Abstract Rosa, belonging to the family Rosaceae, encompasses more than 150 species widely distributed across the northern hemisphere. Renowned for their beauty, roses are cultivated throughout the world for ornamental purposes and the production of essential oils and perfumes. Despite their cultural and commercial significance, the genomic resources of wild Rosa species have not been studied comprehensively, hampering the understanding of their genetic diversity, evolutionary history, and breeding potential. Here we present a Rosaceae panproteome and a Rosa pangenome, spanning wild, traditional garden, and modern rose lineages, constructed using a De Bruijn graph (DBG)-based approach, and introduce two high-quality de novo genomes for Rosa sericea and Rosa rugosa. A phylogeny of 18 Rosa haplotypes based on 4367 single-copy core homology groups (genes) provided robust evolutionary inference. Our analysis revealed substantial interspecific genomic diversity in core gene repertoires, structural features, and a transposable element (TE) landscape that shaped genome size differences and is potentially linked to phenotypic plasticity. We provide two examples of the types of analyses that become possible with this pangenome. First, the pangenome serves as a quality-aware lens, exposing discrepancies arising from assembly and annotation variability and helping separate technical artifacts from genuine biological signal. Second, the pangenome provides locus-level resolution: analysis of MYB114, a key regulator of anthocyanin accumulation, reveals lineage-specific presence–absence patterns and TE-associated regulatory variation. This pangenomic study deepens our understanding of the genetic diversity and genome evolution of Rosa species and establishes a resource to resolve the genetic bases of key traits, thereby informing and supporting rose breeding.

Abstract Modern agriculture faces major sustainability challenges, including stagnating yields, dependence on fossil resources, and severe environmental impacts. Increasing intra- and interspecific diversity within plots through agroecological design is a promising method for enhancing crop productivity and stability. However, mixed-crop performance remains highly variable, and the genetic architecture of interactions within heterogeneous canopies is poorly understood. Two quantitative genetic frameworks have been proposed: trait-based models, which describe how interacting traits shape phenotypes, and variance-based models, which treat neighbor genotype effects as “black-box” social effects. However, existing variance-based models have been developed almost exclusively for intraspecific interactions and simple neighborhoods. We propose a general multispecies framework describing how a focal plant’s phenotype and total breeding value arise from its own direct effects and from the indirect effects of conspecific and heterospecific neighbors. We derived analytical expressions for phenotypic variance, inter-individual covariance, total breeding value variance, and relative heritable variance, which explicitly account for spatial structure, relatedness, and environmental similarities. Using a two-species alternating-row field layout and extensive simulations based on flexible variance–covariance structures, we evaluated the statistical power and bias of joint mixed-model estimators of direct and indirect genetic and environmental effects under a wide range of parameter combinations. Our results show that accurate separation of direct and indirect effects depends on trait heritability and replication, and that modeling genetic covariances across effects and species substantially improves estimation accuracy. This framework provides a unified, individual-centered basis for analyzing complex multispecies neighborhoods and quantifying the breeding potential of plant communities. Article Summary Growing several crop species or varieties together in the same field can boost yield and stability, but the outcome is unpredictable and the genetic causes remain unclear. We developed a theoritical & statistical framework that links each plant’s performance to its own genes and to those of its neighbors, both from the same and from a different species. Computer simulations of a two-species field showed that these direct and neighbor-driven genetic effects can be reliably separated when enough plants are measured per variety. The framework opens the way to breeding crop mixtures that perform well specifically when grown alongside another species.

This paper reviews recent research articles published in Euphytica on breeding for intraspecific diversification (genotype mixtures) and mixed cropping of different species. We highlight several aspects that these papers have in common, including the use of novel approaches to overcome resource constraints, such as low mechanization, external input availability, or farm labor scarcity. We also discuss how these research articles demonstrate a conceptual overlap with Frugal Innovation, which involves finding elegant solutions despite financial or technological constraints. Our analysis suggests that breeding for diversified cropping systems is a key strategy to improve livelihoods and to intensify agricultural production in resource-limited contexts. Finally, we recommend best practices for successful approaches to breed for low-input and diversified cropping systems, including improving nutrient use efficiency, plant community improvement, research using improved phenotyping techniques, and policy frameworks for farmers and breeders. The approaches presented in this article have implications for promoting agricultural intensification and reducing poverty in rural areas.

