数字农科院2.0

Dissection of the Genetic Basis of Yield Traits in Line per se and Testcross Populations and Identification of Candidate Genes for Hybrid Performance in Maize

文献类型: 外文期刊

作者: Yuting Ma;Dongdong Li;Zhenxiang Xu;Riliang Gu;Pingxi Wang;Junjie Fu;Jianhua Wang;Wanli Du;Hongwei Zhang

作者机构:

关键词: candidate gene;hundred kernel weight;maize;testcross;yield per plant

期刊名称: International Journal of Molecular Sciences

ISSN: 1661-6596

年卷期: 2022 年 23 卷 9 期

页码:

摘要: Dissecting the genetic basis of yield traits in hybrid populations and identifying the candidate genes are important for molecular crop breeding. In this study, a BC1F3:4 population, the line per se (LPS) population, was constructed by using elite inbred lines Zheng58 and PH4CV as the parental lines. The population was genotyped with 55,000 SNPs and testcrossed to Chang7-2 and PH6WC (two testers) to construct two testcross (TC) populations. The three populations were evaluated for hundred kernel weight (HKW) and yield per plant (YPP) in multiple environments. Marker–trait association analysis (MTA) identified 24 to 151 significant SNPs in the three populations. Comparison of the significant SNPs identified common and specific quantitative trait locus/loci (QTL) in the LPS and TC populations. Genetic feature analysis of these significant SNPs proved that these SNPs were associated with the tested traits and could be used to predict trait performance of both LPS and TC populations. RNA-seq analysis was performed using maize hybrid varieties and their parental lines, and differentially expressed genes (DEGs) between hybrid varieties and parental lines were identified. Comparison of the chromosome positions of DEGs with those of significant SNPs detected in the TC population identified potential candidate genes that might be related to hybrid performance. Combining RNA-seq analysis and MTA results identified candidate genes for hybrid performance, providing information that could be useful for maize hybrid breeding.

分类号:

  • 相关文献

[1]Genomic Prediction Across Structured Hybrid Populations and Environments in Maize. Li D, Xu Z, Gu R, Wang P, Xu J, Du D, Fu J, Wang J, Zhang H, Wang G. 2021

[2]Genomic Prediction Across Structured Hybrid Populations and Environments in Maize. Li D, Xu Z, Gu R, Wang P, Xu J, Du D, Fu J, Wang J, Zhang H, Wang G. 2021

[3]Meta-analysis and candidate gene mining of low-phosphorus tolerance in maize. Zhang, Hongwei,Uddin, Mohammed Shalim,Zou, Cheng,Xie, Chuanxiao,Xu, Yunbi,Li, Wen-Xue,Uddin, Mohammed Shalim,Xu, Yunbi. 2014

[4]Joint-linkage mapping and GWAS reveal extensive genetic loci that regulate male inflorescence size in maize. Wu, Xun,Li, Yongxiang,Shi, Yunsu,Song, Yanchun,Zhang, Dengfeng,Li, Chunhui,Li, Yu,Wang, Tianyu,Buckler, Edward S.,Buckler, Edward S.,Zhang, Zhiwu,Wu, Xun,Zhang, Zhiwu.

[5]Numerous genetic loci identified for drought tolerance in the maize nested association mapping populations. Li, Chunhui,Li, Yongxiang,Wu, Xun,Zhang, Dengfeng,Shi, Yunsu,Song, Yanchun,Wang, Tianyu,Li, Yu,Sun, Baocheng,Liu, Cheng,Buckler, Edward S.,Buckler, Edward S.,Zhang, Zhiwu. 2016

[6]Genome-wide association studies of leaf angle in maize. Bo Peng,Xiaolei Zhao,Yi Wang,Chunhui Li,Yongxiang Li,Dengfeng Zhang,Yunsu Shi,Yanchun Song,Lei Wang,Yu Li,Tianyu Wang. 2021

[7]Using a high density bin map to analyze quantitative trait locis of germination ability of maize at low temperatures. Yu Zhou,Qing Lu,Jinxin Ma,Dandan Wang,Xin Li,Hong Di,Lin Zhang,Xinge Hu,Ling Dong,Xianjun Liu,Xing Zeng,Zhiqiang Zhou,Jianfeng Weng,Zhenhua Wang. 2022

[8]Linkage mapping combined with GWAS revealed the genetic structural relationship and candidate genes of maize flowering time-related traits. Jian Shi,Yunhe Wang,Chuanhong Wang,Lei Wang,Wei Zeng,Guomin Han,Chunhong Qiu,Tengyue Wang,Zhen Tao,Kaiji Wang,Shijie Huang,Shuaishuai Yu,Wanyi Wang,Hongyi Chen,Chen Chen,Chen He,Hui Wang,Peiling Zhu,Yuanyuan Hu,Xin Zhang,Chuanxiao Xie. 2022

