Basic leucine zippers (bZIP) constitute one of the biggest protein families and evolutionarily conserved transcription factors (TFs) in plants. We obtained mutant lines for two bZIP TFs, HvbZIP33 and HvbZIP76, in the genetic background of the barley cultivar Golden Promise (GP) via targeted gene-specific mutagenesis using an RNA-guided Cas9 endonuclease. A comprehensive morphological, physiological and transcriptomic analysis was performed in wild-type GP compared with hvbzip33 and hvbzip76 mutants under drought stress. The morphological and physiological changes were similar in both mutants and in the wild-type GP. Most strikingly, the mutants exhibited accelerated wilting and increased water loss. This effect was primarily caused by higher stomatal conductance (gs) and transpiration rate (E) in mutants compared to wild-type GP under both control and drought conditions, which in turn had a detrimental effect on the mutant's intrinsic water use efficiency (iWUE). Likewise, the transcriptome profiles of hvbzip33 and hvbzip76 were more similar to each other than those of wild-type GP. We found that the number of differentially regulated genes under control versus drought-stress conditions was higher in the mutants than in wild-type GP, suggesting that the mutants try to compensate for accelerated foliar water loss. The study highlights the essential roles of HvbZIP33 and HvbZIP76 in balancing water loss in barley. These findings provide a foundation for engineering enhanced drought tolerance in barley through targeted manipulation of these genes to optimize transpiration rates. ### Competing Interest Statement The authors have declared no competing interest. Deutsche Forschungsgemeinschaft, https://ror.org/018mejw64, GRK2064

The optimal choice of parents and crosses and, therefore, the prediction of the segregation variance are of high relevance to maximize genetic gain in breeding programs. Several methods have been developed for the prediction of segregation variance, including correlation with genotypic diversity, progeny simulations, or algebraic derivations in case of a diploid inheritance. To the best of our knowledge, no algebraic derivation using parental genotypic information is available to predict segregation variance for autotetraploid species. The objectives of our study were to (1) derive algebraic derivation based on linkage disequilibrium (LD) between linked loci to predict the segregation variance in autotetraploid species; (2) compare the performance of segregation variance estimated based on simulated progenies and the algebraic derivations; (3) investigate by simulations how experimental parameters affect the accuracy of segregation variance prediction; and (4) compare the segregation variance estimated in empirical data of potato and the one based on the algebraic derivations. The segregation variance estimated by the developed derivations showed very high correlations with the one observed in large simulated progenies, but those were lower when phased parental haplotypes were not available or family size decreased. The correlation between segregation variance estimated by the developed derivation and the empirical data was low. This could be attributed to the small family size used in the study, which we could show to increase LD between unlinked loci. The proposed algebraic derivations promise to be a precise alternative to simulations to help breeders in optimizing their family choices and sizes considering the segregation variance.

The wide range of tools and methods available to plant breeders today has the potential to increase the gain of selection. However, they also result in numerous complex choices in the design of efficient crossing and selection strategies. Computer simulations are essential to optimize breeding programs that are multi-year, high-effort endeavors and to compare process efficiencies without going through field experiments, thus saving both time and field resources. In addition, computer simulations are key to evaluate statistical properties of new methods and procedures as well as to exploit crop growth models and G*E interactions. The first two areas are discussed in great detail in our review. Furthermore, the review evaluates the capabilities, assumptions, and limitations of all publicly available simulation tools highlighting their relevance for the different areas of application.

We provide quantitative evidence that rye chromosome arm 1RS exerts background- and context-dependent effects on yield, stability, and grain protein content in contemporary elite winter wheat. Integrated multi-environment analyses demonstrate that translocation lines exhibit pronounced yield stability across 14 contrasting environments, with ‘Insave’- and ‘Petkus’-derived segments contributing through distinct performance profiles relevant for climate-resilient wheat breeding. Climate extremes increasingly threaten wheat production and yield stability globally. Rye (Secale cereale L.) chromosome arm 1RS has long been deployed in wheat breeding, yet its agronomic performance under drought and its interaction with elite genetic backgrounds remain insufficiently characterized. We evaluated 1RS translocations in elite German winter wheat using integrated molecular diversity analyses, multi-environment field phenotyping, and genomic modeling. Under near-optimal precipitation in 2021, matching the 1961–1990 reference period, the investigated winter wheat panel exhibited considerable genetic variation for yield and agronomic traits, indicating that high yield potential is still present in current German winter wheat breeding germplasm. In contrast, severe drought conditions in 2022 resulted in a 13.7

