Fusarium Head Blight (FHB) is a serious disease of wheat, causing yield and quality losses to the grain. Genetic resistance is one of the most important strategies to manage FHB. QTL from unadapted genetic backgrounds, such as wild relative species and exotic landraces, have been used in wheat breeding lines across the world. However, deployment of such sources poses problems associated with linkage drag, that impede their quick deployment in cultivars. Previously, we reported mapping of an FHB resistance QTL on chromosome 1B of an elite soft red winter wheat (SRW) cultivar Jamestown using Recombinant Inbred Lines (RIL) populations. In this work, we developed a high-resolution mapping population with 475 individuals by crossing resistant and susceptible RILs. Genome-specific KASP markers were developed and used to genotype the population. Phenotyping, followed by progeny testing of the recombinants, reduced the genetic interval to a 3 Mb region predicted to contain 17 high-confidence genes in the corresponding region in the Chinese Spring reference genome ver 1.0. The KASP marker flanking the 1B QTL region was validated on the SRW breeding germplasm of the Eastern US, which showed improved correlation over the previous markers used for MAS of this QTL. This work will facilitate breeding of 1B_FHB QTL from Jamestown to wheat varieties widely.

Grain characteristics are the cumulative product of growth and development throughout the growing season. In barley (Hordeum vulgare), these traits determine the grain's value for malting purposes. The ability to accurately predict the genetic merit for malting quality is of great interest for barley breeding programs. Same-season selection on malting quality traits is nearly impossible due to the costly, destructive, time-intensive, malting, and testing procedures. This study examined the temporal relationships of growth and development with grain quality measures through genetic correlations of vegetative indices and end-use traits. Normalized difference vegetation index (NDVI) calculated from aerial imagery is rapid to collect and models photosynthesis and vegetative growth. Malting quality, agronomic traits, and NDVI were measured in 385 lines of two-row winter barley breeding germplasm across 2 years. Genetic and residual correlations between malt quality and agronomic traits from bivariate genomic prediction models point to environmental variation due to field heterogeneity and batch malting effects. The reliability of NDVI was high during fall growth, reduced during spring vegetative growth, and increased again after flowering. Heading date was positively correlated (mean ) with NDVI during heading, but genetic correlations between NDVI and other traits were not consistent across environments. Multi-trait models using NDVI as the secondary trait were fit for each end-use trait. Compared to single-trait genomic prediction models, multi-trait genomic prediction models increased predictive abilities by an average of 0.04 across multiple traits and environments. However, NDVI was not found to be effective as an economic secondary trait for selection of malting quality traits.

In plant breeding, selecting cross-combinations that are more likely to result in superior lines for cultivar development is critical. This step, however, is subjective with decisions being based on available genomic and phenotypic data for prospective parents. Genomic prediction (GP) provides new opportunities to accelerate genetic gain for a target trait by identifying superior crosses through simulation of progeny performance. In this context, this study deployed GP using the phenotype and genotype of potential parents to predict the progeny genetic variance (VG) and means of overall, inferior 10%, and superior 10% (mu, mu ip, and mu sp, respectively). This retrospective experimental design investigated whether the crosses that produced superior soft red winter wheat breeding lines would have been made if progeny simulations had guided crossing decisions of breeding programs. Here, data from historical wheat breeding lines were used to train GP models and predict VG and means for yield, test weight, heading date, and plant height for all combinations of 217 parents. Predicted and observed data for 670 lines derived from biparental crosses were compared to assess the accuracy of progeny simulations, and low-to-moderate prediction accuracy was observed for the four traits (0.25-0.52). Of the pedigrees that produced lines that were selected and advanced into later stage nurseries, 76% were predicted to give rise to progeny with above-average yield. The moderate correlation found between predicted progeny means and observed line per se performance justifies using cross-combination prediction as a tool to reduce crossing number and focus on segregating populations that harbor future cultivars. In plant breeding, selecting parents to be crossed is critical for developing superior progeny. Historical winter wheat data were used to assess the usefulness of genomic prediction for parental selection. Predicted yield and SunGrains breeders' assessment and selection largely agreed. Simulated progeny performance could allow breeders to focus on the most promising crosses.

