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Abstract Foliar fungal diseases are a major limitation in organic naked barley (Hordeum vulgare L.) production. The lack of conventional fungicides in organic systems increases reliance on genetic resistance. We evaluated the severity of barley stripe rust (Puccinia striiformis f. sp. hordei Westend), leaf rust (Puccina hordei sp. hordei), spot blotch (Cochliobolus sativus, anamorph Bipolaris sorokiniana (S. Ito & Kurib.) Drechsler ex Dastur), and scald (Rhynchosporium commune Zaffarano, McDonald and Linde sp. nov) on a naked barley diversity panel of 350 genotypes grown in 13 environments to identify quantitative trait loci associated with disease resistance. Genome‐wide association analyses across and within environments found 10 marker trait associations for barley stripe rust, four marker trait associations for leaf rust, one marker trait association for scald, and five marker trait associations for spot blotch. Structure analysis identified six Ward groups based on genotypic diversity. Resistance to susceptible allele ratios were high for stripe rust and spot blotch, moderate for leaf rust, and low for scald. Combined phenotypic analysis values for each disease overlayed by a principal component analysis found distinct resistance and susceptibility patterns for barley stripe rust and scald. Most significant marker trait associations were previously identified in the literature, providing confirmation and potential new sources of disease resistance for genetic improvement of naked barley germplasm.

Abstract Selection for more nutritious crop plants is an important goal of plant breeding to improve food quality and contribute to human health outcomes. While there are efforts to integrate genomic prediction to accelerate breeding progress, an ongoing challenge is identifying strategies to improve accuracy when predicting within biparental populations in breeding programs. We tested multiple genomic prediction methods for 12 seed fatty acid content traits in oat (Avena sativa L.), as unsaturated fatty acids are a key nutritional trait in oat. Using two well‐characterized oat germplasm panels and other biparental families as training populations, we predicted family mean and individual values within families. Genomic prediction of family mean exceeded a mean accuracy of 0.40 and 0.80 using an unrelated and related germplasm panel, respectively, where the related germplasm panel outperformed prediction based on phenotypic means (0.54). Within family prediction accuracy was more variable: training on the related germplasm had higher accuracy than the unrelated panel (0.14–0.16 and 0.05–0.07, respectively), but variability between families was not easily predicted by parent relatedness, segregation of a locus detected by a genome‐wide association study in the panel, or other characteristics. When using other families as training populations, prediction accuracies were comparable to the related germplasm panel (0.11–0.23), and families that had half‐sib families in the training set had higher prediction accuracy than those that did not. Overall, this work provides an example of genomic prediction of family means and within biparental families for an important nutritional trait and suggests that using related germplasm panels as training populations can be effective.

Abstract Background Pre-harvest sprouting (PHS) is a major problem for wheat production due to its direct detrimental effects on wheat yield, end-use quality and seed viability. Annually, PHS is estimated to cause > 1.0 billion USD in losses worldwide. Therefore, identifying PHS resistance quantitative trait loci (QTLs) is crucial to aid molecular breeding efforts to minimize losses. Thus, a doubled haploid mapping population derived from a cross between white-grained PHS susceptible cv AAC Innova and red-grained resistant cv AAC Tenacious was screened for PHS resistance in four environments and utilized for QTL mapping. Results Twenty-one PHS resistance QTLs, including seven major loci (on chromosomes 1A, 2B, 3A, 3B, 3D, and 7D), each explaining ≥10% phenotypic variation for PHS resistance, were identified. In every environment, at least one major QTL was identified. PHS resistance at most of these loci was contributed by AAC Tenacious except at two loci on chromosomes 3D and 7D where it was contributed by AAC Innova. Thirteen of the total twenty-one identified loci were located to chromosome positions where at least one QTL have been previously identified in other wheat genotype(s). The remaining eight QTLs are new which have been identified for the first time in this study. Pedigree analysis traced several known donors of PHS resistance in AAC Tenacious genealogy. Comparative analyses of the genetic intervals of identified QTLs with that of already identified and cloned PHS resistance gene intervals using IWGSC RefSeq v2.0 identified MFT-A1b (in QTL interval QPhs.lrdc-3A.1) and AGO802A (in QTL interval QPhs.lrdc-3A.2) on chromosome 3A, MFT-3B-1 (in QTL interval QPhs.lrdc-3B.1) on chromosome 3B, and AGO802D, HUB1, TaVp1-D1 (in QTL interval QPhs.lrdc-3D.1) and TaMyb10-D1 (in QTL interval QPhs.lrdc-3D.2) on chromosome 3D. These candidate genes are involved in embryo- and seed coat-imposed dormancy as well as in epigenetic control of dormancy. Conclusions Our results revealed the complex PHS resistance genetics of AAC Tenacious and AAC Innova. AAC Tenacious possesses a great reservoir of important PHS resistance QTLs/genes supposed to be derived from different resources. The tracing of pedigrees of AAC Tenacious and other sources complements the validation of QTL analysis results. Finally, comparing our results with previous PHS studies in wheat, we have confirmed the position of several major PHS resistance QTLs and candidate genes.