Vicine and convicine (VC) are antinutritional pyrimidine glycosides of faba bean (Vicia faba L.), commonly quantified either by spectrophotometric assays or by chromatographic methods. While spectrophotometric analysis is widely used for high-throughput screening because of its simplicity and low analytical costs, its reliability across biologically diverse sample matrices has not been systematically evaluated. This study therefore compared spectrophotometric extinction at 273 nm with high-performance liquid chromatography cou­pled to photodiode array detection (HPLC-PDA) across diverse biologically and analytically distinct sample sets. A total of 301 samples from five experimental studies were analysed, covering multi-year and multi-location variety trials, drought stress conditions, tissue-specific samples, seed developmen­tal stages, and different post-harvest drying procedures. Across the complete dataset, spectrophotometric extinction and HPLC-PDA-determined total VC showed a significant but moderate correlation (R = 0.77). However, stratified analysis revealed strong to very strong correlations within four of the five studies (R = 0.93–0.98), particularly in mature, homoge­neous seed material. In contrast, the developmental series exhibited a lower correlation (R = 0.85) and marked shifts in regression parameters, reflecting matrix-dependent devia­tions. Differences among studies were driven by context-de­pendent shifts in slope and intercept rather than random analytical error, indicating that spectrophotometric and chro­matographic methods are linked by context-specific relation­ships rather than a universal calibration. Untargeted LC-MS/ MS analysis of aqueous spectrophotometric extracts identi­fied a wide range of co-extracted polar metabolites, including nucleosides, nucleotides, sugars, and phosphorylated com­pounds, particularly enriched in immature seeds. Many of these compounds absorb in the same UV range as vicine and convicine and provide a mechanistic explanation for inflated extinction values in matrix-rich samples. The presented results define the analytical boundaries within which spectrophotometric VC determination can be applied reliably in faba bean seeds. While the method is well suited for high-throughput screening of mature and composition­ally homogeneous seed material, chromatographic analysis remains indispensable whenever biological matrix composi­tion changes substantially or compound-specific VC quantifi­cation is required.

Rising nighttime temperature (Tnight) can reduce crop yields while low Tnight may restrict plant growth and development. Despite quantifiable effects of Tnight, genetic basis underlying plant responses to Tnight remains unclear. We investigated natural variation in long-term response of effective photosynthetic efficiency (Fq'/Fm') to Tnight among Arabidopsis accessions. Genome-wide association study (GWAS) was conducted for Fq'/Fm' of the plants grown under 15°C or 20°C Tnight. GWAS revealed highly polygenic architecture of Fq'/Fm', with associated single nucleotide polymorphisms (SNPs) varying across Tnight conditions and measurement days. Notably, 15°C Tnight stabilised the contributions of a subset of SNPs, whereas 20°C Tnight enhanced day-to-day variations in SNP-trait associations. We then incorporated the associated SNPs in genomic prediction (GP) models to assess the improvement of prediction accuracy. The GWAS-derived SNPs significantly improved the prediction ability of GP models, indicating collective influence of numerous small-effect SNPs. Finally, the model predictions were experimentally validated in an independent, genetically diverse population, which confirmed the correct identification of low-Fq'/Fm' accessions in 15°C Tnight. These results uncover the genetic underpinnings of long-term Fq'/Fm' response to cool vs warm nights and establish a framework for leveraging GWAS and GP to explore complex traits, such as photosynthesis, toward breeding climate-resilient crops.

The flag leaf is a major contributor of photosynthetic assimilates to developing grains. We investigated the genetic architecture and cellular basis of flag leaf length (FLL) and width (FLW) in a multiparent population of 45 recombinant inbred line (RIL) populations (HvDRR) in barley. Fine-mapping of a major quantitative trait locus (QTL) was performed to prepare the isolation of the causal gene. Natural variation of FLL and FLW across environments was highly heritable, and genotypes from warm climates produced longer and wider flag leaves than those from cooler regions. Variation in flag leaf size was quantitatively inherited and influenced by 24 consensus QTLs, of which 17 have not previously been reported. Validation of QTLs qHvDRR-FLS-8 and qHvDRR-FLS-17 in nearly isogenic RILs showed that these QTLs also controlled length and width of leaves older than the flag leaf. The number of epidermal cells primarily determined FLL, whereas the number and size of epidermal cells collectively determined FLW differences. In addition, we identified the previously unknown effect of genic alleles and epialleles at Vrn-H1 on flag leaf size variation in spring barley. Furthermore, we fine-mapped qHvDRR-FLS-8, narrowing the interval from 8.7 Mb to 3.5 Mb. In conclusion, our study identified the genomic regions associated with morphological and anatomical variation for leaf size and set the stage to uncover causal genes.

