The Plant Genome

The field of genomics has enabled extraordinary progress in horticultural crop research. However, there is still a need for cost-effective, high-resolution technologies flexible to the diversity found in emerging crops. To this end, we introduce CannSelect, a high-quality genotyping platform for Cannabis sativa. Designed for use in diversity analyses and trait mapping, probe targets were selected from four genotyped diversity panels and a curated gene list. This platform has been used to effectively map day-neutrality in a segregating population to the Autoflower1 locus with average capture efficiencies of 88.5%. With broad genome coverage, demonstrated target specificity, and reproducibility, CannSelect is expected to perform well across the diversity of C. sativa. We describe the methodology used to design CannSelect v1.0 and performance metrics for testing capture efficiency and target alignment in diverse genome assemblies. The CannSelect platform represents a robust and scalable, genome-wide genotyping tool for C. sativa researchers and breeders.

White lupin (Lupinus albus L.) is a cool-season grain legume with seed crude protein of 33-47%, competitive with soybean (Glycine max L.) meal. It also fixes nitrogen and mobilizes soil phosphorus. Because soybean is a summer crop, white lupin can occupy Southeastern winter fields as a complementary protein source. Breeding for seed protein is limited by the cost and throughput of reference phenotyping. To determine how each is best deployed, we compared the utility of near-infrared spectroscopy (NIRS)-based phenomic selection with genomic selection based on 246,847 SNPs from low-pass, whole genome sequencing in a panel of Auburn University breeding lines and USDA National Plant Germplasm System germplasm. A handheld NIR calibration against Dumas reference protein reached screening-grade accuracy (R2 = 0.81). Under common cross-validation, phenomic predictive ability was 0.93 and genomic was 0.12. The low genomic value was consistent with moderate heritability (H2 = 0.33) and strong genotype-by-year interaction. Beyond predictive ability, NIRS recovered superior accessions the strictest selection intensity, and 40 to 60 reference assays sufficed to calibrate the model. Handheld NIRS is a low-cost tool for protein calibration and early-generation screening, while genomic prediction remains suited to parental selection, together supporting a complementary strategy for legume breeding Plain Language SummarySoybean meal is the main protein source for livestock and fish farms in the United States. Because soybean is a summer crop, many Southeastern fields sit idle or grow low-value cover crops in winter. White lupin, a cool-season legume whose seeds are as protein-rich as soybean meal, makes a good complementary winter crop: it yields high-protein grain while serving as a cover crop that fixes nitrogen and frees up soil phosphorus for later crops. In our early-stage lupin breeding program, measuring seed protein by standard lab methods is slow and costly. We built a calibration that lets a handheld scanner estimate protein from light, and compared it with predicting protein from the plants DNA. The scanner gave accurate, low-cost protein screening from only about 40-60 lab tests, while DNA-based prediction remains suited to guiding parent selection. Used together, these tools offer breeders a practical path to develop high-protein white lupin. Core ideasO_LIHandheld NIRS provides screening-grade prediction of white lupin seed crude protein. C_LIO_LISpectra carried more usable protein signal than markers by measuring seed chemistry directly. C_LIO_LINIRS and genomic prediction serve different stages of a white lupin breeding program. C_LIO_LIAbout 40 to 60 reference assays sufficed to calibrate NIRS to near-full accuracy. C_LI

Tropical maize is a rich source of genetic diversity that could enhance temperate maize breeding programs, but its sensitivity to long-day photoperiods, resulting in delayed flowering, limits its widespread use. To overcome this barrier, the Genome Engineering to Sustain Crop Improvement (GETSCI) project used CRISPR/Cas9 to mutate three flowering repressor genes, ZmCCT9, ZmCCT10, and ZmRAP2.7, in the tropical inbred Tzi8. A single sgRNA targeting the first exon of each target gene was combined with an excision cassette carrying the morphogenic genes Babyboom (Bbm) and Wuschel2 (Wus2) to enable efficient transgenic plant regeneration. Transgenic plants carrying frameshift edits in each target gene were recovered, and subsequent crosses produced two non-transgenic genotypes: a double-edited zmcct10, zmrap2.7 line and a triple-edited zmcct9, zmcct10, zmrap2.7 line. Multiple flowering traits were measured for the edited genotypes and unedited Tzi8 inbred in short-day (Hawai i) and long-day (Iowa) field conditions. Both edited genotypes flowered significantly earlier than Tzi8 in both environments. Notably, under long-day conditions, flowering of the two edited lines overlapped with that of the temperate inbred B73, whereas Tzi8 did not. Together, these results demonstrate that targeted, multiplex gene editing can reduce photoperiod sensitivity in a tropical inbred, expanding access to previously untapped genetic diversity for temperate maize improvement.