Abstract Prediction of trait values in plant breeding populations typically relies on assumptions about marker effect homogeneity across populations. Evidence is presented for winter malting barley (Hordeum vulgare L.) germination traits that a single, causative, large‐effect gene in the Seed dormancy 1 region on Chromosome 5H, HvAlaAT1 (Qsd1), leads to heterogeneous estimated marker effects genome wide between groups of otherwise related individuals carrying different Qsd1 alleles. This led to reduced prediction accuracy across alleles when a model was trained either on individuals carrying both alleles or one allele. Several genomic prediction models were tested to increase prediction accuracy within the Qsd1 allele groups. Small gains (5–12%) in prediction accuracy were realized using structured genomic best linear unbiased predictor models when information about the Qsd1 allele was used to stratify the population. We concluded that a single large‐effect locus can lead to heterogeneous marker effects in the same breeding family. Variance partitioning based on large‐effect loci can be used to inform best practices in designing genomic prediction models; however, there are likely few cases for which it may be practical to do this. For malting barley, if germination traits are highly associated with malting quality traits, then similar steps should be considered for malting quality trait prediction.

Abstract Plant metabolites are important traits for plant breeders seeking to improve nutrition and agronomic performance yet integrating selection for metabolomic traits can be limited by phenotyping expense and degree of genetic characterization, especially of uncommon metabolites. As such, developing generalizable genomic selection methods based on biochemical pathway biology for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for >600 metabolites measured by gas chromatography–mass spectrometry (GC‐MS) and liquid chromatography–mass spectrometry (LC‐MS) in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite genome‐wide association study (mGWAS) and selected loci to use in multikernel models that encompassed metabolome‐wide mGWAS results or mGWAS from specific metabolite structures or biosynthetic pathways. Metabolite kernels developed from LC‐MS metabolites in the discovery panel improved prediction accuracy of LC‐MS metabolite traits in the validation panel consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for GC‐MS metabolites. We ranked model performance by metabolite and found that metabolites with similar polarity had consistent rankings of models. Overall, testing biological rationales for developing kernels for genomic prediction across populations contributes to developing frameworks for plant breeding for metabolite traits.

Teff is an indigenous cereal critical to food security in the Horn of Africa. Here, the authors report an improved genome assembly and observe the surprisingly low levels of large-scale structural rearrangement, homoeologous exchanges, or bias gene loss after the formation of this tetraploid species.

