The Plant Genome

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 Publicly available plant genomic resources are often based on outdated reference genomes, heterogenous pipelines, and generic variant hard-filtering thresholds derived from human studies.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.

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.

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.

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.

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.

Lipolytic enzymes play critical roles in plant development, growth, and stress responses. In oat (Avena sativa), they are of particular interest because lipid hydrolysis contributes to rancidity and reduced grain quality, yet little is known about genomic variation in oat lipolytic enzymes. Here, we investigated genomic and expression diversity of the GDSL-esterase/lipase gene family using pangenome, transcriptome, and genome-wide association analyses. Across 22 A. sativa and two A. sterilis genome assemblies, we identified 7,775 GDSL genes (~324 per assembly), revealing wide conservation of ancestral orthologs alongside extensive copy-number variation among homoeologs and paralogs. Transcriptome profiling across 20 genotypes and six tissues demonstrated preferential expression in embryo and root, with tissue-specific patterns highlighting candidates for specific functions, such as seed lipid metabolism. Associations with structural variants further uncovered candidate mutations underlying expression variation among accessions. Comparisons of cultivated and wild oat detected loci with signatures of breeding-based selection, suggesting that GDSL genes have contributed to diversification of the cultivated gene pool. Using 629 whole-genome sequenced accessions, we also identified associations between GDSL regions and agronomic traits, particularly plant height and effective tiller number. Several regions showed patterns consistent with selection, highlighting candidate loci for further investigation of agronomic variation. Together, our results reveal substantial genomic and transcriptional diversity within the oat GDSL gene family and identify candidate genes and genomic regions for improving seed quality and agronomic performance. More broadly, our study demonstrates how pangenomic resources can be used to investigate gene-family diversity and function in complex polyploid crops.

Sudden Death Syndrome (SDS), caused by Fusarium virguliforme, can significantly impact soybean yields. To advance the understanding of SDS resistance in plant introductions, we conducted genome-wide association studies (GWAS) on a set of 351 accessions from the USDA Soybean Germplasm Collection. The analysis revealed seven single nucleotide polymorphisms (SNPs) significantly associated with foliar SDS resistance; three significant QTL (on chromosomes 3, 9 and 11) were detected with Fixed and Random Model Circulating Probability Unification (FarmCPU) and four (on chromosomes 5, 6 and two on chromosome 7) with Bayesian-information and Linkage disequilibrium Iterative Nested Keyway (BLINK). Localized linkage disequilibrium blocks were estimated for these regions. Notably, all of them overlapped with regions where QTL associated with SDS resistance had been previously reported and where within, or close to, sequences annotated as implicated in plant defense mechanisms, such as toxin transporters and transcription factors. The architecture of SDS resistance appears quantitative and polygenic, with most loci explaining modest phenotypic variance. Six plant introductions in this study--PI 175188, PI 307889A, PI 323559, PI 578307C, PI 445683, and PI 323558--harbor favorable alleles at all seven loci, providing high-value resources for resistance introgression. This study underscores the value of leveraging the USDA Soybean Germplasm Collection for the discovery of resistance loci and provides a foundation for marker-assisted breeding to enhance SDS resistance in elite soybean lines.

Breeding programmes choose genomic prediction models by accuracy rankings, and the metric behind a ranking decides which model wins. Selecting the best genotypes within a trial depends only on the order of the predictions, so a metric that guides the choice must be unchanged by any order-preserving change of the predictions and respond when their order is reversed. Of the 22 official metrics of the Genomes to Fields maize competition, one meets both conditions, the within-environment rank correlation; the within-environment Pearson correlation is protected against rescaling only; and twenty fail, including the root mean squared error that decided the 2022 ranking. Switching between the competitions two official metrics reverses 27 % of team pairs, and an exact decomposition shows why pooled metrics mislead: in three constructed panels, 94-95 % of the attainable square of pooled correlation is carried by the environment means. The accuracy difference a trial can resolve falls as about 3/{surd}N with its N genotype-environment cells: at least 0.03 at the competitions design and 0.04-0.12 once calibrated on real panels, against 0.003 between the first two teams; tests that two correlations differ declare far smaller differences, which do not reliably change selection. In soybean, where the two most accurate methods stand clear of most others by more than the trial resolves, ranking by either within-environment correlation selects better material, 98-99 % of the attainable selection gain in held-out environments against 13-34 % for error-magnitude rankings; in common bean and a Chinese maize population, where the leading methods lie within the resolvable gap, all rules pick material of similar value. GPverdict runs these checks on any programmes cross-validated predictions.

