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Genome-wide association study of agronomic traits related to nitrogen use efficiency in wheat

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Abstract

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GWAS identified 347 QTLs associated with eight traits related to nitrogen use efficiency in a 389-count wheat panel. Four novel candidate transcription factor genes were verified using qRT-PCR.

Abstract

Nitrogen is an essential nutrient for plants that determines crop yield. Improving nitrogen use efficiency (NUE) should considerably increase wheat yield and reduce the use of nitrogen fertilisers. However, knowledge on the genetic basis of NUE during wheat maturity is limited. In this study, a diversity panel incorporating 389 wheat accessions was phenotyped for eight NUE-related agronomic traits across five different environments. A total of 347 quantitative trait loci (QTLs) for low nitrogen tolerance indices (ratio of agronomic characters under low and high nitrogen conditions) were identified through a genome-wide association study utilising 397,384 single nucleotide polymorphisms (SNPs) within the MLM (Q + K) model, including 11 stable QTLs. Furthermore, 69 candidate genes were predicted for low nitrogen tolerance indices of best linear unbiased predictions values of the eight studied agronomic traits, and four novel candidate transcription factors (TraesCS5A02G237500 for qFsnR5A.2, TraesCS5B02G384500 and TraesCS5B02G384600 for qSLR5B.1, and TraesCS3B02G068800 for qTKWR3B.1) showed differing expression patterns in contrasting low-nitrogen-tolerant wheat genotypes. Moreover, the number of favourable marker alleles calculated using NUE that were significantly related to SNP in accessions decreased over the decades, indicating a decline in the NUE of the 389 wheat varieties. These findings denote promising NUE markers that could be useful in breeding high-NUE wheat varieties, and the candidate genes could further detail the NUE-related regulation network in wheat.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  • An D, Su J, Liu Q, Zhu Y, Tong Y, Li J, Jing R, Li B, Li Z (2006) Mapping QTLs for nitrogen uptake in relation to the early growth of wheat (Triticum aestivum L.). Plant Soil 284:73–84

    CAS  Google Scholar 

  • Boisson M, Mondon K, Torney V, Nicot N, Laine A-L, Bahrman N, Gouy A, Daniel-Vedele F, Hirel B, Sourdille P (2005) Partial sequences of nitrogen metabolism genes in hexaploid wheat. Theor Appl Genet 110:932–940

    CAS  Google Scholar 

  • Cormier F, Gouis JL, Dubreuil P, Lafarge S, Praud S (2014) A genome-wide identification of chromosomal regions determining nitrogen use efficiency components in wheat (Triticum aestivum L.). Tagtheor Appl Geneticstheor Angew Genet 127:2679–2693

    CAS  Google Scholar 

  • Cui F, Na Z, Fan XL, Wei Z, Li JM (2017) Utilization of a Wheat660K SNP array-derived high-density genetic map for high-resolution mapping of a major QTL for kernel number. Sci Rep 7:3788

    Google Scholar 

  • Doyle J, Doyle J (1986) A rapid DNA isolation procedure from small quantities of fresh leaf tissues. Phytochemistry 19:11–15

    Google Scholar 

  • El-Kereamy A, Bi Y-M, Ranathunge K, Beatty PH, Good AG, Rothstein SJ (2012) The rice R2R3-MYB transcription factor OsMYB55 is involved in the tolerance to high temperature and modulates amino acid metabolism. PLoS ONE 7:e52030

    CAS  Google Scholar 

  • Fan X, Naz M, Fan X, Xuan W, Miller AJ, Xu G (2017) Plant nitrate transporters: from gene function to application. J Exp Bot 68:2463–2475

    CAS  Google Scholar 

  • Fang Q, Wang Q, Hui M, Xu J, Ying W, Hu H, He S, Tu J, Cheng C, Tian G (2018) AtDIV2, an R-R-type MYB transcription factor of Arabidopsis, negatively regulates salt stress by modulating ABA signaling. Plant Cell Rep 37:1499–1511

