Literature DB >> 26922174

Association mapping and genetic dissection of nitrogen use efficiency-related traits in rice (Oryza sativa L.).

Zhiyi Liu1, Chengsong Zhu2, Yue Jiang1, Yunlu Tian1, Jun Yu1, Hongzhou An1, Weijie Tang1, Juan Sun1, Jianpeng Tang1, Gaoming Chen1, Huqu Zhai3, Chunming Wang4,5, Jianmin Wan6,7.   

Abstract

The increases in the usage of nitrogen fertilizer result in deleterious impacts on the environment; thus, there is an urgent need to improve nitrogen use efficiency (NUE) in crops including rice (Oryza sativa L.). Attentions have focused on quantitative trait loci (QTL) mapping of NUE-related traits using single experimental population, but to date, very few studies have taken advantage of association mapping to examine hundreds of lines for identifying potentially novel QTLs in rice. Here, we conducted association analysis on NUE-related traits using a population containing 184 varieties, which were genotyped with 157 genome-wide simple sequence repeat (SSR) markers. We detected eight statistically significant marker loci associating with NUE-related traits, of which two QTLs at RM5639 and RM3628 harbored known NUE-related genes GS1;2 and AspAt3, respectively. At a novel NUE-related locus RM5748, we developed Kompetitive Allele Specific PCR (KASP) single nucleotide polymorphism (SNP) markers and searched for putative NUE-related genes which are close to the associated SNP marker. Based on a transcriptional map of N stress responses constructed by our lab, we evaluated expressions of the NUE-related genes in this region and validated their effect on NUE. Meanwhile, we analyzed NUE-related alleles of the eight loci that could be utilized in marker-assisted selection. Moreover, we estimated breeding values of all the varieties through genomic prediction approach that could be beneficial for rice NUE enhancement.

Entities:  

Keywords:  Association mapping; Breeding value; NUE; Rice

Mesh:

Substances:

Year:  2016        PMID: 26922174     DOI: 10.1007/s10142-016-0486-z

Source DB:  PubMed          Journal:  Funct Integr Genomics        ISSN: 1438-793X            Impact factor:   3.410


  28 in total

1.  Association mapping in structured populations.

Authors:  J K Pritchard; M Stephens; N A Rosenberg; P Donnelly
Journal:  Am J Hum Genet       Date:  2000-05-26       Impact factor: 11.025

2.  Inference of population structure using multilocus genotype data.

Authors:  J K Pritchard; M Stephens; P Donnelly
Journal:  Genetics       Date:  2000-06       Impact factor: 4.562

3.  Genomic control for association studies.

Authors:  B Devlin; K Roeder
Journal:  Biometrics       Date:  1999-12       Impact factor: 2.571

4.  cDNA macroarray analysis of gene expression in ineffective nodules induced on the Lotus japonicus sen1 mutant.

Authors:  Norio Suganuma; Atsuko Yamamoto; Ai Itou; Tsuneo Hakoyama; Mari Banba; Shingo Hata; Masayoshi Kawaguchi; Hiroshi Kouchi
Journal:  Mol Plant Microbe Interact       Date:  2004-11       Impact factor: 4.171

Review 5.  Genetic association mapping and genome organization of maize.

Authors:  Jianming Yu; Edward S Buckler
Journal:  Curr Opin Biotechnol       Date:  2006-02-28       Impact factor: 9.740

6.  TASSEL: software for association mapping of complex traits in diverse samples.

Authors:  Peter J Bradbury; Zhiwu Zhang; Dallas E Kroon; Terry M Casstevens; Yogesh Ramdoss; Edward S Buckler
Journal:  Bioinformatics       Date:  2007-06-22       Impact factor: 6.937

7.  Rapid isolation of high molecular weight plant DNA.

Authors:  M G Murray; W F Thompson
Journal:  Nucleic Acids Res       Date:  1980-10-10       Impact factor: 16.971

8.  Heterotrimeric G proteins regulate nitrogen-use efficiency in rice.

Authors:  Hongying Sun; Qian Qian; Kun Wu; Jijing Luo; Shuansuo Wang; Chengwei Zhang; Yanfei Ma; Qian Liu; Xianzhong Huang; Qingbo Yuan; Ruixi Han; Meng Zhao; Guojun Dong; Longbiao Guo; Xudong Zhu; Zhiheng Gou; Wen Wang; Yuejin Wu; Hongxuan Lin; Xiangdong Fu
Journal:  Nat Genet       Date:  2014-04-28       Impact factor: 38.330

9.  Variation in NRT1.1B contributes to nitrate-use divergence between rice subspecies.

Authors:  Bin Hu; Wei Wang; Shujun Ou; Jiuyou Tang; Hua Li; Ronghui Che; Zhihua Zhang; Xuyang Chai; Hongru Wang; Yiqin Wang; Chengzhen Liang; Linchuan Liu; Zhongze Piao; Qiyun Deng; Kun Deng; Chi Xu; Yan Liang; Lianhe Zhang; Legong Li; Chengcai Chu
Journal:  Nat Genet       Date:  2015-06-08       Impact factor: 38.330

10.  Accumulated expression level of cytosolic glutamine synthetase 1 gene (OsGS1;1 or OsGS1;2) alter plant development and the carbon-nitrogen metabolic status in rice.