Climate changes leading to higher summer temperatures can adversely affect cool season crops like spring barley. In the Upper Midwest region of the United States, one option for escaping this stress factor is to plant winter or facultative type cultivars in the autumn and then harvest in early summer before the onset of high-temperature stress. However, the major challenge in breeding such cultivars is incorporating sufficient winter hardiness to survive the extremely low temperatures that commonly occur in this production region. To broaden the genetic base for winter hardiness in the University of Minnesota breeding program, 2,214 accessions from the N. I. Vavilov Institute of Plant Industry (VIR) were evaluated for winter survival (WS) in St. Paul, Minnesota. From this field trial, 267 (>12%) accessions survived [designated as the VIR-low-temperature tolerant (LTT) panel] and were subsequently evaluated for WS across six northern and central Great Plains states. The VIR-LTT panel was genotyped with the Illumina 9K SNP chip, and then a genome-wide association study was performed on seven WS datasets. Twelve significant associations for WS were identified, including the previously reported frost resistance gene FR-H2 as well as several novel ones. Multi-allelic haplotype analysis revealed the most favorable alleles for WS in the VIR-LTT panel as well as another recently studied panel (CAP-LTT). Seventy-eight accessions from the VIR-LTT panel exhibited a high and consistent level of WS and select ones are being used in winter barley breeding programs in the United States and in a multiparent population.

Abstract New breeding programs are faced with many challenges including evaluation of unknown germplasm, initiation of breeding populations that will satisfy short‐ and long‐term breeding goals, and implementation of efficient phenotyping strategies for multiple traits. Genomic selection (GS) is a potentially valuable tool for recently established breeding programs to quickly accelerate genetic gain. Genomic selection on selection index (SI) values may increase gain over phenotypic selection but empirical studies remain limited. We compared gain in overall SI value for height, heading date, preharvest sprouting (PHS) resistance, and spot blotch resistance and component traits in two cycles of GS with one round of phenotypic selection (PS) in two‐row spring malting barley (Hordeum vulgare L.). Higher realized gain for SI value, height, and PHS was observed with GS compared with PS but GS did not result in significant gain for heading date and spot blotch. Genetic variances for height and heading date, which had small index weights, were not reduced with GS but variances were substantially reduced for heavily weighted PHS and correlated seed germination traits. Inbreeding was increased by GS compared with PS but restricted mating of high breeding value individuals limited potential inbreeding. Our results indicate GS is a useful method to improve selection on index values with different weights.

Abstract Many of the major stem rust resistance genes deployed in commercial wheat (Triticum spp.) cultivars and breeding lines become ineffective over time because of the continuous emergence of virulent races. A genome‐wide association study (GWAS) was conducted using 26,439 single nucleotide polymorphism (SNP) markers and 280 durum wheat [Triticum turgidum L. subsp. Durum (Desf.) Husnot] lines from CIMMYT to identify genomic regions associated with seedling resistance to races TTKSK, TKTTF, JRCQC, and TTRTF and field resistance to TKTTF and JRCQC. The phenotypic data analysis across environments revealed 61–91 and 59–77% of phenotypic variation was explained by the genotypic component for seedling and adult plant response of lines, respectively. For seedling resistance, mixed linear model (MLM) identified eight novel and nine previously reported quantitative trait loci (QTL) while a fixed and random model circulating probability unification (FarmCPU) detected 12 novel and eight previously reported QTL. For field resistance, MLM identified 12 novel and seven previously reported loci while FarmCPU identified seven novel and nine previously reported loci. The regions of Sr7a, Sr8155B1, Sr11, alleles of Sr13, Sr17, Sr22/Sr25, and Sr49 were identified. Novel loci on chromosomes 3B, 4A, 6A, 6B, 7A, and 7B could be used as sources of resistance to the races virulent on durum wheat. Two large‐effect markers on chromosome 6A could potentially be used to differentiate resistant haplotypes of Sr13 (R1 and R3). Allelism tests for Sr13, breaking the deleterious effect associated with Sr22/Sr25 and retaining the resistance allele at the Sr49 locus, are needed to protect future varieties from emerging races.

