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PubMed2025

Optimising parent selection in plant breeding: comparing metaheuristic algorithms for genotype building.

Stacking desirable haplotypes across the genome to develop superior genotypes has been implemented in several crop species. A major challenge in Optimal Haplotype Selection is identifying a set of parents that collectively contain all desirable haplotypes, a complex combinatorial problem with countless possibilities. In this study, we evaluated the performance of metaheuristic search algorithms (MSAs)-genetic algorithm (GA), differential evolution (DE), particle swarm optimisation (PSO), and simulated annealing (SA) for optimising parent selection under two genotype building (GB) objectives: Optimal Haplotype Selection (OHS) and Optimal Population Value (OPV). Using a diverse wheat population of 583 lines genotyped for 29,972 SNPs, forming 7645 haplotype blocks and phenotyped for stripe rust scores, we assessed each algorithm's performance across fitness optimisation, convergence speed, and computational efficiency. GA consistently achieved high fitness and rapid convergence, while DE showed robustness but required longer runtime and careful tuning. PSO performed well under the OHS criterion but was less effective for OPV. SA, although computationally lighter, was less consistent in finding optimal solutions. Simulation over 100 breeding cycles showed that OHS outperformed both OPV and GEBV-based selection in long-term genetic gain and diversity retention. OHS maintained heterozygosity and additive variance, which are key for sustainable improvement, while GEBV selection led to early allele fixation. Our findings underscore the potential of GB strategies that prioritise the collective performance of parent sets rather than individual ranking to enhance selection outcomes in genomic-assisted breeding programmes.

Plant Breeding↗
PubMed2026

The Rise of Plant Pan-Genomes: From Genome Variation to Predictive Breeding.

Plant pan-genomics is entering a new phase beyond genome variation discovery, requiring a shift from cataloguing genomic diversity toward understanding how variation generates biological function and breeding value. Here, we propose that the future of plant pan-genomics will be shaped by three conceptual transitions. First, structural variation (SV), presence-absence variation (PAV), and haplotype diversity should be interpreted not merely as genomic differences, but as regulatory components that influence gene networks, chromatin organization, and complex traits. Second, the expansion from species-level pan-genomes to genus-level super pan-genomes provides an evolutionary framework for uncovering adaptive genetic modules preserved in wild relatives and overlooked during domestication. Third, integrating pan-genomes with pan-omics, three-dimensional genome analyses, and artificial intelligence will enable the transformation of genomic variation into predictive models for crop improvement. We further propose that the ultimate value of pan-genomes lies not in generating increasingly complete genome collections, but in establishing a mechanistic bridge between genome diversity, biological function, and breeding decisions. This transition will move crop improvement from empirical selection toward rational genome design, where evolutionary diversity can be systematically interpreted, predicted, and engineered.

Journal Article↗
PubMed2026

Translating functional molecular knowledge into crop-breeding success.

Historical plant breeding, which optimizes phenotypes through selective crossing guided by phenotypic evaluation and molecular markers, is limited by evolutionary constraints that hinder rapid crop improvement. A new paradigm, precision breeding, circumvents these limitations by targeting genetic variants through functional molecular knowledge. To generate this knowledge at scale, sequence-based deep learning leverages high-quality genome sequence data to predict variant effects at base-pair resolution. When linked to agronomically important traits, these predictions enable breeders to prioritize variants for precision selection or editing. Although it is still in the early stages of development, we foresee three key applications for this approach: introgressing genes from distant breeding pools, purging deleterious mutations and designing new plant ideotypes. Looking ahead, refined computational models will facilitate targeted editing and the systematic redesign of complex physiological processes to address emerging breeding goals under shifting environmental conditions.

Crops, Agricultural↗
PubMed2026

From feasibility to predictability: prime editing redefines precision breeding in plants.

Originally developed in mammalian systems as a genome editing strategy without double-strand breaks, prime editing (PE) has been adapted for precise genome modifications. However, its deployment revealed key limitations, including reduced efficiency, strong locus dependency, low germline transmission, and somatic chimerism. Consequently, diverse PE variants have emerged, resulting in fragmented landscape of architectures with context-dependent and inconsistent performance. This review consolidates these advances and outlines emerging design principles behind plant PE systems. It evaluates optimization strategies at multiple levels, discusses their applications in monocots and eudicots, and highlights persistent bottlenecks and future directions, including AI-guided protein engineering and improved delivery strategies. These advances position PE as a rapidly evolving platform toward enabling precision breeding in plants.

cis-regulatory engineering↗
PubMed2024

Editing of SlWRKY29 by CRISPR-activation promotes somatic embryogenesis in Solanum lycopersicum cv. Micro-Tom.

