Theoretical and Applied Genetics

PurposeGenotype-by-environment (G x E) interactions represent a major obstacle to increasing genetic gain in crop breeding, with the underlying physiological drivers often remaining obscured within conventional statistical models. This case study presents a novel framework that transforms the latent factors from Factor Analytic (FA) multi-environment trial (MET) models into heritable quantitative traits, enabling the genetic dissection of adaptive response patterns. MethodsA Factor Analytical Linear Mixed Model (FA-LMM) was fit to plot-level yield data for 1,036 barley genotypes across eight Australian trials. ResultsCorrelation of the factor loadings with APSIM-simulated environmental covariates demonstrated that the second latent factor FA2 was strongly correlated with the Water Stress Index (r = -0.83) during the critical flowering period, establishing water availability as the main biological axis of crossover Gx E. Genotypic scores for the derived traits, Overall Performance (OP) and Water Stress Response (WSR), were subjected to high-resolution haplotype-based mapping using local Genomic Estimated Breeding Values (GEBV). ConclusionThis analysis successfully identified major genomic regions that accounted for a substantial proportion of the additive genetic variance. Gene Ontology enrichment of candidate genes within the top haploblocks implicated fundamental pathways related to energy homeostasis, root development, and stress response, with notable candidates including FTsH11, BPS1, and TDP1. The distribution of favourable Haplotypes of Interest (HOI) in elite cultivars suggested a historical signature of inadvertent selection for these adaptive mechanisms. This framework provides an explicit bridge between statistical modelling and functional genomics, offering breeders actionable genetic targets for accelerated development of climate-resilient cereals.

Key message Characterized and unknown septoria nodorum blotch susceptibility/resistance genes were identified in contemporary U.S. hard winter wheat. The necrotrophic fungus Parastagonospora nodorum is the causal agent of septoria nodorum blotch (SNB) of wheat. To determine the prevalence of SNB sensitivity genes in a contemporary U.S. hard winter wheat (HWW), we evaluated a panel of 619 breeding lines and cultivars against five P. nodorum isolates and five necrotrophic effectors (NEs), SnToxA, SnTox1, SnTox3, SnTox267 and SnTox5, and genotyped the panel using genotyping-by-sequencing (GBS) markers and diagnostic Kompetetive-allele specific PCR (KASP) markers for the sensitivity genes Tsn1-B1, Snn1-B1, and Snn3-B1/B2. GBS analysis identified 34,357 GBS-single nucleotide polymorphism (SNP) markers. Evaluations against P. nodorum isolates showed that 40-67% of the genotypes were susceptible in the panel. Toxin infiltration assays showed that 54%, 2%, 37%, 13%, and 15% of the genotypes were sensitive to SnToxA, SnTox1, SnTox3, SnTox267, and SnTox5, respectively. Diagnostic KASP markers for Tsn1-B1, Snn1-B1, and Snn3-B1/B2 showed prediction accuracies of 98%, 75%, and 92% for the corresponding effectors SnToxA, SnTox1, and SnTox3, respectively. Genome-wide association studies (GWAS) not only confirmed the presence of the previously characterized sensitivity genes Tsn1-B1, Snn1-B1, Snn2, Snn3-B1/B2, and Snn5-B1, but also identified new loci to be associated with responses to P. nodorum isolates and NEs. Of which, Qsnb.osu-2AS on chromosome 2AS was associated with responses to all five isolates. We developed KASP markers KASP_S4B_643615365, KASP_ S2D_16184991, and KASP_S2A_9833162 linked to Snn5-B1, Snn2, and Qsnb.osu-2AS, respectively. These findings should guide breeding for SNB resistance in hard winter wheat.

