Editorial: Multi-omics and computational biology in horticultural plants: From genotype to phenotype III

Yunpeng Cao^1*Mohammad Shah Jahan^2*Boping Wu^3*Lin Zhang^4*

• ¹Guangxi University, Nanning, China
• ²Sher-e-Bangla Agricultural University, Dhaka, Bangladesh
• ³Zhejiang A and F University, Hangzhou, China
• ⁴Hubei University of Chinese Medicine, Wuhan, China

Horticultural plants underpin human nutrition, health, and well-being by providing essential vitamins, minerals, bioactive metabolites, and aesthetic values across fruits, vegetables, ornamentals, spices, and medicinal species (Lutaladio et al., 2010). Over the past decade, rapid advances in sequencing, phenotyping, and computational methods have transformed our capacity to interrogate the genetic and molecular bases of horticultural traits and to translate discoveries into breeding practice (Mansoor et al., 2025). Building on the momentum of our previous Research Topics-Volumes I and II-which together highlighted genome resources, integrative multi-omics, and emerging computational tools across a wide diversity of species (Mondal et al., 2022;Cao et al., 2024b)-this third volume continues the central mission: to bridge genotype and phenotype in horticultural plants through the integration of multi-omics with computational biology. Our goal with Volume III is twofold. First, to bring into focus integrative, cross-layered analyses that move beyond association toward mechanism and, ultimately, translatable targets. Second, to showcase methodological and infrastructural advances-ranging from single-cell and spatial omics to high-throughput phenotyping and interpretable machine learning-that strengthen the evidentiary chain linking genetic variation to complex traits under realistic environments (Großkinsky et al., 2015;Ferrão et al., 2023;Yan and Wang, 2023). This Research Topic comprises two reviews and 14 research papers. The research papers include four on multi-omics (including transcriptomics, proteomics, and metabolomics) in horticultural crops, six on fruit crops, two on vegetables, one on spices, and one on model crop species. Multi-omics integration combines genomic, epigenomic, transcriptomic, proteomic, and metabolomic data to provide a comprehensive view of biological systems. These contributions showcase how comprehensive molecular profiling, when combined with sophisticated analytical frameworks, enables unprecedented insights into complex trait formation in horticultural species. In this Research Topic, several studies demonstrate how combining multiple omics layers reveals regulatory networks and metabolic pathways that would remain hidden when analyzing single data types in isolation. For instance, Zhu et al. identified the molecular basis for color variations in Cistanche deserticola, showing that the purple hue of 'oil cistanche' stems from increased flavonoids and terpenoids, while its darker dried color is linked to higher levels of In horticultural plants, such as tomato, strawberry, grape, apple, and peach, integrated datasets elucidate regulatory networks of ripening, color, flavor, texture, and nutrition; map stress-response pathways for heat, cold, drought, and salinity; and reveal disease-resistance mechanisms against major pathogens, while ionomics clarifies nutrient homeostasis and disorders such as calcium-related defects. Pangenomes and structural-variant catalogs expose presence-absence genes underlying quality and resilience traits, and haplotype-aware genomic prediction improves selection in perennials despite heterozygosity, clonality, and long juvenility. In grafted systems, multi-omics resolves rootstock-scion signaling that modulates vigor, nutrient uptake, stress tolerance, and fruit quality, and postharvest metabolomic and proteomic biomarkers guide shelf-life and cold-chain optimization. Single-cell and spatial transcriptomics have pinpointed key tissue-specific functions, such as sugar metabolism and flavonoid biosynthesis in the pericarp, embryo development and dormancy pathways in seeds, and cell division and differentiation programs in meristems, while rhizosphere and phyllosphere profiling has provided a basis for developing microbiome-informed strategies for biocontrol and nutrition (Deng et al.). Interoperable, FAIR databases (e.g., Sol Genomics Network, Genome Database for Rosaceae, Pear genomics database) harmonize data and ontologies to power germplasm discovery, marker-assisted and genomic selection, genome editing, and speed breedingcapabilities that are increasingly vital for sustaining quality, yield, and resilience under climate and resource constraints (Jung et al., 2007;Fernandez-Pozo et al., 2015;Chen et al., 2023). While maintaining strong foundations in basic research, Volume III emphasizes the translational aspects of multi-omics discoveries. Multiple studies demonstrate clear pathways from molecular insights to practical applications in crop improvement. For example, the identification of key genes controlling stress tolerance, nutritional quality, and yield components provides immediate targets for marker-assisted selection and proposing them as a key mechanism for regulating plant growth and driving phenotypic evolution within the Rosaceae family (Cao et al., 2024a;Jiang et al., 2025). Ding et al. conducted a pear pangenome analysis and identified a SNP mutation and a promoter insertion in PsbMGH3.1 that likely enhance sepal abscission in the 'Xuehuali' cultivar, a trait critical for fruit quality (Ding et al., 2024). Utilizing two newly assembled high-quality pear genomes for a genome-wide association study, Cao et al. identified and functionally validated the novel CCCH-type zinc finger gene PbdsZF as a key transcriptional regulator of lignin biosynthesis and stone cell formation, a critical determinant of fruit texture (Cao et al., 2025). In addition, the combination of biological big data with artificial intelligence is facilitating the transition from reactive to predictive and ultimately prescriptive approaches in crop management and improvement. Volume III of "Multi-omics and computational biology in horticultural plants: From genotype to phenotype" demonstrates the continued evolution and maturation of this interdisciplinary field. The 16 articles presented here showcase how integrative approaches combining multiple omics technologies are accelerating our understanding of complex traits in horticultural crops. Despite this remarkable progress, several challenges remain in fully realizing the potential of multi-omics approaches for horticultural crop improvement. Data integration across different omics layers and experimental conditions remains technically challenging, requiring sophisticated normalization and harmonization methods. Furthermore, the heterozygous and often polyploid nature of many horticultural crops presents unique challenges for genomic analyses and the functional validation of candidate genes. Developing robust analytical frameworks that account for this genetic complexity while maintaining computational efficiency remains an active area of research.Additionally, the translation of omics discoveries into field-relevant traits requires careful consideration of environmental variability and agricultural management practices. As we face mounting global challenges in food security, nutrition, and environmental sustainability, the approaches and discoveries presented in this Research Topic provide essential tools and knowledge for developing the next generation of improved horticultural varieties. The success of this Research Topic series reflects the growing recognition that bridging the genotype-phenotype gap requires not just more data, but smarter integration of diverse data types to address these very challenges. Looking forward, we anticipate that continued technological innovations, particularly in single-cell omics, spatial biology, and artificial intelligence, will be pivotal in overcoming these obstacles and will further enhance our ability to decode and manipulate the molecular basis of horticultural traits.