At present, the development of plants with improved traits like superior quality, high yield, or stress resistance, are highly desirable in agriculture. Accelerated crop improvement, however, must capitalize on revolutionary new plant breeding technologies, like genetically modified and gene-edited crops, to heighten food crop traits. Genome editing still faces ineffective methods for the transformation and regeneration of different plant species and must surpass the genotype dependency of the transformation process. Tomato is considered an alternative plant model system to rice and Arabidopsis, and a model organism for fleshy-fruited plants. Furthermore, tomato cultivars like Micro-Tom are excellent models for tomato research due to its short life cycle, small size, and capacity to grow at high density. Therefore, we developed an indirect somatic embryo protocol from cotyledonary tomato explants and used this to generate epigenetically edited tomato plants for the SlWRKY29 gene via CRISPR-activation (CRISPRa). We found that epigenetic reprogramming for SlWRKY29 establishes a transcriptionally permissive chromatin state, as determined by an enrichment of the H3K4me3 mark. A whole transcriptome analysis of CRISPRa-edited pro-embryogenic masses and mature somatic embryos allowed us to characterize the mechanism driving somatic embryo induction in the edited tomato cv. Micro-Tom. Furthermore, we show that enhanced embryo induction and maturation are influenced by the transcriptional effector employed during CRISPRa, as well as by the medium composition and in vitro environmental conditions such as osmotic components, plant growth regulators, and light intensity.

Solanum lycopersicum↗
PubMed2025

Genomic prediction-aided incorporation of genetic resources into elite breeding: lessons from a collaborative multiparental design in flint maize.

A public private cooperative mating design between elite maize inbred lines and diversity donors shows that genomic prediction holds great promise to improve the use of genetic resources. Genetic diversity is essential for plant breeding, enabling long-term gains and adaptation to climate change and new agronomical practices. Breeders can access diverse genetic resources to enhance elite germplasm and introduce new favorable variations. The limited performance of genetic resources may hamper their use. To overcome this, a bridging population can be implemented to evaluate and select progenies from crosses between diversity donors and elite lines before their introduction in breeding programs. The choice of such crosses can be dealt with the usefulness criterion (UC), which determines its ability to produce transgressive individuals. This paper investigates the use of genome-wide marker effects to predict (i) the performance of individuals derived from crosses between donors and elite lines and (ii) the UC of crosses not observed yet. It also compares donor introduction strategies based on the UC or the H criterion, which considers the genome-wide donor-elite complementarity. We used a flint maize collaborative multi-parental BC1-S2 population, consisting in materials from six breeding companies and one public institute crossed to different donors. The 20 crosses had contrasted means and genetic variances, and most of them presented transgressive individuals above the elite parent. Results emphasize the importance of half-siblings derived from the elite line parent of the predicted cross to efficiently predict progeny performances or the UC. They also showed that using the H criterion appears promising to select iteratively donors that best complement initial elite materials. The paper concludes with guidelines for implementing a bridging population using genome-wide marker-based predictions.

Zea mays↗
PubMed2026

Development of a low-coverage whole genome sequencing screen for apomixis using a diverse set of Malus germplasm.

In the past decade, plant biologists have made several major discoveries pertaining to the genetic basis of apomixis (clonal propagation by seed) that have shown promise in preserving high-value hybrid rice and sorghum genotypes. This progress was made possible by foundational gene discovery efforts in model species and natural apomicts, but pleiotropic obstacles still limit its broad agricultural adoption, especially in eudicots. Thus, it follows that investigations of novel apomicts should lead to the development of new molecular tools for plant breeding. The two most common ways to identify clonal seed production are flow-cytometry seed screens and genome sequencing to compare the DNA sequences of the maternal parent and progeny, traditionally using low-throughput markers. While flow-cytometry has been the dominant method for more than two decades, it provides indirect information on the genetics of a resulting embryo and can be ineffective in certain species. Here we developed a method using short-read whole-genome sequencing at moderately low coverage (averaging 3X and 6X) to screen diverse Malus genotypes maintained in a USDA germplasm collection for clonal seed production. In total, we sequenced 55 genotypes, 1,216 of their embryos, and identified 17 previously undescribed apomictic genotypes. Several more were detected with the flow cytometry seed screen, which helped resolve certain types of reproduction and sources of noise in low-coverage datasets. This low-pass screening-by-sequencing method is a relatively low-cost, rapid method for detecting apomictic genotypes in diverse plant germplasm and when used thoughtfully in conjunction with flow cytometry, provides a new way to visualize the genetic outcomes of sexual and asexual reproduction in plants.