Flowering time and monocarpic senescence are tightly environmentally and genetically controlled. Typically, early flowering and staygreen traits are associated with opposing life-history strategies; stress avoidance versus adaptation; with flowering time an overarching regulator of crop cycle length. We developed RIL populations segregating for Ppd-1 and NAM-1 variation, which are otherwise isogenic. Multi-year field experiments enabled exploration and uncoupling of the relationship between heading and staygreen traits. Heading date manipulation enabled introduction of staygreen traits to their target breeding environments, characterised by a hot-finish. Under moderate stress, we report a 2.9% and 1.9% increase in grain width (P<0.0001), and 5.8% and 3.7% increase in TGW (P<0.0001), plus significantly greater yield (P<0.1) for late heading staygreen RILs homozygous for NAM-A1, and NAM-D1 missense variants, respectively. Grain yield increases were proportionate to the delay in senescence, being greater for the NAM-A1 than the NAM-D1 variant. For RIL populations segregating for both traits, senescence variation was observed relative to heading-date. Regarding grain yield, the staygreen trait-associated increase in source size could not compensate for the Ppd-1a associated pleiotropic reduction in sink size, even under hypothesised continental target breeding environments, with trait competition identified. Therefore, to maximise the benefits associated with staygreen traits, especially in early-heading favouring environments required targeted manipulation of source-sink dynamics, and we propose multiple strategies. HighlightStaygreen traits were associated with extending grain fill duration, increasing grain width, TGW and grain yield. There appears an antagonist relationship between earlier heading and staygreen traits.

Genomic prediction models that account genotype-by-environment (GxE) have the potential to accelerate the rate of genetic gain for yield and agronomic performance, yet relatively few studies have applied GxE prediction in public soft red winter wheat (Triticum aestivum) breeding programs. In this study, we extended a reaction norm-based genomic prediction framework by integrating weather-based environmental covariates to more effectively capture genotype- environment interactions. Key agronomic traits, including seed yield, plant height, test weight, and heading date, were evaluated across 33 environments (location-year) using over 3,200 breeding lines from the North Carolina State University small grains breeding program. Multiple genomic prediction models were compared using several cross-validation (CV) schemes representing common breeding scenarios. Across traits, the reaction norm M5 model, which incorporates both GxE and genotype-by-environmental covariate interactions (GxO), achieved the highest prediction accuracy (PA) in CV2 (predicting incomplete field trials) and CV1 for yield and test weight (predicting new lines). The highest PA was observed for test weight under CV2 (0.54) and for yield under CV1 (0.41). Under CV0 (predicting new environments), the M3 model incorporating GxE produced highest PA across traits, with the greatest accuracy for plant height (0.45), although differences among M2, M3, and M4 were small. Prediction under CV00 (predicting new lines in new environments) remained more challenging, with PA values 0.10 - 0.20 across traits. Overall, our results demonstrate that integrating environmental covariates into genomic prediction models can improve predictive performance across diverse wheat-growing environments in North Carolina, supporting their utility for applied breeding efforts. CORE IDEASO_LIIntegrating genotype-by-environment (GxE) interactions with environmental covariates improves prediction accuracy across environments. C_LIO_LIModel performance varies by prediction scenario, with different approaches performing best for new lines, incomplete trials, or new environments. C_LIO_LIPrediction of new lines in new environments remains challenging. C_LI PLAIN LANGUAGE SUMMARYThis study explores how adding environmental information to genomic prediction models can improve prediction accuracy in a public winter wheat breeding program. Using data from multi-environment trials conducted across diverse conditions in North Carolina, we evaluated statistical models that capture how different wheat lines respond to changing environments. By incorporating weather data, we improved the ability to predict performance across locations and years. These findings provide practical insights for refining selection strategies and accelerating genetic gain in wheat breeding.