Abstract Positional‐based cloning is a foundational method for understanding the genes and gene networks that control valuable agronomic traits such as grain yield components. In this study, we sought to positionally clone the causal genetic variant of a 1000‐grain weight (TGW) quantitative trait loci (QTL) on wheat (Triticum aestivum L.) chromosome arm 5AL. We developed heterogenous inbred families (HIFs) (>5,000 plants) for enhanced genotypic resolution and fine‐mapped the QTL to a 10‐Mbp region. The transcriptome of developing grains from positive and negative control HIF haplotypes revealed presence–absence chromosome arm 5AS structural variation and unexpectedly no differential expression of genes within the chromosome arm 5AL candidate region. Evaluation of genomic, transcriptomic, and phenotypic data, and predicted function of genes, identified that the 5AL QTL was the result of strong linkage disequilibrium (LD) with chromosome arm 5AS presence or absence (HIF r2 = 0.91). Structural variation is common in wheat, and our results highlight that the redundant polyploid genome's masking of such variation is a significant barrier to positional cloning. We propose recommendations for more efficient and robust detection of structural variation, including transitioning from a single nucleotide polymorphism (SNP) to a haplotype‐based approach to identify positional cloning targets. We also present nine candidate genes for grain yield components based on chromosome arm 5AS presence or absence, which may unveil hidden variation of homoeolog dosage‐dependent genes across the group five chromosome short arms. Taken together, our discovery demonstrates the phenotypic resiliency of polyploid genomic structural variation and highlights a considerable challenge to routine positional cloning in wheat.

Oat (Avena sativa L.) has a high concentration of oils, comprised primarily of healthful unsaturated oleic and linoleic fatty acids. To accelerate oat plant breeding efforts, we sought to identify loci associated with variation in fatty acid composition, defined as the types and quantities of fatty acids. We genotyped a panel of 500 oat cultivars with genotyping-by-sequencing and measured the concentrations of ten fatty acids in these oat cultivars grown in two environments. Measurements of individual fatty acids were highly correlated across samples, consistent with fatty acids participating in shared biosynthetic pathways. We leveraged these phenotypic correlations in two multivariate genome-wide association study (GWAS) approaches. In the first analysis, we fitted a multivariate linear mixed model for all ten fatty acids simultaneously while accounting for population structure and relatedness among cultivars. In the second, we performed a univariate association test for each principal component (PC) derived from a singular value decomposition of the phenotypic data matrix. To aid interpretation of results from the multivariate analyses, we also conducted univariate association tests for each trait. The multivariate mixed model approach yielded 148 genome-wide significant single-nucleotide polymorphisms (SNPs) at a 10% false-discovery rate, compared to 129 and 73 significant SNPs in the PC and univariate analyses, respectively. Thus, explicit modeling of the correlation structure between fatty acids in a multivariate framework enabled identification of loci associated with variation in seed fatty acid concentration that were not detected in the univariate analyses. Ultimately, a detailed characterization of the loci underlying fatty acid variation can be used to enhance the nutritional profile of oats through breeding.

This study aimed to understand how genetics and environment influence organic winter naked barley composition and functionality, and to identify traits that might effectively categorize basic physicochemical functionality of food barley. Across three years, two locations, and 15 genotypes, genotype significantly influenced all 10 food-related traits and was the dominant influence for three. Location significantly influenced eight traits and was dominant for three. Year significantly influenced all traits but was dominant only for one. Of the interactions location * year was the most influential and was the dominant effect for two traits. For all interaction terms where genotype was a component, the effect sizes were either small or non-significant suggesting that even with low leverage traits there is the potential for genetic gain by observing trait rankings across environments. Principal component analysis identified six traits that could serve to categorize basic physicochemical functionality of food barley. These were grain protein content, beta-glucan content, flour-water batter flow, water solvent retention capacity, time to peak viscosity of cooked flour, and hardness of cooked intact grains.

Amylase/trypsin-inhibitors (ATIs) comprise about 2–4% of the total wheat grain proteins and may contribute to natural defense against pests and pathogens. However, they are currently among the most widely studied wheat components because of their proposed role in adverse reactions to wheat consumption in humans. ATIs have long been known to contribute to IgE-mediated allergy (notably Bakers' asthma), but interest has increased since 2012 when they were shown to be able to trigger the innate immune system, with attention focused on their role in coeliac disease which affects about 1% of the population and, more recently, in non-coeliac wheat sensitivity which may affect up to 10% of the population. This has led to studies of their structure, inhibitory properties, genetics, control of expression, behavior during processing, effects on human adverse reactions to wheat and, most recently, strategies to modify their expression in the plant using gene editing. We therefore present an integrated account of this range of research, identifying inconsistencies, and gaps in our knowledge and identifying future research needs.Note This paper is the outcome of an invited international ATI expert meeting held in Amsterdam, February 3-5 2020