Fruit quality traits play an important role in consumption of kiwiberry (Actinidia arguta). The genetic basis of fruit quality traits in this woody, perennial and dioecious fruit crop remains largely unknown. This study aimed to identify the underlying genetic basis of fruit quality traits in A. arguta, using a single nucleotide polymorphism (SNP) genetic linkage map previously developed in a tetraploid F1 population of ‘Ruby-3’ × ‘KuiLv-M’. The F1 population was phenotyped over three years (2020–2022) for fruit quality traits, including skin color, flesh color, fruit weight, fruit diameter, total soluble solids, fruit longitudinal diameter and fruit shape index. A total of nine QTLs were detected for five traits, explaining 10%–32% of the trait variation. For fruit color, the support interval of a major QTL on LG9 contained an MYB transcription factor MYB110, which was previously demonstrated to control color regulation in kiwifruit, thus suggesting that the MYB110 is the candidate gene for fruit color in kiwiberry. The linked marker for fruit color was validated in an F1 population and 25 kiwiberry cultivars. In conclusion, the knowledge obtained through the QTL mapping is applicable to improve the efficiency and cost-effectiveness in kiwiberry breeding.

Rosa , belonging to the family Rosaceae, encompasses more than 150 species which are widely distributed in the northern hemisphere. Renowned for their beauty, roses are cultivated throughout the world for ornamental purposes and the production of essential oils and perfumes. Despite their cultural and commercial significance, the genomic resources of wild Rosa species have not been studied comprehensively, hampering the understanding of their genetic diversity, evolutionary history, and breeding potential. Here we report on high-quality de novo genomes for Rosa sericea and Rosa rugosa . By integrating these two de novo genomes with existing public genomic resources, we have built a Rosaceae panproteome and a Rosa pangenome (spanning wild, traditional garden, and modern rose lineages) using a De Bruijn graph (DBG)-based approach. A maximum likelihood (ML) phylogeny of 18 Rosa haplotypes based on 4,367 single-copy core homology groups (genes) provided robust evolutionary inference, confirming the basal position of R. sericea , and enabled a gene-based macrosynteny analysis across the pangenome. Our analyses revealed significant genomic diversity among species, extensive variation in core gene content, and lineage-specific transposable element (TE) expansion patterns that contribute to the variation in Rosa genome size and to species-specific adaptations. The pangenome also revealed biased diversification of homology groups potentially linked to phenotypic plasticity in Rosa . Specifically, our analysis of the rose scent-related gene family, NUDX1 , uncovered its evolutionary trajectory in Rosa , in which TEs insertions provided putative novel regulatory elements that facilitated adaptive evolution in metabolic pathways. This pangenomic study deepens our understanding of the genetic diversity and evolution of traits within the Rosa genus. In addition, the findings lay the foundation for future efforts to understand the genetic mechanisms driving trait evolution, which can support rose breeding. ### Competing Interest Statement The authors have declared no competing interest.