Genotype-by-environment interactions can complicate plant breeding because genetic effects may vary across weather regimes, locations, and years. Although genomic prediction models can use dense marker data to predict complex crop traits, they often provide limited information about how marker relevance changes across environmental conditions. This study introduces a graph-based framework for estimating environment-associated marker rankings from multi-environment crop trial data. We introduce an attention-based graph neural network that jointly represents SNP markers and weather-based environment vectors as interacting nodes and is designed to learn environment-associated marker rankings from supervised yield data. The model is trained using yield prediction as the supervised objective, and the learned marker-level attention signals are aggregated to estimate marker rankings overall and within groups of similar environments. We evaluate the framework on two genotype-by-environment datasets. In both datasets, we examine whether learned marker rankings are stable across independent training runs and determine the overlap between high-ranked markers and external GWAS references. In a maize dataset with data from 212 different site-years, we rank markers separately within each weather-defined environment cluster and define a global marker set as the markers shared across all clusters. This provides a structured way to examine how marker rankings vary across weather-defined groups and which markers remain important across environments. We further encode each weather variable with a separate LSTM combined by attention, which makes the contribution of individual weather variables readable, and compare the resulting variable ranking against an independent SHAP-based attribution. Genes located near the top-ranked markers are also examined for functional enrichment. Overall, the results suggest that graph-based, environment-conditioned marker attribution may complement genomic prediction as an exploratory tool for studying context-dependent genomic signals in multi-environment trials.

Efficient yield prediction under field conditions remains a key challenge for crop breeders and agronomists. YieldLearn is an interpretable machine-learning pipeline developed to estimate yield-related traits in durum wheat (Triticum durum) using physiological indicators measured by the Hansatech Instruments Pocket PEA. The workflow integrates fluorescence parameters (Area, Fo, Fm, Fv/Fm, PI Inst.) with field-measured agronomic traits (Booting, Height, Maturity, Lodging, Yield kg/plot, and Adjusted Yield kg/ha). Five supervised learning algorithms multinomial logistic regression, random forest, support vector machine (radial kernel), k-nearest neighbors, and decision tree were evaluated using five-fold cross-validation. Models achieved average classification accuracy around 0.57 with AUC values exceeding 0.58. SHAP (SHapley Additive exPlanations) visualizations revealed that high Fv/Fm and PI Inst. values were positively associated with yield potential, whereas elevated Fo indicated stress-induced yield reduction. An interactive Shiny App enables real-time prediction and feature-importance exploration. Given the small sample size (165 plots) and absence of environmental covariates such as temperature or soil moisture, YieldLearn should be regarded as a reproducible exploratory framework rather than a production-ready predictor. Nonetheless, it offers a valuable foundation for guiding experimental design, validating physiological indicators, and supporting the next generation of scalable, interpretable crop-yield models.

Amaranthus tricolor is a climate-resilient orphan crop whose betalain pigments contribute to nutritional quality and stress tolerance. However, breeding efforts have traditionally relied on field yield trials due to the limited availability of genomic information and genetic resources. To address this gap, we first used the Illumina and PacBio sequencing platforms to assemble a novel reference genome of A. tricolor, using the Japanese cultivar Bayam. By combining this reference genome with a core collection developed in our previous study, we aimed to identify genomic regions associated with pigmentation-related traits across winter and summer seasonal environments. Betalains are antioxidant phytonutrients that contribute to abiotic stress tolerance, making the regulation of their biosynthesis under heat stress particularly relevant for developing climate-resilient crops such as A. tricolor. As a result, ATR1.0CH03_5131103, which is associated with ATR1.0ch03g000565, was identified under the winter seasonal environment. Meanwhile, transcriptomic profiles in this study revealed that ATR1.0ch03g000565 was differentially expressed under both control and heat stress conditions, suggesting that it may be associated with the regulation of CYP76AD1 expression under optimal conditions. Under the elevated-temperature treatment, transcript levels of CYP76AD1 and 5GT decreased in Am087, coinciding with lower betacyanin-dominated absorbance indices. cDOPA-like homologs were upregulated; however, pigmentation-related absorbance remained reduced. Further molecular experiments, such as CRISPR-Cas9 knockouts or overexpression studies, are necessary to validate the regulatory mechanisms of CYP76AD1 and 5GT under heat stress.