Genomic prediction (GP) across diverse environments has a potential to accelerate genetic gain in cotton breeding programs. A major challenge in GP is modelling genotype-by-environment interactions (GEI), which is essential for selecting stable and high-performing genotypes under variable production conditions. However, incorporating GEI into GP models increases the dimensionality and computational complexity, risking complex models that are impractical to use on commercial breeding-scale data sets because of run times and computational demands. This study addresses two primary aims. Firstly, we evaluate the practical benefits of GEI-informed GP for predicting economically important cotton traits. Second, advanced statistical modelling strategies are developed and assessed for integrating genomic and environmental data at scale. We propose a dimensionality reduction approach that combines linkage disequilibrium network analysis with principal component techniques to reduce redundancy while preserving informative variation. Using this reduced dataset, we implement Bayesian linear regression models and, for comparison, deep residual neural networks for genomic prediction. Analyses were conducted on a large multi-environment dataset from the CSIRO cotton breeding program, comprising 3,236 breeding lines, 54 environmental covariates, and 8,049 yield and fibre quality phenotype records collected over 10 years and 9 locations representing 41 year-location combinations. Results demonstrate that generally Bayesian linear regression approaches outperform BG-BLUP models, with all three linear/linear mixed methods providing clearly more reliable performance than the deep learning models. These findings highlight the value of using interpretable statistical models for integrating genomic and environmental information to support selection decisions under diverse environmental conditions.

In sunflower (Helianthus annuus L.), the composition of fatty acids in the seeds, primarily oleic, linoleic, stearic and palmitic acid, is of utmost importance for oil quality. Despite this, the genetic basis of this trait and its interaction with the environment is poorly understood. Understanding this interaction is critical to improvement of sunflower within the context of climate change. In this work, we incorporated fatty acid composition measurements from the sunflower SAM population and eight environments across an extensive geographic cline into GWAS. The SAM panel consists of 287 varieties representing approximately 90% of sunflower diversity, for which 2.2 million high-quality SNPs with a MAF > 5% are available. For increased power, multivariate GWAS was performed with four different inputs: (i) mean fatty acid composition within each environment, (ii) mean fatty acid composition within each environment omitting high oleic varieties, (iii) trait stability within environments quantified by standard errors among replicate samples ( stability) and (iv) Eberhart and Russells {beta} which quantifies trait stabilities across environments ({beta} stability). All four analyses yielded highly significantly associated SNPs. We found that high oleic varieties exhibited high {beta} trait stability, resulting in substantial overlap in markers between analyses (i) and (iv), with signals being fairly consistent between environments in analysis (i). For analyses (ii) and (iii), significant markers tended to vary between trials. For significant SNPs across all analyses, 147 candidate genes were identified, including promising candidates such as 15 fatty acid metabolism genes, 6 heat shock proteins and 22 transcription factors. Lastly, a large introgression consisting of two flanking inverted sequences on Chromosome 5 was found to coincide with stability in the Georgia trial, suggesting a role in FA composition stability under high heat conditions.

In quantitative genetics, candidate SNPs are identified through genotype-phenotype associations inferred with genome-wide association studies (GWAS). In this study, we explore an alternative approach to detect genetic variants with non-neutral effects by tracking temporal trends in allele frequency in a winter wheat (Triticum aestivum L.) breeding population over an eight-year period, from which signals of selection may be inferred. Selection signatures were inferred with a generalized linear model, where we modeled trends in allele frequency as a function of time (crossing year). These signatures of selection were used to prioritize variants. Associations between phenotypic performance and individual load of prioritized variants were then investigated. Furthermore, we assessed whether incorporating selection information into a genomic best linear unbiased prediction (GBLUP) model improves model performance in terms of quality of fit and prediction ability. Our findings indicate that the inferred signals of selection are effective in identifying non-neutral variants. Variants under strong negative selection were associated with a decrease in protein content adjusted for grain yield (p-value < 0.01), while genetic variants that had been under moderate to high levels of positive selection were associated with increased grain yield (p-value < 0.01). However, incorporating selection information did not improve prediction accuracy. In conclusion, temporal trends in allele frequency can be used to detect non-neutral variants. The proposed approach may hence complement traditional quantitative genetic methods for detecting non-neutral genetic variation. This approach may allow breeders to detect non-neutral variants earlier in the breeding cycle, without resorting to phenotypic data.