Societal Impact Statement Agricultural production systems in the global North combine monocultures of specialised varieties and breeds with external interventions and inputs. Increasing the diversity of varieties, breeds and species may increase the system's resilience to external pressures through beneficial interactions. However, more diverse agricultural systems actually show large variation in their response to stresses. We propose to use functional-structural modelling, alternating with field experiments, to optimise the design of biodiverse systems and validate predicted interactions under stresses. This information can then be used to design biodiverse production systems that are more resilient and guide crop variety and livestock breeding for them. Summary Many arable, horticultural, livestock and forestry production systems are optimised for productivity in monocultures, with environmental factors managed primarily through external interventions and inputs (fertilizers, herbicides, pesticides, antibiotics, irrigation). Enhancing system resilience may be achieved by (re)introducing diversity within and between species and breeds of crops, livestock and trees. While the ecological concepts underlying the effect of biodiversity on resilience are known, in practice the response of more biodiverse agricultural production systems to external pressures shows large variation. Hence, resilient production systems do not emerge automatically from greater diversity but must be designed. Studying the specific combining advantages of different species/varieties/breeds at the production system level may provide the required information, but the number of possible combinations and interactions is too large to screen with controlled field experiments only. We argue that modelling provides a solution to this combinatorial problem by allowing for an in silico exhaustive search of possible interactions. We describe various crop models and discuss the use of functional-structural models for (re-)designing production systems, specifying the functional traits to be selected for in crop variety and livestock breeding. While process-based functional-structural models can predict how genotypes will respond to environmental stressors, experimental trials measure these responses in the field. An iterative process- where models inform experiments and experiments, in turn, refine models- may lead to a nuanced understanding of resilience mechanisms and a robust set of tools for designing diversified production systems. We discuss opportunities and pitfalls of this combined approach.

The cultivation and domestication of roses reflects cultural exchanges and shifts in aesthetics that have resulted in today's most popular ornamental plant group. However, the narrow genetic foundation of cultivated roses limits their further improvement. Wild Rosa species harbour vast genetic diversity, yet their utilization is impeded by taxonomic confusion. Here we generated a phased and gap-free reference genome of Rosa persica for phylogenetic and population genomic analyses of a large collection of Rosa samples. The robust nuclear and plastid phylogenies support most of the morphology-based traditional taxonomy of Rosa. Population genomic analyses disclosed potential genetic exchanges among sections, indicating the northwest and southwest of China as two independent centres of diversity for Rosa. Analyses of domestication traits provide insights into selection processes related to flower colour, fragrance, double flower and resistance. This study provides a comprehensive understanding of rose domestication and lays a solid foundation for future re-domestication and innovative breeding efforts using wild resources.

Population genetic studies have long been an essential part of dissecting important traits of crops. The advent of genomic resources has significantly enhanced the efficiency of genetic mapping studies. Concurrently, the integration of multi-omics approaches affords a comprehensive perspective of plant systems, enabling researchers to investigate the correlations between genetic variations and horticultural traits across multiple dimensions. This review summarized studies combining forward genetics with multi-omics resources to analyze the genetic basis of important traits in horticultural crops. The strategies for the effective application of these integrated approaches in the genetic dissection of ornamental plant populations was discussed. The challenges in processing large dataset were acknowledged and promising prospects of artificial intelligence (AI) when addressing such issues were outlined. The review aims to provide guidance for population genetic research of horticultural crops and ornamental plants in the multi-omics era.

Multiple QTLs control unreduced pollen production in potato. Two major-effect QTLs co-locate with mutant alleles of genes with homology to AtJAS, a known regulator of meiotic spindle orientation. In diploid potato the production of unreduced gametes with a diploid (2n) rather than a haploid (n) number of chromosomes has been widely reported. Besides their evolutionary important role in sexual polyploidisation, unreduced gametes also have a practical value for potato breeding as a bridge between diploid and tetraploid germplasm. Although early articles argued for a monogenic recessive inheritance, the genetic basis of unreduced pollen production in potato has remained elusive. Here, three diploid full-sib populations were genotyped with an amplicon sequencing approach and phenotyped for unreduced pollen production across two growing seasons. We identified two minor-effect and three major-effect QTLs regulating this trait. The two QTLs with the largest effect displayed a recessive inheritance and an additive interaction. Both QTLs co-localised with genes encoding for putative AtJAS homologs, a key regulator of meiosis II spindle orientation in Arabidopsis thaliana. The function of these candidate genes is consistent with the cytological phenotype of mis-oriented metaphase II plates observed in the parental clones. The alleles associated with elevated levels of unreduced pollen showed deleterious mutation events: an exonic transposon insert causing a premature stop, and an amino acid change within a highly conserved domain. Taken together, our findings shed light on the natural variation underlying unreduced pollen production in potato and will facilitate interploidy breeding by enabling marker-assisted selection for this trait.