Apomixis↗
PubMed2026

Finlay-Wilkinson random regression for yield and yield stability prediction in cereals.

Year-to-year climate variability poses a challenge for agriculture by increasing crop yield variability; therefore, there is a need to identify genotypes that can withstand these fluctuations. With the right selection criteria, genotypes with yield stability across variable environmental conditions can be selected. Methods such as Finlay-Wilkinson random regression (FWRR) may allow us to use sparse datasets-common in plant breeding pipelines-and incorporate genomic data to leverage phenotypic information from related genotypes to predict yield stability. Our objective was to examine how the number of environments and the variance among those environments affect stability predictions. We also integrate FWRR as a genomic prediction tool for characterizing yield stability, comparing it to the traditional genomic prediction models as a reference. We used three datasets: one highly unbalanced dataset for oats (Avena sativa L.) and two completely balanced datasets with different numbers of environments for barley (Hordeum vulgare L.) and wheat (Triticum aestivum L.). We fit standard Finlay-Wilkinson (FW) and FWRR models to estimate grain yield and stability under various scenarios. We found that the estimated stability values obtained were similar using balanced datasets for FW or FWRR. FWRR also achieved moderate predictive ability for stability using unbalanced datasets under 10-fold cross-validation (CV1) with new genotypes. In terms of environmental representation, selecting the right set of environments for inclusion in the model was more important than adding more environments. Our results suggest the possibility of using FWRR to select stable genotypes earlier in line development, as well as to design resource-efficient stability-testing schemes.

Hordeum↗
PubMed2025

Development of recombinant inbred lines and QTL analysis of plant height and fruit shape-related traits in Cucurbita pepo L.

UNLABELLED: Zucchini (Cucurbita pepo subsp. pepo) stands as an economically vital crop in China. In zucchini breeding, plant architectural patterns and fruit morphological characteristics serve as pivotal traits. In this study, we employed quantitative trait locus (QTL) analysis using recombinant inbred lines (RILs) derived from two distinct inbred lines, JinGL (subsp. ovifera) and HM-S2 (subsp. pepo), in conjunction with a high-density genetic map. Our investigation focused on ten QTLs associated with six horticulturally significant traits, including hypocotyl length (HL), plant height (PH), and four fruit-related traits: fruit length (FL), fruit diameter (FD), fruit shape index (FSI), and fruit weight (FW). The QTLs governing HL and PH were mapped to Chr03/LG10 and named qhl3.1 and qph3.1, respectively. The candidate gene Cp4.1LG10g05910/CpDw for qph3.1 was successfully identified. Additionally, three novel QTLs related to fruit size and shape were discovered. Among them, qfsi8.1/qfl8.1, demarcated by Marker238258 and Marker240069 on Chromosome 08/Linkage group 17 (Chr08/LG17), is a new major QTL regulating the fruit shape of zucchini. Through genomic insertion-deletion (InDel) and qRT-PCR analyses, we predicted genes within the qfsi8.1/qfl8.1 candidate interval, uncovering Cp4.1LG17g02030/CpIAA12 and Cp4.1LG17g02010/CpCalB as potential candidate genes. We developed molecular markers tightly linked to qph3.1 and qfl8.1 and validated them in 171 and 224 Cucurbita pepo germplasms, achieving accuracy rates of 96% and 100%, respectively. This study deepens our understanding of the genetic basis of key traits and provides valuable references for molecular breeding in Cucurbita pepo. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11032-025-01592-y.

Cucurbita pepo↗
PubMed2025

Genomic mechanism of aroma terpenoids biosynthesis in plants.

