This study investigated the quantitative changes in 33 stress- or resistance-related metabolites induced by Bipolaris sorokiniana in barley leaves of quantitatively resistant and susceptible barley lines of the multiparental nested association mapping (NAM) population HEB-25. The analyses were based on ultrahigh-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS). Twenty-nine infected and noninfected barley genotypes were analyzed at four different time points after inoculation. The method provided quantification of hordatines, phenolamides, hydroxycinnamic acids, flavone glucosides, hydroxynitrile glucosides, apocarotenoids, and indole derivatives. In leaves infected with B. sorokiniana, phenolamide levels were elevated compared to noninfected plants. A correlation between metabolite levels and the severity of infection showed that the more resistant barley lines contained higher amounts of hordatines and hordatine glucosides.

The nested association mapping (NAM) population, first developed in maize, have been widely adopted across diverse crop species including barley, sorghum, rice, and soybean. These populations enable high-resolution mapping of complex traits and have been extensively utilized worldwide to dissect the genetic architecture of agronomically important traits such as yield, disease resistance, and seed composition. The parents were selected from a Korean soybean collection widely used in the Korean soybean breeding program, along with a soybean reference genotype. The K-SoyNAM population was initially developed using 27 donor parents crossed with 'Daepung', generating 2,831 F5-derived recombinant inbred lines (RILs) from 27 families. After quality control filtering, 2,669 RILs from 24 families were retained for downstream analyses. The parental lines were deeply sequenced using whole genome reference sequencing, and the RILs were genotyped using the Axiom 180K SoyaSNP array. Genetic linkage maps were constructed for each family, and a composite linkage map was developed across families. Linkage analysis of three agronomic traits, flowering time, flower color, and seed protein content, the utility of the K-SoyNAM population for quantitative trait locus (QTL) mapping. Genotype imputation based on a composite genetic linkage map generated 4,057,789 high-confidence single nucleotide polymorphisms (SNPs) for the RILs. Comparison of genome-wide association study (GWAS) results before and after imputation demonstrated improved detection of small significant peaks in the imputed GWAS dataset. The genotypic datasets for the RILs and parents are publicly available and are expected to be helpful in dissecting the genetic architecture and mapping QTLs controlling important complex traits in soybean.

GrowScreen-Rhizo 3 - automated large-scale high throughput greenhouse phenotyping of plant root and shoot development.

The genomic landscape of recombination in rice revealed by a large nested association mapping population.

While various genomic prediction models have been evaluated for their potential to accelerate genetic gain for multiple traits, no individual genomic prediction model has outperformed all others across all applications. As an alternative approach, ensembles of multiple individual genomic prediction models can be applied to utilize the complementary strengths of individual prediction models and offset the prediction errors of each. We used the EasiGP (Ensemble AnalySis with Interpretable Genomic Prediction) pipeline to investigate the performance of an ensemble approach, targeting flowering-time traits measured in 2 maize nested association mapping datasets. For both datasets, the ensemble-based prediction approach achieved higher prediction accuracy and lower prediction error across the flowering-time traits compared to each individual model. Multiple genomic regions known to contain key flowering-time-related genes were repeatedly included as features across individual genomic prediction models, indicating the models successfully captured SNPs as features that are associated with genomic regions known to contain flowering-time genes. Although repeatability was high for some genomic regions, estimated marker effects varied across many genomic regions, suggesting that the models might also have captured different aspects of the genetic variation underlying the traits. The ensemble combination of the diverse views likely contributed to the improvement of prediction performance by the ensemble-based approach over the individual prediction models. Ensemble-based prediction can be applied to overcome limitations observed in the continuous exploration for the best individual genomic prediction models that can consistently achieve the highest prediction performance, thereby potentially contributing to improved prediction accuracy for applications in crop breeding.