Dietary fibre intake remains below recommended levels, and increasing fibre content of widely consumed white wheat flour (derived from the starchy endosperm) represents a scalable strategy to improve public health. In wheat starchy endosperm, arabinoxylan (AX) is the dominant fibre component, with the water-extractable (WE) fraction being particularly beneficial to health. We assembled an Elite Fibre Panel (EFP) of 384 elite modern wheat genotypes from UK commercial breeding programmes and quantified the content of WE-AX in wholemeal, as a proxy for soluble AX in white flour, across two UK field environments. Wholemeal WE-AX, showed substantial quantitative variation and moderate genotype-by-environment interaction, with a broad-sense heritability of 0.68. Genome-wide association analyses using 6,791 SNPs identified seven loci associated with WE-AX content. The strongest and most stable effects were mapped to major loci on chromosomes 1B and 6B, previously implicated in AX regulation, while additional loci of smaller and sometimes environment-dependent effect were detected on chromosomes 3A, 5B, and 7A. Favourable alleles increased WE-AX content by [~]5-15% and combined additively. LD-defined intervals contained several high-confidence candidate genes involved in cell-wall biosynthesis, remodelling and post-depositional modification, including PER1, a validated regulator of arabinoxylan cross-linking, together with genes encoding a UTP-glucose-1-phosphate uridylyltransferase, trichome birefringence-like proteins and xyloglucan endotransglucosylase/hydrolases. These findings demonstrate that substantial gains in soluble AX can be achieved by pyramiding favourable alleles already segregating within elite germplasm, providing a practical route for breeding wheat with enhanced dietary fibre content and improved nutritional quality. Key messageMultiple additive loci controlling soluble arabinoxylan content were identified in elite wheat germplasm, enabling marker-assisted breeding for increased dietary fibre in white flour.

Genomic selection (GS) and trait discovery in crops require reliable marker datasets, yet variant discovery remains constrained by the lack of standardised, high-confidence reference resources. Publicly available plant genomic resources are often based on outdated reference genomes, heterogenous pipelines, and generic variant hard-filtering thresholds derived from human studies. Consequently, implementation of the Genome Analysis Toolkit (GATK) Best Practices framework, including machine learning-based variant quality score recalibration (VQSR), remains challenging in crop genomes. Here, we present a crop-adapted framework for unsupervised variant quality assessment and VQSR enabling advanced SNP and insertion-deletion (INDEL) discovery without predefined high-confidence reference variant sets. Using Brassica napus as a model, we use the Illumina 60k Brassica SNP array to generate a reference-specific truth set for data pre-processing and independent evaluation of an unsupervised machine-learning approach for constructing cohort-specific VQSR resources. Gaussian mixture model (GMM)-based clustering of multidimensional variant annotation profiles identifies high-, intermediate-, and low-quality variant classes, providing flexible training resources for VQSR without requiring labelled training data. We further validate the framework in diploid Brassica oleracea and hexaploid wheat (Triticum aestivum) across cohorts differing in population size, genetic diversity, and genome architecture. Despite population- and species-specific differences in variant annotation profiles, GMM-based clustering consistently identifies informative variant quality classes and enables cohort-specific recalibration of both SNPs and INDELs. Our results demonstrate that unsupervised machine learning can extend GATK Best Practices to crop species lacking comprehensive genomic resources, providing a scalable approach for generating high-quality variant datasets for crop genomics and downstream breeding applications.