BACKGROUND: Aroma terpenoids are crucial plant secondary metabolites with physiological and commercial importance. Interestingly, both closely and distantly related species can synthesize identical aroma terpenoids. With the development of genome sequencing technology, it has become possible to elucidate the genomic mechanism underlying this phenomenon. AIM: This review highlights whole-genome data as a robust strategy for investigating the genomic mechanism of aroma terpenoids biosynthesis in plants, and provides new perspectives on the origin, evolution, and engineering of terpene synthases (TPSs). This aims to significantly benefit plant breeding and enhance suitability for industrial production. KEY SCIENTIFIC CONCEPTS OF REVIEW: Genomic mechanism of aroma terpenoids biosynthesis in plant genomes is the genetic and evolutionary dynamics. We elaborate the genomic mechanism governing the biosynthesis of plant-derived aroma terpenoids in three dimensions: (1) Genome-wide identification and phylogenetic analyses of TPSs. The same aroma terpenoids were produced by numerous plant species with chromosome-level genomes. Based on 34 plant genomes, we identified 1643 TPSs and classified them into seven subfamilies. (2) Functional and structural basis of TPSs. We found that TPSs with identical functions in distant species exhibit low sequence similarity but conserved active cavity architectures. Conversely, functionally distinct TPSs in closely related species cluster phylogenetically but differ in active cavity structures. (3) Patterns of TPS gene origination. Comparative genomic analyses within and between species revealed three patterns enabling TPSs to acquire the same functions: tandem duplications, dispersed duplications, and genes without duplication.

Terpenes↗
PubMed2026

Analyzing Meiosis in Maize.

Meiosis is central to sexual reproduction and the main source of genetic diversity in plants. Understanding how meiotic processes are regulated has direct relevance to agriculture. As meiotic recombination is the vehicle of plant breeding, gaining the ability to influence recombination patterns can accelerate crop improvement. Maize is a powerful model for studying plant meiosis, thanks to its large chromosomes, well-developed genetics, and the availability of diverse cytogenetic and molecular tools. Insights gained from maize studies can extend to other species. In this review, we describe a variety of approaches for examining meiosis and meiotic recombination in maize. Cytological techniques, including protein immunolocalization and fluorescence in situ hybridization (FISH), enable visualization of chromosome structure and behavior, as well as crossover (CO) formation. Chromatin immunoprecipitation (ChIP) is used in meiosis research to determine locations of recombination proteins, identify recombination sites, and elucidate chromatin features, such as histone modifications. Quantification of COs at specific genomic sites through pollen typing by droplet digital PCR allows precise high-resolution measurement of recombination rates. Combining cytology, protein localization, and molecular assays provides a multiscale picture of meiosis, linking molecular mechanisms to chromosome behavior and, ultimately, to genetic variation.

Journal Article↗
PubMed2025

Unveiling Potato Cultivars With Microbiome Interactive Traits for Sustainable Agricultural Production.

Root traits significantly shape rhizosphere microbiomes, yet their interaction with microbes is often overlooked in plant breeding programs. Here, we propose that selecting modern cultivars based on microbiome interactive trait (MIT), such as root biomass, exudate patterns and the rhizosphere microbiome, can enhance agricultural sustainability by interacting effectively with soil microbiomes, which in turn, promotes plant growth and resistance to stress, thereby reducing reliance on synthetic crop protectants. Through a stepwise selection process (in silico and in vitro) that started with approximately 1000 potato genotypes, we chose 51 potato cultivars based on known phenotypical properties and distinct root exudate patterns. We conducted a greenhouse experiment to evaluate their capacity to interact with the soil microbiome and to assess their MIT scores. Our findings revealed that cultivars significantly influence plant growth, metabolite profiles, and rhizosphere fungal community composition. Moreover, we observed a positive correlation between microbial community diversity and root biomass. Additionally, leaf metabolites were correlated with rhizosphere bacterial composition, supporting the plant holobiont framework. Utilising z-scores, we aggregated all data related to plant growth, metabolomes, and microbiomes, creating a classification of 51 cultivars based on a gradient of MIT scores. By examining the distribution of low, intermediate, and high MIT, we identified a group of 11 potato cultivars suitable for further studies to assess their resilience and productivity under low-input production systems. This study provides an in-depth correlation between microbiome and several plant traits across 51 cultivars, offering tools to facilitate and expedite the incorporation of microbiome traits into breeding goals to support sustainable agriculture.

