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Background A sustainable breeding program requires a minimum level of germplasm diversity to provide varied options for the selection of new breeding lines. To maximize genetic gain of the North Dakota State University (NDSU) flax breeding program, we aimed to increase the genetic diversity of its parental stocks by incorporating diverse genotypes. For this purpose, we analyzed the genetic diversity, linkage disequilibrium, and population sub-structure of 350 globally-distributed flax genotypes with 6200 SNP markers. Results All the genotypes tested clustered into seven sub-populations (P1 to P7) based on the admixture model and the output of neighbor-joining (NJ) tree analysis and principal coordinate analysis were in line with that of structure analysis. The largest sub-population separation arose from a cluster of NDSU/American genotypes with Turkish and Asian genotypes. All sub-populations showed moderate genetic diversity (average H  = 0.22 and I  = 0.34). The pairwise F st comparison revealed a great degree of divergence ( F st  > 0.25) between most of the combinations. A whole collection mantel test showed significant positive correlation (r = 0.30 and p  < 0.01) between genetic and geographic distances, whereas it was non-significant for all sub-populations except P4 and P5 (r = 0.251, 0.349 respectively and p  < 0.05). In the entire collection, the mean linkage disequilibrium was 0.03 and it decayed to its half maximum within < 21 kb distance. Conclusions To maximize genetic gain, hybridization between NDSU stock (P5) and Asian individuals (P6) are potentially the best option as genetic differentiation between them is highest ( F st  > 0.50). In contrast, low genetic differentiation between P5 and P2 may enhance the accumulation of favorable alleles for oil and fiber upon crossing to develop dual purpose varieties. As each sub-population consists of many genotypes, a Neighbor-Joining tree and kinship matrix assist to identify distantly related genotypes. These results also inform genotyping decisions for future association mapping studies to ensure the identification of a sufficient number of molecular markers to tag all linkage blocks.

Background Hexaploid oat ( Avena sativa L.) is a commercially important cereal crop due to its soluble dietary fiber β-glucan, a hemicellulose known to prevent cardio-vascular diseases. To maximize health benefits associated with the consumption of oat-based food products, breeding efforts have aimed at increasing the β-glucan content in oat groats. However, progress has been limited. To accelerate oat breeding efforts, we leveraged existing breeding datasets (1,230 breeding lines from South Dakota State University oat breeding program grown in multiple environments between 2015 and 2022) to conduct a genome-wide association study (GWAS) to increase our understanding of the genetic control of beta-glucan content in oats and to compare strategies to implement genomic selection (GS) to increase genetic gain for β-glucan content in oat. Results Large variation for β-glucan content was observed with values ranging between 3.02 and 7.24%. An independent GWAS was performed for each breeding panel in each environment and identified 22 loci distributed over fourteen oat chromosomes significantly associated with β-glucan content. Comparison based on physical position showed that 12 out of 22 loci coincided with previously identified β-glucan QTLs, and three loci are in the vicinity of cellulose synthesis genes, Cellulose synthase-like ( Csl ). To perform a GWAS analysis across all breeding datasets, the β-glucan content of each breeding line was predicted for each of the 26 environments. The overall GWAS identified 73 loci, of which 15 coincided with loci identified for individual environments and 37 coincided with previously reported β-glucan QTLs not identified when performing the GWAS in single years. In addition, 21 novel loci were identified that were not reported in the previous studies. The proposed approach increased our ability to detect significantly associated markers. The comparison of multiple GS scenarios indicated that using a specific set of markers as a fixed effect in GS models did not increase the prediction accuracy. However, the use of multi-environment data in the training population resulted in an increase in prediction accuracy (0.61–0.72) as compared to single-year (0.28–0.48) data. The use of USDA-SoyWheOatBar-3 K genotyping array data resulted in a similar level of prediction accuracy as did genotyping-by-sequencing data. Conclusion This study identified and confirmed the location of multiple loci associated with β-glucan content. The proposed genomic strategies significantly increase both our ability to detect significant markers in GWAS and the accuracy of genomic predictions. The findings of this study can be useful to accelerate the genetic improvement of β-glucan content and other traits.