The observable phenotype is the manifestation of information that is passed along different organization levels (transcriptional, translational, and metabolic) of a biological system. The widespread use of various omic technologies (RNA-sequencing, metabolomics, etc.) has provided plant genetics and breeders with a wealth of information on pertinent intermediate molecular processes that may help explain variation in conventional traits such as yield, seed quality, and fitness, among others. A major challenge is effectively using these data to help predict the genetic merit of new, unobserved individuals for conventional agronomic traits. Trait-specific genomic relationship matrices (TGRMs) model the relationships between individuals using genome-wide markers (SNPs) and place greater emphasis on markers that most relevant to the trait compared to conventional genomic relationship matrices. Given that these approaches define relationships based on putative causal loci, it is expected that these approaches should improve predictions for related traits. In this study we evaluated the use of TGRMs to accommodate information on intermediate molecular phenotypes (referred to as endophenotypes) and to predict an agronomic trait, total lipid content, in oat seed. Nine fatty acids were quantified in a panel of 336 oat lines. Marker effects were estimated for each endophenotype, and were used to construct TGRMs. A multikernel TRGM model (MK-TRGM-BLUP) was used to predict total seed lipid content in an independent panel of 210 oat lines. The MK-TRGM-BLUP approach significantly improved predictions for total lipid content when compared to a conventional genomic BLUP (gBLUP) approach. Given that the MK-TGRM-BLUP approach leverages information on the nine fatty acids to predict genetic values for total lipid content in unobserved individuals, we compared the MK-TGRM-BLUP approach to a multi-trait gBLUP (MT-gBLUP) approach that jointly fits phenotypes for fatty acids and total lipid content. The MK-TGRM-BLUP approach significantly outperformed MT-gBLUP. Collectively, these results highlight the utility of using TGRM to accommodate information on endophenotypes and improve genomic prediction for a conventional agronomic trait.

Stem rust of wheat caused by Puccinia graminis Pers. f.sp. trtici Eriks and E. Henn., is the most damaging fungal disease of both common (Triticum aestivum L.) and durum (Triticum turgidum L., ssp. Durum) wheat. Continuously emerging races virulent to many of the commercially deployed qualitative resistance genes have caused remarkable loss worldwide and threaten global wheat production. The objectives of this study were to evaluate the response of a panel of 283 durum wheat lines assembled by the International Maize and Wheat Improvement Center (CIMMYT) to multiple races of stem rust in East Africa at the adult plant stage and map loci associated with field resistance. The lines were evaluated in Debre Zeit, Ethiopia and Njoro, Kenya from 2018 to 2019 in five environments (year × season). The panel was genotyped using genotyping-by-sequencing. After filtering, 26,439 Single Nucleotide Polymorphism (SNP) markers and 280 lines and three checks were retained for analysis. Population structure was assessed using principal component analysis. Genome-wide association analysis (GWAS) was conducted using Genomic Association and Prediction Integrated Tool (GAPIT). The broad-sense heritability of the phenotype data revealed that 64–83% of the variation in stem rust response explained by the genotypes and lines with multiple race resistance were identified. GWAS analysis detected a total of 160 significant marker trait associations representing 42 quantitative trait loci. Of those, 21 were potentially novel and 21 were mapped to the same regions as previously reported loci. Known stem rust resistance genes/alleles were postulated including Sr8a, Sr8155B1, SrWeb/Sr9h, Sr11, Sr12, Sr13/Sr13 alleles, Sr17, Sr28/Sr16, Sr22, and Sr49. Lines resistant to multiple races in East Africa can be utilized as parents in durum wheat breeding programs. Further studies are needed to determine if there are new alleles at the Sr13 locus and potential markers for the known Sr13 alleles.