Summary Kiwifruit ( Actinidia spp) is a woody, perennial and deciduous vine. In this genus, there are multiple ploidy levels but the main cultivated cultivars are polyploid. Despite the availability of many genomic resources in kiwifruit, SNP genotyping is still a challenge given these different levels of polyploidy. Recent advances in SNP array technologies have offered a high‐throughput genotyping platform for genome‐wide DNA polymorphisms. In this study, we developed a high‐density SNP genotyping array to facilitate genetic studies and breeding applications in kiwifruit. SNP discovery was performed by genome‐wide DNA sequencing of 40 kiwifruit genotypes. The identified SNPs were stringently filtered for sequence quality, predicted conversion performance and distribution over the available Actinidia chinensis genome. A total of 134 729 unique SNPs were put on the array. The array was evaluated by genotyping 400 kiwifruit individuals. We performed a multidimensional scaling analysis to assess the diversity of kiwifruit germplasm, showing that the array was effective to distinguish kiwifruit accessions. Using a tetraploid F1 population, we constructed an integrated linkage map covering 3060.9 cM across 29 linkage groups and performed QTL analysis for the sex locus that has been identified on Linkage Group 3 (LG3) in Actinidia arguta . Finally, our dataset presented evidence of tetrasomic inheritance with partial preferential pairing in A. arguta. In conclusion, we developed and evaluated a 135K SNP genotyping array for kiwifruit. It has the advantage of a comprehensive design that can be an effective tool in genetic studies and breeding applications in this high‐value crop.

Background The termite-fungus symbiosis is an ancient stable mutualism of two partners that reproduce and disperse independently. With the founding of each termite colony the symbiotic association must be re-established with a new fungus partner. Complementarity in the ability to break down plant substrate may help to stabilize this symbiosis despite horizontal symbiont transmission. An alternative, non-exclusive, hypothesis is that a reduced rate of evolution may contribute to stabilize the symbiosis, the so-called Red King Effect. Methods To explore this concept, we produced the first linkage map of a species of Termitomyces , using genotyping by sequencing (GBS) of 88 homokaryotic offspring. We constructed a highly contiguous genome assembly using PacBio data and a de-novo evidence-based annotation. This improved genome assembly and linkage map allowed for examination of the recombination landscape and its potential effect on the mutualistic lifestyle. Results Our linkage map resulted in a genome-wide recombination rate of 22 cM/Mb, lower than that of other related fungi. However, the total map length of 1370 cM was similar to that of other related fungi. Conclusions The apparently decreased rate of recombination is primarily due to genome expansion of islands of gene-poor repetitive sequences. This study highlights the importance of inclusion of genomic context in cross-species comparisons of recombination rate.

Linkage mapping is an approach to order markers based on recombination events. Mapping algorithms cannot easily handle genotyping errors, which are common in high-throughput genotyping data. To solve this issue, strategies have been developed, aimed mostly at identifying and eliminating these errors. One such strategy is SMOOTH, an iterative algorithm to detect genotyping errors. Unlike other approaches, SMOOTH can also be used to impute the most probable alternative genotypes, but its application is limited to diploid species and to markers heterozygous in only one of the parents. In this study we adapted SMOOTH to expand its use to any marker type and to autopolyploids with the use of identity-by-descent probabilities, naming the updated algorithm Smooth Descent (SD). We applied SD to real and simulated data, showing that in the presence of genotyping errors this method produces better genetic maps in terms of marker order and map length. SD is particularly useful for error rates between 5% and 20% and when error rates are not homogeneous among markers or individuals. With a starting error rate of 10%, SD reduced it to ∼5% in diploids, ∼7% in tetraploids and ∼8.5% in hexaploids. Conversely, the correlation between true and estimated genetic maps increased by 0.03 in tetraploids and by 0.2 in hexaploids, while worsening slightly in diploids (∼0.0011). We also show that the combination of genotype curation and map re-estimation allowed us to obtain better genetic maps while correcting wrong genotypes. We have implemented this algorithm in the R package Smooth Descent.