Background Stem development is crucial for plant lodging, nutrients and water transport, and structural support for other organs. Understanding stem development and growth is essential for ensuring global food security. Although numerous lodging-resilient and high-yielding crop varieties have been developed in the Green Revolution by controlling plant height, the molecular mechanism underlying stem development, particularly for cereals, is not fully understood. The allelic stem mutants in barley ( Hordeum vulgare subsp. vulgare ),  single internode dwarf 1  ( sid1 ), provide a model system for genetic studies on stem development. Results We characterized and genetically analyzed the  sid1.b  mutation. To determine the precise position of  Sid1 , a high-resolution genetic map was constructed. Segregating F 2 plants derived from a cross between wild type (WT) and the mutant were genotyped with the barley 50 k iSelect SNP Array, and the detected SNPs were converted to PCR-based markers for fine mapping. The  Sid1  gene was mapped to a 429-kb region on chromosome 4H. Illumina sequencing of WT and sid1 identified a C → T transition in an epidermal pattern factor (EPF)-coding gene, which introduces a premature stop codon in the mutant allele. Conclusions In the present study, we genetically characterized and mapped the  sid1.a  mutation, which causes a dwarfed phenotype with single internode stems in barley. The EPF-encoding gene in the delimited region is a promising candidate for  Sid1 . Therefore, our study provides a foundation for cloning of  Sid1 , which will enhance our understanding of the molecular mechanisms underlying stem development, particularly in monocot plants.

Background Fusarium Head Blight (FHB) is a destructive fungal disease affecting wheat and barley, leading to significant yield losses and reduced grain quality. Susceptibility to FHB is influenced by genetic factors, environmental conditions, and genotype-by-environment interactions (GxE), making it challenging to predict disease resistance across diverse environments. This study investigates GxE in a long-term spring wheat multi-environment uniform nursery trial focusing on the evaluation of resistant lines in northern US breeding programs. Results Traditionally, GxE has been analyzed as a reaction norm over an environment index. Here, we computed the environment index as a linear combination of environmental covariables specific to each environment, and we derived an environment relationship matrix. Three methods were compared, all aimed at predicting untested genotypes in untested environments: the widely used Finlay-Wilkinson regression (FW), the joint-genomic regression analysis (JGRA) method, and mixed models incorporating an environmental covariates matrix. These were benchmarked against a baseline genomic selection model (GS) without environmental covariates. Predictive abilities were assessed within and across environments. The results revealed that the JGRA marker effect method was more accurate than GS in within- and across-environment predictions, although the differences were small. The predictive ability slightly decreased when the target environment was less related to the training environments. Mixed models performed similarly to JGRA within-environment, but JGRA outperformed the other methods for across-environment predictions. Additionally, JGRA identified significant genetic markers associated with baseline FHB resistance and environmental sensitivity. Furthermore, location-specific genomic estimated breeding values were predicted, providing insights into genotype stability across varying locations. Conclusion These findings highlight the value of incorporating environmental covariates to increase predictive ability and improve the selection of resistant genotypes for diverse, untested environments. By leveraging this approach, breeders can effectively exploit GxE interactions to improve disease management at no additional cost.