Key messageA single-kernel near-infrared reflectance spectroscopy-based sorter can effectively identify haploid kernels for doubled haploid production in field and sweet corn backgrounds. Doubled haploid (DH) technology significantly shortens the breeding cycle for developing homozygous inbred lines in maize (Zea mays). Manual sorting of haploids from a larger bulk of hybrid kernels in an induction cross is a major bottleneck in DH development. Automated systems based on near-infrared (NIR) reflectance spectroscopy can be valuable tools for rapid haploid sorting, provided that sorting accuracy is sufficient for incorporation into the DH process. In this study, we evaluated the accuracy of a custom-built single-kernel NIR (skNIR) sorter for classifying haploid kernels from 12 high-oil haploid induction populations generated from two sweet corn and two field corn donors and four high-oil haploid inducers (HOHIs). We evaluated several general classification models that can be applied without population-specific recalibration or prior genotyping, including models that classified haploids based solely on predicted oil content, as well as multivariate methods that used all wavelengths of the NIR spectra. The highest classification accuracy was obtained using a general multivariate support vector machine (SVM) model. When combined with the two best-performing HOHIs, the general SVM model accurately sorted induction populations from two of the three donor backgrounds crossed with these inducers. Two oil-based methods showed less accurate classification than the multivariate SVM model, due to overlapping oil content distributions across the two kernel classes. Overall, this study demonstrates effective skNIR-based sorting of haploid kernels from diverse induction populations using a single general model. The practical deployment of this instrument in maize breeding programs is discussed.

Indonesian rice cultivars represent valuable genetic resources, yet many remain poorly characterized at the genomic level. Here, we generated 95.40 Gb of PacBio HiFi sequence data from seven Indonesian rice cultivars and constructed cultivar-specific consensus genomes using the telomere-to-telomere Nipponbare reference AGIS1.0. Sequencing coverage ranged from 27.92x to 41.58x, and the resulting consensus genomes spanned 387.93-390.54 Mb, with BUSCO completeness of approximately 98.3-98.5%. OrthoFinder assigned 99.1% of predicted proteins to 40,737 orthogroups, including 27,514 core orthogroups represented across all seven cultivars, indicating a highly conserved predicted gene space within the reference-guided framework. Targeted analysis recovered 278 of 280 cultivar-by-locus combinations representing 40 genes or gene family entries associated with grain pigmentation, nitrogen and amino-acid metabolism, and starch properties. Comparative predicted protein analysis prioritized ANS1, SBE2b, SSIIa/ALK, Wx/GBSSI, OsAAP6/qPC1, and SSI as candidates for further investigation. Among 269 completed AGIS1.0-anchored promoter comparisons, 159 passed quality-control criteria, whereas 110 were flagged for gene-model, boundary, synteny, or structural concerns. Notably, these flagged comparisons accounted for more than 90% of the alignment-derived sequence variation, emphasizing the importance of rigorous quality control when interpreting apparent promoter divergence. Collectively, these reference-guided genomic resources provide a standardized framework for investigating sequence variation in Indonesian rice germplasm and prioritize testable coding and regulatory candidates for functional validation and future genomics-assisted crop improvement.

Enhancing grain carotenoid concentration to increase yellowness and provitamin A content in sorghum (Sorghum bicolor [L.] Moench) is a major breeding objective across sub-Saharan Africa. Carotenoid breeding is constrained by costly post-harvest phenotyping and can be accelerated through genetic markers that capture functional variation in key carotenoid biosynthesis genes. Zeaxanthin epoxidase (ZEP) is a major gene controlling sorghum grain carotenoid accumulation, and SNP-based KASP markers facilitate selection of high-carotenoid grain, although quantitative variation remains among lines carrying the favorable allele. We hypothesized that additional functional variation within ZEP and other carotenoid biosynthesis pathway genes contributes to this variation and can be revealed by a pangenome-informed analysis. Using a 33-member pangenome reference, we characterized sequence and structural variation at ZEP and other key carotenoid biosynthesis genes and applied pangenome-derived genotyping to study marker diversity associated with carotenoid accumulation. Evaluation of ZEP in the pangenome reference revealed previously uncharacterized structural variation that is absent from the BTx623 primary reference genome. A pangenome-based association analysis identified genetic markers associated with the accumulation of carotenoids which were specific to particular pangenome reference members, including in RTx430 and SRN39, both yellow endosperm lines. Machine learning identified markers in ZEP, {beta}-OH, ZDS, and Z-ISO as the most predictive for all carotenoid traits, suggesting a multi-locus genetic architecture underlies the accumulation of carotenoids in sorghum grain. By integrating sorghum pangenomic resources with machine learning, this study establishes a framework for pangenome-accelerated trait discovery and identifies new genetic targets for carotenoid biofortification in sorghum.