Climate change is intensifying the frequency and severity of abiotic stresses that threaten global food security by reducing crop productivity. Among these, saline stress poses a serious threat to barley (Hordeum vulgare L.) production. These conditions are increasingly prevalent in arid and semiarid regions, as well as in regions with limited access to freshwater resources, making the identification of salt tolerance genes essential for breeding resilient varieties. In this study, we evaluated 400 genotypes from the barley nested association mapping population HEB‐25 under control conditions and 40% seawater irrigation to simulate moderate‐to‐high salinity stress. A genome‐wide association study (GWAS) was conducted to identify alleles from wild barley [H. vulgare L. subsp. spontaneum (C. Koch) Thell.] associated with enhanced salt tolerance. Phenotypic evaluation included germination percentage (Ger%), shoot length (SL), root length (RL), root–shoot length ratio, seedling fresh weight, seedling dry weight, and salt tolerance index of the different traits. The HEB‐25 families exhibited significant variation in seedling responses to seawater‐induced salinity, with contrasting effects on SL, RL, and dry weight. Compared to the elite parental Barke, several genotypes demonstrated high tolerance under seawater stress, maintaining stable Ger% and exhibiting the highest tolerance indices. Moreover, GWAS results identified 60 highly significant single nucleotide polymorphisms associated with seedling growth parameters under both conditions. These findings underscore the value of the HEB‐400 panel as a genetic resource for dissecting salinity tolerance mechanisms, identifying stress‐adaptive alleles lost during domestication and a source of pre‐breeding material for developing genotypes with enhanced salinity tolerance.

Fusarium head blight (FHB), caused by Fusarium graminearum, is one of the most devastating diseases of wheat (Triticum aestivum) and other cereal crops worldwide. Improving FHB resistance has been a major focus in many wheat genetics and breeding programs globally. However, only a few major loci have been effectively deployed, limiting progress in breeding for FHB resistance. To expand the genetic basis of resistance, we developed a modified nested association mapping (NAM) panel comprising four synthetic hexaploid wheat (SHW) lines, ten hard red spring wheat (HRSW) varieties/lines, and 276 BC1-derived progenies. The panel was genotyped using the 90K SNP (single nucleotide polymorphism) Infinium array and evaluated for Type II FHB resistance across six environments. The disease evaluations revealed that 15 resistant lines, primarily derived from backcrossing SHW line SW183 (T. dicoccum PI 191091/Aegilops tauschii CIae 26) with HRSW 'Linkert' or 'Glenn,' exhibited resistance levels comparable to the well-known FHB resistance source, Sumai 3. A genome-wide association study (GWAS) identified 19 significant marker-trait associations (MTAs) for FHB resistance. Of these, three SNPs carried resistance alleles from SHW, two from HRSW, and 14 from both parental sources. Sixteen MTAs co-localized with previously reported quantitative trait loci (QTLs) linked to FHB resistance, while three, located on the short arms of chromosomes 1D, 2B, and 4A, appear to be associated with novel resistance loci. The resistant lines developed in this study could serve as new sources for FHB resistance, and their associated resistance loci can be further validated and incorporated into breeding programs. KEY MESSAGE: Three potentially novel loci and sixteen loci overlapping with previously reported regions for Fusarium head blight resistance were identified in a mapping panel derived from synthetic and bread wheat germplasm.

Variation in protein 3D structures reflects genetic variation and contributes to phenotypic diversity, yet its underlying genetic mechanisms remain unclear. To investigate the relationship between protein 3D structure and phenotype, we predict the 3D structures of 795,649 proteins from 26 maize (Zea mays L.) inbred lines using AlphaFold2. Population genetics analysis of these protein 3D structures reveal that buried residues held greater genomic evolutionary rate profiling (GERP) scores than exposed residues, indicating that buried residues are under stronger purifying selection. The design of the maize nested association mapping population makes it possible to utilize haplotype information and protein 3D structural variation to reveal the molecular mechanisms linking genetic diversity and phenotypic variation for a population with about 5000 individuals. Associating protein 3D structure variation with phenotypes (structure-based proteome-wide association study [PWAS]) identifies 14.2% more (96 vs. 84) significant proteins compared with associating protein sequence with phenotypes (sequence-based PWAS) using 32 agronomic traits. Moreover, structure-based PWAS identifies 24 additional significant proteins unique to predicted structures, whereas sequence-based PWAS identifies 12 additional significant proteins. Structure-based proteome-wide predictions (PWPs) improve genomic prediction accuracy by an average of 3.8% compared with sequence-based PWPs. In general, predicted protein 3D structures represent a powerful approach for understanding the natural diversity of protein haplotypes.