Marker‐trait associations (MTAs) are invaluable to the understanding of biological processes and implementation of marker‐assisted selection (MAS) to increase genetic gain in modern breeding programs. In this study, a genome‐wide association study (GWAS) was performed to identify MTAs that influence oat nutritional quality, as well as agronomic and milling traits, in advanced breeding spring oat (Avena sativa L.) germplasm from North Dakota State University. High‐density sequence‐based molecular markers (15,037) were identified in a population of 1,092 unique lines evaluated in 11 field conditions over three years. Linkage disequilibrium analysis confirmed that decay metrics of 5, 11.5, and 2.2 Mb for the A, C, and D sub‐genomes, respectively, matched the 1.25 Mb/single nucleotide polymorphism marker density used in this study. We identified 160 MTAs and 44 durable quantitative trait loci (QTL) for nine traits that individually explained 3.7%–43.9% of the variation in the data. Haplotypes assembled from the three most predictive QTL for each trait increased the variation explained to 16%–46%. The haplotype effects are substantial, with their presence resulting in predicted mean percentage increases—greater than 20% for many of the investigated traits. These results confirm that significant MTAs for oat can be identified directly within breeding germplasm using GWAS. These markers are excellent candidates for the implementation of MAS to increase the selection efficiency and genetic gain. Core Ideas A genome‐wide association study was conducted on advanced germplasm of an oat breeding program. Several marker‐trait associations (MTAs) were found for breeding‐related traits. Haplotype analysis of the most important MTAs highlights their potential for early screening. Plain Language Summary Oat is an important small grain that provides numerous health benefits for human consumption. To maintain a durable supply of oat to the consumer, new varieties need to be constantly developed that can yield high‐quality grain in the face of environmental, disease, and market pressures. In this study, we used genetic analysis of existing trait data from 1,092 lines in the North Dakota State University public breeding program to discover gene regions that significantly influence almost every trait that was evaluated. We identified molecular markers that flag these gene regions, which enables the selection of breeding lines without directly screening in the field. The discovery of these gene regions is a first step in uncovering the molecular mechanisms of how these traits develop, and deployment of this information in a marker‐assisted selection scheme is a very promising method to improve the genetics of the lines and quickly deliver superior varieties to the producers.

Aegilops umbellulata serve as an important reservoir for novel biotic and abiotic stress tolerance for wheat improvement. However, chromosomal rearrangements and evolutionary trajectory of this species remain to be elucidated. Here, we present a comprehensive investigation into Ae. umbellulata genome by generating a high‐quality near telomere‐to‐telomere genome assembly of PI 554389 and resequencing 20 additional Ae. umbellulata genomes representing diverse geographical and phenotypic variations. Our analysis unveils complex chromosomal rearrangements, most prominently in 4U and 6U chromosomes, delineating a distinct evolutionary trajectory of Ae. umbellulata from wheat and its relatives. Furthermore, our data rectified the erroneous naming of chromosomes 4U and 6U in the past and highlighted multiple major evolutionary events that led to the present‐day U‐genome. Resequencing of diverse Ae. umbellulata accessions revealed high genetic diversity within the species, partitioning into three distinct evolutionary sub‐populations and supported by extensive phenotypic variability in resistance against several races/pathotypes of five major wheat diseases. Disease evaluations indicated the presence of several novel resistance genes in the resequenced lines for future studies. Resequencing also resulted in the identification of six new haplotypes for Lr9 , the first resistance gene cloned from Ae. umbellulata. The extensive genomic and phenotypic resources presented in this study will expedite the future genetic exploration of Ae. umbellulata , facilitating efforts aimed at enhancing resiliency and productivity in wheat.