Solanum tuberosum↗
PubMed2026

Pleiotropic mutation in a tendril TCP gene underlies the yield-enhancing multiple-flowering trait in summer squash (Cucurbita pepo).

Crop yield is a focal point in plant breeding. Regulation of lateral budding through apical dominance was a central target of crop domestication, directly affecting crop production. The young fruits of Cucurbita pepo, summer squash, are produced on plants characterized by apical dominance and differentiation of a single flower bud per leaf axil. A single recessive mutation, mf, results in differentiation of more than one flower per leaf axil, thereby directly increasing production because of the continual day-to-day harvest of the summer squash crop. Positional cloning of the Cucurbita pepo mf (Cpmf) gene denoted a frameshift mutation in a TCP transcription factor, Cp4.1LG13g07780, as causative for the increase in axillary flowering. Cpmf is an ortholog of a tendril-development TCP gene in other cucurbits, and likewise, the recessive allele of Cpmf is associated with distorted tendril development. Gene function is context dependent, and we propose that multiple flowering is a unique pleiotropic attribute of mutation in a tendril-development gene of C. pepo. Characterization of a C. pepo collection confirmed a significant association of the Cpmf mutation with multiple flowering and showed that the mutant allele is absent in ancestral C. pepo and one of its two cultivated subspecies. The beneficial mutation occurred and was selected after the domestication of the other subspecies, during its cultivation for young fruit production. We demonstrate the discovery of a causative yield-increasing sequence variant and its practical utilization in breeding. Our findings provide a molecular target for creation of high-yielding, multiple-flowering summer squash cultivars through marker-assisted breeding or precise genome editing.

Cucurbita↗
PubMed2026

Mul-PheG2P: decoupled learning and prediction-space fusion enables robust and interpretable multi-phenotype genomic prediction.

Genomic prediction of multiple phenotypes is crucial in modern plant breeding; however, existing methods struggle with negative transfer and lack interpretability, particularly across high-dimensional small-sample data and diverse species. To address this, we propose Mul-PheG2P, a novel paradigm based on decoupled learning and predictive space fusion. It employs a two-stage design: first training phenotype-specific encoders using genetic data, then decoupling phenotype-specific learning from cross-phenotype aggregation via an interpretable prediction layer. Mul-PheG2P outperforms existing methods across diverse crop datasets, including maize (Zea mays), wheat (Triticum aestivum), and tomato (Solanum lycopersicum). It provides a multi-scale interpretability chain: at the macro level, it quantifies phenotypic contributions via attention-based weighting; at the micro level, Integrated Gradients reveal the genetic basis of predictions. Notably, the model successfully identified the CCT (CONSTANS, CO-like, and TOC) motif regulating photoperiodism and the SQUAMOSA (SQUAMOSA promoter binding protein) promoter for inflorescence development, confirming its ability to capture functional biological mechanisms. These results highlight the high performance and interpretability of Mul-PheG2P, showcasing its value for low-cost, large-scale screening to advance precision breeding.

Phenotype↗
PubMed2024

Over expression of modified Isomaltulose Synthase Gene II (ImSyGII) under single and double promoters drive unprecedented sugar contents in sugarcane.

Sugarcane has been grown all around the world to meet sugar demands for industrial sector. The current sugar recovery percentage in sugarcane cultivars is dismally low which demands scientific efforts for improvements. Multiple approaches were adopted to enhance sugar contents in commercial sugarcane plants in contrast to conventional plant breeding methods. The exploitation of biotechnological methods and exploration of isomaltulose synthetic genes presented a promising solution to increase the existing low level of sugar recovery percentage in Saccharum officinarum L. Isomaltulose synthase gene II was employed and integrated into plant expression vector driven under the leaf and stem specific promoters terminated by nopaline synthase gene in a cloning strategy shown in the present study. Three gene constructs were developed in various combinations driven under promoters Zea mays ubiquitin and Cestrum Yellow Leaf Curl virus in the single and double combined stacked system. The transformation was executed in multiple formats with single transformed events, double promoter transformation events and triple construct stacked promoters in sugarcane induced calli via the particle gene gun. The transformation of ImSyGII in sugarcane genotype HSF-240 was confirmed by molecular gene analysis while expression quantification was determined through Real Time PCR. Furthermore, HPLC was also done to harvest the increased amounts of Isomaltulose in transgenic sugarcane juice. The present work upheld the enhanced ImSyGII expression in leaves owing to the exploitation of ubiquitin, while the Cestrum Yellow Leaf Curl virus promoter enhanced gene expression in sugarcane stems. The employment of three gene constructs collectively produced elite sugar lines producing more than 78% enhancements in whole sugar recovery percentage. The mature internode proved highly efficient and receptive regarding the production of isomaltulose. Quantifications and sugar contents evaluations upheld an increased Brix ratio of transgenic sugarcane lines than control lines.