Additional file 7: Supplemental File 1 and Supplemental File 2. Original, uncropped, gel electrophoresis images used for Supplementary Fig. 1.

Genome-wide association studies (GWAS) are a useful tool to unravel the genetic architecture of complex traits, but the results can be difficult to interpret. Population structure, genetic heterogeneity, and rare alleles easily result in false positive or false negative associations. This paper describes the analysis of a GWAS panel combined with three bi-parental mapping populations to validate GWAS results, using phenotypic data for steroidal glycoalkaloid (SGA) accumulation and the ratio (SGR) between the two major glycoalkaloids α-solanine and α-chaconine in potato tubers. SGAs are secondary metabolites in the Solanaceae family, functional as a defence against various pests and pathogens and in high quantities toxic for humans. With GWAS, we identified five quantitative trait loci (QTL) of which Sga1.1, Sgr8.1, and Sga11.1 were validated, but not Sga3.1 and Sgr7.1. In the bi-parental populations, Sga5.1 and Sga7.1 were mapped, but these were not identified with GWAS. The QTLs Sga1.1, Sga7.1, Sgr7.1, and Sgr8.1 co-localize with genes GAME9, GAME 6/GAME 11, SGT1, and SGT2, respectively. For other genes involved in SGA synthesis, no QTLs were identified. The results of this study illustrate a number of pitfalls in GWAS of which population structure seems the most important. We also show that introgression breeding for disease resistance has introduced new haplotypes to the gene pool involved in higher SGA levels in certain pedigrees. Finally, we show that high SGA levels remain unpredictable in potato but that α-solanine/α-chaconine ratio has a predictable outcome with specific SGT1 and SGT2 haplotypes.

Cultivated potato is a clonally propagated autotetraploid species with a highly heterogeneous genome. Phased assemblies of six cultivars including two chromosome-scale phased genome assemblies revealed extensive allelic diversity, including altered coding and transcript sequences, preferential allele expression, and structural variation that collectively result in a highly complex transcriptome and predicted proteome, which are distributed across the homologous chromosomes. Wild species contribute to the extensive allelic diversity in tetraploid cultivars, demonstrating ancestral introgressions predating modern breeding efforts. As a clonally propagated autotetraploid that undergoes limited meiosis, dysfunctional and deleterious alleles are not purged in tetraploid potato. Nearly a quarter of the loci bore mutations are predicted to have a high negative impact on protein function, complicating breeder’s efforts to reduce genetic load. The StCDF1 locus controls maturity, and analysis of six tetraploid genomes revealed that 12 allelic variants of StCDF1 are correlated with maturity in a dosage-dependent manner. Knowledge of the complexity of the tetraploid potato genome with its rampant structural variation and embedded deleterious and dysfunctional alleles will be key not only to implementing precision breeding of tetraploid cultivars but also to the construction of homozygous, diploid potato germplasm containing favorable alleles to capitalize on heterosis in F1 hybrids.