    CAS  Google Scholar 

  • Gaillochet C, Jamge S, van der Wal F, Angenent G, Immink R, Lohmann JU (2018) A molecular network for functional versatility of HECATE transcription factors. Plant J Cell Mol Biol 95:57–70

    CAS  Google Scholar 

  • Gooding MJ, Addisu M, Uppal RK, Snape JW, Jones HE (2011) Effect of wheat dwarfing genes on nitrogen-use efficiency. J Agric Sci 150:3–22

    Google Scholar 

  • He X, Qu B, Li W, Zhao X, Teng W, Ma W, Ren Y, Li B, Li Z, Tong Y (2015) The nitrate-inducible NAC transcription factor TaNAC2-5A controls nitrate response and increases wheat yield. Plant Physiol 169:1991–2005

    CAS  Google Scholar 

  • Hu M, Zhao X, Liu Q, Hong X, Zhang W, Zhang Y, Sun L, Li H, Tong Y (2018) Transgenic expression of plastidic glutamine synthetase increases nitrogen uptake and yield in wheat. Plant Biotechnol J 16:1858–1867

    CAS  Google Scholar 

  • Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42:961

    CAS  Google Scholar 

  • Islam S, Zhang J, Zhao Y, She M, Ma W (2021) Genetic regulation of the traits contributing to wheat nitrogen use efficiency. Plant Sci Int J Exp Plant Biol 303:110759

    CAS  Google Scholar 

  • Jamil M, Ali A, Gul A, Ghafoor A, Napar AA, Ibrahim AM, Naveed NH, Yasin NA, Mujeeb-Kazi A (2019) Genome-wide association studies of seven agronomic traits under two sowing conditions in bread wheat. BMC Plant Biol 19:1–18

    Google Scholar 

  • Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, Eskin E (2008) Efficient control of population structure in model organism association mapping. Genetics 178:1709–1723

    Google Scholar 

  • Lei L, Li G, Zhang H, Powers C, Fang T, Chen Y, Wang S, Zhu X, Carver BF, Yan L (2018) Nitrogen use efficiency is regulated by interacting proteins relevant to development in wheat. Plant Biotechnol J 16:1214–1226

    CAS  Google Scholar 

  • Li H, Hu B, Chu C (2017) Nitrogen use efficiency in crops: lessons from Arabidopsis and rice. J Exp Bot 68:2477–2488

    CAS  Google Scholar 

  • Li S, Tian Y, Wu K, Ye Y, Yu J, Zhang J, Liu Q, Hu M, Li H, Tong Y, Harberd NP, Fu X (2018) Modulating plant growth-metabolism coordination for sustainable agriculture. Nature 560:595–600

    CAS  Google Scholar 

  • Li L, Mao X, Wang J, Chang X, Reynolds M, Jing R (2019) Genetic dissection of drought and heat-responsive agronomic traits in wheat. Plant Cell Environ 42:2540–2553

    CAS  Google Scholar 

  • Liu Y, Wang H, Jiang Z, Wang W, Xu R, Wang Q, Zhang Z, Li A, Liang Y, Ou S, Liu X, Cao S, Tong H, Wang Y, Zhou F, Liao H, Hu B, Chu C (2021) Genomic basis of geographical adaptation to soil nitrogen in rice. Nature 590:600–605

    CAS  Google Scholar 

  • Ma Z, He S, Wang X, Sun J, Zhang Y, Zhang G, Wu L, Li Z, Liu Z, Sun G (2018) Resequencing a core collection of upland cotton identifies genomic variation and loci influencing fiber quality and yield. Nat Genet 50:803–813

    CAS  Google Scholar 

  • Marco M, Walid EF, Ghasemali N, Silvio S, Angela CM, Chiara CM, Sandra S, Roberto T (2016) Prioritizing quantitative trait loci for root system architecture in tetraploid wheat. J Exp Bot 67:1161–1178

