Vanika Garg

Genomic prediction (GP) in mango breeding faces challenges due to the species’ complex biology, long cycles, and limited reference populations. To accelerate genetic improvement, this study integrated data from diverse global populations to increase the reference population size. It included three mango collections reserved in Australia (225), USA (161), and China (224), totalling 610 individuals. Fruit weight (FW) and total soluble solids (TSS) were measured in multiple datasets, while several other traits were measured in specific datasets. We evaluated genetic diversity, performed genome-wide association studies (GWAS), and assessed GP accuracy using standard, genotype-by-environment (GxE), and multi-trait models, both within and across collections. Findings revealed a highly admixed genetic structure, with faster linkage disequilibrium (LD) decay in the Chinese collection, indicating higher genetic diversity. Data integration significantly enhanced GWAS power, identifying 19 quantitative trait loci (QTL) for FW and 9 for TSS. GxE models consistently achieved higher or comparable prediction accuracies for FW and TSS compared to the non-GxE models, especially when combining Australian and USA collections. This was not the case when predicting into or from the Chinese collection, mostly due to differences in the phenotyping protocol. While single-trait models performed comparably to multi-trait models in predicting new individuals (Coss-Validation: CV1), multi-trait models significantly improved prediction accuracy in scenarios with incomplete phenotypic records (CV2). This study demonstrates that strategic global data integration significantly enhances GWAS power and GP accuracy in mango. This collaborative approach is crucial for developing more efficient and accelerated breeding programs for mango and other perennial trees.

Adzuki bean (Vigna angularis), a globally important legume crop, faces breeding bottlenecks due to limited genomic resources and an insufficient understanding of its genetic basis for key traits, which constrains the efficient utilization of its genetic diversity in breeding programs. To address this, a high-quality genome assembly is developed for the elite cultivar ZH20 and a comprehensive genetic variation map is constructed by resequencing of 546 diverse adzuki bean accessions. Genomic and phenotypic analyses of this diversity panel reveal distinct population structures and identify genomic variations underlying key agronomic traits, including seed coat color, size, shape, and flowering time, linked to adaptation and selection. This analysis pinpointed 251 loci significantly associated with eight key agronomic traits, highlighting promising candidate genes, such as ANKRD50 and NAC73 for seed morphology, ANR1 for flavonoid content, and NPF5.4 for flowering time. Furthermore, comparative genomics provides insights into domestication processes. These datasets are integrated to develop AdzukiBeanAtlas (https://www.cgris.net/AdzukiBeanAtlas), a versatile toolkit to facilitate breeding strategies. These resources provide a valuable foundation for understanding adzuki bean diversity, while AdzukiBeanAtlas serves as a user-friendly, cross-platform tool for molecular marker development, helping to accelerate future breeding programs.

Accumulating evidences have shown that the mid-oleic fatty acid phenotype in peanuts cannot be explained by the traditional two-gene model involving AhFAD2A and AhFAD2B, which are genes encoding fatty-acid desaturase 2. But the underlying genetic mechanism remains unclear. Here, we present a population-specific pangenome using the eight founder genomes of the PeanutMAGIC population. This graph-based pangenome serves as a comprehensive reference, capturing all segregating haplotypes within the population. We conduct whole genome sequencing for the MAGIC Core, a subset of 310 RILs, for genotyping. Using pangenome-based genotypes, we trace recombination for detailed genomic analysis and phenotypic association. This investigation identifies a unique third gene, named AhFAD2C, near AhFAD2B. When recombination occurs, AhFAD2C segregates from AhFAD2B. We reveal the genotype determining mid-oleic fatty acid phenotype. Our findings underscore the limitations of a single-reference genome, which leads to false association and marker discovery. In contrast, a population-specific pangenome provides a more reliable framework for genomic studies. This study reveals insights into the genetic mechanism of peanut oil quality and demonstrates the advantages of population-specific pangenomes.

Spatial omics technologies enable unraveling of single-cell heterogeneity and characterizing diverse cell types in plants while preserving their spatial arrangement.Spatial transcriptomics facilitates visualization and quantification of gene expression across the entire transcriptome in plant tissue cryosections, using strategies such as barcoded oligo(dT) arrays and high-throughput sequencing.Spatial proteomics and metabolomics are advancing in resolution, field of view, and cost-efficiency. Achieving single-cell resolution in plants requires overcoming challenges in both experimental techniques and computational analysis.Spatially resolved multiomics profiling and 3D spatial omics hold potential to shape future crop improvement strategies by providing a holistic understanding of molecular and cellular features that control agronomically important traits.
Plant cells communicate information to regulate developmental processes and respond to environmental stresses. This communication spans various ‘omics’ layers within a cell and operates through intricate regulatory networks. The emergence of spatial omics presents a promising approach to thoroughly analyze cells, allowing the combined analysis of diverse modalities either in parallel or on the same tissue section. Here, we provide an overview of recent advancements in spatial omics and delineate scientific discoveries in plant research enabled by these technologies. We delve into experimental and computational challenges and outline strategies to navigate these challenges for advancing breeding efforts. With ongoing insightful discoveries and improved accessibility, spatial omics stands on the brink of playing a crucial role in designing future crops.
Plant cells communicate information to regulate developmental processes and respond to environmental stresses. This communication spans various ‘omics’ layers within a cell and operates through intricate regulatory networks. The emergence of spatial omics presents a promising approach to thoroughly analyze cells, allowing the combined analysis of diverse modalities either in parallel or on the same tissue section. Here, we provide an overview of recent advancements in spatial omics and delineate scientific discoveries in plant research enabled by these technologies. We delve into experimental and computational challenges and outline strategies to navigate these challenges for advancing breeding efforts. With ongoing insightful discoveries and improved accessibility, spatial omics stands on the brink of playing a crucial role in designing future crops.