Stripe rust and leaf rust, caused by Puccinia striiformis f. sp. tritici and P. triticina, respectively, are the most destructive wheat diseases in the southern Great Plains. Green Hammer is a hard red winter wheat (HRWW) cultivar released by Oklahoma State University in 2018 and has demonstrated a stable adult plant resistance to stripe rust and race-specific seedling resistance to leaf rust. To identify and map rust resistance loci, 109 doubled haploid (DH) lines derived from the cross between Green Hammer and another HRWW cultivar, Lonerider, were developed. Lonerider showed adult plant resistance to stripe rust but was susceptible to multiple P. triticina races. The DH lines were evaluated for stripe rust at the adult plant stage in greenhouse and field environments across Oklahoma, Kansas, and Washington, and for leaf rust at the seedling stage against seven U.S. P. triticina races and at the adult plant stage in Oklahoma and Texas. Genotyping-by-sequencing generated 6,078 polymorphic single-nucleotide polymorphisms used for genetic mapping. Quantitative trait loci (QTL) analysis identified 14 stripe rust and 8 leaf rust resistance QTL. For stripe rust, a major QTL in Green Hammer, QYr.osughln-2AS, was identified in the proximity of the 2NvS translocation. Three other major stripe rust resistance QTL were identified in Lonerider on chromosomes 2AL (two QTL) and 2BS (one QTL). For leaf rust, QLr.osughln-1DS and QLr.osughln-2DS.1 were the two major QTL identified in Green Hammer and most likely correspond to the all-stage resistance genes Lr21 and Lr39, respectively. In this study, we identified previously characterized genes as well as unknown genes that can be utilized in wheat breeding programs to enhance resistance to leaf rust and stripe rust.

Accurate modeling of genotype-by-environment (GxE) interaction is critical for genomic prediction in plant breeding but remains challenging due to complex interaction structures. Conventional models often use the Hadamard product of genotype and environment covariance matrices to capture joint similarity, which may not fully represent GxE complexity. Here we propose a novel framework that derives covariance structures from the matrix multiplication of genotype and environment kernels, decomposing these into symmetric components incorporated as random effects in mixed models. Evaluated for 11 wheat and rice multi-environment datasets and across, this approach consistently outperformed the traditional Hadamard-based model, improving prediction accuracy by up to 13.2% in Pearsons correlation and enhancing top-selection accuracy. Combining both methods yielded the highest performance, indicating complementary information capture. This framework offers a flexible, interpretable, and computationally feasible extension for modeling GxE interaction, potentially enhancing genomic selection effectiveness under diverse environmental conditions.

The number of pollen grains, which carry male gametes in seed plants, has attracted interest in genetics, evolution, and breeding. Rapid evolutionary reductions in pollen number and anther length were reported in selfing species as well as domesticated species, although this poses a challenge for hybrid breeding. Here, we studied the variation of pollen number and anther length of the hexaploid bread wheat (Triticum aestivum) by employing a quick pollen counting method. Pollen numbers in cultivars were lower than those in landraces among 54 lines of diverse geographic origins. Using the year of registration of traditional and modern cultivars, we found a reduction in pollen number over the past 150 years. We detected high heritability and variation among Asian landraces and cultivars. Thus, we conducted QTL mapping of pollen number as well as of anther length using nested association mapping lines in which Norin 61 was the common parent. Genomic loci encompassing Green Revolution genes (Rht-B1, Rht-D1, and Ppd-D1) showed significant effects on pollen number and anther length, but their contributions were relatively minor. Although anther length has often been used as a proxy for pollen number in bread wheat, our data showed that their correlations are not necessarily high. Interestingly, we identified a new QTL of pollen number that was not detected by measuring anther length, and, vice versa, a new QTL specific to anther length. These data suggest that pollen number has reduced rapidly in bread wheat but can be modified using the genetic diversity of landraces. Significance statementWe found that modern cultivars of bread wheat have reduced pollen number and shorter anther length, which are common in domesticated species but can be a challenge for hybrid breeding. Using underutilized Asian landraces and cultivars, we reported that new quantitative trait loci as well as loci used in the Green Revolution, are responsible for the traits, which can be employed to increase pollen numbers.