Missing hills affect the yield of a single plant in experimental plots, as the remaining plants have more space and resources compared to plants in plots without missing hills. However, up to date, no universal way on how to correct for missing hills exists in the context of potato breeding programs. The aim of our study was to (i) compare different approaches to adjust the yield when hills are missing in a plot, (ii) propose a universal approach for correcting missing hills in potato plots, and (iii) test if the estimated parameters are transferable from a training population to an independent validation population. We evaluated in a set of 1066 different clones approaches to adjust single plant yield for the proportion of missing hills in a plot. These approaches were a grid search, a linear mixed effects model, or approaches previously proposed in the literature. We compared these approaches based on the heritability of single plant yield. Our results showed that all advanced correction approaches improved the heritability and reduced the residuals compared to the standard approaches, where the yield of the plot was just divided by the actual plant number or the intended plant number. The most promising results were observed for the four-parameter Weibull type 2 regression of the effects for each proportion of missing hills derived by a linear mixed model analysis with the proportion of missing hills used as a cofactor. The approach derived here is transferable across populations and increases heritability of yield estimates without new parameter adaptation. Therefore, our approach provides an easily applicable procedure to improve the comparison of single plant yield between clones in potato breeding programs.

To meet the growing demand for agricultural products, optimizing photosynthesis is a promising strategy to improve the crop yields. Phenotypic variance in photosynthesis has been observed within or between species. To explore the potential of integrating photosynthetic parameters into crop breeding programs, we explored the genetic variation in photosynthesis by assessing photosynthesis-related parameters across plant development in 631 barley recombinant inbred lines (RILs) from eight HvDRR sub-populations under field conditions. The genetic complexity of these parameters was resolved by bi-parental and multi-parental quantitative trait loci (QTL) analyses. Finally, we examined the merit of integrating photosynthesis-related parameters in genomic prediction of yield and its components. Significant genotypic variations of the photosynthesis-related parameters were found among the RILs, with their heritability ranging from 0.38 to 0.54. The multiple QTL and dynamic QTL for photosynthesis observed across different developmental stages underlined the complexity of the genetics of photosynthesis in barley. The considerably higher percentage of phenotypic variance explained for genomic prediction than multi-parental QTL analysis illustrates that the photosynthesis-related parameters are inherited in a more complex way than classical agronomic traits. Notably, the prediction ability for yield was increased by integrating the photosynthesis-related parameters of some developmental stages into genomic prediction models. Therewith, our results suggest a novel perspective on increasing the efficiency of crop breeding programs by integrating photosynthesis-related parameters into prediction models.

Epigenetic variation can play a crucial role in explaining the missing heritability of complex traits. To investigate genome-wide methylation in spring barley (Hordeum vulgare subsp. vulgare), we performed whole genome bisulfite sequencing on 23 parental inbreds from a community resource for genetic mapping. Our objectives were to characterize methylation variation, explore its association with single nucleotide polymorphisms (SNPs), and examine links to gene expression. The barley genome showed high average methylation levels of 88.6%, 58.1%, and 1.4% in the CpG, CHG, and CHH contexts, respectively. We identified nearly 500 000 differentially methylated regions (DMRs), with 64%, 64%, and 83% of DMRs in CpG, CHG, and CHH contexts, respectively, not associated with sequence variation. Around 6% of all DMRs showed significant associations with gene expression, with the direction of the correlations varying based on the DMR's location relative to the gene with a recognizable pattern. We exemplified this association between DNA methylation and gene expression on the known flowering promoting gene VRN-H1, identifying a highly methylated epiallele linked to earlier flowering. Lastly, methylation improved the prediction abilities of genomic prediction models for various traits, outperforming models based solely on SNPs and gene expression. These findings emphasize the independent role of DNA methylation to sequence variation.