    Google Scholar 

  • Marien H, Anne M, Fabien C, Céline M-D (2016) Nitrogen remobilisation during leaf senescence: lessons from Arabidopsis to crops. J Exp Bot 68:2513–2529

    Google Scholar 

  • McAllister CH, Beatty PH, Good AG (2012) Engineering nitrogen use efficient crop plants: the current status. Plant Biotechnol J 10:1011–1025

    CAS  Google Scholar 

  • Meng L, Li HH, Zhang LY, Wang JK (2015) QTL IciMapping: Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. Crop J 3:269–283

    Google Scholar 

  • Monostori I, Szira F, Tondelli A, Arendas T, Gierczik K, Cattivelli L, Galiba G, Vagujfalvi A (2017) Genome-wide association study and genetic diversity analysis on nitrogen use efficiency in a Central European winter wheat (Triticum aestivum L.) collection. PLoS ONE 12:e0189265

    Google Scholar 

  • Morosini JS, Mendonça LdF, Lyra DH, Galli G, Vidotti MS, Fritsche-Neto R (2017) Association mapping for traits related to nitrogen use efficiency in tropical maize lines under field conditions. Plant Soil 421:453–463

    CAS  Google Scholar 

  • Nigro D, Gadaleta A, Mangini G, Colasuonno P, Marcotuli I, Giancaspro A, Giove SL, Simeone R, Blanco A (2019) Candidate genes and genome-wide association study of grain protein content and protein deviation in durum wheat. Planta 249:1157–1175

    CAS  Google Scholar 

  • Ogbonnaya FC, Rasheed A, Okechukwu EC, Jighly A, Makdis F, Wuletaw T, Hagras A, Uguru MI, Agbo CU (2017) Genome-wide association study for agronomic and physiological traits in spring wheat evaluated in a range of heat prone environments. Theor Appl Genet 130:1819–1835

    Google Scholar 

  • Paffendorf B, Qassrawi R, Meys AM, Trimborn L, Schrader A (2020) Transparent testa glabra 1 participates in flowering time regulation in arabidopsis thaliana. PeerJ 8:e8303

    Google Scholar 

  • Piepho HP, Mohring J (2007) Computing heritability and selection response from unbalanced plant breeding trials. Genetics 177:1881–1888

    Google Scholar 

  • Pritchard JK, Stephens MJ, Donnelly PJ (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959

    CAS  Google Scholar 

  • Ren D, Fang X, Jiang P, Zhang G, Hu J, Wang X, Meng Q, Cui W, Lan S, Ma X, Wang H, Kong L (2018) Genetic architecture of nitrogen-deficiency tolerance in wheat seedlings based on a nested association mapping (NAM) population. Front Plant Sci 9:845

    Google Scholar 

  • Su Q, Zhang X, Zhang W, Zhang N, Song L, Liu L, Xue X, Liu G, Liu J, Meng D, Zhi L, Ji J, Zhao X, Yang C, Tong Y, Liu Z, Li J (2018) QTL detection for kernel size and weight in bread wheat (Triticum aestivum L.) using a high-density SNP and SSR-based linkage map. Front Plant Sci 9:1484

    Google Scholar 

  • Tang W, Ye J, Yao X, Zhao P, Xuan W, Tian Y, Zhang Y, Xu S, An H, Chen G, Yu J, Wu W, Ge Y, Liu X, Li J, Zhang H, Zhao Y, Yang B, Jiang X, Peng C, Zhou C, Terzaghi W, Wang C, Wan J (2019) Genome-wide associated study identifies NAC42-activated nitrate transporter conferring high nitrogen use efficiency in rice. Nat Commun 10:5279

    Google Scholar 

  • Thomsen HC, Eriksson D, Møller IS, Schjoerring JK (2014) Cytosolic glutamine synthetase: a target for improvement of crop nitrogen use efficiency? Trends Plant Sci 19:656–663

