There is a paucity of available information on lentil (Lens culinaris Medikus) qualitative traits and their associations with agronomic traits, despite such information being essential for the selection of best genotypes. Hence, this study was undertaken to evaluate relationship among qualitative and agronomic traits, and to explore the significance of these relationships for lentil breeding programs. One hundred ninety-two lentil germplasm were evaluated in 2025 at Dogolo and Enewari in the Amhara Region of Ethiopia. Pot experiment was also undertaken at Debre Berhan Agricultural Research Centre for two consecutive years (2024 and 2025). Alpha lattice experimental design was used and data on 24 agro-morphological traits were collected. Chi-square tests, Cramér's V, Spearman's rank correlation, point-biserial correlation, and principal component analysis were employed to assess relationships and effect sizes among qualitative and agronomic traits. Additionally, Welch's t-test and the Welch's ANOVA were used to compare the mean values of agronomic traits across different groups. Large association was observed among seed and flower characters (Cramer (V) = 0.61 to 0.76), and also seed with leaf related characters (V = 0.30 to 0.35) across both experiments. On the other hand, leaf pubescence showed a strong association with days to maturity (ρ = 0.41-0.53), as well as with seed (ρ = -0.27 to -0.35) and pod (ρ = -0.23 to -0.35) development across both experiments. Mean comparison also showed that germplasm with medium leaf pubescence tend to have longer time to mature while producing fewer seeds and pods per plant compared to those with absent or slightly dense pubescence. Seed pigmentation pattern, cotyledon and flower color also showed a larger effect size on the phenology, seed size and yield traits where highly pigmented type tended to mature earlier and produce smaller seeds but showed improved yield performance. Overall, this study demonstrates that strong associations exist between several qualitative and agronomic traits, which provide valuable insights for breeders in the selection of desirable traits for adaptation and variety development, and also offer a useful basis for further studies on linkage analysis, association mapping, underlying molecular mechanisms controlling those traits in diverse lentil germplasm.

Efforts are underway to increase the efficiency and precision of selection hop (Humulus lupulus L.) breeding using genomics. Little is known, however, about the genetic control of important traits like α-and β-acids contents, oil content, and cone morphological characteristics, all of which play an important role in determining the utility and harvestability of a hop and are targets of selection. In this study, we utilized association mapping with a collection of 529 female hop plants evaluated in Prosser, WA USA in 2023 and 2024, single nucleotide polymorphism data derived from genotyping-by-sequencing with 20,861 markers, and phenotype data generated from near-infrared (NIR) spectroscopy and image analyses of hop cones. A total of 49 significant marker trait associations were detected across five traits with 43 unique loci. High correlation estimates between wet lab and near-infraredspectroscopy data (R=0.54-0.94), high broad-sense heritability estimates (H2=0.32-0.71), and logical associated candidate genes illustrate the validity of the methods used in detecting meaningful associations. Furthermore, existing germplasm in our study containing increasing stacks of favorable alleles showed improvement in all traits, demonstrating the potential for utilizing the markers identified herein in a genomic prediction pipeline to improve hop germplasm for key end-use traits.

