文献类型: 外文期刊
作者: Lu, Shi 1 ; Li, Mu 1 ; Zhang, Mo 2 ; Lu, Ming 1 ; Wan, Xinqi 1 ; Wang, Piwu 2 ; Liu, Wenguo 1 ;
作者机构: 1.Jilin Acad Agr Sci, Key Lab Biol & Genet Improvement Maize Northeast, Natl Engn Res Ctr Maize Jilin, Natl Engn Lab Maize Changchun,Minist Agr, Shengtai St 1363, Changchun 130033, Peoples R China
2.Jilin Agr Univ, Xincheng St 1288, Changchun 130118, Peoples R China
关键词: Maize; Plant height; Ear height; Genome-wide association study (GWAS); Candidate genes verification
期刊名称:TROPICAL PLANT BIOLOGY ( 影响因子:1.512; 五年影响因子:2.011 )
ISSN: 1935-9756
年卷期: 2020 年 13 卷 3 期
页码:
收录情况: SCI
摘要: Both plant height (PH) and ear height (EH) are key agronomic traits in maize that are associated with plant lodging resistance and population density. To explore the genetic basis for PH and EH in maize, we conducted a genome-wide association study (GWAS) based upon 1.49 x 10(6) single nucleotide polymorphisms (SNPs) identified following the sequencing of 80 backbone inbred maize lines in Jilin Province. By comparing genotypic data and these two traits of interest, we identified 27 total SNPs significantly associated with PH and EH (P < 0.000001). Of these SNPs, 12 were significantly associated with PH and were found on chromosomes 1, 3, 4, 6, 7, and 9, accounting for 25.8% of the phenotypic variability for this trait. The remaining 15 SNPs were significantly linked with EH and were located on chromosomes 1, 2, 4, 6, 7, 8, 9, and 10, accounting for 30% of the phenotypic variability for this trait. Within a mean linkage disequilibrium (LD) distance of 9.7 kb from these SNP loci, we identified 5 candidate genes associated with PH, with one of these candidate genes harboring a significant SNP. Similarly, we identified 12 candidate genes associated with EH, of which 3 harbored significant SNPs. We then isolated RNA from 8 different inbred maize lines from this GWAS study cohort and assessed the expression of these candidate genes of interest via quantitative real-time PCR (qRT-PCR). Through this analysis, we were able to verify that there were significant differences in the expression of these four SNP-harboring candidate genes in plants with a range of EH and PH phenotypes.
- 相关文献
作者其他论文 更多>>
-
Natural polymorphisms in ZMET2 encoding a DNA methyltransferase modulate the number of husk layers in maize
作者:Wang, Zi;Xia, Aiai;Wang, Qi;He, Yan;Cui, Zhenhai;Lu, Ming;Ye, Yusheng;Wang, Yanbo
关键词:
-
Genome-wide association analysis was used to discover genes related to soybean grain weight per plant and 100-grain weight
作者:Sun, Tingting;Zhang, Qi;Liu, Lu;Wang, Jiabao;Wang, Kun;Yuan, Boran;Wang, Piwu;Tang, Yujie
关键词:soybean; GWAS; 100-grain weight; grain weight per plant
-
The RHW1-ZCN4 regulatory pathway confers natural variation of husk leaf width in maize
作者:Xia, Aiai;Zheng, Leiming;Wang, Zi;Wang, Qi;He, Yan;Lu, Ming;Cui, Zhenhai;He, Yan
关键词:GWAS; husk leaf; maize; RHW1; ZCN4
-
Nitrate improves aluminium resistance through SLAH-mediated citrate exudation from roots
作者:Wang, Peng;Cao, Hongrui;Zhang, Meng;Wang, Hui;Ma, Hongyu;Yang, Zhong-Bao;Quan, Shuxuan;Wang, Yong;Li, Mu;Wei, Ping;Li, Xiaofeng;Yang, Zhong-Bao
关键词:Al toxicity; Arabidopsis; Maize; Root growth; Transcriptional regulation
-
Genomic selection to improve husk tightness based on genomic molecular markers in maize
作者:Ao, Man;Zhu, Fangbo;Guan, Yixin;Cui, Zhenhai;Ruan, Yanye;Zhang, Ao;Lu, Ming;Zheng, Shubo
关键词:husk tightness; sequencing platforms; population structure; genomic selection (GS); marker density
-
Gradual daylength sensing coupled with optimum cropping modes enhances multi-latitude adaptation of rice and maize
作者:Wang, Xiaoying;Han, Jiupan;Li, Rui;Qiu, Leilei;Huang, Rongyu;Huang, Xi;Ouyang, Xinhao;Zhang, Cheng;Lu, Ming;Wang, Xiangfeng;Zhang, Jianfu;Xie, Huaan;Li, Shigui
关键词:multi-latitude adaptation; daylength sensing; cropping mode
-
Rapid and Accurate Varieties Classification of Different Crop Seeds Under Sample-Limited Condition Based on Hyperspectral Imaging and Deep Transfer Learning
作者:Wu, Na;Liu, Fei;He, Yong;Meng, Fanjia;Li, Mu;Zhang, Chu
关键词:crop seeds; hyperspectral imaging; classification model; spectroscopic analysis; deep learning