The performance of a single cross is determined by the average additive effects of the parents, as well as the interactions between them. These quantities can be estimated using an appropriate genetic design, allowing for the estimation of general (GCA) and specific (SCA) combining abilities. The prediction of GCA for new parents and the total genetic value of unrealized crosses can be made when genome-wide marker information is available. Several studies in crops such as maize and rice have demonstrated the potential of genomic-assisted prediction of single-cross performance in economically important crops. However, no study to date has explored its relevance in perennial ryegrass, an obligate allogamous species that is bred in genetically heterogeneous families. In this study, we aimed to estimate genetic parameters and assess the ability of genomic models to predict the performance of F2 families in terms of dry matter yield and nutritive quality traits. We used data from a large partial diallel involving 104 parents from two distinct subpopulations, as inferred by admixture analysis. F2 families were evaluated in multiple environments and under two nitrogen availability conditions. Genotyping-by-sequencing of the parent plants produced 42,145 variants after quality control, which were used to estimate genomic relationships based on identity-by-state. Variance component estimation revealed limited GCA and SCA interactions with the environment, and particularly with nitrogen management. The predictive abilities of two parental models exceeded 0.60 and often surpassed 0.70 for most traits. However, incorporating non-additive effects into the model did not improve predictive ability. We leveraged the genetic diversity among parents to map genomic regions associated with all recorded traits. Genome-wide association studies (GWAS) by genomic best linear unbiased prediction (GBLUP) identified six quantitative trait loci (QTL) regions, with 45 candidate genes within the linkage disequilibrium range, estimated at approximately 92 kb. Our results demonstrate that genomic prediction of single crosses can be performed with high accuracy, especially when both parents are also progenitors of families in the training set.

Maximum utilization of existing genetic variability in a breeding program depends on the efficient classification of the inbred lines into heterotic groups, particularly under stress conditions. This study applied practical breeding approaches to determine the mode of genetic inheritance for Striga resistance and proposes a weighted heterotic grouping method based on the general combining ability of multiple traits (WHGCAMT) and compares its effectiveness with other existing methods in classifying the inbred lines into heterotic groups in Striga-infested and optimum environments. Using Diallel design IV, 300 crosses were generated from 21 inbred lines and 4 standard testers. The crosses, along with six checks, were evaluated in an 18 x 17 alpha lattice design with two replications at two locations, in both artificial Striga-infested and Striga-free environments. The inbred lines were genotyped using DArTtag SNP markers. Phenotypic and genotypic data were analyzed using R. Analysis of variance revealed significant mean squares for hybrid, general combining ability (GCA), specific combining ability (SCA) and their interactions with environment. Significant positive and negative GCA and SCA effects were detected for grain yield and other measured traits. However, a larger proportion of additive gene action than non-additive gene action was observed for grain yield and most measured traits. The analysis of molecular variance also showed substantial genetic differences within and between clusters. Except for HSCA, the mean grain yield between the inter-group and intra-group hybrids was significant for each method. Pairwise comparison of the inter- and intra-group hybrids of all the methods showed significant differences between the WHGCAMT and all other methods in most cases. WHGCAMT consistently produced higher-yielding inter-group hybrids and lower-yielding intra-group hybrids, achieving breeding efficiency improvements of 55.8%, 4.3%, 15.7%, and 11.4% over the HSCA, HSGCA, HGCAMT and molecular marker methods, respectively, under Striga infestation. Thus, WHGCAMT offers more precise, reliable and biologically meaningful heterotic groups among early-maturing maize inbred lines.

