Construction of a Quantitative Genomic Map, Identification and Expression Analysis of Candidate Genes for Agronomic and Disease-Related Traits in Brassica napus.
Construction of a Quantitative Genomic Map, Identification and Expression Analysis of Candidate Genes for Agronomic and Disease-Related Traits in Brassica napus.
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甘蓝型油菜农艺及病害相关性状的定量基因组图谱构建、鉴定及表达分析
DOI:
10.3389/fpls.2022.862363
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发表时间:
2022
影响因子:
5.6
通讯作者:
Li M
中科院分区:
文献类型:
--
作者:
Raboanatahiry N;Chao H;He J;Li H;Yin Y;Li M
Rapeseed is the second most important oil crop in the world. Improving seed yield and seed oil content are the two main highlights of the research. Unfortunately, rapeseed development is frequently affected by different diseases. Extensive research has been made through many years to develop elite cultivars with high oil, high yield, and/or disease resistance. Quantitative trait locus (QTL) analysis has been one of the most important strategies in the genetic deciphering of agronomic characteristics. To comprehend the distribution of these QTLs and to uncover the key regions that could simultaneously control multiple traits, 4,555 QTLs that have been identified during the last 25 years were aligned in one unique map, and a quantitative genomic map which involved 128 traits from 79 populations developed in 12 countries was constructed. The present study revealed 517 regions of overlapping QTLs which harbored 2,744 candidate genes and might affect multiple traits, simultaneously. They could be selected to customize super-rapeseed cultivars. The gene ontology and the interaction network of those candidates revealed genes that highly interacted with the other genes and might have a strong influence on them. The expression and structure of these candidate genes were compared in eight rapeseed accessions and revealed genes of similar structures which were expressed differently. The present study enriches our knowledge of rapeseed genome characteristics and diversity, and it also provided indications for rapeseed molecular breeding improvement in the future.
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影响因子:
14.9
作者:
Babicki S;Arndt D;Marcu A;Liang Y;Grant JR;Maciejewski A;Wishart DS
通讯作者:
Wishart DS
影响因子:
5.3
作者:
Chen F;Zhang W;Yu K;Sun L;Gao J;Zhou X;Peng Q;Fu S;Hu M;Long W;Pu H;Chen S;Wang X;Zhang J
通讯作者:
Zhang J
影响因子:
2.9
作者:
Conner, Jeffrey K.;Karoly, Keith;Shaw, Frank H.
通讯作者:
Shaw, Frank H.
影响因子:
2.9
作者:
Bouchet AS;Laperche A;Bissuel-Belaygue C;Baron C;Morice J;Rousseau-Gueutin M;Dheu JE;George P;Pinochet X;Foubert T;Maes O;Dugué D;Guinot F;Nesi N
通讯作者:
Nesi N
影响因子:
4.6
作者:
Chao H;Wang H;Wang X;Guo L;Gu J;Zhao W;Li B;Chen D;Raboanatahiry N;Li M
通讯作者:
Li M