Oil Crops Research Institute

Peanut (Arachis hypogaea L.) is a globally important legume crop, the yield and quality traits are governed by complex polygenic interactions that are difficult to resolve using conventional biparental populations or natural association panels, particularly given the crop’s narrow cultivated gene pool. Multi-parent Advanced Generation Inter-Cross (MAGIC) populations have emerged as a powerful alternative, combining structured recombination with high allelic diversity to enable high-resolution dissection of complex traits. This review synthesizes recent progress and emerging directions in peanut MAGIC research, highlighting five key frontiers: pan-genome-informed founder selection; multi-environment MAGIC for genotype-by-environment (G × E) dissection; systems genetics integration of multi-omics data; introgression MAGIC to incorporate wild Arachis diversity; and AI-guided simulation for optimized population design. By integrating genomics, multi-omics phenotyping, and artificial intelligence, MAGIC populations provide a high-resolution platform for precision breeding and the development of climate-resilient, high-yielding, and nutritionally enhanced peanut cultivars. Beyond trait discovery, this integration enables a shift from descriptive genetic mapping toward predictive and systems-level breeding, in which allelic effects can be evaluated across environments, genetic backgrounds, and management scenarios. Future progress will therefore depend on translating MAGIC-derived insights into subgenome-aware selection strategies, environment-informed genomic prediction models, and shared breeding resources that support sustainable and coordinated peanut improvement under increasing climatic and production constraints.

Siberian kale (Brassica napus subsp. pabularia, AACC, 2n = 38) is a distinct subspecies of B. napus, characterized by its deeply lobed leaves and primarily cultivated as a nutritious leafy vegetable. Here, we present a chromosome-level genome of Beta, a Siberian kale variety, integrating Illumina short reads, PacBio HiFi long reads, and Hi-C data. The final assembly size is 1,078.8 Mb, with a scaffold N50 of 57.5 Mb and a genome BUSCO completeness of 99.7%. 954.0 Mb (88.4%) of sequences were successfully anchored to 19 pseudo-chromosomes. The configuration of Beta genome chromosomes is consistent with the distribution of ten A subgenome and nine C subgenome chromosomes in rapeseed. In total, 98,882 protein-coding genes were predicted ab initio in the Beta genome, with an average gene length of 1,997 bp, and 90,415 (91.44%) genes were functionally annotated. Overall, the high-quality genome provides a valuable resource for bridging current knowledge gaps and offers key genetic insights into deeply lobed leaf formation and improvement of Brassica crops.

Ricinoleic acid is a high-value hydroxy fatty acid with broad industrial applications, which is the main composition of castor oil (approximately 87%). Elucidating the molecular mechanisms underlying seed oil synthesis and identifying key candidate genes are essential for increasing ricinoleic acid production. In this study, we employed an integrated metabolomic and transcriptomic approach to investigate the dynamic synthesis of castor oil during seed development in Ricinus communis (R. communis). A total of 790 structurally identified metabolites were detected across 5 distinct developmental stages. These differentially abundant metabolites (DAMs) were classified into 19 clusters based on their expression trends throughout seed development. Annotation results highlighted three key DAMs, i.e., hydroxy ricinoleic acid, ricinoleic acid methyl ester and ricinoleic acid, associated with ricinoleic acid, all of which exhibited a gradual increase in abundance as seed development progressed. Transcriptomic analysis identified 9,940 differentially expressed genes (DEGs), which were grouped into five distinct clusters. Among these, 30 enzyme genes related to fatty acid biosynthesis and six oil body-related genes were screened as candidate genes, with the majority predominantly enriched in clusters I and II. Co-expression network analysis of transcription factors (TFs) and oil-related genes in these two clusters further identified candidate key TFs potentially involved in oil synthesis, including WRI1, DREB, LOB and eight other families. Integrated metabolomic and transcriptomic analysis revealed that the co-upregulation of five candidate genes with three dominant accumulated metabolites (hydroxy ricinoleic acid, ricinoleic acid methyl ester and ricinoleic acid), including an enzyme gene (FAH12) directly involved in ricinoleic acid biosynthesis and four oil body-associated protein genes (OLEs and PXG) associated with oil accumulation and storage. Candidate alternative splicing events were identified during seed development, notably within lipid metabolism genes (SCAD, LACS1, SBH2 and LIP1P) and transcription factor ARF2. This study provides a theoretical foundation and valuable reference for future high-oil breeding and metabolic engineering efforts aimed at optimizing ricinoleic acid production.