Intercropping is a promising strategy to improve productivity and sustainability in agricultural systems, but designing effective genotype combinations remains a major challenge owing to the rapid increase in possible pairings as the number of candidate genotypes increases. This creates a practical bottleneck because field evaluation of all combinations is infeasible under realistic resource constraints. Here, we propose a framework that integrates genomic prediction and Bayesian optimization to support efficient decision-making for intercropping system design. Using genome-wide marker data from sorghum and soybean, we simulated intercropping performance across 5,214 genotype pairs under certain genetic architectures, including variation in heritability, correlations between direct and indirect genetic effects, and the contribution of pair-specific interactions. Genomic prediction models incorporating direct and indirect genetic effects substantially improved prediction accuracy compared with models based on direct genetic effects alone, and inclusion of specific mixing ability further enhanced the performance under high-heritability conditions. When coupled with Bayesian optimization, the models rapidly identified superior genotype pairs, requiring fewer evaluation cycles than random or prediction-only search strategies. Acquisition functions that account for predicted uncertainty were most effective in complex scenarios involving interaction effects or negative correlations between direct and indirect effects. These results demonstrate that combining genomic prediction with Bayesian optimization can substantially reduce the experimental burden associated with intercropping design, while improving the efficiency of identifying high-performing genotype pairs. The proposed framework provides a practical approach for prioritizing candidate mixtures in breeding and field evaluation, and contributes to the development of data-driven strategies for sustainable agricultural systems. HighlightsO_LIA data-driven framework was developed to optimize genotype pairs in intercropping. C_LIO_LIModeling indirect effects improved prediction accuracy across genotype pairs. C_LIO_LIPair-specific interactions enhanced prediction under high-heritability conditions. C_LIO_LIBayesian optimization identified superior pairs under limited evaluation capacity. C_LIO_LIThe framework reduces field-testing requirements for intercropping system design. C_LI

Crop improvement can enhance food security, but side effects, such as trade-offs between valuable agronomic traits, are common. Likewise, fertilisation helps ensure high yields, but can devalue mutualisms with soil microbes that would otherwise be essential for nutrient acquisition. If the need for nutritional mutualisms is reduced in crops, mutualisms could be disrupted by selection relaxation or allocation trade-offs. Thus, crops could achieve high yields in spite of, or because of, disruption of nutritional mutualisms. Alternatively, the highest-yielding plants might flourish because they maximise nutrient acquisition from both symbionts and the soil. Here, enhanced mutualism could evolve over the course of agricultural crop improvement. To investigate whether high yields in cultivars and wild accessions are negatively or positively genetically correlated with outcomes in the legume-rhizobia mutualism, we measured whether yield and symbiosis traits trade-off or are positively genetically correlated among cultivars and wild accessions. We also tested whether this relationship differs between accessions released before or after 1950. We measured genetic correlations between yield and mutualism traits in 87 domesticated pea (Pisum sativum) accessions in a common garden agricultural field across three years. Seed yield and N2 fixation (%Ndfa) were positively genetically correlated. While N fixation was more strongly predictive of yield in the pre-1950 accessions than the post-1950 accessions, the underlying positive genetic correlation between the traits did not differ between the groups. The positive genetic correlation between yield and N2 fixation indicates that selection to increase yields has maintained or increased the benefits of the rhizobial mutualism in pea. Our findings predict that breeding to increase yield may continue to produce pea cultivars that get a greater proportion of their N from rhizobia, enhancing symbiotic mutualism and reducing the proportion of N supplied by fertilisation.

The transformation of the free nuclear syncytium into cellular endosperm tissue with starch and protein accumulation is a well-established phenomenon, at least in the fruits of cereals of the Triticeae tribe. The present article demonstrates that there is considerable diversity inherent in this type of caryopsis morphogenesis. By examining various taxa (species, varieties, and cultivars) of wheat, oats, and some wild grasses, this research reveals significant deviations in endosperm morphogenesis from the typical state. A new developmental pattern of endosperm was identified, characterized by several distinctive features such as incomplete cellularization of the syncytium and starch accumulation within the acellular endosperm domains and the endosperm cavity. A large number of plastids were observed in the syncytium stage, which served as the basis for the later amyloplast stage. The acellular endosperm domains and the cavity domain exhibited connections at specific discontinuities in the modified aleurone layer surrounding the cavity. The peripheral parts of the caryopsis received fewer assimilates necessary for starch synthesis, which was attributed to their increased distance from the transfer system and a likely reduction in the efficiency of assimilate transport through the apoplast in these areas. The starch cavity volume constituted a few percent of the overall caryopsis volume, which could serve as a foundation for potential breeding improvements to enhance starch yields across different varieties.