Perennial grains represent a promising frontier for sustainable agriculture, but breeding progress is constrained by the accessibility of genotyping and the difficulty of evaluating complex traits expressed for multiple years after establishment across heterogeneous environments. Phenomic selection may help address these challenges by using inexpensive, scalable, high-dimensional phenotypes collected early in development, although the robustness of such predictions across breeding cycles remains uncertain. Here, we compared genomic selection and phenomic selection across two breeding cycles of Thinopyrum intermedium (intermediate wheatgrass; IWG; Kernza(R)), comprising approximately 2,280 individuals from maternal half-sib families evaluated across multiple field sites and years. We constructed relationship matrices from genomic markers and early-life stage phenomic data, including seed and leaf color (HSV), CropReporter multispectral reflectance and indices, and cycle-specific hyperspectral reflectance sensors. Genomic models provided the strongest predictions on average across all field traits in both cycles. Among phenomic predictors, leaf HSV was consistently the most informative, whereas CropReporter and hyperspectral data showed lower and more trait-dependent performance and seed HSV provided little predictive value. Genomic, leaf HSV, and CropReporter models transferred across breeding cycles with little apparent loss of predictive ability relative to within-cycle validation, demonstrating that their predictive signals were not restricted to a single breeding cycle. Early-life stage leaf HSV emerged as a practical, accessible tool for germplasm thinning and early-stage prioritization in perennial breeding programs. Despite limited similarity among relationship matrices, multi-relationship-matrix models rarely improved prediction beyond the stronger constituent single-relationship-matrix model. Together, these results show that early-life stage phenomic data provide reproducible information about agronomic performance expressed years later, but that predictor complexity and data integration do not guarantee improved prediction.

Micronutrient deficiencies remain a major public-health challenge, and genetic improvement of grain iron and zinc concentrations in wheat offers a sustainable biofortification strategy. We evaluated 563 CIMMYT advanced wheat lines and six checks under restricted irrigation (two irrigations) and well-watered conditions (five irrigations) at Ciudad Obregon, Mexico. Grain iron and zinc concentrations were quantified by energy-dispersive X-ray fluorescence spectrometry. Best linear unbiased estimates were calculated, and genome-wide association analyses were conducted using 8,687 high-quality single-nucleotide polymorphisms and a multi-locus mixed model that accounted for population structure. Five marker-trait associations were detected in at least two datasets. For grain zinc concentration, S3B_811421507 was detected under both irrigation regimes and in the combined analysis, whereas S3B_814372642 was detected under well-watered conditions and in the combined analysis. For grain iron concentration, S2B_73321328 and S4B_20679079 were associated with variation under restricted irrigation and in the combined analysis, while S2B_72162723 was detected under well-watered conditions and in the combined analysis. Individual loci explained 0.34% to 3.36% of phenotypic variance, consistent with the quantitative inheritance of grain micronutrient concentration. The identified alleles provide candidate targets for validation and marker-assisted biofortification breeding, while their environment-dependent effects emphasize the need to evaluate micronutrient traits across contrasting moisture conditions.

Background Sesame (Sesamum indicum L.) is one of the oldest oilseed crops. Its seeds accumulate lignans and antioxidants that determine their nutritional value. Global demand for sesame is rising rapidly, but climate change increasingly threatens sesame yields and seed quality. Here, we analyzed a worldwide panel of 300 sesame accessions to explore the genetic basis of key adaptive and quality traits, specifically flowering time, seed lignan content, and seed antioxidant capacity. Results Whole-genome resequencing revealed previously unreported genetic diversity, expanding the resources available for sesame breeding. By integrating k-mer-based genome-wide association analysis with a graph-based sesame genome, we identified structural variants associated with differences in flowering time and lignan accumulation. A 9.4-kb deletion on chromosome 6 that disrupted SIN_1018434 (an ortholog of Arabidopsis PHOTOPERIOD-INDEPENDENT EARLY FLOWERING 1) and a 6.2-kb Copia-type retrotransposon insertion upstream of SIN_1004470 on chromosome 11 were associated with early flowering. The intact alleles at both loci were associated with delayed flowering and were predominant in low-latitude accessions. High seed lignan content was associated with non-synonymous mutations and copy number variants in glycosyl hydrolase genes on chromosome 6 and an 8.2-kb deletion spanning SIN_1019378 on chromosome 13. Association signals for seed antioxidant traits coincided with loci involved in abiotic stress responses and seed-coat pigmentation. Conclusions These findings highlight the importance of exploring diverse germplasm to uncover previously unrecognized adaptive and quality-associated genomic variation. The loci identified here provide molecular targets for developing climate-adapted cultivars with improved seed quality.