Abstract Classical plant breeding has been instrumental in changing the genetic makeup of crop plants for better ecological adaptation and improved quality. This paper provides insights of the genomic changes effected in hard winter wheat (Triticum aestivum L.) through decades of breeding and selection in the Great Plains of the United States. Population structure and differentiation analyses were conducted on 185 wheat cultivars released from 1943 to 2013. Cultivars were grouped into four distinct clusters using discriminant analysis of principal components (DAPC). One of the clusters was unique in that 15 out of the 18 individuals were recent releases (2000–2010), while 12 of the 18 shared the cultivar ‘Jagger’ in their genetic background. Jagger carries a 2NS/2AS translocation segment from Aegilops ventricosa, an important segment for resistance to several foliar diseases. Using the outlier approach, Wright's population fixation index (Fst) identified 450 loci that were directionally selected. The largest signature of selection was found on chromosome 2A. Genetic diversity was high while the inbreeding coefficient was low, indicating extensive hybridization and germplasm exchange among breeding programs within the region. Foliar disease pressure and selection for resistance helped shape the microevolution of wheat in the southern Great Plains. The results showed that high genetic diversity remains in hard winter wheat cultivars adapted to the Great Plains of the USA, and modern plant breeding did not cause any sizable reduction in genetic diversity of the crop in this region.

Most oat grains destined for human consumption must possess the ability to pass through an industrial de-hulling process with minimal breakage and waste. Uniform grain size and a high groat to hull ratio are desirable traits related to milling performance. The purpose of this study was to characterize the genetic architecture of traits related to milling quality by identifying quantitative trait loci (QTL) contributing to variation among a diverse collection of elite and foundational spring oat lines important to North American oat breeding programs. A total of 501 lines from the Collaborative Oat Research Enterprise (CORE) panel were evaluated for genome-wide association with 6 key milling traits. Traits were evaluated in 13 location years. Associations for 36,315 markers were evaluated for trait means across and within location years, as well as trait variance across location years, which was used to assess trait stability. Fifty-seven QTL influencing one or more of the milling quality related traits were identified, with fourteen QTL mapped influencing mean and variance across location years. The most prominent QTL was Qkernel.CORE.4D on chromosome 4D at approximately 212 cM, which influenced the mean levels of all traits. QTL were identified that influenced trait variance but not mean, trait mean only and both.

Efforts focused on the genetic improvement of seed morphometric and color traits would greatly benefit from efficient and reliable quantitative phenotypic assessment in a nondestructive manner. Although several seed phenotyping systems exist, none of them combine the cost effectiveness, identity preservation, throughput, and accuracy needed for implementation in plant breeding. We integrated an image analysis component into a single-seed analyzer (SSA) system that also captures near-infrared reflectance (NIR) and weight data. Through the development and utilization of an open-source computational image analysis pipeline, image data acquired by two cameras mounted on the SSA machine were automatically processed to derive estimates of length, width, height, volume, and color for 96 individual dehulled groats (seeds) of five oat ( L.) genotypes replicated across days. With the exception of color, the four traits were found to be strongly correlated with and have repeatability comparable to manual measurements. The seed color values had moderately strong correlation with those measured by a colorimeter, but further improvements to the SSA system are needed to increase measurement accuracy. These results demonstrate that the SSA system has the potential to provide a low-cost solution for the rapid, accurate measurement of morphological traits on individual seeds of oat and potentially other crop species, allowing the screening of seeds from numerous genotypes in breeding programs.

Genetic diversity of durum wheat landraces is a powerful tool for the introgression of new alleles of commercial interest in breeding programs. In a previous study, our team structured a collection of 172 durum wheat landraces from 21 Mediterranean countries in four genetic populations related to their geographical origin: east Mediterranean (17), east Balkan and Turkey (23), west Balkan and Egypt (25), and West Mediterranean (73), leaving 34 genotypes as admixed, and association mapping was carried out for important agronomic traits. Using a subset of this collection, the current study identified 23 marker alleles with a differential frequency in landraces from east and west regions of the Mediterranean Basin, which affected important agronomic traits. Eastern landraces had higher frequencies than the western ones of alleles increasing the number of spikes (wPt-5385 on chromosome 1B), grains per m2 (wPt-0841 on chromosome 7B), and grain filling duration (7 significant marker trait associations). Eastern landraces had higher frequencies of marker alleles located on chromosomes 4A, 5B, and 6B associated with reduced cycle length, and lighter grains than the western ones. Also for lower kernel weight, four marker alleles were located on chromosome 1A. Breeders may use the molecular markers identified in the current study for improving yield under specific Mediterranean environments.