More biodiversity and genetic diversity in crops, trees, and livestock is considered an important strategy to improve resilience and sustainability of agricultural and forestry production systems that contribute to climate mitigation and adaptation. Plant and animal breeding need to provide well-adapted varieties and breeds that fit into these systems.In this report, we draw visions of five innovative agricultural production and forestry systems and used them in focus groups of scientists, breeders, and pioneer entrepreneurs to discuss the contribution of biodiversity and genetic diversity to the sustainability and resilience of the system, breeding goals, access to genetic material to realize the breeding goals, and the priorities for breeding and research. Although transition pathways can differ between systems, strategic research i should concentrate on the relationship between wider use of genetic resources, increasing beneficial species/breed/variety interactions, and the resilience of production systems.---Meer biodiversiteit en genetische diversiteit in gewassen, bossen en landbouwhuisdieren wordt beschouwd als een belangrijke strategie voor veerkrachtige en duurzame land- en bosbouwsystemen die bijdragen aan klimaatmitigatie en -adaptatie. Veredeling en fokkerij moeten goed aangepaste rassen leveren die passen bij deze systemen. In ditr apport schetsen wij vijf visies van innovatieve land- en bosbouwsystemen en hebben deze gebruikt in focusgroepdiscussies met wetenschappers, fokkers en veredelaars, en pioniers. Met hen hebben we de bijdrage van biodiversiteit en genetische diversiteit aan de duurzaamheid en weerbaarheid van het systeem, de fok- en veredelingsdoelen, de toegang tot genetisch materiaal om die doelen te realiseren, en de prioriteiten voor onderzoek bediscussieerd. Hoewel systemen kunnen verschillen in het transitiepad, is strategisch onderzoek nodig, gericht op de relatie tussen een breder gebruik van genetische bronnen, verbetering van gunstige interacties tussen soorten/rassen, en de veerkracht van het gehele productiesysteem.

In polyploids, linkage mapping is carried out using genotyping with discrete dosage scores. Here, we use probabilistic genotypes and we validate it for the construction of polyploid linkage maps. Marker genotypes are generally called as discrete values: homozygous versus heterozygous in the case of diploids, or an integer allele dosage in the case of polyploids. Software for linkage map construction and/or QTL analysis usually relies on such discrete genotypes. However, it may not always be possible, or desirable, to assign definite values to genotype observations in the presence of uncertainty in the genotype calling. Here, we present an approach that uses probabilistic marker dosages for linkage map construction in polyploids. We compare our method to an approach based on discrete dosages, using simulated SNP array and sequence reads data with varying levels of data quality. We validate our approach using experimental data from a potato (Solanum tuberosum L.) SNP array applied to an F1 mapping population. In comparison to the approach based on discrete dosages, we mapped an additional 562 markers. All but three of these were mapped to the expected chromosome and marker position. For the remaining three markers, no physical position was known. The use of dosage probabilities is of particular relevance for map construction in polyploids using sequencing data, as these often result in a higher level of uncertainty regarding allele dosage.

Intercropping is both a well-established and yet novel agricultural practice, depending on one's perspective. Such perspectives are principally governed by geographic location and whether monocultural practices predominate. Given the negative environmental effects of monoculture agriculture (loss of biodiversity, reliance on non-renewable inputs, soil degradation, etc.), there has been a renewed interest in cropping systems that can reduce the impact of modern agriculture while maintaining (or even increasing) yields. Intercropping is one of the most promising practices in this regard, yet faces a multitude of challenges if it is to compete with and ultimately replace the prevailing monocultural norm. These challenges include the necessity for more complex agricultural designs in space and time, bespoke machinery, and adapted crop cultivars. Plant breeding for monocultures has focused on maximizing yield in single-species stands, leading to highly productive yet specialized genotypes. However, indications suggest that these genotypes are not the best adapted to intercropping systems. Re-designing breeding programs to accommodate inter-specific interactions and compatibilities, with potentially multiple different intercropping partners, is certainly challenging, but recent technological advances offer novel solutions. We identify a number of such technology-driven directions, either ideotype-driven (i.e., "trait-based" breeding) or quantitative genetics-driven (i.e., "product-based" breeding). For ideotype breeding, plant growth modeling can help predict plant traits that affect both inter- and intraspecific interactions and their influence on crop performance. Quantitative breeding approaches, on the other hand, estimate breeding values of component crops without necessarily understanding the underlying mechanisms. We argue that a combined approach, for example, integrating plant growth modeling with genomic-assisted selection and indirect genetic effects, may offer the best chance to bridge the gap between current monoculture breeding programs and the more integrated and diverse breeding programs of the future.