IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0159371.html
   My bibliography  Save this article

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

Author

Listed:
  • Saba Mahjourimajd
  • Julian Taylor
  • Zed Rengel
  • Hossein Khabaz-Saberi
  • Haydn Kuchel
  • Mamoru Okamoto
  • Peter Langridge

Abstract

Genetic variation has been observed in both protein concentration in wheat grain and total protein content (protein yield). Here we describe the genetic analysis of variation for grain protein in response to nitrogen (N) supply and locate significant genomic regions controlling grain protein components in a spring wheat population. In total, six N use efficiency (NUE) field trials were carried out for the target traits in a sub-population of doubled haploid lines derived from a cross between two Australian varieties, RAC875 and Kukri, in Southern and Western Australia from 2011 to 2013. Twenty-four putative Quantitative Trait Loci (QTL) for protein-related traits were identified at high and low N supply and ten QTL were identified for the response to N for the traits studied. These loci accounted for a significant proportion of the overall effect of N supply. Several of the regions were co-localised with grain yield QTL and are promising targets for further investigation and selection in breeding programs.

Suggested Citation

  • Saba Mahjourimajd & Julian Taylor & Zed Rengel & Hossein Khabaz-Saberi & Haydn Kuchel & Mamoru Okamoto & Peter Langridge, 2016. "The Genetic Control of Grain Protein Content under Variable Nitrogen Supply in an Australian Wheat Mapping Population," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-13, July.
    • Handle: RePEc:plo:pone00:0159371
    DOI: 10.1371/journal.pone.0159371
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0159371
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0159371&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0159371?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Alison Smith & Brian Cullis & Robin Thompson, 2001. "Analyzing Variety by Environment Data Using Multiplicative Mixed Models and Adjustments for Spatial Field Trend," Biometrics, The International Biometric Society, vol. 57(4), pages 1138-1147, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Saba Mahjourimajd & Julian Taylor & Beata Sznajder & Andy Timmins & Fahimeh Shahinnia & Zed Rengel & Hossein Khabaz-Saberi & Haydn Kuchel & Mamoru Okamoto & Peter Langridge, 2016. "Genetic Basis for Variation in Wheat Grain Yield in Response to Varying Nitrogen Application," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-18, July.
    2. T. Caliński & S. Czajka & Z. Kaczmarek & P. Krajewski & W. Pilarczyk, 2005. "Analyzing Multi-environment Variety Trials Using Randomization-Derived Mixed Models," Biometrics, The International Biometric Society, vol. 61(2), pages 448-455, June.
    3. Joel Jorge Nuvunga & Carlos Pereira da Silva & Luciano Antonio de Oliveira & Renato Ribeiro de Lima & Marcio Balestre, 2019. "Bayesian factor analytic model: An approach in multiple environment trials," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-26, August.
    4. Sudipto Banerjee & Gregg A. Johnson, 2006. "Coregionalized Single- and Multiresolution Spatially Varying Growth Curve Modeling with Application to Weed Growth," Biometrics, The International Biometric Society, vol. 62(3), pages 864-876, September.
    5. