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Abstract As genotyping by sequencing (GBS) becomes more prevalent and cost‐effective, there is a benefit in being able to apply the data to solve a variety of problems. However, high degrees of missing data and overreliance on single nucleotide polymorphisms (SNPs), while ignoring other forms of genetic variation, frequently plague attempts to make full use of GBS sequence data. Here we have developed two R scripts to serve as a tool in haplotype determination at loci of interest within biparental populations. One of these scripts, Sparse Tag Allele Caller (STAC), provides both automated calling and visual representations of the data around a locus of interest to assist in rapid data compilation decision‐making. The other script, STAC Integrate, allows automated quality control and logic‐based integration of presence/absence data with SNP data, while also rendering global overviews of recombination and coverage across the genome. These scripts are designed to be used together to maximize the utility of the available data. These tools were validated on a biparental population of wheat that was genotyped through GBS. They successfully enabled haplotype determination of a locus that was difficult to directly genotype, and their systemic accuracy was demonstrated in multiple populations and species. These scripts may serve as a tool for researchers attempting to make better use of GBS and other genetic marker data for both research and crop breeding decisions.

Abstract The Hagberg–Perten falling number (HFN) method is the international standard used to evaluate the damage to wheat (Triticum aestivum) grain quality due to preharvest sprouting (PHS) and late maturity alpha‐amylase (LMA). However, the HFN test requires specialized laboratory facilities and is time consuming. Spectrometers were known as a potential tool for quick HFN assessment, but none of the studies have validated the assessment results across different datasets. In this study, an independent validation was conducted using independent samples and spectral instruments. The calibration set had 462 grain samples of 92 varieties grown at 24 locations in 2019 and examined using a near‐infrared spectrometer. In the validation set, 19 varieties collected from 10 locations in 2 years that experienced either PHS or LMA were scanned with a hyperspectral camera. The association between spectra and HFN was modeled by partial least square regression. As a result, the independent validation correlation accuracy was r = 0.72 and a mean absolute error of 56 s. Furthermore, this study showed a cost‐effective alternative using only 10 spectral bands to predict HFN, and it achieved better performance than the full spectrum of the hyperspectral system. In conclusion, this is the first study that showed the potential that wheat HFN could be predicted on an independent dataset measured by a different instrument. The result suggested that spectrometers can potentially serve as a faster alternative for plant breeders to develop varieties resistant to PHS and LMA, and for growers to screen damaged grains in transportation processes.

Abstract Background Genetic improvement of end-use quality is an important objective in wheat breeding programs to meet the requirements of grain markets, millers, and bakers. However, end-use quality phenotyping is expensive and laborious thus, testing is often delayed until advanced generations. To better understand the underlying genetic architecture of end-use quality traits, we investigated the phenotypic and genotypic structure of 14 end-use quality traits in 672 advanced soft white winter wheat breeding lines and cultivars adapted to the Pacific Northwest region of the United States. Results This collection of germplasm had continuous distributions for the 14 end-use quality traits with industrially significant differences for all traits. The breeding lines and cultivars were genotyped using genotyping-by-sequencing and 40,518 SNP markers were used for association mapping (GWAS). The GWAS identified 178 marker-trait associations (MTAs) distributed across all wheat chromosomes. A total of 40 MTAs were positioned within genomic regions of previously discovered end-use quality genes/QTL. Among the identified MTAs, 12 markers had large effects and thus could be considered in the larger scheme of selecting and fixing favorable alleles in breeding for end-use quality in soft white wheat germplasm. We also identified 15 loci (two of them with large effects) that can be used for simultaneous breeding of more than a single end-use quality trait. The results highlight the complex nature of the genetic architecture of end-use quality, and the challenges of simultaneously selecting favorable genotypes for a large number of traits. This study also illustrates that some end-use quality traits were mainly controlled by a larger number of small-effect loci and may be more amenable to alternate selection strategies such as genomic selection. Conclusions In conclusion, a breeder may be faced with the dilemma of balancing genotypic selection in early generation(s) versus costly phenotyping later on.

Super soft kernel texture is associated with superior milling and baking performance in soft wheat. To understand the mechanism underlying super soft kernel texture, we studied proteomic changes between a normal soft and a super soft during kernel development. The cultivar ‘Alpowa’, a soft white spring wheat, was crossed to a closely related super soft spring wheat line ‘BC2SS163’ to produce F6 recombinant inbred lines (RILs). Four normal soft RILs and four super soft RILs along with the parents were selected for proteomic analysis. Alpowa and the normal soft RILs showed hardness indices of 20 to 30, whereas BC2SS163 and the super soft RILs showed hardness indices of -2 to -6. Kernels were collected from normal soft and super soft genotypes at 7 days post anthesis (dpa), 14 dpa, 28 dpa, and maturity and were subject to quantitative proteomic analysis. Throughout kernel development, 175 differentially abundant proteins (DAPs) were identified. Most DAPs were observed at 7 dpa, 14 dpa, and 28 dpa. Of the 175 DAPs, 32 had higher abundance in normal soft wheat, whereas 143 DAPs had higher abundance in super soft wheat. A total of 18 DAPs were associated with carbohydrate metabolism and five DAPs were associated with lipids. The gene TraesCS4B02G091100.1 on chromosome arm 4BS, which encodes for sucrose-phosphate synthase, was identified as a candidate gene for super soft kernel texture in BC2SS163. This study enhanced our understanding of the mechanism underlying super soft kernel texture in soft white spring wheat.

Abstract End‐use quality phenotyping is laborious and expensive, thus, testing may not occur until later generations in wheat breeding programs. We investigated the pattern of genotype × environment (G × E) interaction for end‐use quality traits in soft white wheat (Triticum aestivum L.) and tested the effectiveness of implementing genomic selection to optimize breeding for these traits. We used a multi‐environment unbalanced dataset comprised of 672 breeding lines and cultivars adapted to the Pacific Northwest region of the United States, which were evaluated for 14 end‐use quality traits. Genetic correlations between environments based on factor analytic models showed low‐to‐moderate G × E interaction for most traits but high G × E interaction for grain and flour protein. A total of 40,518 single‐nucleotide polymorphism markers were used for genomic prediction. Genomic prediction accuracies were high for most traits thereby justifying the use of genomic selection to assist breeding for superior end‐use quality in soft white wheat. Excluding outlier environments based on genetic correlations between environments was more effective in increasing genomic prediction accuracies compared with that based on environment clustering analysis. For kernel size, kernel weight, milling score, ash, and flour swelling volume, excluding outlier environments increased prediction accuracies by 1–11%. However, for grain and flour protein, flour yield, and cookie diameter, excluding outlier environments did not improve genomic prediction performance.

Kernel texture (grain hardness) is a fundamental and determining factor related to wheat (Triticum spp.) milling, baking and flour utilization. There are three kernel texture classes in wheat: soft and hard hexaploid (T. aestivum), and very hard durum (T. turgidum subsp. durum). The genetic basis for these three classes lies with the Puroindoline genes. Phenotypically, the easiest means of quantifying kernel texture is with the Single Kernel Characterization System (SKCS), although other means are valid and can provide fundamental material properties. Typical SKCS values for soft wheat would be around 25 and for durum wheat≥80. Soft kernel durum wheat was created via homeologous recombination using the ph1b mutation, which facilitated the transfer of ca. 28 Mbp of 5DS that replaced ca. 21 Mbp of 5BS. The 5DS translocation contained a complete and intact Hardness locus and both Puroindoline genes. Expression of the Puroindoline genes in durum grain resulted in kernel texture and flour milling characteristics nearly identical to that of soft wheat, with high yields of break and straight-grade flours, which had small particle size and low starch damage. Dough water absorption was markedly reduced compared to durum flour and semolina. Dough strength was essentially unchanged and reflected the inherent gluten properties of the durum background. Pasta quality was essentially equal-to-or-better than pasta made from semolina. Agronomically, soft durum germplasm showed good potential with moderate grain yield and resistance to a number of fungal pathogens and insects. Future breeding efforts will no doubt further improve the quality and competitiveness of soft durum cultivars.