Considering the increasing water and labor constraints in rice production, direct-seeded rice is becoming increasingly important. However, direct-seeded rice is vulnerable to water stress and inefficient nutrient use. Nitrogen use efficiency in rice is generally low, and improving nitrogen acquisition and assimilation is important for sustainable productivity. In this study, 48 recombinant inbred lines were evaluated under ammonium and nitrate nutrition in irrigated and rainfed environments across two cropping seasons. Agronomic, physiological, biochemical and molecular responses were assessed. Significant effects of genotype, nitrogen form, environments and their interactions were observed for several traits. Rainfed conditions reduced grain and biological yield compared with irrigated conditions. Ammonium produced a higher mean grain yield than nitrate by approximately 26.2 g m-2 under irrigated conditions and 154.2 g m-2 under rainfed conditions, although the responses varied among genotypes. Biological yield and SPAD values were generally higher with ammonium, whereas total chlorophyll showed season and environment-dependent responses. Glutamine synthetase activity was highest under ammonium treatment, while glutamate synthase activity was higher under nitrate treatment. Grain yield showed strong positive correlations with biological yield, but correlations between grain yield and GS or GOGAT activity were not significant. Expression of OsGln1.2, OsGln2 and OsGlt1 varied according to genotype, tissue and nitrogen treatment. Overall, the results demonstrate that responses to nitrogen forms are genotype- and environment-dependent, and that ammonium may provide a yield advantage under rainfed conditions. Integrating field performance with biochemical and molecular traits may help identify rice genotypes suited to water-limited systems.

Bread wheat harbours low genetic diversity due to bottlenecks during speciation, domestication, and selection; wild relatives therefore provide critical reservoir for climate-resilient breeding. Aegilops mutica, one of the few self-incompatible diploids in the Aegilops-Triticum species complex, is a putative parent in the homoploid hybridisation event that formed the D-genome clade and is a proven donor of disease resistance, winter hardiness and grain zinc accumulation. Ae. mutica is globally endangered, with a reference-quality genome available for only a single Central Anatolian accession. Using HiFi sequencing, we generated genome assemblies from two accessions from opposite extremes of the species' range: western Turkiye and Armenia. The four haploid assemblies recovered ~93% complete BUSCOs and comprised ~81% repetitive sequence, predominantly LTR retrotransposons (~55%), closely matching the reference. Comparison against the reference identified 145,871-155,535 structural variants per haplotype, including an 8.4 Mb inversion on chromosome 6T. Between 96,860 and 105,472 structural variants segregated between haplotypes within individuals and classic domestication loci did not overlap with any large structural variants. These assemblies and structural variant catalogue extend genomic resources for Ae. mutica, providing a basis for targeted introgression into wheat and conservation of a species for which populations in the South Caucasus have severely declined.

We performed a screening to evaluate the responses to water deficit before the flowering stage in a panel of 31 genotypes composed of one commercial hybrid, one wild species, multiple ex-PVP (vintage) lines, several commercial heirlooms from the Northeast USA, Mexican landraces from hot and humid environments and genome edited lines with altered flowering and inflorescence traits. We characterized 18 different morphological, physiological and biochemical traits to assess the response to 40% water deficit. We observed diverse morphological, physiological and biochemical responses across the 31 genotypes screened, suggesting the presence of useful variation for breeding for drought tolerance in cultivated tomato. Interestingly, some heirlooms exhibited superior performance under drought, suggesting they can be used as donors to discover novel genes involved in drought responses. Unexpectedly, we found mutants in SlCLE9, a paralog of the stem cell regulator SlCLV3 known to co-regulate meristem proliferation in tomato, exhibited enhanced drought tolerance. Notably, the slcle9 mutant also conferred drought tolerance when used as rootstock with grafted scions from our reference cultivar M82, suggesting SlCLE9 influence root architecture and drought response. Consistent with this, we found SlCLE9 is transcriptionally upregulated in roots and leaves during drought stress, and exogenous application of SlCLE9 modifies root growth and response to drought. Altogether, our screening uncovered new potential trait donors for breeding against drought stress and describes a novel role for SlCLE9 in drought tolerance.