Maize (Zea mays L.) is the most recently domesticated and dispersed worldwide among major cereals. Chloroplast genomes (plastome), with their highly conserved tetrad structure, hold a significant value in phylogenetic research due to their typical maternal inheritance. In this study, a total of 286 maize plastomes, 262 newly assembled and 24 publicly available, were investigated which included worldwide landraces, nested association mapping founders, and teosinte accessions. The maize plastomes were assembled with the filtered reads of the whole genome sequences from our re-seq data and public database by mapping to the reference plastome of B73. The maize plastomes were put into the phylogenetic analyses, with Z. perennis accession as the outgroup. The maize landraces were divided into four clades, showing a closer relationship with the Z. mays ssp. parviglumis plastome than with mexicana, indicating that parviglumis was the direct donor of the plastome to maize. Landrace diversities in Europe were quite similar to those in China and the Korean Peninsula, suggesting that the dispersal of maize following European contact with America was both rapid and accompanied by the translocation of a broad spectrum of maize germplasm. The introduced landraces have less diversity than the old landraces found in South America. This study provides information for understanding the genetic diversity of maize landraces and the process of maize dispersal in the world for exploiting the maize germplasm in breeding.

Abstract Unoccupied aerial vehicle (UAV)‐based high‐throughput phenotyping provides scalable and cost‐effective access to phenotypic information for crop improvement, yet its application in minor crops such as lentil ( Lens culinaris Medik.) remains limited. This study applied UAV‐derived canopy traits and crop growth regression modeling to a nested association mapping population developed from CDC Redberry crossed with 32 diverse founder lines. UAV imagery collected across four site‐years was used to capture canopy height, crop area, and crop volume per plot basis at multiple time points. Crop growth regression models were fitted to derive crop growth parameters, maximum canopy size, growth rate, and cumulative growth anchored to phenological stages. These static and time‐series traits were evaluated for seed yield prediction using partial least squares regression with a 70:20:10 data split and 10‐fold cross‐validation. Static traits such as maximum crop volume and maximum crop area were consistently associated with yield. Dynamic trait‐based models improved prediction accuracy and identified the swollen pod stage (R5–R6) as the most informative forecasting window. External validation using an independent trial confirmed the generalizability of the approach. This study presents a UAV phenotyping framework that supports crop growth dissection and early yield prediction and downstream trait discovery in lentil.

Oat crown rust, caused by the fungus Puccinia coronata f. sp. avenae (Pca), is the most destructive foliar pathogen of oat. Almost 100 genes conferring resistance to Pca have been cataloged. However, only limited genes have been mapped, and the chromosomal location of most remains undetermined. The goals of this study were to detect the chromosomal locations of 13 cataloged Pc genes and one uncharacterized but highly effective resistance gene and to identify functions related to them. We used an A. sativa L. nested association mapping population comprising 14 biparental F2:3 families, derived from crosses between a donor carrying Pca resistance and the Pca susceptible variety "Swan." A total of 2,356 F2:3 lines were phenotyped for response to pathotypes of Pca, from which the final AsNAM population of 707 individuals were selected. Based on DArT-Seq genotype data 15,940 high-quality single nucleotide polymorphisms were identified. Using the IBD mixed model, eight resistance QTLs to Pca with varying phenotypic variance were identified. The locations of four previously mapped genes were confirmed (Pc38, chr7D; Pc45, chr2D; Pc46, chr3D; Pc50, chr3D), and two genes were mapped for the first time (Pc36, chr1C; Pc70, chr5D). Resistance QTLs from the highly resistant Ensiler variety were also identified for the first time. The results revealed that some families had a single dominant gene controlling resistance, while others had more complex resistance. Several genes were linked or allelic (Pc13, Pc46, and Pc50 on chr3D; Pc36 and Pc60 on chr 1C; Pc38 and Pc64 on chr 7D). A total of 31 putative genes belonging to eight protein families related with disease resistance were identified in detected QTL regions.