Puroindolines are small, amphipathic, wheat proteins that determine the hardness of the wheat kernel and protect crops from different pathogens. Puroindoline A (PinA) and puroindoline B (PinB) are two major isoforms of puroindolines. These proteins have antibacterial and antifungal properties mainly attributed to their characteristic tryptophan-rich domains (TRDs). In this in vitro study, we investigated the antimicrobial effect of PinA and PinB synthetic peptides against the growth and biofilm formation of Campylobacter jejuni. C. jejuni is an important microaerobic, foodborne pathogen that causes gastrointestinal and neurological diseases in humans. Our results showed that: (1) PinA, but not PinB, has strong antimicrobial activity against C. jejuni clinical strains 81-176 and F38011, Escherichia coli O157:H7, methicillin-resistant Staphylococcus aureus, Salmonella enterica serovar Typhimurium, and Listeria monocytogenes; (2) The substitution of two tryptophan residues to glycine (W→G) in the TRD of PinA abolishes its antimicrobial activity against these microorganisms; (3) PinA functions additively with two common antibiotics (ciprofloxacin and erythromycin) to inhibit or inactivate C. jejuni strains; (4) PinA damages the C. jejuni cellular membrane, (5) PinA is cytotoxic to human INT 407 cells at high concentrations; and (6) PinA inhibits C. jejuni biofilm formation. In summary, this study demonstrates the antimicrobial activity of PinA against C. jejuni growth and biofilm formation and further confirms the potential use of PinA as a therapeutic agent in health care or as preservatives in the agri-food industry.

Although durum wheat (Triticum durum L. ssp. durum Desf.) has traditionally been used to make a range of food products, its use has been restricted due to the absence of the D-genome glutenin proteins, the relatively low variability in starch composition, and its very hard grain texture. This review focuses on the manipulation of the starch and protein composition and modification of the hardness of durum wheat in order to improve its technological and nutritional value and expand its utilization for application to a wider number of end products. Starch is composed of amylopectin and amylose in a 3:1 ratio, and their manipulation has been explored for achieving starch with modified composition. In particular, silencing of the genes involved in amylose and amylopectin synthesis has made it possible to isolate durum wheat lines with amylose content varying from 2–3% up to 75%. This has created opportunities for new products with different properties and enhanced nutritional value. Durum-made bread has generally inferior quality to bread made from common wheat. Attempts to introduce the Glu-D1 subunits 1Dx5 + 1Dy10 and 1Dx2 + 1Dy12 produced stronger dough, but the former produced excessively strong, inelastic doughs, and loaf volume was either inferior or not affected. In contrast, the 1Dx2 + 1Dy12 sometimes improved bread loaf volume (LV) depending on the glutenin subunit background of the genotype receiving these genes. Further breeding and selection are needed to improve the dough extensibility to allow higher LV and better texture. The versatility of durum wheat has been greatly expanded with the creation of soft-textured durum via non-GMO introgression means. This soft durum mills like soft hexaploid wheat and has similar baking properties. The pasta quality is also not diminished by the soft-textured kernels. The Glu-D1 locus containing the subunits 1Dx2 + 1Dy12 has also been introgressed to create higher quality soft durum bread.

Durum wheat is an important crop worldwide. In many areas, durum wheat appears to have competitive yield, and biotic and abiotic advantages over bread wheat. What limits durum production? In one respect, the comparatively more limited processing and food functionality. Two traits directly relate to these limitations: kernel texture (hardness) and gluten strength. We have addressed both using ph1b-mediated translocations from bread wheat. For kernel texture, ca. 28 Mbp of chromosome 5D short arm replaced about 20 Mbp of 5B short arm. Single Kernel Characterization System (SKCS) hardness was reduced from ca. 80 to 20 as the puroindolines were expressed and softened the endosperm. Break flour yields increased from 17 to >40%. Straight-grade flour had low starch damage (2%), and a mean particle size of 75 μm. Crosses with CIMMYT durum lines all produced soft kernel progeny and a high degree of genetic variance for milling and baking quality. Solvent Retention Capacities (SRC) and cookie diameters were similar to soft white hexaploid wheat, showing that soft durum can be considered a “tetraploid soft white spring wheat.” Regarding gluten strength, CIMMYT durums contributed a high genetic variance, with the “best” progeny exhibiting Na-dodecylsulfate (SDS) sedimentation volume, SRC Lactic Acid and Mixograph characteristics that were similar to medium-gluten-strength U.S. hard red winter. The best loaf volume among these progeny was 846 cm3 at ca. 12.8% flour protein. To further address the issue of gluten strength, Soft Svevo was crossed with durum lines possessing Dx2+Dy12 and Dx5+Dy10. Bread baking showed that Dx5+Dy10 was overly strong, whereas Dx2+Dy12 significantly improved bread loaf volume. The best progeny produced a loaf volume of 1,010 cm3 at 12.1% protein. As a comparison, the long-term in-house regression for loaf volume-flour protein for hard “bread” wheats is 926 cm3 at 12.1% protein. Obviously, from these results, excellent bread making potential has been achieved.

