QTL Alignment for Seed Yield and Yield Related Traits in Brassica napus.
QTL Alignment for Seed Yield and Yield Related Traits in Brassica napus.
复制标题
甘蓝型油菜种子产量和产量相关性状的 QTL 比对
DOI:
10.3389/fpls.2018.01127
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发表时间:
2018
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;Dalin H;Pu S;Yan W;Yu L;Wang B;Li M
Worldwide consumption of oil is increasing with the growing population in need for edible oil and the expansion of industry using biofuels. Then, demand for high yielding varieties of oil crops is always increasing. Brassica napus (rapeseed) is one of the most important oil crop in the world, therefore, increasing rapeseed yield through breeding is inevitable in order to cater for the high demand of vegetable oil and high-quality protein for live stocks. Quantitative trait loci (QTL) analysis is a powerful tool to identify important loci and which is also valuable for molecular marker assisted breeding. Seed-yield (SY) is a complex trait that is controlled by multiple loci and is affected directly by seed weight, seeds per silique and silique number. Some yield-related traits, such as plant height, biomass yield, flowering time, and so on, also affect the SY indirectly. This study reports the assembly of QTLs identified for seed-yield and yield-related traits in rapeseed, in one unique map. A total of 972 QTLs for seed-yield and yield-related were aligned into the physical map of B. napus Darmor-bzh and 92 regions where 198 QTLs overlapped, could be discovered on 16 chromosomes. Also, 147 potential candidate genes were discovered in 65 regions where 131 QTLs overlapped, and might affect nine different traits. At the end, interaction network of candidate genes was studied, and showed nine genes that could highly interact with the other genes, and might have more influence on them. The present results would be helpful to develop molecular markers for yield associated traits and could be used for breeding improvement in B. napus.
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影响因子:
11.6
作者:
Blázquez, MA;Green, R;Weigel, D
通讯作者:
Weigel, D
影响因子:
4.6
作者:
Daviere, Jean-Michel;Achard, Patrick
通讯作者:
Achard, Patrick
影响因子:
5.6
作者:
Gu J;Chao H;Gan L;Guo L;Zhang K;Li Y;Wang H;Raboanatahiry N;Li M
通讯作者:
Li M
影响因子:
2.1
作者:
Frank, SA
通讯作者:
Frank, SA
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