Background: Dirigent genes play crucial roles in regulating plant architecture development and responses to environmental stress. However, the pan-genomic attributes of these genes remain poorly characterized. Method: The dirigent pan-gene family was reconstructed using the public genome assemblies from the 26 maize Nested Association Mapping project founder lines. Orthogroup classification based on multiple sequence alignment revealed both core and variable family members. Evolutionary pressures were assessed through Ka/Ks ratio analysis, and promoter regions were examined for cis-acting regulatory elements. Haplotype, transcriptomic and genome-wide association study (GWAS) analyses were integrated to explore genetic diversity and functional relevance. Results: Most dirigent members were under purifying selection, whereas a subset may have undergone positive selection. Promoter analysis demonstrated enrichment of stress- and phytohormone-responsive cis-acting regulatory elements, suggesting that regulatory divergence was associated with environmental adaptation. Haplotype analysis revealed allelic diversity among heterotic clusters, potentially contributing to heterosis. Integration with public genome-wide association study datasets identified candidate genes significantly associated with plant architecture and kernel-quality-related traits. Transcriptome profiles indicated that several dirigent genes were preferentially expressed in the roots, suggesting their involvement in root development and nutrient uptake. In addition, public gene expression data showed that certain dirigent genes are induced in response to salt stress, supporting their putative roles in abiotic stress tolerance. Conclusions: These findings provide insights into the molecular mechanisms underlying dirigent gene functions and reveal candidate genes with potential utility for improving maize performance and stress resilience through molecular breeding.

An ensemble of multiple genomic prediction models has grown in popularity due to consistent prediction performance improvements in crop breeding. However, technical tools that analyze the predictive behavior at the genome level are lacking. Here, we develop a computational tool called Ensemble AnalySis with Interpretable Genomic Prediction (EasiGP) that uses circos plots to visualize how different genomic prediction models quantify contributions of marker effects to trait phenotypes. As a demonstration of EasiGP, multiple genomic prediction models, spanning conventional statistical and machine learning algorithms, were used to infer the genetic architecture of days to anthesis (DTA) in a maize mapping population. The results indicate that genomic prediction models can capture different views of trait genetic architecture, even when their overall profiles of prediction accuracy are similar. Combinations of diverse views of the genetic architecture for the DTA trait in the teosinte nested association mapping study might explain the improved prediction performance achieved by ensembles, aligned with the implication of the Diversity Prediction Theorem. In addition to identifying well-known genomic regions contributing to the genetic architecture of DTA in maize, the ensemble of genomic prediction models highlighted several new genomic regions that have not been previously reported for DTA. Finally, different views of trait genetic architecture were observed across subpopulations, highlighting challenges for between-population genomic prediction. A deeper understanding of genomic prediction models with enhanced interpretability using EasiGP can reveal several critical findings at the genome level from the inferred genetic architecture, providing insights into the improvement of genomic prediction for crop breeding programs.

The interplay between nuclear and cytoplasmic genomes, collectively known as cytonuclear interactions (CNIs), is increasingly recognized as a key driver of phenotypic variation and adaptive potential across diverse organisms. Yet, leveraging cytoplasmic diversity and fully understanding the role of CNIs in agriculturally important traits remain major challenges in crop improvement. Here, we present the Cytonuclear Multi-Parent Population (CMPP), a novel interspecific resource comprising 951 doubled haploid lines, generated from 2 backcrosses between ten genetically diverse wild barley accessions (Hordeum vulgare ssp. spontaneum) used as female founders and the elite cultivar Noga (H. vulgare). Phenotyping across multiple environments revealed that up to 5% of variation in key spike and grain trait values are explained by cytoplasm (η2 = 0.05). Notably, wild cytoplasms influenced trait stability, with the B1K-50-04 cytoplasm increasing grain weight stability based on Shukla's measure. Genome-wide association studies employing Nested Association Mapping (NAM), FASTmrMLM, and MatrixEpistasis (ME) identified 76 marker-trait associations (MTAs). The ME approach specifically uncovered 16 cytonuclear QTL (cnQTL) exhibiting cytoplasm-dependent effects. Furthermore, we developed a genomic prediction strategy incorporating interactions between significant MTAs and population structure variables (subfamily and cytoplasm), which achieved cross-validation accuracies comparable to, or even exceeding, models using the full set of 6,679 SNPs, despite utilizing substantially fewer predictors, enabling quicker and more efficient validation runs. The CMPP provides a unique platform for dissecting cytoplasmic effects and CNIs, highlighting the importance of incorporating cytonuclear context in genetic mapping and prediction to effectively harness both nuclear and cytoplasmic diversity for crop improvement.