[9]Integration of GWAS, linkage analysis and transcriptome analysis to reveal the genetic basis of flowering time-related traits in maize. Xun Wu,Ying Liu,Xuefeng Lu,Liang Tu,Yuan Gao,Dong Wang,Shuang Guo,Yifei Xiao,Pingfang Xiao,Xiangyang Guo,Angui Wang,Pengfei Liu,Yunfang Zhu,Lin Chen,Zehui Chen. 2023

[10]DissectingthemaizedirectandindirectdefenseresponseagainstAsianCornBorer. 汪海,李圣彦,查象敏,朱莉,黄大昉,郎志宏. 2015

[11]TheDifferentialTranscriptionNetworkbetweenEmbryoandEndospermintheEarlyDevelopingMaizeSeed. XiaoduoLu,DijunChen,DefengShu,ZhaoZhang,WeixuanWang,ChristianKlukas,Ling-lingChen,YunliuFan,MingChen,ChunyiZhang. 2015

[12]Identification of quantitative trait loci across interspecific F-2, F-2:3 and testcross populations for agronomic and fiber traits in tetraploid cotton. Yu, Shuxun,Wu, Man,Zhai, Honghong,Li, Xingli,Fan, Shuli,Song, Meizhen,Gore, Michael,Zhang, Jinfa.

[13]Genome-wide study refines the quantitative trait locus for number of ribs in a Large White x Minzhu intercross pig population and reveals a new candidate gene. Zhang, Long-Chao,Yue, Jing-Wei,Pu, Lei,Wang, Li-Gang,Liu, Xin,Liang, Jing,Yan, Hua,Zhao, Ke-Bin,Li, Na,Shi, Hui-Bi,Zhang, Yue-Bo,Wang, Li-Xian.

[14]Quantitative trait loci for the number of vertebrae on Sus scrofa chromosomes 1 and 7 independently influence the numbers of thoracic and lumbar vertebrae in pigs. Zhang Long-chao,Liu Xin,Liang Jing,Yan Hua,Zhao Ke-bin,Li Na,Pu Lei,Shi Hui-bi,Zhang Yue-bo,Wang Li-gang,Wang Li-xian. 2015

[15]Up-regulation of NLRC5 and NF-kappa B signaling pathway in carrier chickens challenged with Salmonella enterica Serovar Pullorum at different persistence periods. Liu, Xiangping,Sheng, Zhongwei,Dou, Xinhong,Wang, Kehua,Ma, Teng,Wang, Hongzhi,Li, Zhiteng,Pan, Zhiming,Chang, Guobin,Chen, Guohong. 2015

[16]Identification of candidate genes associated with male sterility in CMS7311 of heading Chinese cabbage (Brassica campestris L. ssp pekinensis). Xu, Xiaoyong,Sun, Xilu,Zhang, Jing,Huang, Weiwei,Zhang, Lugang,Xu, Xiaoyong,Sun, Xilu,Zhang, Jing,Zhang, Lugang,Fang, Zhiyuan,Huang, Weiwei,Fang, Zhiyuan. 2013

[17]Zea mays NAC transcription factor family members: their genomic characteristics and relationship with drought stress. Li, Liang,Ma, Yiwen,Li, Liang,Ma, Yiwen,Zhang, Shihuang,Hao, Zhuanfang,Li, Xinhai. 2015

[18]Identification of favorable SNP alleles and candidate genes for traits related to early maturity via GWAS in upland cotton. Junji Su,Chaoyou Pang,Hengling Wei,Libei Li,Bing Liang,Caixiang Wang,Meizhen Song,Hantao Wang,Shuqi Zhao,Xiaoyun Jia,Guangzhi Mao,Long Huang,Dandan Geng,Chengshe Wang,Shuli Fan. 2016

[19]Meta-Analysis And Overview Analysis Of Q.uantitative Trait Locis Associated W ith Fatty Acid Content In Soybean For Candidate Gene Mining. Qin, HT, Liu, ZX, Wang, YY, Xu, MY, Mao, XR, Qi, HD, Yin, ZG, Li, YL, Jiang, HW, Hu, ZB, Wu, XX, Zhu, RS, Liu, CY, Chen, QS, Xin, DW, Qi, ZM. 2018

[20]Genome-wide association study of blast resistance in indica rice. Caihong Wang , Yaolong Yang , Xiaoping Yuan , Qun Xu , Yue Feng , Hanyong Yu , Yiping Wang , Xinghua Wei *. 2014

作者其他论文 更多>>