Intermediate wheatgrass (IWG) is a perennial species and has edible and nutritious grain and desirable agronomic traits, including large seed size, high grain yield, and biomass. It also has the potential to provide ecosystem services and an economic return to farmers. However, because of its allohexaploidy and self-incompatibility, developing molecular markers for genetic analysis and molecular breeding has been challenging. In the present study, using genotyping-by-sequencing (GBS) technology, 3436 genomewide markers discovered in a biparental population with 178 genets, were mapped to 21 linkage groups (LG) corresponding to 21 chromosomes of IWG. Genomic prediction models were developed using 3883 markers discovered in a breeding population containing 1126 representative genets from 58 half-sib families. High predictive ability was observed for seven agronomic traits using cross-validation, ranging from 0.46 for biomass to 0.67 for seed weight. Optimization results indicated that 8 to 10 genets from each half-sib family can form a good training population to predict the breeding value of their siblings, and 1600 genomewide markers are adequate to capture the genetic variation in the current breeding population for genomic selection. Thus, with the advances in sequencing-based marker technologies, it was practical to perform molecular genetic analysis and molecular breeding on a new and challenging species like IWG, and genomic selection could increase the efficiency of recurrent selection and accelerate the domestication and improvement of IWG.

Prediction accuracy of genomic selection (GS) has been previously evaluated through simulation and cross-validation; however, validation based on progeny performance in a plant breeding program has not been investigated thoroughly. We evaluated several prediction models in a dynamic barley breeding population comprised of 647 six-row lines using four traits differing in genetic architecture and 1536 single nucleotide polymorphism (SNP) markers. The breeding lines were divided into six sets designated as one parent set and five consecutive progeny sets comprised of representative samples of breeding lines over a 5-yr period. We used these data sets to investigate the effect of model and training population composition on prediction accuracy over time. We found little difference in prediction accuracy among the models confirming prior studies that found the simplest model, random regression best linear unbiased prediction (RR-BLUP), to be accurate across a range of situations. In general, we found that using the parent set was sufficient to predict progeny sets with little to no gain in accuracy from generating larger training populations by combining the parent set with subsequent progeny sets. The prediction accuracy ranged from 0.03 to 0.99 across the four traits and five progeny sets. We explored characteristics of the training and validation populations (marker allele frequency, population structure, and linkage disequilibrium, LD) as well as characteristics of the trait (genetic architecture and heritability, H 2 ). Fixation of markers associated with a trait over time was most clearly associated with reduced prediction accuracy for the mycotoxin trait DON. Higher trait H 2 in the training population and simpler trait architecture were associated with greater prediction accuracy.

Intermediate wheatgrass [IWG; Thinopyrum intermedium (Host) Barkworth & D.R. Dewey subsp. intermedium] is being developed as a new perennial grain crop that has a large allohexaploid genome similar to that of wheat (Triticum aestivum L.). Breeding for increased seed weight is one of the primary goals for improving grain yield of IWG. As a new crop, however, the genetic architecture of seed weight and size has not been characterized, and selective breeding of IWG may be more intricate than wheat because of its self-incompatible mating system and perennial growth habit. Here, seed weight, seed area size, seed width, and seed length were evaluated across multiple years, in a heterogeneous breeding population comprised of 1126 genets and two clonally replicated biparental populations comprised of 172 and 265 genets. Among 10,171 DNA markers discovered using genotypingby-sequencing (GBS) in the breeding population, 4731 markers were present in a consensus genetic map previously constructed using seven full-sib populations. Thirty-three quantitative trait loci (QTL) associated with seed weight and size were identified using association mapping (AM), of which 23 were verified using linkage mapping in the biparental populations. About 37.6% of seed weight variation in the breeding population was explained by 15 QTL, 12 of which also contributed to either seed length or seed width. When performing either phenotypic selection or genomic selection for seed weight, we observed the frequency of favorable QTL alleles were increased to >46%. Thus, by combining AM and genomic selection, we can effectively select the favorable QTL alleles for seed weight and size in IWG breeding populations. Intermediate wheatgrass (2n = 6x = 42) is a new perennial grain crop (Wagoner, 1990;Kantar et al., 2016). Compared with annual grain crops, it has an extended growing season and deep roots, which increase carbon sequestration and help prevent runoff and improve water quality (Glover et al., 2010;Culman et al., 2013). Moreover, Core Ideas• Twenty-three shared QTL were identified using linkage and association mapping• Overlapped QTL explained the high genetic correlation among seed weight and size• QTL responded positively to either phenotypic selection or genomic selection• Combining association mapping and genomic selection would increase genetic gain

Stem rust was one of the most devastating diseases of barley in North America. Through the deployment of cultivars with the resistance gene Rpg1, losses to stem rust have been minimal over the past 70 yr. However, there exist both domestic (QCCJB) and foreign (TTKSK aka isolate Ug99) pathotypes with virulence for this important gene. To identify new sources of stem rust resistance for barley, we evaluated the Wild Barley Diversity Collection (WBDC) (314 ecogeographically diverse accessions of Hordeum vulgare subsp. spontaneum) for seedling resistance to four pathotypes (TTKSK, QCCJB, MCCFC, and HKHJC) of the wheat stem rust pathogen (Puccinia graminis f. sp. tritici, Pgt) and one isolate (92-MN-90) of the rye stem rust pathogen (P. graminis f. sp. secalis, Pgs). Based on a coefficient of infection, the frequency of resistance in the WBDC was low ranging from 0.6% with HKHJC to 19.4% with 92-MN-90. None of the accessions was resistant to all five cultures of P. graminis. A genome-wide association study (GWAS) was conducted to map stem rust resistance loci using 50,842 single-nucleotide polymorphic markers generated by genotype-by-sequencing and ordered using the new barley reference genome assembly. After proper accounting for genetic relatedness and structure among accessions, 45 quantitative trait loci were identified for resistance to P. graminis across all seven barley chromosomes. Three novel loci associated with resistance to TTKSK, QCCJB, MCCFC, and 92-MN-90 were identified on chromosomes 5H and 7H, and two novel loci associated with resistance to HKHJC were identified on chromosomes 1H and 3H. These novel alleles will enhance the diversity of resistance available for cultivated barley.

Genomic selection uses marker‐based predictions to improve and accelerate the breeding process. Numerous studies have investigated the accuracy of genomic predictions; however, few studies have directly compared genomic and phenotypic selection. We compared genomic and phenotypic selection in five sets of selection candidates from a barley breeding program. In each set, about 96 breeding lines were genotyped with 1536 single nucleotide polymorphism (SNP) markers and phenotyped for yield, Fusarium head blight (FHB) severity, and deoxynivalenol (DON) concentration. A set of 168 lines and the same set of SNP markers were used to train a prediction model and predict the performance of the selection candidates using ridge regression best linear unbiased prediction. The best‐performing 10% of the breeding lines in each selection candidate set were selected using both methods and revaluated in several trials. We found similar significant response to selection using genomic and phenotypic selection for FHB severity and DON concentration, and no significant response for yield using either method. For all traits, genomic selection significantly increased genetic similarity compared with the selection candidates. In addition, genomic selection, compared with phenotypic selection, resulted in an increase in the frequency of favorable alleles. Three indirect selection methods for DON concentration, (predicted FHB severity, empirical FHB severity, and predicted DON concentration) performed similarly to direct phenotypic selection for DON, but differed considerably in cost. We conclude that the use of genomic selection for yield and FHB breeding in barley should result in gains from response to selection that are similar to the gains obtained using phenotypic selection, but with a shorter breeding cycle time and lower cost.