Functional & Integrative Genomics

, Volume 16, Issue 3, pp 323–333 | Cite as

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

  • Zhiyi Liu
  • Chengsong Zhu
  • Yue Jiang
  • Yunlu Tian
  • Jun Yu
  • Hongzhou An
  • Weijie Tang
  • Juan Sun
  • Jianpeng Tang
  • Gaoming Chen
  • Huqu Zhai
  • Chunming Wang
  • Jianmin Wan
Original Article

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.

Keywords

Rice NUE Association mapping Breeding value 

Abbreviations

ANOVA

Analysis of variance

BIC

Bayesian information criterion

EST

Expressed sequence tag

GBS

Genotyping by sequencing

GLR

Grain length ratio of low N/normal N

LD

Linkage disequilibrium

MAS

Marker-assisted selection

NUE

Nitrogen use efficiency

PHR

Plant height ratio of low N/normal N

PVE

Phenotypic variation explained

QTL

Quantitative trait loci

TNR

Tiller number ratio of low N/normal N

Notes

Acknowledgments

This work was supported by a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD, 010809001), the Fundamental Research Funds for the Central Universities (KYRC201208, KYZ201202-6), and “Shuangchuang” projects, Jiangsu Province, China. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Authors’ contributions

CW conceived the experiments, performed the experiments, and wrote the manuscript. JW and HZ supervised this project and revised the manuscript. CZ conceived the experiments, carried out the statistical analysis, and wrote the manuscript. ZL performed the experiments and analyzed the data. YJ, YT, JY, HA, WT, YL, JS, JT, and GC participated in the experiments. All authors read and approved the final manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Supplementary material

10142_2016_486_MOESM1_ESM.docx (118 kb)
ESM 1 (DOCX 117 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Zhiyi Liu
    • 1
  • Chengsong Zhu
    • 2
  • Yue Jiang
    • 1
  • Yunlu Tian
    • 1
  • Jun Yu
    • 1
  • Hongzhou An
    • 1
  • Weijie Tang
    • 1
  • Juan Sun
    • 1
  • Jianpeng Tang
    • 1
  • Gaoming Chen
    • 1
  • Huqu Zhai
    • 3
  • Chunming Wang
    • 1
    • 4
  • Jianmin Wan
    • 1
    • 3
  1. 1.State Key Laboratory of Crop Genetics and Germplasm EnhancementNanjing Agricultural UniversityNanjingChina
  2. 2.Division of Plant ScienceUniversity of MissouriColumbiaUSA
  3. 3.National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop ScienceChinese Academy of Agricultural SciencesBeijingChina
  4. 4.Jiangsu Collaborative Innovation Center for Modern Crop ProductionNanjingChina

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