Roots play a pivotal role for plant performance, but they are difficult to access, which hampers quantitative measurements. Repeated imaging of rhizotrons, flat growth containers with a transparent side, has proven suitable to assess dynamics of root traits in indoor experiments. However, measuring hundreds of soil-grown plants with high temporal resolution remains a laborious challenge. We introduce a novel whole-plant phenotyping platform with a capacity of almost 900 rhizotrons, which we named GrowScreen-Rhizo 3. This platform was designed to image shoots and roots of individual plants simultaneously and derive digital proxy traits for biomass and growth. In addition, built-in weighing and watering stations deliver water use data for each rhizotron. To achieve the desired throughput (image all 896 plants once a day) a high degree of automatization and standardization was required. We realized a modular plant-to-sensor solution, using a fleet of automated guided vehicles (AGVs) to transport large rhizotrons (80 × 40 × 5 cm) to four measurement chambers for daily imaging, weighing, and watering. Simultaneous imaging of the root system with a high-resolution camera (116 μm per px) and the shoot from six different viewing angles allows to monitor plant growth with high spatial and temporal accuracy. First, we verified that moving plants to the measurement chambers did not significantly affect above- or belowground plant growth. Next, we measured phenotypic variation in root and shoot traits of 24 barley genotypes, parents of a nested association mapping population. Our analysis revealed that heritability of root traits such as root system depth and seminal root length was moderate to high (r2 = 0.52 and r2 = 0.93, respectively), enabling further assessment of increasing numbers of recombinant genotypes. The results demonstrate the suitability of GrowScreen-Rhizo 3 to phenotype a range of plant species characterized by various growth habits, including crop, niche, and wild plant species. We conclude that GrowScreen-Rhizo 3 will contribute significantly to the development of phenotyping pipelines for the identification of candidate genotypes with improved resource use efficiency and to pre-breeding processes of climate-resilient crops.

Polygenic and spatial insights into the genetic uniqueness of essential tremor using common variants.

In response to the technical requirements for real-time quality control in the hot pressing process of intelligent plywood production, this study proposes a real-time process control framework driven by edge AI. This framework employs a three-layer edge intelligence architecture. This work shows a practical and efficient boundary node model application scheme for defect detection with multi-level lightweight strategies. In particular, this work builds a decision level data fusion approach for visual detection data and process parameters based on rules for defect-process parameter association mapping. Experimental results have shown that this designed scheme can efficiently detect defects in an edge computing environment. Additionally, with more multi-source fusion being considered in the site environment, the overall detection efficiency might be improved while maintaining a stable closed-loop control system. After that, quality enhancement for products and efficiency improvement for detection were realized. The results provide a feasible method for utilizing engineering processes for enhanced online quality detection for the plywood hot-pressing process based on practical experiences for intelligence upgrades in wood processing.

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

Genome-wide association mapping reveals pleiotropic loci coupling antioxidant defense with redox homeostasis in barley under combined drought and salinity.

Phaseolus acutifolius (tepary bean) is a heat- and drought-tolerant legume adapted to semi-arid environments with emerging genomic resources, yet the genetic architecture of biomass and nitrogen-related traits remains poorly resolved. Here, we aimed to resolve the genetic architecture of cover-crop-relevant traits in tepary bean, anticipating predominantly polygenic control but allowing for a major-effect component of flowering time. We assessed 206 accessions and four commercial checks for biomass, flowering time, leaf amino acids, and a Relative NUE-Index that integrates plant N uptake with seasonal soil N changes. Genotyping-by-sequencing produced 49,384 high-quality SNPs for multi-model genome-wide association studies (GWAS). The results show substantial natural variation across traits, enabling association mapping. Biomass-associated loci on chromosomes 6, 7, and 11 align with candidate genes involved in structural growth and development, including hydroxyproline-rich glycoproteins and carotenoid cleavage dioxygenase 1. Relative NUE-Index loci implicate trehalose-6-phosphate signaling and phosphatase activity, supporting a role for coordinated carbon-nitrogen regulation in NUE-related physiology. Leaf ureide and amino-acid profiles showed pronounced among-accession variation, providing a complementary physiological context for NUE-related trait variation. A major QTL on chromosome 3 for flowering time, near a BTB-domain gene, highlights a candidate region for phenology tuning in tepary bean. Overall, SNP data from GBS and GWAS reveal a largely distinct polygenic structure across traits in tepary bean, providing actionable loci and hypotheses for marker-assisted breeding and introgression toward resilient summer cover crop varieties.