Forward genetics is a powerful approach for gene discovery, but identifying causal mutations becomes difficult when mutants are maintained in heterogeneous populations with uncertain pedigrees. This is exemplified by classical tasselseed (ts) mutants, which have long served as a genetic model for studying sex determination and carpel suppression. Decades of repeated outcrossing to diverse inbred lines have created substantial genetic heterogeneity, limiting the effectiveness of conventional bulked-segregant analysis sequencing (BSA-Seq). To address this, we developed a BSA-Seq framework that integrates flexible experimental designs, multiple reference genomes, and complementary statistical methods tailored for genetically heterogeneous populations. Applying this framework revealed that reference genome selection is critical for mapping success and that Euclidean distance raised to the fourth power (ED4) outperformed homozygosity mapping (HM). Furthermore, the framework enables simultaneous mapping of multiple mutations within a single population, eliminating the need for additional mapping populations. Applying this framework to 26 ts mutant stocks from the Maize Genetics Cooperation Stock Center, we successfully mapped 24 mutants to genomic intervals containing known ts genes, while the remaining mutants mapped to distinct genomic intervals, defining novel candidate regions underlying carpel suppression. Together, these results demonstrate that historical mutant collections represent an underutilized resource for gene discovery and establish a generalizable mapping strategy for unlocking their genetic potential across diverse species.

Reproductive stage drought disrupts flag leaf metabolism in rice (Oryza sativa), limiting photoassimilate translocation to developing grains and reducing grain yield (GY). How natural variation in this response relates to GY remains poorly understood. This study combines untargeted metabolomics of flag leaves from 271 indica rice accessions grown under well-watered (WW) and reproductive-stage water-limited (WL) field conditions with genome-wide association studies of 89 annotated polar metabolites. A total of 180 metabolite quantitative trait loci (mQTLs) were identified: 93 detected only under WL, 68 only under WW, and 19 under both conditions, indicating a predominant stress-dependent genetic control; mapping of GY itself detected only four marginal QTLs, none overlapping the mQTLs. Four high-density regions (HDRs) of mQTLs were identified: HDR1-1, detected only under WW conditions and associated mainly with raffinose family oligosaccharides; HDR1-2, HDR4, and HDR11, detected only under WL conditions and associated mainly with tricarboxylic acid cycle intermediates and amino acids. After correcting for genetic relatedness, accessions carrying the favourable haplotype at both HDR1-2 and HDR4 yielded 21.5% more under WL conditions than accessions carrying the unfavourable haplotype at both loci and lost 11.4 percentage points less GY to stress. Accessions carrying the favourable haplotype at only one locus showed a comparable reduction in yield loss. Haplotype-guided variant detection against the 3,000 Rice Genomes Project panel identified major-impact variants in candidate genes, including a polyphenol oxidase at HDR1-2 and tandem cytochrome P450s at HDR4. These hubs are promising, metabolically defined targets for breeding drought-resilient rice.

Breedbase ( is a comprehensive, web-based, open-source breeding management and analysis software for plant breeding that was introduced to address gaps with existing software for plant breeding data. Breedbase is continually improved and developed, with new features being added as required by the breeding community. Since its inception, the capabilities of Breedbase were improved to better serve the diverse data modeling needs of additional crops beyond our original ensemble of cassava, banana, yam, sweet potato, tomato, rice, and wheat. We describe here major updates that have been made in the past 4 years, including (1) improved handling of repetitive measurements; (2) a redesigned experimental treatments interface; (3) a more fine-grained user permission system; (4) improved trial quality control workflows; (5) intercropping support; (6) the addition of a decision meeting feature that helps decision makers make the best selection with the available data; (7) transgenics and tracking tools and finally, (8) an improved field layout tool which consolidates several trials in one field. These new features integrate seamlessly with the previous feature set of Breedbase - making it one of the most feature rich breeding management softwares available.