    CAS  Google Scholar 

  • Tsay Y-F, Chiu C-C, Tsai C-B, Ho C-H, Hsu P-K (2007) Nitrate transporters and peptide transporters. FEBS Lett 581:2290–2300

    CAS  Google Scholar 

  • Vidal EA, Alvarez JM, Araus V, Riveras E, Brooks MD, Krouk G, Ruffel S, Lejay L, Crawford NM, Coruzzi GM, Gutiérrez RA (2020) Nitrate in 2020: thirty years from transport to signaling networks. Plant Cell 32:2094–2119

    CAS  Google Scholar 

  • Vlieghe K, Vuylsteke M, Florquin K, Rombauts S, Maes S, Ormenese S, Van Hummelen P, Van de Peer Y, Inze D, De Veylder L (2003) Microarray analysis of E2Fa-DPa-overexpressing plants uncovers a cross-talking genetic network between DNA replication and nitrogen assimilation. J Cell Sci 116:4249–4259

    CAS  Google Scholar 

  • Xu G, Fan X, Miller AJ (2012) Plant nitrogen assimilation and use efficiency. Annu Rev Plant Biol 63:153–182

    CAS  Google Scholar 

  • Xu Y, Wang R, Tong Y, Zhao H, Xie Q, Liu D, Zhang A, Li B, Xu H, An D (2014) Mapping QTLs for yield and nitrogen-related traits in wheat: influence of nitrogen and phosphorus fertilization on QTL expression. Theor Appl Genet 127:59–72

    CAS  Google Scholar 

  • Xu N, Wang R, Zhao L, Zhang C, Li Z, Lei Z, Liu F, Guan P, Chu Z, Crawford NM, Wang Y (2016) The Arabidopsis NRG2 protein mediates nitrate signaling and interacts with and regulates key nitrate regulators. Plant Cell 28:485–504

    CAS  Google Scholar 

  • Yaish MW, El-Kereamy A, Zhu T, Beatty PH, Good AG, Bi Y-M, Rothstein SJ (2010) The APETALA-2-like transcription factor OsAP2-39 controls key interactions between abscisic acid and gibberellin in rice. PLoS Genet 6:e1001098

    Google Scholar 

  • Yan J, Warburton M, Crouch J (2011) Association mapping for enhancing maize (Zea mays L.) genetic improvement. Crop Sci 51:433–449

    Google Scholar 

  • Yan H, Shi H, Hu C, Luo M, Xu C, Wang S, Li N, Tang W, Zhou Y, Wang C, Xu Z, Chen J, Ma Y, Sun D, Chen M (2021) Transcriptome differences in response mechanisms to low-nitrogen stress in two wheat varieties. Int J Mol Sci 22:12278

    CAS  Google Scholar 

  • Yanagisawa S (2014) Transcription factors involved in controlling the expression of nitrate reductase genes in higher plants. Plant Sci 229:167–171

    CAS  Google Scholar 

  • Yang J, Lee SH, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Human Genet 88:76–82

    CAS  Google Scholar 

  • Yang J, Wang M, Li W, He X, Teng W, Ma W, Zhao X, Hu M, Li H, Zhang Y, Tong Y (2019) Reducing expression of a nitrate-responsive bZIP transcription factor increases grain yield and N use in wheat. Plant Biotechnol J 17:1823–1833

    CAS  Google Scholar 

  • Yu J, Xuan W, Tian Y, Fan L, Sun J, Tang W, Chen G, Wang B, Liu Y, Wu W, Liu X, Jiang X, Zhou C, Dai Z, Xu D, Wang C, Wan J (2021) Enhanced OsNLP4-OsNiR cascade confers nitrogen use efficiency by promoting tiller number in rice. Plant Biotechnol J 19:167–176

    CAS  Google Scholar 

  • Zaveri E, Lobell DB (2019) The role of irrigation in changing wheat yields and heat sensitivity in India. Nat Commun 10:4144

