with U.K. and Israeli populations.

The structure of the U.S. wheat powdery mildew population (Blumeria graminis f. sp. tritici) has not been previously investigated, and the global evolutionary history of B. graminis f. sp. tritici is largely unknown. After gathering 141 single-ascosporic B. graminis f. sp. tritici isolates from 10 eastern U.S. locations, 34 isolates from the United Kingdom, and 28 isolates from Israel, we analyzed pathogen population structure using presumptively neutral markers. DNA was extracted from conidia, primers for 12 "housekeeping" genes were designed, and amplicons were examined for polymorphism. Four genes were found to contain a total of 12 single-nucleotide polymorphisms in the U.S. population and were also analyzed in the U.K. and Israeli populations. In total, 25 haplotypes were inferred from the four concatenated genes, with 2 haplotypes comprising over 70% of the U.S. population. Using Hudson's tests and analysis of molecular variance, we found the wheat mildew isolates subdivided into four groups corresponding to distinct regions: the mid-Atlantic United States, the southern United States, the United Kingdom, and Israel. Genotypic diversity was greatest in samples from the United Kingdom, Israel, Virginia, and Kingston, NC. Using rarefaction, a procedure that compensates for differing sample sizes when estimating population richness and diversity, we found that cooler locations with greater conduciveness to regular powdery mildew epidemics had the greatest haplotype richness. Our results suggest that the eastern U.S. B. graminis f. sp. tritici population is young, descended recently from Old World populations with isolation and genetic drift, and is currently subdivided into northern and southern subpopulations.

Virulence Structure of the Eastern U.S. Wheat Powdery Mildew Population.

Little is known about the population structure of wheat powdery mildew in the eastern United States, and the most recent report on virulence in this population involved isolates collected in 1993-94. In the present study, wheat leaves naturally infected with powdery mildew were collected from 10 locations in the southeastern United States in 2003 and 2005 and a collection of 207 isolates was derived from single ascospores. Frequencies of virulence to 16 mildew resistance (Pm) genes were determined by inoculating the isolates individually on replicated plates of detached leaves of differential wheat lines. These virulence frequencies were used to infer local effectiveness of Pm genes, estimate virulence complexity, detect significant associations between pairs of pathogen avirulence loci, and assess whether phenotypic differences between pathogen subpopulations increased with geographic distance. In both years, virulence to Pm3a, Pm3c, Pm5a, and Pm7 was present in more than 90% of sampled isolates and virulence to Pm1a, Pm16, Pm17, and Pm25 was present in fewer than 10% of isolates. In each year, 71 to 88% of all sampled isolates possessed one of a few multilocus virulence phenotypes, although there were significant differences among locations in frequencies of virulence to individual Pm genes. Several significant associations were detected between alleles for avirulence to pairs of Pm genes. Genetic (phenotypic) distance between isolate subpopulations increased significantly (R2 = 0.40, P < 0.001) with increasing geographic separation; possible explanations include different commercial deployment of Pm genes and restricted gene flow in the pathogen population.

Artificial Inoculation of Wheat for Selecting Resistance to Stagonospora Nodorum Blotch.

In the eastern United States, natural epidemics of Stagonospora nodorum blotch (SNB) are not consistently severe enough to facilitate substantial progress in breeding moderately resistant cultivars of soft red winter wheat. We compared three artificial inoculation methods to natural inoculum in a field experiment involving seven wheat (Triticum aestivum) cultivars with varying levels of SNB resistance. Artificial inoculation methods were: Phaeosphaeria nodorum conidia applied by atomization to three- to four-leaf wheat in early winter, P. nodorum conidia applied by atomization at boot stage in late spring, and P. nodorum-infected wheat straw applied in early winter. The experiment was conducted at Kinston and Plymouth, NC, in 2003-2004, 2004-2005, and 2005-2006, and all treatments had three replicates. Percent diseased canopy was assessed and comparisons were made using disease severity at a single date (early to soft dough stage) and area under the disease progress curve (AUDPC). The relative resistance level of cultivars was consistent across sites, years, and inoculum methods, although the rankings of moderately susceptible and susceptible cultivars were sometimes switched. On average, late spores and straw caused significantly more disease than early spores or natural inoculum (P ≤ 0.05). Biplot analysis indicated that all artificial methods had a higher mean capacity to discriminate among cultivars than did natural inoculum (P ≤ 0.05). On average, artificial inoculation increased the capacity of environments to separate wheat cultivars by SNB resistance.