High-throughput phenotyping (HTP) platforms can be used to measure traits that are genetically correlated with wheat ( L.) grain yield across time. Incorporating such secondary traits in the multivariate pedigree and genomic prediction models would be desirable to improve indirect selection for grain yield. In this study, we evaluated three statistical models, simple repeatability (SR), multitrait (MT), and random regression (RR), for the longitudinal data of secondary traits and compared the impact of the proposed models for secondary traits on their predictive abilities for grain yield. Grain yield and secondary traits, canopy temperature (CT) and normalized difference vegetation index (NDVI), were collected in five diverse environments for 557 wheat lines with available pedigree and genomic information. A two-stage analysis was applied for pedigree and genomic selection (GS). First, secondary traits were fitted by SR, MT, or RR models, separately, within each environment. Then, best linear unbiased predictions (BLUPs) of secondary traits from the above models were used in the multivariate prediction models to compare predictive abilities for grain yield. Predictive ability was substantially improved by 70%, on average, from multivariate pedigree and genomic models when including secondary traits in both training and test populations. Additionally, (i) predictive abilities slightly varied for MT, RR, or SR models in this data set, (ii) results indicated that including BLUPs of secondary traits from the MT model was the best in severe drought, and (iii) the RR model was slightly better than SR and MT models under drought environment.

The leaf spotting diseases in wheat that include Septoria tritici blotch (STB) caused by , Stagonospora nodorum blotch (SNB) caused by , and tan spot (TS) caused by pose challenges to breeding programs in selecting for resistance. A promising approach that could enable selection prior to phenotyping is genomic selection that uses genome-wide markers to estimate breeding values (BVs) for quantitative traits. To evaluate this approach for seedling and/or adult plant resistance (APR) to STB, SNB, and TS, we compared the predictive ability of least-squares (LS) approach with genomic-enabled prediction models including genomic best linear unbiased predictor (GBLUP), Bayesian ridge regression (BRR), Bayes A (BA), Bayes B (BB), Bayes Cπ (BC), Bayesian least absolute shrinkage and selection operator (BL), and reproducing kernel Hilbert spaces markers (RKHS-M), a pedigree-based model (RKHS-P) and RKHS markers and pedigree (RKHS-MP). We observed that LS gave the lowest prediction accuracies and RKHS-MP, the highest. The genomic-enabled prediction models and RKHS-P gave similar accuracies. The increase in accuracy using genomic prediction models over LS was 48%. The mean genomic prediction accuracies were 0.45 for STB (APR), 0.55 for SNB (seedling), 0.66 for TS (seedling) and 0.48 for TS (APR). We also compared markers from two whole-genome profiling approaches: genotyping by sequencing (GBS) and diversity arrays technology sequencing (DArTseq) for prediction. While, GBS markers performed slightly better than DArTseq, combining markers from the two approaches did not improve accuracies. We conclude that implementing GS in breeding for these diseases would help to achieve higher accuracies and rapid gains from selection.

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.

Genomic selection (GS) uses genome-wide molecular marker data to predict the genetic value of selection candidates in breeding programs. In plant breeding, the ability to produce large numbers of progeny per cross allows GS to be conducted within each family. However, this approach requires phenotypes of lines from each cross before conducting GS. This will prolong the selection cycle and may result in lower gains per year than approaches that estimate marker-effects with multiple families from previous selection cycles. In this study, phenotypic selection (PS), conventional marker-assisted selection (MAS), and GS prediction accuracy were compared for 13 agronomic traits in a population of 374 winter wheat ( L.) advanced-cycle breeding lines. A cross-validation approach that trained and validated prediction accuracy across years was used to evaluate effects of model selection, training population size, and marker density in the presence of genotype × environment interactions (G×E). The average prediction accuracies using GS were 28% greater than with MAS and were 95% as accurate as PS. For net merit, the average accuracy across six selection indices for GS was 14% greater than for PS. These results provide empirical evidence that multifamily GS could increase genetic gain per unit time and cost in plant breeding.