The Puccinia graminis f. sp. tritici (Pgt) Ug99-emerging virulent races present a major challenge to global wheat production. To meet present and future needs, new sources of resistance must be found. Identification of markers that allow tracking of resistance genes is needed for deployment strategies to combat highly virulent pathogen races. Field evaluation of a DH population located a QTL for stem rust (Sr) resistance, QSr.nc-6D from the breeding line MD01W28-08-11 to the distal region of chromosome arm 6DS where Sr resistance genes Sr42, SrCad, and SrTmp have been identified. A locus for seedling resistance to Pgt race TTKSK was identified in a DH population and an RIL population derived from the cross AGS2000 × LA95135. The resistant cultivar AGS2000 is in the pedigree of MD01W28-08-11 and our results suggest that it is the source of Sr resistance in this breeding line. We exploited published markers and exome capture data to enrich marker density in a 10 Mb region flanking QSr.nc-6D. Our fine mapping in heterozygous inbred families identified three markers co-segregating with resistance and delimited QSr.nc-6D to a 1.3 Mb region. We further exploited information from other genome assemblies and identified collinear regions of 6DS harboring clusters of NLR genes. Evaluation of KASP assays corresponding to our co-segregating SNP suggests that they can be used to track this Sr resistance in breeding programs. However, our results also underscore the challenges posed in identifying genes underlying resistance in such complex regions in the absence of genome sequence from the resistant genotypes.

A doubled haploid mapping population was developed from a cross between the hard red winter wheat (Triticum aestivum L. subsp. aestivum) landrace PI 173438 and WA 8137, a soft white winter wheat breeding line developed by the Washington State University winter wheat breeding program in Pullman, WA. The PI 173438/WA 8137 (Reg. no. MP-16, NSL 543858 MAP) population consists of 437 individuals, with 358 of these individuals used to identify quantitative trait loci associated with snow mold (Typhula spp.) tolerance in PI 173438. Genotypic information was gathered on these individuals using genotype-by-sequencing and processed using a pipeline developed by the USDA Eastern Regional Small Grains Genotyping Laboratory. Twenty-three linkage groups covering the whole genome with groups for the long and short arms of chromosomes 3D and 7D were constructed using 4,029 single nucleotide polymorphisms from the sequenced individuals. Quantitative trait loci associated with snow mold tolerance and snow mold recovery attributed to PI 173438 were successfully identified in this population. PI 173438 has also been used to identify other traits associated with snow mold tolerance, such as freezing tolerance and carbohydrate reserves, and has been important in research investigating dwarf and common bunt. This population may continue to expand current knowledge in these areas, and, given the landrace status of PI 173438, the population may be useful in identifying other novel traits of interest.

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. In wheat, awns are associated with better yield in higher temperature conditions and worse yields in cooler environments, but maturity differences change the environmental conditions plants experience during grain fill.

Conventional selected-bulk breeding is a low cost means of advancing populations but requires years of selection in the field to generate fixed lines. Doubled haploid (DH) methods produce fixed lines quickly but without selection and at high cost. The 'Minibulk' system was developed to combine the speed of DHs with the population size and crossover opportunities of selected-bulk breeding. Breeding populations of winter wheat (Triticum aestivum L.) were vernalized and advanced at high density in the greenhouse from the F-2 to the F-4 generation. F-4 populations underwent visual selection in the field, and derived lines were genotyped for variants at photoperiod and vernalization alleles and across the genome using genotyping-by-sequencing. The number of crossover events and parental genome contributions were determined for recombinant inbred lines (RILs) within populations and among RILs across populations. During vernalization, seeds in all populations germinated and underwent vegetative growth, forming a dense seed mat that was transplanted directly into greenhouse pots. A 22-h photoperiod accelerated development, and many populations reached physiological maturity as soon as five weeks after transplanting. Increasing the number of seeds planted from 300 in the F-2 to 500 in the F-3 increased the number of fertile spikes produced, thereby maintaining a larger population size. The number of crossovers detected differed significantly between populations and chromosomes, while the number of crossovers detected in each population was related to marker density. Adoption of the minibulk system by winter cereal breeding programs can lead to significant cost savings and acceleration of the breeding cycle.

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-enabled1 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.