Genome-Wide Association Mapping of Crown Rust Resistance in Oat Elite Germplasm.

Oat crown rust, caused by f. sp. , is a major constraint to oat ( L.) production in many parts of the world. In this first comprehensive multienvironment genome-wide association map of oat crown rust, we used 2972 single-nucleotide polymorphisms (SNPs) genotyped on 631 oat lines for association mapping of quantitative trait loci (QTL). Seedling reaction to crown rust in these lines was assessed as infection type (IT) with each of 10 crown rust isolates. Adult plant reaction was assessed in the field in a total of 10 location-years as percentage severity (SV) and as infection reaction (IR) in a 0-to-1 scale. Overall, 29 SNPs on 12 linkage groups were predictive of crown rust reaction in at least one experiment at a genome-wide level of statistical significance. The QTL identified here include those in regions previously shown to be linked with seedling resistance genes , , , , , and and also with adult-plant resistance and adaptation-related QTL. In addition, QTL on linkage groups Mrg03, Mrg08, and Mrg23 were identified in regions not previously associated with crown rust resistance. Evaluation of marker genotypes in a set of crown rust differential lines supported as the identity of . The SNPs with rare alleles associated with lower disease scores may be suitable for use in marker-assisted selection of oat lines for crown rust resistance.

Population Genomics Related to Adaptation in Elite Oat Germplasm.

Six hundred thirty five oat ( L.) lines and 4561 single-nucleotide polymorphism (SNP) loci were used to evaluate population structure, linkage disequilibrium (LD), and genotype-phenotype association with heading date. The first five principal components (PCs) accounted for 25.3% of genetic variation. Neither the eigenvalues of the first 25 PCs nor the cross-validation errors from = 1 to 20 model-based analyses suggested a structured population. However, the PC and = 2 model-based analyses supported clustering of lines on spring oat vs. southern United States origin, accounting for 16% of genetic variation ( < 0.0001). Single-locus -statistic () in the highest 1% of the distribution suggested linkage groups that may be differentiated between the two population subgroups. Population structure and kinship-corrected LD of = 0.10 was observed at an average pairwise distance of 0.44 cM (0.71 and 2.64 cM within spring and southern oat, respectively). On most linkage groups LD decay was slower within southern lines than within the spring lines. A notable exception was found on linkage group Mrg28, where LD decay was substantially slower in the spring subpopulation. It is speculated that this may be caused by a heterogeneous translocation event on this chromosome. Association with heading date was most consistent across location-years on linkage groups Mrg02, Mrg12, Mrg13, and Mrg24.

Identifying Soft Red Winter Wheat Cultivars Tolerant to Barley yellow dwarf virus.

Barley yellow dwarf virus (BYDV) is a serious disease of soft red winter wheat. Although there has been interest in tolerant cultivars, identification and development has been slow due to a lack of precision in rating plants for response to BYDV. Visual ratings of symptoms are commonly used to evaluate cultivars, but these ratings have proven to be inconsistent. The objectives of this research were to assess BYDV visual symptom ratings of wheat cultivars under field conditions, to measure disease-related yield reductions in these cultivars, to determine if a relationship exists between BYDV visual symptoms and yield reductions, and to determine BYDV cultivar tolerance. A split-plot design with insecticide treatment (main plot) and 11 cultivars (subplots) was employed over 4 years. The overall relationship between symptom ratings and BYDV yield reductions was weak (R2 = 0.40) and not consistent across years or cultivars. A consistency of performance analysis showed cultivars clustered into five distinct tolerance classes. Under conditions of high BYDV infestation, visual symptom ratings could be cautiously used to identify highly tolerant cultivars. The most reliable method for rating cultivar tolerance was a direct measure of disease-induced yield reduction across multiple environments.