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.

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.

HaploCatcher: An R package for prediction of haplotypes.

Wheat (Triticum aestivum L.) is crucial to global food security but is often threatened by diseases, pests, and environmental stresses. Wheat-stem sawfly (Cephus cinctus Norton) poses a major threat to food security in the United States, and solid-stem varieties, which carry the stem-solidness locus (Sst1), are the main source of genetic resistance against sawfly. Marker-assisted selection uses molecular markers to identify lines possessing beneficial haplotypes, like that of the Sst1 locus. In this study, an R package titled "HaploCatcher" was developed to predict specific haplotypes of interest in genome-wide genotyped lines. A training population of 1056 lines genotyped for the Sst1 locus, known to confer stem solidness, and genome-wide markers was curated to make predictions of the Sst1 haplotypes for 292 lines from the Colorado State University wheat breeding program. Predicted Sst1 haplotypes were compared to marker-derived haplotypes. Our results indicated that the training set was substantially predictive, with kappa scores of 0.83 for k-nearest neighbors and 0.88 for random forest models. Forward validation on newly developed breeding lines demonstrated that a random forest model, trained on the total available training data, had comparable accuracy between forward and cross-validation. Estimated group means of lines classified by haplotypes from PCR-derived markers and predictive modeling did not significantly differ. The HaploCatcher package is freely available and may be utilized by breeding programs, using their own training populations, to predict haplotypes for whole-genome sequenced early generation material.

Utilization of a publicly available diversity panel in genomic prediction of Fusarium head blight resistance traits in wheat.

Fusarium head blight (FHB) is an economically and environmentally concerning disease of wheat (Triticum aestivum L). A two-pronged approach of marker-assisted selection coupled with genomic selection has been suggested when breeding for FHB resistance. A historical dataset comprised of entries in the Southern Uniform Winter Wheat Scab Nursery (SUWWSN) from 2011 to 2021 was partitioned and used in genomic prediction. Two traits were curated from 2011 to 2021 in the SUWWSN: percent Fusarium damaged kernels (FDK) and deoxynivalenol (DON) content. Heritability was estimated for each trait-by-environment combination. A consistent set of check lines was drawn from each year in the SUWWSN, and k-means clustering was performed across environments to assign environments into clusters. Two clusters were identified as FDK and three for DON. Cross-validation on SUWWSN data from 2011 to 2019 indicated no outperforming training population in comparison to the combined dataset. Forward validation for FDK on the SUWWSN 2020 and 2021 data indicated a predictive accuracy r ≈ 0.58 $r \approx 0.58$ and r ≈ 0.53 $r \approx 0.53$ , respectively. Forward validation for DON indicated a predictive accuracy of r ≈ 0.57 $r \approx 0.57$ and r ≈ 0.45 $r \approx 0.45$ , respectively. Forward validation using environments in cluster one for FDK indicated a predictive accuracy of r ≈ 0.65 $r \approx 0.65$ and r ≈ 0.60 $r \approx 0.60$ , respectively. Forward validation using environments in cluster one for DON indicated a predictive accuracy of r ≈ 0.67 $r \approx 0.67$ and r ≈ 0.60 $r \approx 0.60$ , respectively. These results indicated that selecting environments based on check performance may produce higher forward prediction accuracies. This work may be used as a model for utilizing public resources for genomic prediction of FHB resistance traits across public wheat breeding programs.

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.

Training Population Design With the Use of Regional Fusarium Head Blight Nurseries to Predict Independent Breeding Lines for FHB Traits.

Fusarium head blight (FHB) is a devastating disease in cereals around the world. Because it is quantitatively inherited and technically difficult to reproduce, breeding to increase resistance in wheat germplasm is difficult and slow. Genomic selection (GS) is a form of marker-assisted selection (MAS) that simultaneously estimates all locus, haplotype, or marker effects across the entire genome to calculate genomic estimated breeding values (GEBVs). Since its inception, there have been many studies that demonstrate the utility of GS approaches to breeding for disease resistance in crops. In this study, the Uniform Northern (NUS) and Uniform Southern (SUS) soft red winter wheat scab nurseries (a total 452 lines) were evaluated as possible training populations (TP) to predict FHB traits in breeding lines of the UK (University of Kentucky) wheat breeding program. DON was best predicted by the SUS; Fusarium damaged kernels (FDK), FHB rating, and two indices, DSK index and DK index were best predicted by NUS. The highest prediction accuracies were obtained when the NUS and SUS were combined, reaching up to 0.5 for almost all traits except FHB rating. Highest prediction accuracies were obtained with bigger TP sizes (300-400) and there were not significant effects of TP optimization method for all traits, although at small TP size, the PEVmean algorithm worked better than other methods. To select for lines with tolerance to DON accumulation, a primary breeding target for many breeders, we compared selection based on DON BLUES with selection based on DON GEBVs, DSK GEBVs, and DK GEBVs. At selection intensities (SI) of 30-40%, DSK index showed the best performance with a 4-6% increase over direct selection for DON. Our results confirm the usefulness of regional nurseries as a source of lines to predict GEBVs for local breeding programs, and shows that an index that includes DON, together with FDK and FHB rating could be an excellent choice to identify lines with low DON content and an overall improved FHB resistance.

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.