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Rapeseed (Brassica napus L.) is a major agricultural crop with diverse applications, particularly in the production of seed oil for both culinary use and biodiesel. However, its photosynthetic efficiency, a pivotal determinant of yield, remains relatively low compared with other C3 plants such as rice and soybean, highlighting the necessity of identifying the genetic loci and genes regulating photosynthesis in rapeseed. In this study, we investigated 5 photosynthesis traits and 5 leaf morphology traits in a natural population of rapeseed, and conducted a genome-wide association study (GWAS) to identify significantly associated loci and genes. The results showed that the gas-exchange parameters of the dark reactions in photosynthesis exhibited a significant positive correlation with the chlorophyll content, whereas they showed a weaker negative correlation with the leaf area. By GWAS, a total of 538 quantitative trait nucleotides (QTNs) were identified as significantly associated with traits related to both leaf morphology and photosynthesis. These QTNs were classified into 84 QTL clusters, of which, 21 clusters exhibited remarkable stability across different traits and environmental conditions. In addition, a total of 3,129 potential candidate genes were identified to be significantly associated with the above-mentioned 10 traits, most of which were shared by certain traits, further indicating the reliability of the findings. By integrating GWAS data with GO enrichment analysis and gene expression analysis, we further identified 8 key candidate genes that are associated with the regulation of photosynthesis, chlorophyll content, leaf area, and leaf petiole angle. Taken together, this study identified key genetic loci and candidate genes with the potential to improve photosynthetic efficiency in rapeseed. These findings provide a theoretical framework for breeding new rapeseed varieties with enhanced photosynthetic capabilities.
Rapeseed (Brassica napus L.) is a major agricultural crop with diverse applications, particularly in the production of seed oil for both culinary use and biodiesel. However, its photosynthetic efficiency, a pivotal determinant of yield, remains relatively low compared with other C3 plants such as rice and soybean, highlighting the necessity of identifying the genetic loci and genes regulating photosynthesis in rapeseed. In this study, we investigated 5 photosynthesis traits and 5 leaf morphology traits in a natural population of rapeseed, and conducted a genome-wide association study (GWAS) to identify significantly associated loci and genes. The results showed that the gas-exchange parameters of the dark reactions in photosynthesis exhibited a significant positive correlation with the chlorophyll content, whereas they showed a weaker negative correlation with the leaf area. By GWAS, a total of 538 quantitative trait nucleotides (QTNs) were identified as significantly associated with traits related to both leaf morphology and photosynthesis. These QTNs were classified into 84 QTL clusters, of which, 21 clusters exhibited remarkable stability across different traits and environmental conditions. In addition, a total of 3,129 potential candidate genes were identified to be significantly associated with the above-mentioned 10 traits, most of which were shared by certain traits, further indicating the reliability of the findings. By integrating GWAS data with GO enrichment analysis and gene expression analysis, we further identified 8 key candidate genes that are associated with the regulation of photosynthesis, chlorophyll content, leaf area, and leaf petiole angle. Taken together, this study identified key genetic loci and candidate genes with the potential to improve photosynthetic efficiency in rapeseed. These findings provide a theoretical framework for breeding new rapeseed varieties with enhanced photosynthetic capabilities.
Soybean improvement has entered a new era with the advent of multi-omics strategies and bioinformatics innovations, enabling more precise and efficient breeding practices. This comprehensive review examines the application of multi-omics approaches in soybean—encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics. We first explore pre-breeding and genomic selection as tools that have laid the groundwork for advanced trait improvement. Subsequently, we dig into the specific contributions of each -omics field, highlighting how bioinformatics tools and resources have facilitated the generation and integration of multifaceted data. The review emphasizes the power of integrating multi-omics datasets to elucidate complex traits and drive the development of superior soybean cultivars. Emerging trends, including novel computational techniques and high-throughput technologies, are discussed in the context of their potential to revolutionize soybean breeding. Finally, we address the challenges associated with multi-omics integration and propose future directions to overcome these hurdles, aiming to accelerate the pace of soybean improvement. This review serves as a crucial resource for researchers and breeders seeking to leverage multi-omics strategies for enhanced soybean productivity and resilience.
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