Boron toxicity constrains grapevine (Vitis) production in irrigated arid and semi-arid regions, yet most available rootstocks are sensitive to excessive boron. Although underutilized Vitis species may harbor sources of adaptation, the genetic basis of natural variation in boron exclusion remains poorly understood, limiting the development of boron-tolerant rootstocks. We screened 267 wild Vitis accessions representing 16 species for their ability to limit leaf boron accumulation under excess boron conditions. Leaf boron concentration varied 190-fold among accessions and was highly heritable (entry-mean H^2 = 0.71). Its correlation with stomatal conductance was modest (r = 0.34), indicating that transpiration rate explained only a small proportion of the variation in leaf boron accumulation. Leaf boron was correlated with leaf chloride (r = 0.36), but their largely non-overlapping association profiles supported distinct genetic control. Genome-wide association analysis identified a suggestive association on chromosome 11. Independent quantitative trait locus mapping in a full-sib F1 population (n = 158) derived from Longii-9018 (V. acerifolia) x GRN3 (commercial rootstock) identified qBEx11.1 in the same syntenic region on chromosome 11, which explained 19.5% of the phenotypic variance. Positional candidate genes near the association peak included a pectinesterase and a MATE efflux transporter, suggesting roles for cell wall modification and membrane transport. Leaf spectral reflectance was associated with leaf boron concentration in both populations, particularly at visible and red-edge wavelengths, indicating potential for high-throughput phenotyping. Together, these findings identify qBEx11.1 as a priority locus for boron exclusion and support integrated genetic and spectral approaches for breeding boron-tolerant grapevine rootstocks.

Common tansy (Tanacetum vulgare), a significant agricultural invasive species, threatens forage productivity, livestock carrying capacity, and plant biodiversity across North American agroecosystems. Despite being a close relative to the ornamental Asteraceae Chrysanthemum, T. vulgare genomic studies are severely lacking, but remain critical to the development of novel weed management tools and the investigation of plant invasion evolution. In this study, we performed the assembly and annotation of the diploid genome (2n=2x=18) of an invasive North American T. vulgare genotype using long-read PacBio HiFi and Iso-Seq sequencing technology. The assembled haplotype genome comprised 9 pseudomolecules with a total size of ~4.8 Gb and a scaffold N50 of 546 Mb. In addition, both the plastid and mitochondrial genome sequences were assembled. BUSCO analysis revealed a high overall genome completeness with 69,343 predicted protein-coding genes (AED<0.6: 98%). A high-quality North American invasive reference T. vulgare genome, complementing a recent one assembled for a native European accession, is critical for the investigation of plant invasion biology, genome evolution, and the future development of novel genetic biocontrol weed management technologies.

RNA-guided nucleases (RGNs), such as Cas9 from Streptococcus pyogenes (SpCas9), are widely used for plant genome editing. Previous surveys for off-targeting, the modification of unintended targets with similarity to the intended target, indicate high specificity of SpCas9 in plant cells. However, off-targeting has not been assessed for efficiency-optimized editing systems combined with extensive multiplexing, which might increase the likelihood of cleavage at unintended sites. We therefore analyzed Arabidopsis thaliana lines that had been extensively mutagenized using zCas9i and up to 29 gRNAs addressing >45 target sites over several rounds of editing. Genomes were sequenced by short- and long-read technologies, and genome-wide variants were catalogued. Our pipeline for variant calling reliably detected RGN-induced mutations at on-targets. When excluding these on-target modifications, variants were detected in edited lines at frequencies similar to those previously reported for spontaneous mutations. In further analyses, we did not find any evidence for an origin of these variants from RGN activity. Our data are thus consistent with high specificity of SpCas9. In contrast, we detected genomic reorganization events upon editing at two complex loci, RPP1 and RPP7, encompassing multiple homologous genes, and also identified an allele by WGS that had escaped detection by amplicon sequencing. We conclude that, while off-targets may efficiently be avoided by selection of specific gRNAs, on-target modifications may be more extensive than intended, especially at complex loci and/or during multiplexing.