In areas of the Pacific Northwest (PNW), snow mold, a fungal disease with multiple causal agents, can have severe impacts on winter wheat (Triticum aestivum L. subsp. aestivum) yields. Growing tolerant varieties is the best option to manage the disease; however, genetic diversity for the trait is limited in local varieties. The winter wheat landrace PI 173438 has been used in Japan to improve snow mold tolerance but has not been used in PNW breeding programs. A doubled haploid population from a cross between PI 173438 and susceptible variety WA 8137 was used to identify novel quantitative trait loci (QTL) associated with snow mold tolerance that would be validated using marker-assisted selection (MAS) in two separate populations. A linkage map was developed using genotypic data collected on the PI 173438/WA 8137 population, which was then used with phenotypic data in composite interval mapping for QTL analysis. Six QTL were found associated with snow mold tolerance traits, five of which originated from PI 173438. A QTL was found on chromosome 1D that has not been reported before. The remaining QTL occurred on chromosomes that were previously reported to have snow mold tolerant QTL, some of which may be identical. Several of these QTL and linked markers were suitable for testing via MAS on separate populations revealing no significant difference amongst the three or four QTL that could be tested. Such results continue to indicate the complexity of breeding for snow mold tolerance using molecular markers.

Maintaining winter wheat (Triticum aestivum L.) productivity with more efficient nitrogen (N) management will enable growers to increase profitability and reduce the negative environmental impacts associated with nitrogen loss. Wheat breeders would therefore benefit greatly from the identification and application of genetic markers associated with nitrogen use efficiency (NUE). To investigate the genetics underlying N response, two bi-parental mapping populations were developed and grown in four site-seasons under low and high N rates. The populations were derived from a cross between previously identified high NUE parents (VA05W-151 and VA09W-52) and a shared common low NUE parent, 'Yorktown.' The Yorktown × VA05W-151 population was comprised of 136 recombinant inbred lines while the Yorktown × VA09W-52 population was comprised of 138 doubled haploids. Phenotypic data was collected on parental lines and their progeny for 11 N-related traits and genotypes were sequenced using a genotyping-by-sequencing platform to detect more than 3,100 high quality single nucleotide polymorphisms in each population. A total of 130 quantitative trait loci (QTL) were detected on 20 chromosomes, six of which were associated with NUE and N-related traits in multiple testing environments. Two of the six QTL for NUE were associated with known photoperiod (Ppd-D1 on chromosome 2D) and disease resistance (FHB-4A) genes, two were reported in previous investigations, and one QTL, QNue.151-1D, was novel. The NUE QTL on 1D, 6A, 7A, and 7D had LOD scores ranging from 2.63 to 8.33 and explained up to 18.1% of the phenotypic variation. The QTL identified in this study have potential for marker-assisted breeding for NUE traits in soft red winter wheat.

Heading date in wheat (Triticum aestivum L.) and other small grain cereals is affected by the vernalization and photoperiod pathways. The reduced-height loci also have an effect on growth and development. Heading date, which occurs just prior to anthesis, was evaluated in a population of 299 hard winter wheat entries representative of the U.S. Great Plains region, grown in nine environments during 2011-2012 and 2012-2013. The germplasm was evaluated for candidate genes at vernalization (Vrn-A1, Vrn-B1, and Vrn-D1), photoperiod (Ppd-A1, Ppd-B1 and Ppd-D1), and reduced-height (Rht-B1 and Rht-D1) loci using polymerase chain reaction (PCR) and Kompetitive Allele Specific PCR (KASP) assays. Our objectives were to determine allelic variants known to affect flowering time, assess the effect of allelic variants on heading date, and investigate changes in the geographic and temporal distribution of alleles and haplotypes. Our analyses enhanced understanding of the roles developmental genes have on the timing of heading date in wheat under varying environmental conditions, which could be used by breeding programs to improve breeding strategies under current and future climate scenarios. The significant main effects and two-way interactions between the candidate genes explained an average of 44% of variability in heading date at each environment. Among the loci we evaluated, most of the variation in heading date was explained by Ppd-D1, Ppd-B1, and their interaction. The prevalence of the photoperiod sensitive alleles Ppd-A1b, Ppd-B1b, and Ppd-D1b has gradually decreased in U.S. Great Plains germplasm over the past century. There is also geographic variation for photoperiod sensitive and reduced-height alleles, with germplasm from breeding programs in the northern Great Plains having greater incidences of the photoperiod sensitive alleles and lower incidence of the semi-dwarf alleles than germplasm from breeding programs in the central or southern plains.