Seed weight is a key agronomic trait determining soybean yield and quality, yet only a few of genes regulating this trait have been functionally characterized to date. In this study, we identified 155 homologous genes in the soybean genome through BLAST searches using 78 functionally validated rice grain weight-related genes as queries. Haplotype analysis prioritized 40 candidate genes exhibiting significant differences in seed weight between haplotypes. To further refine the candidate list, we integrated haplotype frequency analysis, expression-trait association mapping, and tissue-specific expression profiling, ultimately delineating eight key genes. Given the established role of ubiquitination in seed development, we focused on homologs of OsUBP15 and identified three candidate genes, GmUBP5, GmUBP11, and GmUBP33, that exhibited significant haplotype-dependent variation in seed weight. Subcellular localization assays confirmed their nuclear localization. Haplotype frequency analysis revealed that the superior haplotypes of these genes have been preferentially retained during modern breeding and are widely distributed across major soybean-producing regions. Leveraging non-synonymous SNP variants, we developed and validated robust KASP markers that efficiently discriminate germplasm with contrasting seed weight phenotypes. Collectively, our study provides not only high-confidence genetic targets and actionable molecular markers but also insights into pyramiding breeding strategies for improving seed weight in soybean.

Heart failure (HF) affects over 64 million individuals globally, and its prevalence continues to escalate, driven by population aging and the rising burden of metabolic disorders. Dengzhan Shengmai (DZSM) capsule, a classical Traditional Chinese Medicine formulation, has demonstrated therapeutic potential for HF. However, its bioactive constituents and underlying molecular mechanisms remain largely unexplored. We employed ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) to characterize the metabolic profiles of DZSM in rat serum, cardiac, and hepatic tissues. Network pharmacology and bioinformatics approaches were subsequently applied to identify potential targets of these metabolites. Our integrated analytical framework encompassed protein-protein interaction (PPI) network construction, hub gene identification, Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, miRNA-mRNA regulatory network analysis, core pathway evaluation, and gene-disease association mapping. Molecular docking, Western blot and RNA sequencing were performed to validate key bioactive components and their corresponding targets. We identified 67, 36, and 37 characteristic metabolites in serum, cardiac, and hepatic tissues, respectively. Critical bioactive compounds include caffeic acid, ferulic acid, quercetin-3-O-glucuronide, hyperoside, scutellarin, schizandrin A, and schizandrin B. The core therapeutic targets were identified as STAT3, CDK5, NOX4, and JAK2 in the chemokine signaling pathway. These biomarkers appear to influence HF treatment through modulation of CCL2-CCR2 axis, JAK-STAT, and MAPK signaling pathways. Molecular docking confirmed strong binding affinities between bioactive components and target proteins. Western blot results demonstrated that scutellarin dose-dependently inhibited the phosphorylation of JAK2 and STAT3 induced by oxygen-glucose deprivation/reoxygenation (OGD/R), with JAK2 and STAT3 total proteins remain almost unchanged. Single-cell RNA sequencing spatially mapped the expression patterns of key targets in myocardial tissue. Integrated serum/tissue pharmacochemistry and systems pharmacology elucidate DZSM's bioactive constituents and mechanistic foundations for HF intervention.

Leveraging inbred and hybrid association mapping coupled with transcriptome analyses to decipher the genetics underlying the general combining ability of maize flowering time

Meiotic drivers are selfish genetic elements that subvert Mendelian inheritance to increase their own transmission, yet they are typically found at low frequencies across natural populations. The factors that limit their spread remain unclear. The Segregation Distorter (SD) system, a selfish coadapted gene complex in Drosophila melanogaster, is an excellent model to study this paradox. SD biases its transmission by killing sperm carrying a homologous chromosome bearing a target locus, Responder (Rsp), which corresponds to satellite repeats. Such selfish killing impairs male fertility and imposes selective pressure on the host genome to evolve resistance, either by deleting Rsp copies, or acquiring unlinked suppressors. The interplay of these factors in natural populations is poorly understood. We studied the genetic factors contributing to drive strength in strains derived from a natural population of D. melanogaster from Raleigh, North Carolina known as Drosophila Genome Reference Panel (DGRP). Here we characterized the spectrum of Rsp alleles and the frequency of segregating suppressors in 90 DGRP strains. Our approach disentangles drive resistance at Rsp from suppression. Rather than loss of Rsp, we found that over half of the strains harbor suppressors located on the X chromosome or autosomes, but not the Y chromosome. The widespread presence of strong suppressors limited the resolution of our genome-wide association mapping; however, recombination analysis using a DGRP strain identified a strong X-linked suppressor to a ∼300 kb interval on the chromosome. Together, our findings suggest that pervasive, multilocus suppression constrains the spread of SD in natural populations.