The management of forest trees has long been a critical challenge in the field of forestry, as these essential natural resources face numerous threats, from climate change and pests to deforestation and unsustainable harvesting practices. Maintaining the resilience and adaptability of forest ecosystems to global changes has become a crucial challenge for forest management. The application of genetic tools can play a pivotal role in enhancing the adaptive capacity of forest tree species and ensuring their long-term sustainability. The study aims to investigate the application of genetic tools for the management of forest genetic resources. In recent years, there has been growing interest in the application of genetic tools to address these challenges and enhance the resilience and sustainability of forest ecosystems. One of the key strategies in this regard is the use of genetic data to inform forest management decisions. Powerful forward-genetic techniques, such as DNA/RNA sequencing technologies, marker-based trait selection, quantitative trait locus mapping, genome-wide association studies, and nested association mapping, have been instrumental in pinpointing the genomic regions and underlying causative mutations responsible for traits like yield, stress resistance, and metabolic profiles. The analysis of genetic markers can help identify trees with desirable traits, such as resistance to pests or adaptations to changing climatic conditions. This information can then be used to selectively breed or propagate these high-performing individuals, thereby enhancing the overall genetic diversity and resilience of the forest. Furthermore, genetic tools can also be leveraged to monitor and respond to emerging threats, such as the spread of invasive species or the onset of disease outbreaks. By tracking the genetic profiles of these pests and pathogens, forest managers can develop early warning systems and deploy targeted management strategies to mitigate their impact. As the challenges facing forest ecosystems continue to grow, the application of genetic tools presents a promising approach to enhance the long-term sustainability and resilience of these vital natural resources. In this review, we have highlighted the application of genetic tools in the management of forest genetic resources. The application of genetic tools in forest management has the potential to significantly enhance the resilience and adaptability of forest ecosystems to the challenges posed by global change. By providing insights into the genetic diversity, adaptation mechanisms, and ecosystem function of forests, these tools can enable forest managers to make more informed decisions and develop strategies that balance the various objectives of forest management.

Genomic prediction (GP) significantly enhances genetic gain by improving selection efficiency and shortening crop breeding cycles. Using a nested association mapping population, a set of diverse scenarios were assessed to evaluate GP for important agronomic traits in Brassica napus, including plant height, days to flowering, 1000‐kernel weight, and yield. GP accuracy was examined on each trait by employing eight different models, eight marker sets, varying population sizes and marker densities, and incorporating trait‐associated markers identified through genome‐wide association study analysis. Eight models, including linear and semi‐parametric approaches, were tested. The choice of model minimally impacted GP accuracy across traits. Employing a training population of 1500 lines or more resulted in increased prediction accuracies. Inclusion of single nucleotide absence polymorphism markers with single‐nucleotide polymorphism markers significantly improved prediction accuracy, with gains of up to 15%. The study provided estimates of GPs for major agronomic traits through varied prediction scenarios, shedding light on achievable genetic gains. These insights, coupled with marker application, can advance the breeding cycle acceleration in B. napus.

Spot blotch of barley(Hordeum vulgare L.), caused by Bipolaris sorokiniana, is responsible for major lossesin crop yield. Breeding-resistantbarley varieties have proven to be an effective countermeasure forprotecting agricultural production. Plants react to pathogen attacksby up-regulating secondary metabolites. Marker compounds for a B. sorokiniana infection are examined by untargetedUPLC-TOF-MS metabolomics and lipidomics techniques. Through the analysisof nine quantitatively resistant and susceptible barley genotypes,derived from the nested association mapping population HEB-25, followedby structure identification experiments and spore germination assays,57 metabolites are identified. In addition to previously known metabolites,the unknown compounds 5-carboxydidehydroblumenol C-9-O-ß-d-glucoside (46) and grasshopper ketone3-sulfate (47) were elucidated. 5-Carboxyblumenol C-9-O-ß-d-glucoside (45) was describedfor the first time in barley leaves. Pheophytin derivatives, oxylipins,linolenate-conjugated lipids, and flavone glycosides were describedfor the first time in connection with infections by phytopathogenicfungi or resistance in barley.