Papaya is a nutritionally valuable fruit crop cultivated globally in tropical and subtropical regions. Conventional breeding efforts have prioritized enhancing traits such as yield and fruit size, with notable success in developing high-yielding cultivars. However, other critical areas in papaya improvement, such as enhancing genetic diversity, improving disease resistance, optimizing post-harvest management, and addressing consumer preferences for fruit quality and flavor, have experienced relatively limited progress. Addressing these gaps is essential for meeting both production challenges and market demands. Achieving substantial genetic gains in these traits in the shortest timeframe will require integrating traditional breeding practices with emerging genomics tools. Over the past two decades, substantial advancements in papaya genomics have been achieved, resulting in resources including high-density genetic maps, high-quality reference genomes, and transcriptomic and resequencing datasets. These resources have been utilized to develop genome-wide markers and identify marker-trait associations, supporting the development of disease-resistant varieties and uncovering the genetics of consumer-preferred traits. By utilizing these resources in combination with innovative approaches such as genomic selection and speed breeding, sequence-based breeding approaches can significantly accelerate genetic gains in papaya. This enables the rapid development of elite (high-performance) papaya cultivars that meet both agronomic and consumer expectations.

Plant evolution is largely driven by plant–microbe interactions, yet the ecology of the plant holobiont is not well understood at a molecular level. However, these relationships hold diverse benefits for sustainable agriculture as nature-based solutions (NbS). We propose a workflow to enhance understanding of natural variation in the plant–soil microbiome holobiont, addressing key challenges like growth promotion, stress resilience, nitrogen use efficiency (NUE), biological nitrification inhibition (BNI), healthy soils, and improving fertilization practices towards a more natural agroecosystem. We discuss a panome-wide association study (PWAS) approach to discover and incorporate novel genetic diversity from exotic germplasm into breeding populations. Ultimately, understanding natural variation of the holobiont in agroecosystems will contribute to the development of novel climate-resilient crop varieties for food security.

Chickpea (Cicer arietinum L.) is an important legume crop that has been subjected to intensive breeding, resulting in limited genetic diversity. Australia is the world's second largest producer and the leading exporter of chickpea; the genomic architecture of its cultivars remains largely unexplored. This knowledge gap hinders efforts to enhance their genetic potential for production, protection, and stress adaptation. To address this, we generated high-quality genome assemblies and annotations for 15 leading Australian chickpea cultivars using single-tube long-fragment read technology. The pan-genome analysis identified 34 345 gene families, including 13 986 dispensable families enriched for genes associated with key agronomic traits. Comparative genomic analysis revealed ~2.5 million single-nucleotide polymorphisms, nearly 200 000 insertions/deletions, and over 280 000 structural variations. These variations were found in key flowering time genes, seed weight-related genes, and disease resistance genes, providing insights into the genetic diversity underlying these critical traits. Haplotype analysis of key genes within the 'QTL-hotspot' region revealed the absence of superior haplotypes in Australian cultivars. Validation using Kompetitive allele-specific PCR markers confirmed these findings, highlighting the need to introduce beneficial haplotypes from diverse accessions to enhance drought tolerance in Australian chickpea cultivars. The genomic resources generated in this study provide valuable insights into chickpea genetic diversity and offer potential avenues for crop improvement.

Background
Yellow pitaya (Selenicereus megalanthus, 2n = 4x = 44) breeding remains severely hindered due to the lack of a reference genome.

Results
Here, we present a high-quality chromosome-level genome assembly of yellow pitaya using PacBio HiFi sequencing and Hi-C scaffolding technologies. We identify yellow pitaya as an autotetraploid with a genome size of 1.79 Gb, harboring 27,246 high-confidence genes probably from diploid ancestors, red pitaya (S. undatus). By comparative analysis of the 3D chromatin architecture, we identify varying number of compartment A/B, topologically associated domains (TADs), and structural variations in diploid (red pitaya) and polyploid (yellow pitaya) species. We find that TAD boundaries are enriched with transcription factor motifs in both species. We find significant alterations in expression of genes in the betalain biosynthesis pathway in both species. We detect differential expression of genes encoding key regulators of pericarp color within the TAD regions of polyploid pitaya and diploid pitaya. We also identify the expression differences in candidate genes that likely influence betacyanin and betaxanthin synthesis in both species.

Conclusions
Our findings suggest that differential 3D genome organization, especially differences in TAD boundaries, may impact gene expression, which may further lead to different trait formation in different pitaya species. This provides theoretical implications for fast-forward breeding.