Peroxidase (POD) activity in wheat (Triticum aestivum L.) grain influences natural carotenoid pigment content and is associated with the color of flour, and processing and product quality. Here, we report the molecular characterization and physical mapping of POD genes in bread wheat. The complete genomic DNA (gDNA) sequence of two POD genes (TaPod-A2 and TaPod-D1), and the partial gDNA sequence of two additional POD genes (TaPod-A3 and TaPod-B1) from wheat were characterized using in silico cloning and validated through laboratory experiments. Using a set of 21 nullisomic-tetrasomic (NT) lines, six group-7 ditelosomic (Dt) lines, and 38 group-7 deletion (Del) lines of Chinese Spring (CS), TaPod-A2 and TaPod-D1 were found to be physically located on 0.73–0.83 and on the most distal 0.39 fraction arm length (FL) of 7AS and 7DS in cv. CS, respectively; whereas, TaPod-A3 and TaPod-B1 were assigned to the 0.40–0.49 and 0.40–0.48 FL of 7AL and 7BL, respectively. Based on single nucleotide polymorphisms (SNPs) of two alleles at the TaPod-D1 locus, two functional markers POD-7D1 and POD-7D6 were developed, amplifying 540- and 640-bp, fragments in varieties with higher and lower POD activities, respectively. A total of 224 wheat varieties were analyzed and showed a significant association between the polymorphic fragments and POD activity using POD-7D1 and POD-7D6 markers. The analysis of variance (ANOVA) indicated the average POD activities of 115 varieties with TaPod-D1a were significantly lower than 109 varieties with TaPod-D1b (P < 0.01). This study provides useful information of the POD genes in bread wheat, insight into wheat genome synteny and structure, gene-specific markers, and contributes a valuable resource for quality improvement in wheat breeding programs.

Breeding for grain yield, biotic and abiotic stress resistance, and end-use quality are important goals of wheat breeding programs. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Genomic selection provides an alternative to predict performance using genome-wide markers under forward and across location predictions, where a previous year’s dataset can be used to build the models. Due to large datasets in breeding programs, we explored the potential of the machine and deep learning models to predict fourteen end-use quality traits in a winter wheat breeding program. The population used consisted of 666 wheat genotypes screened for five years (2015–19) at two locations (Pullman and Lind, WA, USA). Nine different models, including two machine learning (random forest and support vector machine) and two deep learning models (convolutional neural network and multilayer perceptron) were explored for cross-validation, forward, and across locations predictions. The prediction accuracies for different traits varied from 0.45–0.81, 0.29–0.55, and 0.27–0.50 under cross-validation, forward, and across location predictions. In general, forward prediction accuracies kept increasing over time due to increments in training data size and was more evident for machine and deep learning models. Deep learning models were superior over the traditional ridge regression best linear unbiased prediction (RRBLUP) and Bayesian models under all prediction scenarios. The high accuracy observed for end-use quality traits in this study support predicting them in early generations, leading to the advancement of superior genotypes to more extensive grain yield trails. Furthermore, the superior performance of machine and deep learning models strengthens the idea to include them in large scale breeding programs for predicting complex traits.

Soft white wheat is used in domestic and foreign markets for various end products requiring specific quality profiles. Phenotyping for end-use quality traits can be costly, time-consuming and destructive in nature, so it is advantageous to use molecular markers to select experimental lines with superior traits. An association mapping panel of 469 soft white winter wheat cultivars and advanced generation breeding lines was developed from regional breeding programs in the U.S. Pacific Northwest. This panel was genotyped on a wheat-specific 90 K iSelect single nucleotide polymorphism (SNP) chip. A total of 15,229 high quality SNPs were selected and combined with best linear unbiased predictions (BLUPs) from historical phenotypic data of the genotypes in the panel. Genome-wide association mapping was conducted using the Fixed and random model Circulating Probability Unification (FarmCPU). A total of 105 significant marker-trait associations were detected across 19 chromosomes. Potentially new loci for total flour yield, lactic acid solvent retention capacity, flour sodium dodecyl sulfate sedimentation and flour swelling volume were also detected. Better understanding of the genetic factors impacting end-use quality enable breeders to more effectively discard poor quality germplasm and increase frequencies of favorable end-use quality alleles in their breeding populations.