The efficient pyramiding of favorable alleles underlying complex traits remains a major challenge in crop breeding as most quantitative trait loci (QTLs) have not been resolved to causal genes, limiting their direct application in marker-assisted breeding. Although haplotypes provide more informative genetic units than individual markers, existing haplotype-based studies have largely focused on genetic interpretation and elite haplotype discovery, whereas computational frameworks for translating haplotypes into breeding decisions remain limited. Here, we developed HAPBDB, a haplotype-guided breeding framework that directly translates regional haplotypes into parental selection, cross design, and elite QTL pyramiding, and applied it to a lettuce genomic breeding panel. HAPBDB accurately reconstructed functional haplotypes at known loci and resolved elite haplotypes for five major QTLs controlling flowering time and yield. Integrating haplotype information across loci enabled systematic identification of accessions carrying complementary elite haplotypes and rational design of crosses that maximized favorable haplotype accumulation while minimizing segregating loci. Experimental validation using QTL-specific molecular markers demonstrated concordance between predicted and observed multi-locus genotypes across all designed F hybrids. Our results demonstrated that regional haplotypes can serve as practical breeding units even when the underlying causal genes remain unknown, thereby enabling the direct utilization of genetically mapped QTLs for precision breeding. By bridging the gap between genomic discovery and practical breeding, HAPBDB provides a practical framework for converting genomic information into breeding decisions and accelerating precision improvement of complex traits.

Accelerating grapevine breeding for disease resistance and climate adaptation remains constrained by long generation cycles. We benchmarked genomic (SNP), phenomic (NIRS), and metabolomic (untargeted LC-MS) prediction for 24 agronomic traits in a biparental population phenotyped over three years. Seven statistical frameworks and four tissue x timepoint combinations (wood; vineyard leaves at budbreak and flowering; greenhouse leaves at flowering) were evaluated, together with feature-wise BLUPs across samples. Cross-year and cross-population analyses with two additional populations assessed temporal robustness and transferability. Genomic prediction was most accurate (up to r = 0.83), metabolomic prediction was intermediate (up to r = 0.59), and phenomic prediction was lowest (up to r = 0.39) despite its lower acquisition cost. Metabolite features were more heritable than NIR wavelengths, for which most unexplained variation remained residual under the fitted model. Multi-omics integration produced limited overall gains. These results support genomic selection as the primary approach, with metabolomic or phenomic screening considered only for traits and sampling designs that show reproducible predictive signal.

Cowpea (Vigna unguiculata [L.] Walp.) is a resilient grain legume and an important global source of dietary protein, yet the genetic and environmental basis of phenological and canopy development, as well as grain composition, remains incompletely characterized across production environments. In this study, we evaluated a cowpea multi-parent advanced generation intercross (MAGIC) population along an environmental gradient in California (with contrasting daylengths, temperatures, and soil types) using agronomic, grain compositional, and uncrewed aerial vehicle (UAV) and rover-enabled phenotyping. Near-infrared spectroscopy (NIRS) enabled assessment of grain compositional traits, while sensing-enabled time-series imaging captured canopy and reproductive dynamics. Quantitative trait locus (QTL) mapping identified 267 QTL, and genome-wide association studies (GWAS) detected 1,973 marker-trait associations. Integrating QTL mapping and GWAS results identified two major genomic hotspots affecting multiple traits. A chromosome 9 hotspot (5.8-6.0 Mb) was associated with flowering time and co-localized with sensing-enabled measures of flower and pod counts, plant height, and vegetation fraction, indicating broad effects on phenological and canopy development. A chromosome 8 hotspot (37.3-37.9 Mb) contained co-localized signals for seed weight, protein, starch, phytate, and moisture. A total of 22 prioritized candidate genes were identified within these and other loci with multi-environment QTL and GWAS support. Genomic predictive abilities were moderate to high for most traits and scenarios, with multi-trait MegaLMM outperforming RR-BLUP. Together, these results define major genomic regions controlling cowpea phenology, canopy development, and grain composition, and provide targets and strategies for breeding cowpea cultivars with favorable and environmentally resilient productivity and grain composition. Significance StatementTo dissect the genetic basis of cowpea productivity, adaptation, and grain composition, and how performance for these traits varies and can be predicted across environments, we combined multi-environment phenotyping, including sensing of canopy and reproductive traits, with quantitative genetic analyses in a multi-parental population. We identified genomic hotspots for seed size/composition and reproductive phenology and an across-environment predictive advantage for multi-trait vs. single-trait genomic prediction. Overall, these findings support the comprehensive improvement of cowpea.

