文献类型: 外文期刊
作者: Havlickova, Lenka;He, Zhesi;Berger, Madeleine;Wang, Lihong;Sandmann, Greta;Chew, Yen Peng;Yoshikawa, Guilherme V.;Lu, Guangyuan;Hu, Qiong;Banga, Surinder S.;Beaudoin, Frederic;Bancroft, Ian
作者机构:
关键词: Brassica napus;radiation mutagenesis;vegetable oil
期刊名称: PLANT BIOTECHNOLOGY JOURNAL
ISSN: 1467-7644
年卷期: 2023 年
页码:
收录情况: SCIE(2023版)
摘要: Rapeseed is a crop of global importance but there is a need to broaden the genetic diversity available to address breeding objectives. Radiation mutagenesis, supported by genomics, has the potential to supersede genome editing for both gene knockout and copy number increase, but detailed knowledge of the molecular outcomes of radiation treatment is lacking. To address this, we produced a genome re-sequenced panel of 1133 M-2 generation rapeseed plants and analysed large-scale deletions, single nucleotide variants and small insertion-deletion variants affecting gene open reading frames. We show that high radiation doses (2000 Gy) are tolerated, gamma radiation and fast neutron radiation have similar impacts and that segments deleted from the genomes of some plants are inherited as additional copies by their siblings, enabling gene dosage decrease. Of relevance for species with larger genomes, we showed that these large-scale impacts can also be detected using transcriptome re-sequencing. To test the utility of the approach for predictive alteration of oil fatty acid composition, we produced lines with both decreased and increased copy numbers of Bna.FAE1 and confirmed the anticipated impacts on erucic acid content. We detected and tested a 21-base deletion expected to abolish function of Bna.FAD2.A5, for which we confirmed the predicted reduction in seed oil polyunsaturated fatty acid content. Our improved understanding of the molecular effects of radiation mutagenesis will underpin genomics-led approaches to more efficient introduction of novel genetic variation into the breeding of this crop and provides an exemplar for the predictive improvement of other crops.
分类号:
- 相关文献
作者其他论文 更多>>
-
Optimization and application of genome prediction model in rapeseed: flowering time, yield components, and oil content as examples
作者:Yu, Wenkai;Wang, Xinao;Wang, Hui;Wang, Wenxiang;Cheng, Hongtao;Mei, Desheng;Jiang, Lixi;Hu, Qiong;Liu, Jia
关键词:Rapeseed; GWAS; genome prediction (GP); flowering time; yield components; oil content
-
Genome-Wide Association Study Reveals Candidate Genes Regulating Plant Height and First-Branch Height in Brassica napus
作者:Cui, Tianyu;Wang, Xinao;Wang, Wenxiang;Cheng, Hongtao;Mei, Desheng;Hu, Qiong;Wei, Wenliang;Liu, Jia
关键词:rapeseed;plant height;first-branch height;GWAS;candidate genes;meta-analysis
-
A real-time orchard navigation path extraction method using semantic segmentation and pixel scanning
作者:Huang, Yuyu;Li, Hui;Wang, Lihong;Li, Chengsong;Niu, Qi;He, Xiongkui;Ma, Wei;Xi, Wanpeng;Yang, Yuheng;Wang, Pei
关键词:Autonomous navigation;Path extraction;Semantic segmentation;Pixel scanning
-
Nitric oxide delays the postharvest nutritional quality decline of Golden Hook beans
作者:He, Xuelian;Wang, Lihong;Watkins, Christopher B.;Bai, Chunmei;Ma, Lili;Guo, Susu;Han, Lichun;Wang, Hongwei;Wang, Qing;Zuo, Jinhua;Zheng, Yanyan
关键词:color change;defensive system;flavonoids;nutritional quality;Phaseolus vulgaris
-
Dissecting the Genetic Mechanisms of Hemicellulose Content in Rapeseed Stalk
作者:Xu, Yinhai;Yang, Yuting;Yu, Wenkai;Liu, Liezhao;Hu, Qiong;Wei, Wenliang;Liu, Jia
关键词:rapeseed;hemicellulose;GWAS;RNA-seq;bioenergy
-
Genetic Programming for High-Level Feature Learning in Crop Classification
作者:Lu, Miao;Bi, Ying;Xue, Bing;Hu, Qiong;Zhang, Mengjie;Wei, Yanbing;Yang, Peng;Wu, Wenbin
关键词:crop classification;genetic programming;feature learning;high-level features;genetic programming representation
-
An Object- and Topology-Based Analysis (OTBA) Method for Mapping Rice-Crayfish Fields in South China
作者:Wei, Haodong;Hu, Qiong;Cai, Zhiwen;Yang, Jingya;Song, Qian;Yin, Gaofei;Xu, Baodong
关键词:rice-crayfish field; object-based method; topology; classification; high-resolution image