The durum wheat ( ssp. (Desf.) Husn.) cultivar Soft Svevo with a soft kernel texture was developed through a -mediated homoeologous 5DS–5BS chromosomal translocation. The soft kernel trait ( locus) derived from chromosome 5D of the common wheat ( L.) cultivar Chinese Spring. Soft Svevo was used as the donor parent to create near-isogenic soft durum germplasm. The size of the translocation, its estimated breakpoint, and the amount of chromosome 5BS translocated, if any, remain unknown. Four near-isogenic pairs of hard and soft kernel durum genotypes, in addition to Soft Svevo and the Chinese Spring deletion line 5DS-2, which lacks a distal 22% terminal segment of chromosome 5DS, were genotyped using Illumina’s 90k wheat single nucleotide polymorphism array. Single nucleotide polymorphism results were processed in GenomeStudio and 164 polymorphic markers were identified between the near-isogenic lines (NILs). Subsequent BLASTn results for two subsets of markers corresponding to the distal ends of chromosomes 5DS and 5BS indicated that the translocation event was nearly reciprocal, as a ∼24.36-Mbp segment of chromosome 5DS was gained, whereas a ∼20.01-Mbp segment of chromosome 5BS was lost. Genomic in situ hybridization images of the soft durum NILs agreed with these estimates and confirmed the absence of additional terminal or interstitial translocations. Soft durum represents the potential of a new wheat market class and these findings will assist durum wheat breeders in the development of new soft durum germplasm.

Quinoa (Chenopodium quinoa Willd.) is an Andean crop with an edible seed that both contains high protein content and provides high quality protein with a balanced amino acid profile in embryonic tissues. Quinoa is a halophyte adapted to harsh environments with highly saline soil. In this study, four quinoa varieties were grown under six salinity treatments and two levels of fertilization, and then evaluated for quinoa seed quality characteristics, including protein content, seed hardness, and seed density. Concentrations of 8, 16, and 32 dS m-1 of NaCl and Na2SO4, were applied to the soil medium across low (1 g N, 0.29 g P, 0.29 g K per pot) and high (3 g N, 0.85 g P, 0.86 g K per pot) fertilizer treatments. Seed protein content differed across soil salinity treatments, varieties, and fertilization levels. Protein content of quinoa grown under salinized soil ranged from 13.0% to 16.7%, comparable to that from non-saline conditions. NaCl and Na2SO4 exhibited different impacts on protein content. Whereas the different concentrations of NaCl did not show differential effects on protein content, the seed from 32 dS m-1 Na2SO4 contained the highest protein content. Seed hardness differed among varieties, and was moderately influenced by salinity level (P = 0.09). Seed density was affected significantly by variety and Na2SO4 concentration, but was unaffected by NaCl concentration. The samples from 8 dS m-1 Na2SO4 soil had lower density (0.66 g/cm3) than those from 16 dS m-1 and 32 dS m-1 Na2SO4, 0.74 and 0.72g/cm3, respectively. This paper identifies changes in critical seed quality traits of quinoa as influenced by soil salinity and fertility, and offers insights into variety response and choice across different abiotic stresses in the field environment.

Seed dormancy and resistance to decay are fundamental survival strategies, which allow a population of seeds to germinate over long periods of time. Seeds have physical, chemical, and biological defense mechanisms that protect their food reserves from decay-inducing organisms and herbivores. Here, we hypothesize that seeds also possess enzyme-based biochemical defenses, based on induction of the plant defense enzyme, polyphenol oxidase (PPO), when wild oat (Avena fatua L.) caryopses and seeds were challenged with seed-decaying Fusarium fungi. These studies suggest that dormant seeds are capable of mounting a defense response to pathogens. The pathogen-induced PPO activity from wild oat was attributed to a soluble isoform of the enzyme that appeared to result, at least in part, from proteolytic activation of a latent PPO isoform. PPO activity was also induced in wild oat hulls (lemma and palea), non-living tissues that cover and protect the caryopsis. These results are consistent with the hypothesis that seeds possess inducible enzyme-based biochemical defenses arrayed on the exterior of seeds and these defenses represent a fundamental mechanism of seed survival and longevity in the soil. Enzyme-based biochemical defenses may have broader implications since they may apply to other defense enzymes as well as to a diversity of plant species and ecosystems.