Hybridization between closely related species is increasingly recognized as a major source of biodiversity. Yet, whether it can create advantageous trait combinations while purging harmful alleles remains unknown. To address this question, we studied Arabis nemorensis and Arabis sagittata, two endangered species that currently hybridize in a single hotspot. We chose two representative individuals originating from the hotspot, generated high-quality annotated genome sequences, crossed them to form an F2 population, quantified segregation distortion along the genome, measured 22 phenotypic traits and mapped their genetic basis. Two genomic regions showed strong segregation distortion favoring A. sagittata alleles in the F2 and potentially accelerating their introgression. Fifty-eight quantitative trait loci (QTLs) were identified for 20 traits, with additive and dominance effects best fitting Gaussian and logistic distributions, respectively. It was found that 48% of QTLs were unlinked to reduced fitness or segregation distortion. A major QTL affecting flowering time implicated Terminal-Flower 1 (TFL1) as a candidate gene for life-history adaptation. QTLs did not overlap with recent selective sweeps, except for those controlling rosette size. Our findings offer unique insights into both the potential for adaptive trait combinations and the constraints imposed by hybrid fitness loss during incipient stages of hybridization.

Nitrate and ammonium are two primary nitrogen forms essential for plant growth. Plants deploy different strategies to optimize the N uptake by roots, based on a complicated regulatory network that controls root phenotype and physiology. Here, we studied the response of root architecture to varying N applications in the model species Brachypodium distachyon. Using a combination of phenotypic and transcriptomic analyses, we examined how different forms and concentrations of ammonium and nitrate affect root growth, biomass allocation, and N uptake. N concentrations significantly influence root traits such as root length, root hair development, and aerenchyma formation in response to nitrate and ammonium. Plants grown in ammonium conditions had thin but highly branched roots, whereas nitrate application resulted in shorter, thicker roots with denser root hair at higher nitrate concentrations. Furthermore, using advanced co-expression network analysis, we identified an Atypical Aspartic Protease (APs) gene encoding an aspartyl protease family protein and a phosphoenolpyruvate carboxylase 1 (PEPC1) gene in brachypodium, which potentially control the root architectural and anatomical adaptions to different N form. APs expression showed a positive correlation with total root length and lateral root development, along with a negative correlation with root hair density. In contrast, PEPC1 exhibited positive correlations with cortex, stele, root cross-sectional areas, and root hair density, while showing a negative correlation with total root length. These genes likely play an important role in the transcriptional regulatory networks involved in these adaptive responses, which highlight the complex interplay between root morphology, nitrogen metabolism, and environmental nutrient conditions.

Potato is a versatile food crop and major component of human nutrition worldwide. Model calculations and computer simulations can be used to optimize the resource allocation in potato breeding programs but require quantitative genetic parameters. The objectives of our study are to (i) estimate quantitative genetic parameters of the most important phenotypic traits in potato breeding programs, (ii) compare the importance of inter- vs. intra-population variance, (iii) quantify genotypic and phenotypic covariances among phenotypic traits, and (iv) examine the effect of a preselection in the single hills stage on variance and covariance components in later stages of the breeding program. Our study was based on a total of 1066 clones from three breeding programs which were evaluated in a non-orthogonal way in 15 environments for a total of 26 phenotypic traits. The examined traits showed an overall high to medium heritability, and variance analysis revealed trait-specific differences in the influence of the genotypic, environmental, and genotype-environment interaction effect. Accounting for heterogeneity in the residual variances between the 15 environments led to a significant improvement of the variance parameter estimation. The result of our study suggested that the first selection step at the single hills stage did not negatively impact the genetic variability of the target traits implying that the traits assessed in the earlier stages were not correlated with the traits influencing market success. Our results can be used as base for further simulation studies and, thus, help to optimize the resource allocation in breeding programs.

Genomic prediction (GP) can help increase the efficiency of breeding programs, as genotypes can be selected based on their predicted performance. However, to the best of our knowledge, this procedure is not yet routine in commercial breeding programs in tetraploid organisms like potato (Solanum tuberosum L.). The objectives of this study were to GP revealed high prediction accuracies using genomic best linear unbiased prediction. Our results indicated that a training set of 280–480 clones and 10,000 markers was sufficient. Prediction within a specific market segment led to a higher prediction accuracy compared to adding clones from other market segments to the training set or to predict between different market segments. Lastly, we found a higher prediction accuracy when in a training set of selected clones, i.e., a training set that consists of clones with high trait values, 20

