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Evaluation of quality traits and their genetic variation in global collections of Brassica napus L

  • Biyun Chen (a1), Kun Xu (a1), Hao Li (a1), Guizhen Gao (a1), Guixin Yan (a1), Jiangwei Qiao (a1) and Xiaoming Wu (a1)...


Evaluating quality traits is important to the selection of elite lines in Brassica napus L. In this study, the quality traits of 488 global collections of B. napus L were evaluated for two consecutive years under central Chinese growing conditions, and a series of phenotypic data was obtained. The measured total glucosinolate content (GLC) and erucic acid content (ERU) values for 95.5% of the accessions were consistent with the original values, and large variations in quality traits were found among these accessions, thus enabling selection for these characters. In general, Chinese accessions tended to have a higher oil content (OC) than foreign accessions, while compared with winter and spring accessions, semi-winter accessions tended to have the highest OC. The mean GLC and ERU of Chinese rapeseed accessions showed gradual downward trends over time, and the genotypic variation in ERU accounted for 98.44% of the total variation, which was the highest among all 10 of the quality traits. Additionally, the heritability for ERU was largest among all 10 of the quality traits. Significant correlations were observed between different traits; OC had significantly (P < 0.01) negative correlation coefficients with oleic acid content, whereas OC had significantly (P < 0.01) positive correlation coefficients with ERU. Principal component analysis revealed that there was no clear boundary among materials of different geographic origins and different ecotypes according to the first two principal coordinates, respectively. This information about variations in quality traits revealed in this study could identify parents for improved rapeseed breeding.


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Evaluation of quality traits and their genetic variation in global collections of Brassica napus L

  • Biyun Chen (a1), Kun Xu (a1), Hao Li (a1), Guizhen Gao (a1), Guixin Yan (a1), Jiangwei Qiao (a1) and Xiaoming Wu (a1)...


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