Although anthocyanin is frequently found in various barley organs, the genetic basis of the pigmentation is still poorly understood. In this study, we examined the development of anthocyanin in GemCraft, a malting barley cultivar showing purple leaf sheath (PLS), and found that the pigmentation became visible on the leaf sheath at the early tillering stage. This study employed single nucleotide polymorphism (SNP) array genotyping data in two F2 populations developed using GemCraft and two barley lines with green leaf sheath throughout the plant development. Genetic and quantitative trait locus (QTL) analyses suggested regulation of the purple pigment accumulation by a single major QTL that was inherited as a dominant allele, which was necessary for the phenotype to develop. A major QTL, named qPLS2 (purple leaf sheath2 locus), was found on chromosome 2H and explained >70% of the trait variation. Nonetheless, the genetic model in the two mapping populations resonated between multiple loci and a single locus that determines the trait variation. Accordingly, in one of the populations, three minor QTL were also detected on chromosomes 1H and 5H: each of these QTL explained <5% variation and showed influence in regulation of the purple pigment intensity. In the qPLS2 QTL interval, comparative genomic analysis of annotated genes that are widely known to regulate anthocyanin development in plants identified a single candidate gene encoding a basic helix–loop–helix (bHLH) transcription factor. The study identified a new major QTL associated with the purple leaf sheath and generated further information for validation and cloning the causal gene for effective utilization of anthocyanin in barley genetic improvement. Core Ideas Two biparental F2 mapping populations were developed to study the genetic loci controlling purple leaf sheath in a barley cultivar, GemCraft. A new major quantitative trait locus (QTL) (purple leaf sheath2 locus [qPLS2]) on 2H and three minor QTL associated with the purple leaf sheath were mapped. Comparative genomic analysis revealed a single candidate gene at the qPLS2 locus. Plain Language Summary Anthocyanin in cereal grains provides some health benefits, but its accumulations on leaf may affect plant photosynthetic capacity. Barley is an important crop, in which the genetic basis of anthocyanin regulation is not well understood. In this study, the genetic loci associated with purple leaf sheath in a malting barley cultivar, GemCraft, were studied using two crossing populations. The development of purple leaf sheath in GemCraft was mainly regulated by a genetic locus on chromosome 2H, and other minor loci or genetic backgrounds may also impact the intensity of the pigment development. Comparative genomic analysis at the major effect locus identified one candidate causal gene, which encodes a basic helix–loop–helix (bHLH) transcription factor.

head blight (FHB) is one of the most destructive diseases in wheat worldwide. Breeding for FHB resistance is hampered by its complex genetic architecture, large genotype by environment interaction, and high cost of phenotype screening. Genomic selection (GS) is a powerful tool to enhance improvement of complex traits such as FHB resistance. The objectives of this study were to (1) investigate the genetic architecture of FHB resistance in a North Dakota State University (NDSU) hard red spring wheat breeding population, (2) test if the major QTL and play an important role in this breeding population; and (3) assess the potential of GS to enhance breeding efficiency of FHB resistance. A total of 439 elite spring wheat breeding lines from six breeding cycles were genotyped using genotyping-by-sequencing (GBS) and 102,147 SNP markers were obtained. Evaluation of FHB severity was conducted in 10 unbalanced field trials across multiple years and locations. One QTL for FHB resistance was identified and located on chromosome arm 1AL, explaining 5.3% of total phenotypic variation. The major type II resistance QTL only explained 3.1% of total phenotypic variation and the QTL was not significantly associated with FHB resistance in this breeding population. Our results suggest that integration of many genes with medium/minor effects in this breeding population should provide stable FHB resistance. Genomic prediction accuracies of 0.22-0.44 were obtained when predicting over breeding cycles in this study, indicating the potential of GS to enhance the improvement of FHB resistance.

