Theoretical and Applied Genetics

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.

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.

Long breeding cycles constrain genetic gain in Scots pine, while mature progeny trials can provide reference populations for genomic selection. We combined phenotypic records from two German trials established in 1990 with dense SNP data. We complemented these data with an offspring-level parentage audit, duplicate filtering, and simulations of breeding strategies. Of 1,986 phenotype-matched genotyped trees, 1,668 formed the parentage-green set and 1,651 remained after exclusion of 17 near-duplicate samples. The final population comprised 1,521 supported controlled-cross offspring and 130 mother-known, father-unknown offspring from 87 progeny labels. Across 14 trait-by-age measurements, GBLUP gave the clearest improvements for diameter and volume from age 20 onwards, whereas height was more mixed. At age 35, ABLUP versus GBLUP heritability was 0.155 versus 0.235 for diameter, 0.265 versus 0.263 for height, and 0.172 versus 0.236 for volume. Leave-progeny-out predictive ability increased from 0.205 to 0.238, 0.249 to 0.265, and 0.203 to 0.239, respectively. SNPscan_breeder simulations compared phenotypic selection, progeny testing, cross-generation genomic selection, and genomic selection with phenotypic thinning under five diversity variants. Progeny testing produced the greatest cumulative gain, but its 45-year cycle reduced annual response. Under the base assumptions, genomic selection with phenotypic thinning gave the highest annual gains for height and fungal resistance, whereas pure genomic selection gave the highest annual diameter gain. Genomic strategies accumulated more kinship than conventional strategies, although a {lambda} = 0.10 kinship penalty improved founder retention with little loss of gain; no mitigation option simultaneously maximised gain, prediction accuracy, and diversity. These results support genomic shortlisting within a field-tested programme with explicit reference-population updating and diversity management.

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.

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.

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.

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.

O_LICereal-legume intercropping is a cornerstone of agroecological systems because interactions between species can enhance agroecosystem resilience. Yet, the mechanisms underlying these interactions remain poorly understood. To address this gap, we investigated the maize-bean association under low-input conditions. C_LIO_LIWe conducted a two-year intercropping experiment with 200 climbing bean lines grown alongside three maize landraces in France and Romania. We evaluated bean phenotypic responses above- and below-ground to 3 maize landraces, treated as distinct biotic environments (E). Direct and indirect genetic effects were assessed by mapping bean and maize phenotypic traits onto the bean genome (G), with G x E interactions tested using contrasts among maize landraces. C_LIO_LICompetitive interactions dominated, maize acting as the stronger competitor. Maize landraces created distinct biotic environments affecting bean traits. Best-performing partners varied across experimental fields, with no evidence of bean local adaptation. The most productive and balanced mixtures were obtained with the traditionally intercropped maize landrace. Genome-wide association analyses identified loci underlying direct and indirect genetic effects, including candidate genes associated with neighbor perception. C_LIO_LIThese findings reveal the genetic complexity of maize-bean interactions and highlight competitive tolerance in bean and reduced aggressiveness in maize as key traits for improving cereal-legume intercrop performance. C_LI

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.

Leptosphaeria maculans is a plant-pathogenic fungus that infects Brassica species, including Brassica napus (oilseed rape). Breeding oilseed rape varieties with genetic resistance is an efficient way to control the disease; however, L. maculans can adapt and overcome these resistances. Understanding the mechanisms that enable L. maculans to adapt is crucial for managing the emergence of better-adapted isolates. Brassica carinata, the Ethiopian Mustard, although closely related to B. napus, is considered a nonhost species of L. maculans because this fungus cannot infect it. Despite the extreme resistance of B. carinata, one natural L. maculans isolate has been identified as unable to infect B. napus, causing moderate and atypical symptoms on this species. We performed a cross between this isolate and an isolate adapted to B. napus, followed by a QTL analysis, which identified seven QTL, each encompassing candidate genes involved in L. maculans adaptation to B. carinata or B. napus. Additionally, we observed transgression in the progeny, wherein a few strains caused significantly more or less aggressive symptoms on both species of Brassica. We found that epistasis within the L. maculans genome contributes to the observed transgression. These initial findings provide further opportunities to study the adaptive capacities of L. maculans, as well as data to initiate analysis of the extreme resistance of B. carinata to L. maculans. HighlightsO_LISeven pathogenicity QTL identified, carrying several interesting candidate genes C_LIO_LITransgression of some progeny isolates on B. napus and B. carinata was reported C_LIO_LIEpistasis plays a significant role in the adaptation of L. maculans toward host and nonhost Brassica species C_LI