Key messageThis study identified two potentially novel yellow rust resistance loci in traditional Asian wheat varieties and gives insights into the distribution of resistances in high disease-pressure regions near the Himalayas.The global spread of yellow rust has posed a significant threat to wheat production, making the identification of novel resistance-conferring genetic loci crucial. The near-Himalayan region has been proposed as the pathogen’s origin and is characterized by strong and diverse disease pressure. Even though this makes wheat varieties from this region likely to harbor resistance, Asian germplasm has been highly underrepresented in modern breeding. To explore this potential, we screened an Asian nested association mapping (NAM) population comprising traditional and modern wheat varieties under artificial epidemics in multiple field trials. Combined quantitative trait locus (QTL) mapping revealed the two resistance genes Lr67/Yr46/Sr55 and Lr34/Yr18/Sr57, as well as two potentially novel yellow rust resistance loci. The resistant allele of the first one, located on chromosome 3D, is unique to a traditional variety from Nepal, while the second one, found on chromosome 5B, is present in several NAM families. The broad geographic distribution of this QTL across regions with high disease pressure suggests it may serve as a durable source of resistance. Strong observed resistances were conferred by a combination of several resistance loci, suggesting the stacking of resistances as a successful strategy in yellow rust hotspot areas.

Mungbean (Vigna radiata), a valuable sub-tropical grain legume, typically has a long, asynchronous flowering window, which increases vulnerability to abiotic stress and complicates harvesting. To facilitate breeding efforts, we conducted an extensive study of days to flowering (DTF) and the novel trait flowering duration (FD) in multi-environment trials. A diverse nested association mapping population was evaluated across four field trials in Queensland, Australia. Extensive phenotypic variation was observed for both DTF (35-70 d after sowing) and FD (20-60 d). Both traits displayed Genotype × Environment interactions, with FD showing stronger environmental interactions than DTF. No relationship was evident between DTF and FD across environments. Genome-wide association studies identified eight quantitative trait loci (QTLs) for DTF and one for FD, with none overlapping. The accumulation of early or late alleles at DTF QTLs was associated with variations in flowering time. These results show for the first time in mungbean that DTF and FD are independent traits with distinct genetic controls and environmental responses, providing a mechanistic understanding of how flowering patterns may be optimised to potentially enhance adaptation and performance in diverse agricultural environments challenged by climate change.

IntroductionMaize is a crucial source of nutrition, and the quality traits such as starch content, oil content, and lysine content are essential for meeting the demands of modern agricultural development. Understanding the genetic basis of these quality traits significantly contributes to improving maize yield and optimizing end-use quality. While previous studies have explored the genetic basis of these traits, further investigation into the quantitative trait loci (QTL) responsible for variations in starch content, oil content, and lysine content still requires additional attention.MethodsDouble haploid (DH) populations were developed via a nested association mapping (NAM) design. Phenotypic data for starch, oil, and lysine content were collected using near-infrared spectroscopy and analyzed via ANOVA. Genotyping employed a 3K SNP panel, and genetic maps were constructed using QTL IciMapping. QTL analysis integrated single linkage mapping (SLM) and NAM approaches, with candidate genes identified via maizeGDB annotation and transcriptome data.ResultsThe broad-sense heritability of the populations with a range of 63.98-80.72% indicated the majority of starch content, oil content and lysine content variations were largely controlled by genetic factors. The genetic maps were constructed and a total of 47 QTLs were identified. The phenotypic variation explained (PVE) of the three traits is in a range of 2.60-17.24% which suggested that the genetic component of starch content, oil content and lysine content was controlled by many small effect QTLs. Five genes encoding key enzymes in regulation of starch, oil and lysine synthesis and metabolism located within QTLs were proposed as candidate genes in this study.DiscussionThe information presented herein will establish a foundation for the investigation of candidate genes that regulate quality traits in maize kernels. These QTLs will prove beneficial for marker-assisted selection and gene pyramiding in breeding programs aimed at developing high-quality maize varieties.