Different cross-selection (CS) methods incorporating genomic selection (GS) have been used in diploid species to improve long-term genetic gain and preserve diversity. However, their application to heterozygous and autotetraploid crops such as potato (Solanum tuberosum L.) is lacking so far. The objectives of our study were to (i) assess the effects of different CS methods and the incorporation of GS and genetic variability monitoring on both short- and long-term genetic gains compared to strategies using phenotypic selection (PS); (ii) evaluate the changes in genetic variability and the efficiency of converting diversity into genetic gain across different CS methods; and (iii) investigate the interaction effects between different genetic architectures and CS methods on long-term genetic gain. In our simulation results, implementing GS with optimal selected proportions had increased short- and long-term genetic gain compared to any PS strategy. The CS method considering additive and dominance effects to predict progeny mean based on simulated progenies (MEGV-O) achieved the highest long-term genetic gain among the assessed mean-based CS methods. Compared to MEGV-O and usefulness criteria (UC), the linear combination of UC and genome-wide diversity (called EUCD) maintained the same level of genetic gain but resulted in higher diversity and a lower number of fixed QTLs. Moreover, EUCD had a relatively high degree of efficiency in converting diversity into genetic gain. However, choosing the most appropriate weight to account for diversity in EUCD depends on the genetic architecture of the target trait and the breeder's objectives. Our results provide breeders with concrete methods to improve their potato breeding programs.

Drought stress alters the plant metabolism, physiology, and growth, and such responses might differ with the intensity of stress. We evaluated the genotypic diversity in plant morphology, photosynthetic responses, metabolite shift, and their relationship in 23 genetically diverse barley inbreds under control, dry down (DD), and moderate drought (MD) stress. DD triggered strong inhibition of photosynthetic health, while reducing plant size was the key strategy under MD stress. We observed that the induced changes under both stress scenarios occurred in a genotype-dependent manner. Compared to control conditions, the metabolism of simple sugars and polyhydroxy acids increased in MD and DD, while the maximum accumulation of amino acids, lipids, and phosphates occurred in DD stress. Accumulation of sugars and metabolites with unknown classification was the metabolic signature of drought-tolerant inbreds. Nevertheless, accumulation of a large pool of metabolites, including lipids, polyhydroxy acids, and amino acids in an inbred did not have a positive effect on drought tolerance and might be metabolically costly. The inbred plants' tolerance to MD and DD originated from the semi-arid or sub-tropical regions, while drought-sensitive inbreds primarily came from temperate regions. Low stomata density, reduced water loss, and retarded growth under drought stress were the key features of inbreds with better survival capacity under severe dehydration. We identified drought-tolerant barley inbreds, and our study offers resources for future genetic research on various drought tolerance strategies.

Arthropods threaten crop production by feeding on plants and, most importantly, by transmitting viruses. BYDV-PAV is the most prevalent virus species that causes barley yellow dwarf disease, one of the most economically important viral diseases affecting cereals worldwide. Maize plays a central role in BYDV-PAV epidemiology, serving as a “green bridge” for BYDV-PAV and its vector Rhopalosiphum padi in summer. Some studies have reported that the incidence of persistently transmitted viruses may be reduced in plants that are resistant to their insect vectors. In contrast, the choice test applied in our study revealed that R. padi is not repelled by the included BYDV-PAV-resistant maize inbreds. Significant differences in phloem architecture observed among the inbreds suggested that aphids feeding on BYDV-PAV-resistant maize may have difficulties reaching the phloem or establishing a stable feeding site. However, monitoring of aphid feeding behavior using the electrical penetration graph technique on maize inbreds that differed in their BYDV-PAV susceptibility revealed no correlation between R. padi feeding and BYDV-PAV resistance. Furthermore, we could not confirm the generation of reactive oxygen species (ROS), a typical reaction of plants during aphid infestation and infection of some viruses. In summary, we conclude that the BYDV-PAV resistance mechanisms in maize act directly on the virus and not on its vector, R. padi.

Comprehensive maps of functional variation at transcription factor (TF) binding sites (cis-elements) are crucial for elucidating how genotype shapes phenotype. Here, we report the construction of a pan-cistrome of the maize leaf under well-watered and drought conditions. We quantified haplotype-specific TF footprints across a pan-genome of 25 maize hybrids and mapped over 200,000 variants, genetic, epigenetic, or both (termed binding quantitative trait loci (bQTL)), linked to cis-element occupancy. Three lines of evidence support the functional significance of bQTL: (1) coincidence with causative loci that regulate traits, including vgt1, ZmTRE1 and the MITE transposon near ZmNAC111 under drought; (2) bQTL allelic bias is shared between inbred parents and matches chromatin immunoprecipitation sequencing results; and (3) partitioning genetic variation across genomic regions demonstrates that bQTL capture the majority of heritable trait variation across ~72% of 143 phenotypes. Our study provides an auspicious approach to make functional cis-variation accessible at scale for genetic studies and targeted engineering of complex traits.