Drought response in plants is complex, involving integration across a range of physiological processes. However, our knowledge of how different mechanisms of drought response are linked at the genetic level is limited. We investigated multi-trait adaptation in Arabidopsis thaliana from the Cape Verde Islands (CVI). Using a high-throughput phenotyping platform that minimizes spatial heterogeneity, we measured variation in rosette area, growth rate, leaf color, water use efficiency (WUE), and stomatal patterning under precisely controlled water conditions. Relative to the Moroccan outgroup, CVI populations evolved earlier flowering, a smaller rosette size with faster growth, and reduced WUE, consistent with drought escape adaptation. Genome-wide association mapping revealed evidence for pleiotropy involving MPK12 (WUE, rosette area, growth rate, and leaf color), NHL26 (WUE and leaf color), SUVH4 (stomatal patterning, rosette area, and leaf color), and FRI (flowering time, WUE, and leaf color), along with an enrichment of signals in ABA response. This study advances our knowledge of the genetic mechanisms driving plant adaptation to a novel precipitation environment. By identifying key genetic components and their contributions to multi-trait adaptation, our findings offer insights into how plants respond to environmental challenges and contribute to predicting plant responses to future climate change.

Spatial multi-omics data offer a powerful framework for integrating diverse molecular profiles while maintaining the spatial organization of cells. However, inherent variations in data quality and noise levels across different modalities pose significant challenges to accurate integration and analyses. In this paper, we introduce CANDIES, which leverages a conditional diffusion model and contrastive learning to effectively denoise and integrate spatial multi-omics data. With our innovative model and algorithm designs, CANDIES not only enhances the quality of spatial multi-omics data, but also yields a unified and comprehensive joint representation, thereby empowering many downstream analyses. We conduct extensive evaluations on diverse synthetic and real datasets, including MISAR-seq data from the mouse brain, spatial CITE-seq data from human skin biopsy tissue, spatial Mux-seq, and spatial ATAC-RNA-seq data from the mouse embryo, and 10 Visium data from human lymph nodes. CANDIES shows superior performance on various downstream tasks, including denoising, spatial domain identification, spatiotemporal trajectory reconstruction, and spatial association mapping for complex human traits. In particular, we show that CANDIES representations can be integrated with the rich resources from genome-wide association studies (GWASs), allowing the spatial domains to be linked with complex human traits, yielding spatially resolved interpretations of complex traits in their relevant tissues.

Domestication has profoundly shaped the phenotypic differentiation and genetic architecture of Perilla. However, analyses of the morphological difference between its cultivated and weedy forms across its varieties remains incomplete. This study analyzed morphological variation, genetic diversity, population structure, and marker-trait associations of 45 accessions representing the cultivated and weedy forms of two Perilla varieties (P. frutescens var. frutescens and var. crispa) collected from South Korea and Japan. Analyses of ten qualitative and quantitative agronomic traits revealed clear domestication-related differentiation. Cultivated var. frutescens showed larger and heavier seeds, whereas cultivated var. crispa and the weedy accessions were characterized by longer inflorescences and higher floret numbers but smaller seeds. Strong positive correlations were observed among seed-related traits, particularly between seed size and seed weight (r = 0.932), indicating coordinated selection of seed traits. Genetic diversity analysis using 70 SSR markers identified 330 alleles consistent with domestication bottlenecks in cultivated forms while higher diversity was generally retained in the weedy accessions. Population structure, UPGMA clustering, and principal coordinate analyses broadly differentiated the cultivated and weedy accessions, although partial admixture indicated shared ancestry and historical gene flow. Association mapping using Q-based GLM and Q + K MLM models identified 23 significant marker-trait associations involving 16 SSR markers consistently detected across both models. Several markers were associated with multiple traits, implying pleiotropy or tight genetic linkage. Notably, five SSR markers (KNUPF192, KNUPF202, KNUPF207, KNUPF230, and KNUPF238) may represent potential candidate loci for marker-assisted selection to improve seed-related traits in var. frutescens and leaf-related traits in var. crispa.