Ensuring global food security under rapid climate change demands accelerated genetic gain and breeding strategies that address complex Genotype-by-Environment (GxE) interactions. Traditional genomic selection models often fail to account for novel or extreme climates.Furthermore, integrating mechanistic crop growth models (CGMs) using traditional Bayesian frameworks to solve this issue presents severe computational bottlenecks. Here, we introduce DeepBioGS, a novel hybrid framework that integrates genomic selection with biophysical growth modelling via a fully differentiable deep learning architecture. DeepBioGS utilises a parameter-prediction multi-layer perceptron to map high-dimensional genomic markers to latent, highly heritable physiological traits (Genotype-Specific Parameters; GSP). These parameters mechanistically predict crop phenology across diverse environments. Using two multi-environment wheat datasets comprising over 6,000 genotypes, DeepBioGS extracted latent traits with near-perfect SNP-based heritability values (0.95-1.00). Crucially, the framework demonstrated superior or comparable predictive accuracy (up to r2 = 0.77) against standard genomic best linear unbiased prediction (GBLUP) and traditional Bayesian CGM-WGP models. Its architecture drastically improved computational scalability by enabling standard backpropagation, effectively bypassing the stochastic sampling limitations of approximate Bayesian methods. Most importantly for climate adaptation, DeepBioGS allowed accurate forecasting of genotype performance in entirely unobserved environmental conditions. By merging the representational power of deep learning with the structural constraints of biophysics, DeepBioGS provides a highly scalable, interpretable tool to navigate GxE interactions, enabling the assessment of cultivars under future climate scenarios, thus optimising crop breeding for a changing global environment.

(1) BackgroundSaffron crocus (Crocus sativus) is the source of saffron, the most expensive spice in the world. It evolved about 3000 years ago as a sterile triploid clone in Greece. Since then, saffron has spread across the globe, where regionally distinct practices of saffron cultivation have developed. Despite differences in morpho-physiological traits, genetic variability is low, if present at all. Here, we aim to resolve chromosomal and sequence-associated variability across saffron crocus cultivars from the crops main cultivation areas in Africa, Asia and Europe. (2) MethodsWe used genome-wide DNA polymorphisms obtained through genotyping-by-sequencing (GBS) of 33 saffron and 14 closely related Crocus accessions, which we place into a phylogenetic context. For karyotyping, we compare nine saffron accessions by multi-color fluorescent in situ hybridisation (FISH) with repetitive DNA probes. (3) Key resultsPhylogenetic analyses confirmed the single origin and clonal nature of all saffron accessions. We detected slight DNA differences among saffron crocus genotypes, which were minor compared with those in wild C. cartwrightianus populations. Still, the Iranian saffron accessions form a genetically very narrow group that differs from the other proveniences in population genetic analyses. However, chromosomes of some saffron accessions display variable FISH signals, likely resulting from gains and losses of tandemly repeated DNA. (4) Main conclusionsBased on the high genetic identity and small karyotypic differences, we confirm the clonal origin of the saffron accessions. Nevertheless, as we detected small and regional chromosomal variability, we conclude that at least four somaclonal saffron lineages emerged after saffrons origin. Societal Impact StatementFor millennia, many cultures developed cultivation practices and regional crop varieties. A notable case is saffron, the worlds most expensive spice that is harvested from stigmas of saffron crocus. This flower crop arose 3000 years ago in a singular genome triplication event and since then spread clonally across the globe. By identifying genetic and chromosomal variability in clonal saffron accessions, we highlight regional diversity, support the preservation of traditional knowledge, and underscore the risk of relying on only one clonal lineage. This informs strategies for saffron cultivation, linking cultural heritage with modern genomics to address biodiversity, evolution, and food security.