Intercropping dominates smallholder cassava production in sub-Saharan Africa, yet cassava breeding programs evaluate genotypes exclusively under monoculture. Despite consistently reported system-level yield advantages, cassava yield is reduced by 17 to 51% under intercropping, indicating a need to reduce this competitive disadvantage through breeding. However, the quantitative-genetic foundations of intercrop breeding remain uncharacterized for tropical root crop systems. We hypothesized that monoculture selection would capture most, but not all, genetic merit for intercropping and that a limited tester set would be sufficient if general mixing ability predominated. We evaluated 120 cassava clones previously selected under monoculture in IITA advanced yield trials, testing them under monoculture and in intercrop with two contrasting cowpea varieties across two years at Ibadan, Nigeria. Spatial mixed models quantified genetic variation, genotype by cropping system interaction, cross system genetic relationships, realized selection gain, mixing ability, tester effects, and land equivalent ratio. Intercropping reduced cassava fresh root yield by 19%, but total land equivalent ratios exceeded 1.0 for all clones, confirming a system-level land use advantage. Cassava performance under intercropping was heritable, with estimates of 0.50 to 0.75, and genotype by cropping system interaction was not significant. Genetic correlations between monoculture and intercrop performance were high and approached unity (rg = 0.92 to 0.99), and selection efficiency was 26 to 44% at 10% intensity, confirming that high genetic correlation does not guarantee effective indirect selection. Monoculture selection captured approximately two-thirds of direct intercrop gain. General mixing ability dominated, specific mixing ability was negligible, and producer effects explained 20 to 47% of intercrop variance. The two architecturally and phenologically contrasting cowpea varieties had limited influence on cassava rankings. Here, we show for the first time that cassava breeding for intercropping can retain monoculture selection during early stages while adding two representative cowpea testers at the advanced trial stage. This staged strategy aligns cassava breeding with diversified smallholder systems without creating a separate pipeline.

Chickpea (Cicer arietinum L.) yield in Algeria still falls short of domestic demand, and low-cost agronomic tools such as plant growth regulators are one of the few levers producers can adjust without heavy investment. We tested the separate and combined effects of foliar-applied indole-3-acetic acid (IAA) and kinetin (0, 10, and 20 mg/L) on two chickpea varieties, FLIP 84-92 and ILC 32-79, grown side by side in the field at the ITGC station of El Khroub and in pots at the Institute of Natural Sciences of Oum El Bouaghi during the 2024/2025 season. Nine hormone treatments were applied to each variety at each site, with three replicates apiece. No single treatment came out on top across the board. A20+K20 gave the largest pot leaf area at both sampling dates (5.67 and 7.20 cm2) and the highest field dry matter weight (95.21 g), yet A10+K10 produced the largest field leaf area after the first spray (16.47 cm2) and the highest pot dry matter weight (6.12 g). Kinetin alone at 20 mg/L (K20) gave the most field pods (21.28) and the most field leaves after the first spray (115.41), while A10+K20 gave the heaviest pot grains (100-grain weight of 21.16 g). Field and pot means are not directly comparable given how different the two growing environments and sampling routines were. Between varieties, FLIP 84-92 germinated better (86% vs. 83%) and produced heavier grains, while ILC 32-79 grew taller stems. Because the manuscript we worked from supplied only treatment means, with no replicate-level data or variance estimates, we report these as descriptive numerical differences rather than statistically tested effects. Read that way, the pattern that emerges is that IAA and kinetin responses are trait- and environment-specific rather than uniformly additive, and a properly replicated factorial analysis will be needed before any interaction or synergy between the two hormones can be claimed.

Characterizing hybrid maize disease resistance is a costly and labor-intensive effort in commercial breeding programs. Field trials are carefully inoculated and managed but remain error-prone due to spatial variability in disease pressure, microclimatic conditions and inter-rater variability. Quantitative ordinal disease rating scales are used to increase scoring speed at the expense of resolution, accuracy, and the ability to use conventional statistical methods. To improve traditional methods of disease resistance characterization, we propose to leverage readily available low-density SNP marker profiles to create genome-informed disease scores. Specifically, a whole genome ordered probit regression (WGOPR) model is used to deconstruct field-observed disease phenotypes into marker effects and reconstruct genome-informed disease scores. This approach is demonstrated in hybrid maize using data from Exserohilum turcicum-inoculated field trials across the central and northern U.S. and Canadian Corn Belt in 2024. Resulting Genomic Estimated Categorical Probabilities (GECPs) are compared to observed frequencies of disease scores to validate the methodology and evaluate the accuracy of regional hybrid maize disease resistance characterization. The benefit of a probabilistic output is demonstrated through two use cases: a comparison of hybrids with highly variable observed disease resistance scores at a single location, and a comparison of breeding selection schemes from a regional analysis. Because GECPs are the product of estimated marker effects, they better represent the expected behavior of a genotype independent of location-, rater- and plot-specific noise, and will therefore offer a step towards improving hybrid maize characterization and better informing breeding decisions.