The mechanisation of agriculture has achieved major increases in labour productivity but has also led to an erosion of agrobiodiversity. Neglected and underutilized crops such as common buckwheat (Fagopyrum esculentum Moench) offer the potential to diversify food production systems but require major genetic improvement to compete with established crops. Although target traits for breeding such as more homogeneous ripening, reduced height, larger seeds and higher and more stable yields are well known, large-scale and field-based phenotyping data of diverse common buckwheat accessions across multiple environments are still scarce. Here, we tested the agronomic performance of a common buckwheat germplasm collection consisting of 170 accessions of diverse origin. The collection was tested at seven year-sites, and 19 agronomic traits related to phenology, growth habit and yield were assessed. Most traits showed high repeatability (0.31 < R2 < 0.98) and heritability (0.45 < H2 < 0.97), indicating genetic control of these traits. Coefficients of variation ranged between 6.19% and 46.15%, showing the high phenotypic diversity within the collection. By combining multiple traits, distinct ideotypes for different roles in the crop rotation were investigated, and accessions with promising agronomic profiles identified. Genome-wide association studies revealed marker-trait associations for 18 traits, allowing the identification of putative candidate genes influencing these traits such as a putative GRF-INTERACTING FACTOR associated with flowering time and biomass production. Our results show that the collection offers vast phenotypic diversity that can be exploited for the development of improved varieties tailored to meet specific agronomic purposes. The efficient utilization of these genetic resources should be supported by the application of genomics-based breeding approaches. The development of genetic markers and search for putative candidate genes mark important steps towards the establishment of efficient breeding schemes. Thus, these genomic resources provide a foundation for accelerating the genetic improvement of common buckwheat and developing agronomically competitive varieties.

Understanding the genetic diversity and population structure of breeding materials is essential for developing stress-resilient cultivars. In tropical maize, where drought and low soil nitrogen (low N) severely limit productivity, continuous development of tolerant varieties remains a priority. This study assessed the genetic diversity and population structure of 250 doubled haploid lines (DHLs) derived from five drought- and low N-tolerant tropical populations. Genotyping was performed using mid-density DArTseq markers, yielding 3,305 high-quality SNPs for analysis. Results revealed a moderate level of diversity among the DHLs, with an average genetic distance of 0.39, a polymorphism information content (PIC) of 0.33, and a minor allele frequency (MAF) of 0.29. These values reflect substantial allelic variation, important for identifying complementary parental combinations in hybrid development. Discriminant analysis of principal components (DAPC) grouped the DHLs into five distinct clusters, largely corresponding to their source populations, although some admixture was observed. This indicates that while the genetic backgrounds of the source populations were mostly retained, recombination introduced useful variation. Overall, the clear population structure and high diversity observed among these DHLs provide a strong genetic foundation for future maize improvement. These lines represent valuable resources for heterotic group formation, hybrid development, and recurrent selection schemes aimed at enhancing drought and low nitrogen tolerance in tropical maize.

Bluebunch wheatgrass (Pseudoroegneria spicata) exhibits substantial variation in its response to salt stress, making it a valuable model for studying salinity-tolerance mechanisms for use in crop improvement. In this study, we identified two P. spicata genotypes with contrasting responses to salt stress: the tolerant W6 56551, which maintained growth with green foliage under saline conditions, and the susceptible PI693916, which exhibited severe leaf chlorosis and stunted growth. To better understand the molecular basis of salt tolerance in blue-bunch wheatgrass, we conducted RNA-sequencing at 0, 1, and 4 days (D0, D1, and D4) after salt treatment at 160 mM level to examine changes in gene expression of salt-tolerant and salt-susceptible genotypes. Comparative analysis across time points identified 6,154 and 1,086 differentially expressed genes (DEGs) at D4 and D1 in PI693916, and 4,638 and 3,302 DEGs at D4 and D1 in W6 56551, respectively, relative to control (D0). Functional analysis of these DEGs showed that the salt-tolerant geno-type displayed an early and broad transcriptional reprogramming, including induction of photosynthesis, carbon metabolism, and flavonoid biosynthesis pathways, whereas the salt-susceptible genotype exhibited delayed and less coordinated responses, with enrichment of cyanoamino acid metabolism and repression of antioxidant-associated pathways. Notably, calcium signaling, ion transporter regulation, and osmolyte biosynthesis genes showed contrasting expression between genotypes, highlighting distinct strategies for ionic and osmotic homeostasis. Collectively, these results demonstrate that salt tolerance in P. spicata is associated with rapid metabolic adjustment, enhanced photosynthetic stability, and differential regulation of ion transport and osmoprotectant pathways.