This project was initiated with the goal of investigating the malt quality of winter rye cultivars and hybrids grown in the United States in 2014 and 2015, but high levels of deoxynivalenol (DON) were subsequently found in many of the malt samples. DON levels in 75% of the investigated rye samples (n = 117) were actually below 1.0 mg/kg, as quantified by a gas chromatography combined with electron capture detector (GC-ECD). However, 83% of the samples had DON in excess of 1.0 mg/kg following malting, and the average DON level in malted rye was 10.6 mg/kg. In addition, relatively high levels of 3-acetate DON (3-ADON), 15-acetate DON (15-ADON), nivalenol (NIV), and DON-3-glucoside (D3G) were observed in some rye malts. Our results show that rye grain DON is likely a poor predicator of type B trichothecenes in malt in practice, because high levels of malt DON, 15-ADONm and D3G were produced, even when the rye samples with DON levels below 0.50 mg/kg were processed. Fusarium Tri5 DNA content in rye was highly associated with malt DON levels (r = 0.83) in a small subset of samples (n = 55). The impact of Fusarium infection on malt quality was demonstrated by the significant correlations between malt DON levels and wort viscosity, β-glucan content, wort color, wort p-coumaric acid content, and total phenolic content. Additional correlations of rye Fusarium Tri5 DNA contents with malt diastatic power (DP), wort free amino nitrogen (FAN) content, and arabinoxylan content were observed.

The partial rust resistance genes and have been used extensively in wheat ( L.) improvement, as they confer exceptional durability. Interestingly, the resistance of is associated with the expression of leaf tip necrosis (LTN) and with pseudo-black chaff (PBC). Genome-wide association mapping using CIMMYT’s stem rust resistance screening nursery (SRRSN) wheat lines was done to identify genotyping-by-sequencing (GBS) markers linked to LTN and PBC. Phenotyping for these traits was done in Ithaca, New York (fall 2011); Njoro, Kenya (main and off-seasons, 2012), and Wellington, India (winter, 2013). Using the mixed linear model (MLM), 18 GBS markers were significantly associated with LTN. While some markers were linked to loci where the durable leaf rust resistance genes (7DS), (1BL), and (7BL) were mapped, significant associations were also detected with other loci on 2BL, 5B, 3BS, 4BS, and 7BS. Twelve GBS markers linked to the locus (3BS) and loci on 2DS, 4AL, and 7DS were significantly associated with PBC. This study provides insight into the complex genetic control of LTN and PBC. Further efforts to validate and study these loci might aid in determining the nature of their association with durable resistance.

Quantitative adult plant resistance (APR) to stem rust ( f. sp. ) is an important breeding target in wheat ( L.) and a potential target for genomic selection (GS). To evaluate the relative importance of known APR loci in applying GS, we characterized a set of CIMMYT germplasm at important APR loci and on a genome-wide profile using genotyping-by-sequencing (GBS). Using this germplasm, we describe the genetic architecture and evaluate prediction models for APR using data from the international Ug99 stem rust screening nurseries. Prediction models incorporating markers linked to important APR loci and seedling phenotype scores as fixed effects were evaluated along with the classic prediction models: Multiple linear regression (MLR), Genomic best linear unbiased prediction (G-BLUP), Bayesian Lasso (BL), and Bayes Cπ (BCπ). We found the region to play an important role in APR in this germplasm. A model using linked markers as fixed effects in G-BLUP was more accurate than MLR with linked markers (-value = 0.12), and ordinary G-BLUP (-value = 0.15). Incorporating seedling phenotype information as fixed effects in G-BLUP did not consistently increase accuracy. Overall, levels of prediction accuracy found in this study indicate that GS can be effectively applied to improve stem rust APR in this germplasm, and if genotypes at linked markers are available, modeling these genotypes as fixed effects could lead to better predictions.