QTL mapping and GWAS for field kernel water content and kernel dehydration rate before physiological maturity in maize
文献类型: 外文期刊
作者: Li, Shufang 1 ; Zhang, Chunxiao 1 ; Lu, Ming 2 ; Yang, Deguang 3 ; Qian, Yiliang 4 ; Yue, Yaohai 2 ; Zhang, Zhijun 2 ; Ji 1 ;
作者机构: 1.Jilin Acad Agr Sci, Crop Germplasm Resources Inst, Kemaoxi St 303, Gongzhuling 136100, Jilin, Peoples R China
2.Jilin Acad Agr Sci, Maize Res Inst, Gongzhuling 136100, Peoples R China
3.Northeast Agr Univ, Coll Agron, Harbin 150030, Peoples R China
4.Anhui Acad Agr Sci, Maize Res Ctr, Hefei 230001, Peoples R China
期刊名称:SCIENTIFIC REPORTS ( 影响因子:4.379; 五年影响因子:5.133 )
ISSN: 2045-2322
年卷期: 2020 年 10 卷 1 期
页码:
收录情况: SCI
摘要: Kernel water content (KWC) and kernel dehydration rate (KDR) are two main factors affecting maize seed quality and have a decisive influence on the mechanical harvest. It is of great importance to map and mine candidate genes related to KWCs and KDRs before physiological maturity in maize. 120 double-haploid (DH) lines constructed from Si287 with low KWC and JiA512 with high KWC were used as the mapping population. KWCs were measured every 5 days from 10 to 40 days after pollination, and KDRs were calculated. A total of 1702 SNP markers were used to construct a linkage map, with a total length of 1,309.02 cM and an average map distance of 0.77 cM. 10 quantitative trait loci (QTLs) and 27 quantitative trait nucleotides (QTNs) were detected by genome-wide composite interval mapping (GCIM) and multi-locus random-SNP-effect mixed linear model (mrMLM), respectively. One and two QTL hotspot regions were found on Chromosome 3 and 7, respectively. Analysis of the Gene Ontology showed that 2 GO terms of biological processes (BP) were significantly enriched (P <= 0.05) and 6 candidate genes were obtained. This study provides theoretical support for marker-assisted breeding of mechanical harvest variety in maize.
- 相关文献
作者其他论文 更多>>
-
Heterotic quantitative trait loci analysis and genomic prediction of seedling biomass-related traits in maize triple testcross populations
作者:Zhang, Tifu;Liang, Shuaiqiang;Lin, Feng;Lu, Haiyan;Zhao, Han;Jiang, Lu;Ruan, Long;Qian, Yiliang;Dai, Huixue
关键词:Seedling biomass-related traits; Heterotic quantitative trait loci; Genomic prediction; Triple testcross; Maize
-
Detection of QTNs for kernel moisture concentration and kernel dehydration rate before physiological maturity in maize using multi-locus GWAS
作者:Li, Shufang;Zhang, Chunxiao;Jin, Fengxue;Liu, Xueyan;Li, Xiaohui;Yang, Deguang;Lu, Ming;Liu, Wenguo;Qian, Yiliang;Wang, Yu
关键词:
-
A cost-effective barcode system for maize genetic discrimination based on bi-allelic InDel markers
作者:Liang, Shuaiqiang;Lin, Feng;Zhang, Tifu;Wu, Yibo;Zhao, Han;Qian, Yiliang;Qi, Yaocheng;Ren, Sihai;Ruan, Long
关键词:Maize; Barcode system; InDel marker; Genetic discrimination
-
Long-term fertilization regimes change soil nitrification potential by impacting active autotrophic ammonia oxidizers and nitrite oxidizers as assessed by DNA stable isotope probing
作者:Kong, Yali;Ling, Ning;Xue, Chao;Ruan, Yang;Guo, Junjie;Zhu, Chen;Wang, Min;Shen, Qirong;Guo, Shiwei;Chen, Huan
关键词:
-
QTL mapping for maize starch content and candidate gene prediction combined with co-expression network analysis
作者:Lin, Feng;Zhou, Ling;He, Bing;Zhang, Xiaolin;Zhao, Han;Dai, Huixue;Qian, Yiliang;Ruan, Long
关键词:
-
Deciphering the associations between soil microbial diversity and ecosystem multifunctionality driven by long-term fertilization management
作者:Luo, Gongwen;Liu, Manqiang;Wang, Min;Guo, Shiwei;Ling, Ning;Shen, Qirong;Rensing, Christopher;Chen, Huan
关键词:ecosystem multifunctionality; enzymatic patterns; long-term fertilization management; microbial diversity; structural equation modelling
-
Long-term fertilization regimes drive the abundance and composition of N-cycling-related prokaryotic groups via soil particle-size differentiation
作者:Luo, Gongwen;Liu, Manqiang;Wang, Min;Guo, Shiwei;Ling, Ning;Shen, Qirong;Friman, Ville-Petri;Chen, Huan
关键词:Long-term fertilization regimes;N-cycling-related prokaryotes;Community structure;Soil particle size fraction;Structural equation modelling