    Google Scholar 

  • Zhang C, Dong SS, Xu JY, He WM, Yang TL (2019a) PopLDdecay: a fast and effective tool for linkage disequilibrium decay analysis based on variant call format files. Bioinformatics 35:1786–1788

    CAS  Google Scholar 

  • Zhang M, Gao M, Zheng H, Yuan Y, Zhou X, Guo Y, Zhang G, Zhao Y, Kong F, An Y, Li S (2019b) QTL mapping for nitrogen use efficiency and agronomic traits at the seedling and maturity stages in wheat. Mol Breed 39:71

    Google Scholar 

  • Zhang X, Guan Z, Li Z, Liu P, Ma L, Zhang Y, Pan L, He S, Zhang Y, Li P, Ge F, Zou C, He Y, Gao S, Pan G, Shen Y (2020) A combination of linkage mapping and GWAS brings new elements on the genetic basis of yield-related traits in maize across multiple environments. Theor Appl Genet 133:2881–2895

    CAS  Google Scholar 

  • Zhang X, Li F, Ding Y, Ma Q, Yi Y, Zhu M, Ding J, Li C, Guo W, Zhu X (2021) Transcriptome analysis of two near-isogenic lines with different NUE under normal nitrogen conditions in wheat. Biology (basel) 10:787

    CAS  Google Scholar 

  • Zhao K, Tung CW, Eizenga GC, Wright MH, Ali ML, Price AH, Norton GJ, Islam MR, Reynolds A, Mezey J (2011) Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun 2:467

    Google Scholar 

  • Zhu Q, Vries WD, Liu X, Hao T, Zeng M, Shen J, Zhang F (2018) Enhanced acidification in Chinese croplands as derived from element budgets in the period 1980–2010. Sci Total Environ 618:1497–1505

    CAS  Google Scholar 

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Acknowledgements

This work was supported by the Fundamental Research Funds for Central Non-profit Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, the Agricultural Science and Technology Innovation Program (ASTIP), and the State Key Laboratory of Sustainable Dryland Agriculture of Shanxi Agricultural University (No. 202002-2). The authors would like to thank professor Jizeng Jia for providing this study’s natural materials.

Funding

Funding was provided by Agricultural Science and Technology Innovation Program, Shanxi Agricultural University (Grant No. 202002-2).

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Author notes

  1. Huawei Shi, Ming Chen and Lifeng Gao have contributed equally.

Authors and Affiliations

  1. Key Laboratory of Sustainable Dryland Agriculture, College of Agriculture, Shanxi Agricultural University, Jinzhong, 030801, China

    Huawei Shi, Huishu Yan, Chengjie Xu, Shuguang Wang, Yugang Shi, Yuxiang Wu & Daizhen Sun

  2. Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), National Key Facility for Crop Gene Resources and Genetic Improvement, Key Laboratory of Biology and Genetic Improvement of Triticeae Crops, Ministry of Agriculture, Beijing, 100081, China

    Huawei Shi, Ming Chen, Lifeng Gao, Yanming Bai, Huishu Yan, Chengjie Xu, Yongbin Zhou, Zhaoshi Xu, Jun Chen, Wensi Tang, Jizeng Jia & Youzhi Ma

  3. Shijiazhuang Academy of Agricultural and Forestry Sciences, Research Center of Wheat Engineering Technology of Hebei, Shijiazhuang, 050041, Hebei, China

    Yanxia Wang

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  1. Huawei Shi
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Contributions

DS, JJ, and YM designed the experiment. HS, YW, YB, HY, CX, and WT collected the phenotypes and performed the data analysis. HS, MC, and LG wrote the manuscript. All authors have read and approved the manuscript.

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Correspondence to Daizhen Sun, Jizeng Jia or Youzhi Ma.

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Shi, H., Chen, M., Gao, L. et al. Genome-wide association study of agronomic traits related to nitrogen use efficiency in wheat. Theor Appl Genet 135, 4289–4302 (2022). https://doi.org/10.1007/s00122-022-04218-5

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