Salinity is a major crop production constraint in dry pea (Pisum sativum L.), making the development of salt-tolerant varieties essential to improve crop productivity and land-use efficiency. The genetic mechanisms of salt tolerance in dry pea is largely unknown, and research on salt-tolerant genes is limited. In this study, we established comprehensive genomic and transcriptomic resources, along with a robust screening protocol, to dissect the genetic basis of salinity tolerance using two germplasm sets: the USDA pea diversity panel, consisting of approximately 200 globally sourced accessions, and a set of 300 modern elite lines from the NDSU Pulse Crops Breeding Program. Genetic variation for the salinity response was assessed based on ten phenotypic traits, with root dry weight, shoot dry weight, and specific root length identified as key indicators based on their heritability. Genome-wide association mapping uncovered significant genomic regions and several candidate genes linked to salt stress, with the strongest association found on chromosome 6. Overlapping QTL signals across traits suggest a shared genetic architecture underlying salinity tolerance. Field-based transcriptomic analysis further identified five putative genes involved in salinity response conserved across multiple crop species. Notably, Psat5g000800, encoding a glycosyl hydrolase gene, was markedly upregulated under salinity stress. These findings highlight the complex, multi-gene regulatory nature of salinity tolerance in dry pea and underscore the importance of functional validation of candidate genes. This study provides key insights and practical tools to support breeding efforts aimed at improving salt tolerance in dry pea.

Indica rice, being a tropical crop, is highly sensitive to cold temperature. Cold stress affects vegetative growth, photosynthetic efficiency, along with reproductive features. Genetic resource screening in diverse landraces is an approach for identifying cold-tolerant traits. Here, we have characterised a boro germplasm, CB1, with an efficient germination rate and growth vigour when treated at chilling temperatures. CB1 seedlings show a higher survival rate compared to IR36 when subjected to prolonged chilling stress. Biochemical analyses indicated efficient ROS modulation, higher chlorophyll content, enhanced photosystem II efficiency and unique stomatal traits, leading to higher relative water content in CB1 plants during stress and recovery. Transcriptome analysis supported upregulation of chlorophyll biosynthesis, photosystem, & light harvesting complex and ROS scavenger genes in CB1 seedlings. Interestingly, high D1 protein turnover in CB1 promotes damage-repair of PSII for efficient photosynthesis. Furthermore, key transcription factors for stomatal development and expression of photosynthetic genes were upregulated in CB1 during stress recovery. Notably, higher expression of OsGLK1 and enrichment of GLK1 targets were observed in CB1 plants during chilling stress and recovery. Taken together, our results suggested that CB1 plants exhibit cold tolerance by modulating photosynthesis efficiency and stomatal behavior for better adaptability and survival against chilling temperature. HIGHLIGHTSThe efficient photosynthetic recovery, active ROS scavenging system and maintenance of water content through regulating stomatal traits, enhance the survival of indica germplasm CB1 against chilling stress.

Apples (Malus x domestica) are popular fruits grown in temperate regions of the world. The various genotypes must meet a specific threshold amount of cold exposure before they are competent to break dormancy, a quantity approximated as "chill hours". Several varieties have been identified that exhibit an ultra-low-chill requirement, or more precisely shallow dormancy, breaking vegetative and floral buds early in spring in response to minimal cold exposure. These ultra-low-chill genotypes originated from the Bahamas ( Dorsett Golden,1960s), Israel ( Anna, 1950s) and Alabama, USA ( Shell of Alabama, 1880s). The separation in time and space implies that each would feature distinct genetic lesions that govern dormancy control, providing discrete mechanisms to incorporate a low-chill trait in variety improvement. However, analysis of microsatellites and ultimately genome sequence indicates that Dorsett Golden and Anna share strong concordance with the Shell of Alabama genotype, as well as other ultra-low-chill varieties. Kinship analysis confirms that all are closely related, despite differences in year and place of origin. All three low-chill genotypes share common mutations in the DORMANCY ASSOCIATED MADS-BOX1(DAM1) gene, a known repressor of vegetative growth during dormancy. Genomic sequence diversity is observed among Shell of Alabama individuals, including differences in DAM1 that match differences in flowering time. The results of this study call into question the pedigrees of the ultra-low-chill apple germplasm and indicate variation in an otherwise narrow genetic base for use in future breeding efforts.