In common wheat (Triticum aestivum L.), variation at the Vrn1 and Ppd1 loci changes plant phenology, resulting in differential adaptation useful to breeders. Because the effects of phenology on grain yield are conditional on environmental factors, the incorporation of markers for Vrn1 and Ppd1 alleles into breeding approaches has proven difficult. Historic phenotypic, genotypic, and environmental data can provide insight into the relationship between major phenology alleles and grain yield as mediated by environmental variables. Analyses of eight years of breeding trials (1,038 lines at 219 site-years) determined that weak vrn1 alleles and Ppd1 insensitivity alleles have variable effects. These effects change depending on an environment's winter temperature and latitude, respectively, but the environmentally-driven changes in effect of vrn1 alleles are much more variable than effects of Ppd1 alleles. Mediation analyses showed phenologically-dependent environmental variables are one mechanism through which phenological changes created by vrn1 and Ppd1 variants alter yield across multiple developmental stages. Environmentally-driven relationships between flowering time and yield were modeled to estimate yield allele effects as a function of effects on phenology and environmental variables. Weak winter alleles at vrn1 may play a stabilizing role on yield, due to the correlation of winter conditions that drive larger effects with later-season warm temperatures that penalize later maturity. A better mechanistic understanding of how these loci drive environment-specific yield effects may facilitate utilization of markers for these alleles and improve predictive modeling of yield.

Table beet (Beta vulgaris subsp. vulgaris var. vulgaris), also known as red beet, garden beet, or beetroot, is an important crop with both nutritional and agricultural value, yet high-quality genomic resources for this taxon are limited. We present the chromosome-scale genome assembly RefTab-1 based on PacBio and Hi-C data of the inbred table beet genotype W357B. The assembly has a size of 668.6 Mbp and an N50 of 72 Mbp, with 96% of the assembly anchored to nine pseudo-chromosomes. We annotated 64% repetitive DNA, 24,863 protein-coding genes, and 10,405 non-coding RNA loci including a 5S rRNA cluster on chromosome 4. Genome-wide methylation profiling using EM-seq revealed high levels of cytosine methylation in repeats and context-specific methylation in gene bodies and promoters. We propose three terpene synthase candidate genes based on functional annotation and based on their location near a genetic marker on chromosome 8 previously associated with geosmin concentration, responsible for the earthy aroma of table beet. Comparative analysis with sugar beet indicated overall structural conservation and a 9 Mbp inversion on chromosome 9. These high-quality resources for table beet will enhance genetic, epigenetic, and comparative studies in Beta vulgaris, advancing beet biology and breeding programs.

Caraway (Carum carvi L., 2n = 2x = 20) is a medicinal and aromatic plant of the Apiaceae family valued for its essential oil with the major terpenoids carvone and limonene. While the essential oil content represents an important quality trait, low seed shattering and high thousand-grain weight (TGW) are key determinants of seed yield. As molecular tools remain scarce within Apiaceae breeding beyond carrot, we conducted a QTL mapping for these seed-related traits using two previously established F2 populations (Effective N(P1) = 236; N(P2) = 266). In P1 with the non-shattering maternal parent Rekord, QTL mapping revealed at least one shattering locus on LG03 (PVE: 9.9%). In addition, loci associated with essential oil (LG08, 10.5% PVE) and TGW (LG09, 11.1% PVE) were detected. In P2, we identified six essential-oil loci (LG02-06, LG10; combined PVE: 49.2%), with five loci affecting both carvone and limonene, and one (LG03) exclusively limonene. Additionally, a major QTL for TGW mapped to LG02 (PVE: 15.6%). For stalk attachment rate one minor QTL was detected (LG07, 8.5% PVE), while no QTL co-localized with the major shattering locus. To pave the path towards fine-mapping, we designed and validated 26 diagnostic PACE markers covering five key QTLs.