The story of a decade: Genomics, functional genomics, and molecular breeding in Brassica napus.

Rapeseed (Brassica napus L.) is one of the major global sources of edible vegetable oil and is also used as a feed and pioneer crop and for sightseeing and industrial purposes. Improvements in genome sequencing and molecular marker technology have fueled a boom in functional genomic studies of major agronomic characters such as yield, quality, flowering time, and stress resistance. Moreover, introgression and pyramiding of key functional genes have greatly accelerated the genetic improvement of important traits. Here we summarize recent progress in rapeseed genomics and genetics, and we discuss effective molecular breeding strategies by exploring these findings in rapeseed. These insights will extend our understanding of the molecular mechanisms and regulatory networks underlying agronomic traits and facilitate the breeding process, ultimately contributing to more sustainable agriculture throughout the world.

Linkage and association mapping of ovule number per ovary (ON) in oilseed rape (Brassica napus L.).

Ovule number (ON) produced during flower development determines the maximum number of seeds per silique and thereby affects crop productivity; however, the genetic basis of ON remains poorly understood in oilseed rape (Brassica napus). In this study, we genetically dissected the ON variations in a double haploid (DH) population and in natural population (NP) by linkage mapping and genome-wide association analysis. Phenotypic analysis showed that ON displayed normal distribution in both populations with the broad-sense heritability of 0.861 (DH population) and 0.930 (natural population). Linkage mapping identified 5 QTLs related to ON, including qON-A03, qON-A07, qON-A07-2, qON-A10, and qON-C06. Genome-wide association studies (GWAS) revealed 214, 48, and 40 significant single-nucleotide polymorphisms (SNPs) by individually using the single-locus model GLM and the multiple-locus model MrMLM and FASTMrMLM. The phenotypic variation explained (PVE) by these QTLs and SNPs ranged from 2.00-17.40% to 5.03-7.33%, respectively. Integration of the results from both strategies identified four consensus genomic regions associated with ON from the chromosomes A03, A07, and A10. Our results preliminarily resolved the genetic basis of ON and provides useful molecular markers for plant yield improvement in B. napus. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-023-01355-7.

A simple and efficient method to quantify the cell parameters of the seed coat, embryo and silique wall in rapeseed.

BACKGROUND: Researchers interested in the seed size of rapeseed need to quantify the cell size and number of cells in the seed coat, embryo and silique wall. Scanning electron microscope-based methods have been demonstrated to be feasible but laborious and costly. After image preparation, the cell parameters are generally evaluated manually, which is time consuming and a major bottleneck for large-scale analysis. Recently, two machine learning-based algorithms, Trainable Weka Segmentation (TWS) and Cellpose, were released to overcome this long-standing problem. Moreover, the MorphoLibJ and LabelsToROIs plugins in Fiji provide user-friendly tools to deal with cell segmentation files. We attempted to verify the practicability and efficiency of these advanced tools for various types of cells in rapeseed. RESULTS: We simplified the current image preparation procedure by skipping the fixation step and demonstrated the feasibility of the simplified procedure. We developed three methods to automatically process multicellular images of various tissues in rapeseed. The TWS-Fiji (TF) method combines cell detection with TWS and cell measurement with Fiji, enabling the accurate quantification of seed coat cells. The Cellpose-Fiji (CF) method, based on cell segmentation with Cellpose and quantification with Fiji, achieves good performance but exhibits systematic error. By removing border labels with MorphoLibJ and detecting regions of interest (ROIs) with LabelsToROIs, the Cellpose-MorphoLibJ-LabelsToROIs (CML) method achieves human-level performance on bright-field images of seed coat cells. Intriguingly, the CML method needs very little manual calibration, a property that makes it suitable for massive-scale image processing. Through a large-scale quantitative evaluation of seed coat cells, we demonstrated the robustness and high efficiency of the CML method at both the single-cell level and the sample level. Furthermore, we extended the application of the CML method to developing seed coat, embryo and silique wall cells and acquired highly precise and reliable results, indicating the versatility of this method for use in multiple scenarios. CONCLUSIONS: The CML method is highly accurate and free of the need for manual correction. Hence, it can be applied for the low-cost, high-throughput quantification of diverse cell types in rapeseed with high efficiency. We envision that this method will facilitate the functional genomics and microphenomics studies of rapeseed and other crops.