Peanut smut, caused by Thecaphora frezzii, is an important constraint to peanut production in Argentina, but quantitative estimates of yield losses across environments remain limited. We quantified the relationship between disease incidence and kernel yield using 922 observations from 26 field studies conducted in Cordoba, Argentina, between 2021 and 2025. Study-specific incidence-yield relationships were analyzed using linear regression, random-effects meta-analysis, and linear mixed-effects models. Peanut smut incidence was consistently associated with yield reduction across studies. The estimated damage coefficient ranged from 24.2 to 28.7 kg ha-{superscript 1} per 1% increase in disease incidence, corresponding to a relative yield reduction of 0.74-0.87% of attainable yield. In contrast, attainable yield varied markedly among studies, ranging from 1,370 to 5,409 kg ha-{superscript 1}. Although an exploratory segmented analysis suggested a breakpoint near 12% incidence, subsequent moderator analyses, study- specific regressions, and normalized response curves provided no evidence of a biologically meaningful change in the damage coefficient across incidence or yield classes. These results indicate that differences among environments were primarily associated with attainable yield rather than with changes in the magnitude of disease-associated yield loss. The resulting damage function provides a quantitative basis for yield-loss assessment and disease management in peanut.

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.

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.

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.

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.

Adventitious root formation (ARF) is a critical trait for the cost-effective propagation of grapevines in commercial nurseries. Poor rooting ability can limit the use and adoption of new rootstocks derived from underutilized Vitis species, constraining breeding efforts largely to the traditional trio: Vitis riparia, V. rupestris, and V. berlandieri. Despite its agronomic relevance, the genetic basis of ARF remains poorly characterized across the broader Vitis genus. In this study, we evaluated 308 accessions representing 18 Vitis species over three growing seasons, quantifying rooting performance at two developmental stages, callus-stage and post-transplant, alongside root biomass, cutting weight, and a derived transplant-response index. We observed extensive phenotypic variation both within and across species, and species rankings depended on the trait considered. V. riparia, V. rupestris and V. californica ranked among the top five species for all four rooting traits, whereas V. cinerea and V. candicans ranked among the lowest for root weight and post-transplant rooting. V. arizonica and V. acerifolia rooted well at the callus stage but were intermediate after transplanting, and V. berlandieri was among the weakest at the callus stage yet intermediate for post-transplant rooting. Repeatability was moderate to high for root weight (0.74) and callus-stage rooting (0.66), and lower for post-transplant rooting (0.47), reflecting both genetic control and season-to-season variation. Between-species differences accounted for 68% of the genetic variance in callus-stage rooting but only 10% in cutting weight. Rooting was associated with the climate of each accession's wild site of origin: after removing differences among species, accessions originating from sites with lower dry-season precipitation rooted better and produced more root biomass. Genome-wide association analysis using 3.4 million SNPs identified 54 significant SNPs resolving into 18 independent loci across four traits, with root weight contributing 12 of them. Candidate genes in linkage with these loci include a mitogen-activated protein kinase, a SCARECROW-LIKE GRAS transcription factor, PASTICCINO1, expansin A1, an AP2/ERF-RAV1 transcription factor, a tandem array of caffeoyl-CoA O-methyltransferases, and several sugar, peptide and nitrate transporters, implicating auxin-linked cell proliferation, cell wall and lignin remodeling, and solute transport. Genomic and phenomic prediction models yielded moderate accuracies across traits and seasons; up to r = 0.67 for post-transplant rooting within a season and r = 0.65 for previously unevaluated accessions. Moreover, the integration of spectral and genotypic data further improved predictive performance. Prediction accuracy was essentially flat between 5,000 and 50,000 markers. This study establishes a foundational framework for the genetic improvement of grapevine rootstocks, promoting broader use of resilient, high-performing, and clonally-propagable germplasm in viticulture.

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.