Saccharum↗
PubMed2026

New Insights into Genomic Variations and Mutational Events Associated with Plant-Pathogen Interactions.

Plant diseases threaten global food security, causing up to 40% crop yield losses and more than $220 billion in annual economic damage. This review synthesizes recent advances in understanding the genomic variations and mutational events underlying plant-pathogen interactions and durable plant disease resistance. Key insights into evolutionary dynamics, genetic variability, and coadaptive strategies reveal the complexity of host-pathogen relationships and the implications for developing durable disease resistance. Integrative approaches combining genome-wide association studies and functional genomics have uncovered the polygenic and epistatic architecture of quantitative resistance. Advances in pan-genomics and high-throughput sequencing have revealed extensive genetic variability in cultivated/elite germplasm and wild relatives. Emerging technologies, including gene editing, multi-omics, and machine learning, enable predictive modeling of resistance traits and support evolution that informs plant breeding strategies. Collectively, these advances provide a robust framework for developing durable resistance and sustainable crop protection in the face of global agricultural challenges.

Host-Pathogen Interactions↗
PubMed2026

Site-Specific Measurement of Meiotic Crossing-Over Rate with Droplet Digital PCR.

Understanding the frequency and distribution of meiotic crossovers (COs) is critical for both fundamental studies on meiosis and for practical applications in plant breeding, where controlling recombination can accelerate crop improvement. Determining CO rates at specific genomic loci has traditionally relied on labor-intensive methods that require the production and genotyping of large progenies. Here, we present a high-throughput protocol for site-specific quantification of meiotic COs in maize using droplet digital PCR (ddPCR). The method is based on genotyping individual pollen nuclei from hybrid plants to detect recombinant and nonrecombinant alleles at defined chromosomal intervals. By distributing several thousands of pollen nuclei into nanoliter-sized droplets and performing PCR with allele-specific fluorescent probes, this method allows precise quantification of CO frequency with high sensitivity. The protocol provides detailed guidance for nuclei isolation, probe master mix preparation, droplet generation, and data interpretation. This method can be easily adapted for use in other plants.

Journal Article↗
PubMed2026

Integrating plant phenotypic and genotypic data in the AGENT project: a BrAPI service implementation.

MOTIVATION: The AGENT project established a network of actively cooperating European genebanks, integrating genomic and phenotypic data from accessions of wheat and barley. Due to specific storage demands for phenotypic and genotypic data, the project used separate database instances and backend technologies to manage integrated phenotypic and genotypic data. RESULTS: We discuss the challenges encountered when integrating dispersed data to serve through a single interface such as the Plant Breeding Application Programming Interface, BrAPI. We examine how the consistent mappability of genebank data to the BrAPI model can enable the implementation of effective services. The advantages of BrAPI in transparently linking distributed data entities through embedded, unique identifiers are highlighted. We present a technical solution involving a BrAPI proxy, which combines and merges separate BrAPI endpoints. Finally, we demonstrate the AGENT BrAPI implementation with an illustrative example that validates a suggested SNP for a trait from the literature by linking phenotypic, genotypic and passport data. AVAILABILITY AND IMPLEMENTATION: The BrAPI proxy implementation and documentation is available at the Python Package Index (https://pypi.org/project/brapi-proxy) and archived in Zenodo (doi: 10.5281/zenodo.19436445). SUPPLEMENTARY INFORMATION: A Jupyter Notebook file for the validation example using a marker-trait relationship found in the literature.

Phenotype↗