Authors:  Aili Bao; Zhuqing Zhao; Guangda Ding; Lei Shi; Fangsen Xu; Hongmei Cai
Journal:  PLoS One       Date:  2014-04-17       Impact factor: 3.240

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  13 in total

1.  Genetic dissection of plant growth habit in chickpea.

Authors:  Hari D Upadhyaya; Deepak Bajaj; Rishi Srivastava; Anurag Daware; Udita Basu; Shailesh Tripathi; Chellapilla Bharadwaj; Akhilesh K Tyagi; Swarup K Parida
Journal:  Funct Integr Genomics       Date:  2017-06-09       Impact factor: 3.410

Review 2.  Biochemical and Genetic Approaches Improving Nitrogen Use Efficiency in Cereal Crops: A Review.

Authors:  Nitika Sandhu; Mehak Sethi; Aman Kumar; Devpriya Dang; Jasneet Singh; Parveen Chhuneja
Journal:  Front Plant Sci       Date:  2021-06-04       Impact factor: 5.753

3.  The Genetic Control of Grain Protein Content under Variable Nitrogen Supply in an Australian Wheat Mapping Population.

Authors:  Saba Mahjourimajd; Julian Taylor; Zed Rengel; Hossein Khabaz-Saberi; Haydn Kuchel; Mamoru Okamoto; Peter Langridge
Journal:  PLoS One       Date:  2016-07-20       Impact factor: 3.240

4.  Identification of Single Nucleotide Polymorphisms Associated with Brown Rust Resistance, α-Amylase Activity and Pre-harvest Sprouting in Rye (Secale cereale L.).

Authors:  Monika Rakoczy-Trojanowska; Paweł Krajewski; Jan Bocianowski; Małgorzata Schollenberger; Wojciech Wakuliński; Paweł Milczarski; Piotr Masojć; Małgorzata Targońska-Karasek; Zofia Banaszak; Katarzyna Banaszak; Waldemar Brukwiński; Wacław Orczyk; Andrzej Kilian
Journal:  Plant Mol Biol Report       Date:  2017-04-26       Impact factor: 1.595

5.  PCR-based assays for validation of single nucleotide polymorphism markers in rice and mungbean.

Authors:  Thu Giang Thi Bui; Nguyen Thi Lan Hoa; Jo-Yi Yen; Roland Schafleitner
Journal:  Hereditas       Date:  2017-01-26       Impact factor: 3.271

6.  A combined association mapping and t-test analysis of SNP loci and candidate genes involving in resistance to low nitrogen traits by a wheat mutant population.

Authors:  Hongchun Xiong; Huijun Guo; Chunyun Zhou; Xiaotong Guo; Yongdun Xie; Linshu Zhao; Jiayu Gu; Shirong Zhao; Yuping Ding; Luxiang Liu
Journal:  PLoS One       Date:  2019-01-30       Impact factor: 3.240

7.  Genetic Basis for Variation in Wheat Grain Yield in Response to Varying Nitrogen Application.

Authors:  Saba Mahjourimajd; Julian Taylor; Beata Sznajder; Andy Timmins; Fahimeh Shahinnia; Zed Rengel; Hossein Khabaz-Saberi; Haydn Kuchel; Mamoru Okamoto; Peter Langridge
Journal:  PLoS One       Date:  2016-07-26       Impact factor: 3.240

8.  QTL Mapping by Whole Genome Re-sequencing and Analysis of Candidate Genes for Nitrogen Use Efficiency in Rice.

Authors:  Xinghai Yang; Xiuzhong Xia; Zongqiong Zhang; Baoxuan Nong; Yu Zeng; Faqian Xiong; Yanyan Wu; Ju Gao; Guofu Deng; Danting Li
Journal:  Front Plant Sci       Date:  2017-09-21       Impact factor: 5.753

Review 9.  Molecular Genetics and Breeding for Nutrient Use Efficiency in Rice.

Authors:  Jauhar Ali; Zilhas Ahmed Jewel; Anumalla Mahender; Annamalai Anandan; Jose Hernandez; Zhikang Li
Journal:  Int J Mol Sci       Date:  2018-06-14       Impact factor: 5.923

10.  Identification of rice landraces with promising yield and the associated genomic regions under low nitrogen.

Authors:  I Subhakara Rao; C N Neeraja; B Srikanth; D Subrahmanyam; K N Swamy; K Rajesh; P Vijayalakshmi; T Vishnu Kiran; N Sailaja; P Revathi; P Raghuveer Rao; L V Subba Rao; K Surekha; V Ravindra Babu; S R Voleti
Journal:  Sci Rep       Date:  2018-06-15       Impact factor: 4.379

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