Genomic selection (GS) is a promising strategy for accelerating genetic gains of complex traits in breeding programs. Despite the recent advancements in high‐throughput genotyping technologies, the selection of the type of marker systems needed for GS remains challenging in breeding programs. In this study, we explored 3K array single nucleotide polymorphisms (SNPs) and genotyping by sequencing (GBS) SNP markers for genomic prediction of oat biomass yield using different statistical and machine learning approaches. An oat panel consisting of 420 lines was phenotyped for biomass‐related traits for 3 years and genotyped using two different marker platforms (3K array and GBS). Our results showed similar performance of both the 3K array and GBS‐based SNPs in terms of training population optimization, forward prediction, and univariate and multivariate genomic prediction of forage yield. The genomic best linear unbiased prediction (GBLUP), Bayes‐B, and random forest models gave similar predictive ability for dry matter yield (DMY) in different harvest–year combinations and for both marker platforms. The multivariate models involving various combinations of secondary traits (simple breeders' field notes and data) resulted in more than twofold increases in predictive abilities compared to the univariate models. Comparison of the 25% top‐performing observed and predicted genotypes showed a higher overlap percentage (30.10%–66.99%) for multivariate GBLUP models compared to the univariate models (27.18%–51.46%). This further elucidates the great potential of multivariate GS models incorporating the more robust and easily reproducible 3K array SNP markers for improving the genetic gains of DMY in breeding programs. Core Ideas Illumina 3K single nucleotide sequencing (SNP) array and genotyping by sequencing (GBS) SNP markers performed similarly for genetic diversity, as well as training population optimization. Illumina 3K and GBS SNPs demonstrated comparable predictive abilities for dry matter yield (DMY) using univariate and multivariate models. Multivariate prediction models had higher predictive ability for oat DMY than univariate models. The reproducibility, speed, and ease of production, as well as the lower marker density of the 3K array, would enhance its utilization for genomic selection (GS). Plain Language Summary In the Southern United States, oats have been extensively used as a forage crop for animal feed. Nevertheless, collecting phenotypic data for aboveground biomass improvement in oat breeding programs is destructive, labor‐intensive, and takes a lengthy time. We investigated two different marker genotyping platforms for genomic prediction of oat biomass yield using different univariate and multivariate models. The inclusion of easy‐to‐measure secondary traits in our models resulted in a better ability to determine oat genotypes that will give higher biomass yield. Comparable predictive abilities were observed for both marker genotyping methods. Thus, the more robust 3K array genotyping method with lower marker density could be utilized by breeders for selection of oat genotypes in early generations using secondary traits in genomic selection to reduce cost, shorten the time required to develop new varieties, and eventually boost the availability of nutritious oat forage for the livestock industry.

Fusarium head blight (FHB) is a fungal disease posing a major threat to wheat production. Plant breeding that leverages genotyping is an effective method to improve the genetic resistance of cultivars. Started in 1995, the uniform regional scab nursery (URSN) consists of germplasm from several public breeding programs in the Northern US region. Its main objective is to showcase new sources of resistance and enable germplasm exchange among the cooperators; however, the data from the URSN have not been studied. Phenotypic and genotypic data from this nursery were gathered, as well as from two current breeding programs in the US Midwest. Genomic prediction on eight traits related to FHB and agronomic traits was applied, and the effects of statistical method, marker density, training set size, genetic structure, and genetic architecture of the trait were studied. Using the URSN population, reproducing kernel Hilbert space was the best method in various prediction settings, with an average accuracy of 0.63, marker density could be as low as 500 without decreasing the prediction accuracy, and training set optimization was useful for two traits. Furthermore, genotypic values were predicted in breeding programs using the URSN population as a training set with various prediction scenarios. Predicting unrelated populations led to a significant decrease in accuracy but with encouraging values for some traits and populations. Ultimately, when progressively decreasing the number of lines from breeding populations in the training set, the advantage of adding the URSN population was more pronounced, with an increase in accuracy up to 0.19. Core Ideas Genomic predictive ability ranged from 0.49 to 0.72 for predicting Fusarium head blight (FHB) and agronomical traits in wheat. Training set size had more impact on accuracy than marker density, which could be reduced to between 500 and 1000 markers. Training set optimization with a sparse selection index increased the accuracy of genomic prediction for two traits. Adding an unrelated population in the training set allows a reduction in phenotyping effort. Plain Language Summary Genomic prediction was used as a tool to improve the genetic resistance of spring wheat to Fusarium head blight, using data from a historical nursery and from two current breeding programs. Parameters related to genomic predictive ability were thoroughly studied. Predictive ability within the historical nursery was medium to high for all the eight traits studied and all methods gave similar results. Marker density did not affect predictive ability compared to training set size. Training set optimization had mixed results depending on the trait. We tested several prediction scenarios useful in a breeding context by harnessing the historical dataset in the training set for predicting breeding lines. While the lack of genetic relatedness decreased the accuracy of genomic prediction, we showed that breeding programs could benefit from these historical data by incorporating their information into training models, thus reducing the phenotyping effort.