Plant-pathogenic microorganisms, including the wheat fungal pathogen Zymoseptoria tritici, adapt to their host environment. In plants, genome-wide association studies (GWAS) have been extensively used to uncover the complexity of local adaptation and disease resistance. However, the application of GWAS to decipher the mechanisms underlying fungal pathogenicity and host adaptation trails far behind. Here we established a genome-host association approach to infer statistical associations between pathogen allele frequencies and host of origin for 832 fungal strains isolated from 12 different host cultivars during a natural field epidemic. We identified 2 to 20 genes associated with specialization to the different wheat cultivars, including one known effector gene, Avr3D1, as well as ten pathogenicity-related genes that provided a proof of concept for our genome-host association approach. Our study highlights the polygenic genetic architecture of host adaptation and provides a new application of GWAS in plant pathogens that transcends the limitations imposed by traditional phenotyping methods.

Heat stress (HS) significantly impedes wheat production, making the development of heat-tolerant cultivars increasingly essential in the context of climate change. This study evaluated 153 elite spring wheat (Triticum aestivum L.) genotypes from the Wheat Association Mapping Initiative (WAMI) panel and three controls in field trials conducted during the 2020-2021 and 2021-2022 growing seasons at the Isfahan University of Technology research farm, Iran. Two sowing dates (SD; fall and spring) under full irrigation were employed to replicate HS conditions, with spring SD simulating terminal HS and reflecting regional farming practices. HS reduced days to flowering (DF), anthesis (DA), and maturity (DM) by 36-45%, shortened the grain-filling period (GFP), and decreased grain yield (GY) by ~25%, while key flour-quality traits (e.g., Zeleny index and grain hardness) remained stable under both SDs. Considerable genotypic variability was observed in both agronomic and quality traits. Stress tolerance and sensitivity indices (STI, MP, YSI, and HSI) were used to classify genotypes, with HSI identified as the most effective index due to its strong association with yield performance under HS. Several WAMI lines (e.g., 029, 123, 104, 067, and 139) demonstrated high yield potential combined with robust heat tolerance, as evidenced by their reduced yield loss under HS. These findings highlight the value of the WAMI panel for identifying heat-resilient wheat genotypes and providing critical insights for breeding programs targeting improved wheat performance under terminal HS and water-limited environments.

Multi-locus genome-wide association mapping reveals loci underlying malt quality traits in heterogeneous Ethiopian Barley (Hordeum vulgare L.) germplasm

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.

Abstract Lentil ( Lens culinaris Medik.), a nutrient-rich legume cultivated worldwide, plays a vital role in combating malnutrition and hidden hunger. Understanding the genetic architecture underlying key phenological and agronomic traits in lentil is crucial for accelerating molecular breeding. In this study, genome-wide association mapping was conducted using 142 genetically diverse lentil accessions, evaluated across two field environments over two years. High-throughput sequencing generated 34,995 high-quality single-nucleotide polymorphisms, which were used for genetic characterization and for the identification of marker-trait associations for phenological and yield-associated traits. Population structure analysis identified three subpopulations (K = 3), with UPGMA clustering showing a similar pattern. Association mapping was performed using multi-locus models and further confirmed through a single-locus generalized linear model. A total of 64 significant associations were identified, of which Chr5_342836807 and Chr6_200603138 were consistently detected across all environments for days to 50% flowering. Putative candidate genes located near these phenology-associated loci such as abscisate β-glucosyltransferase, pentatricopeptide repeat proteins, and transcription factors from the MYB , MADS-box , and GRAS families are likely involved in flowering-time regulation in lentil. These findings reveal novel associations between genetic variants and complex traits and identify putative genes that may be exploited in marker-assisted selection and genomic prediction strategies.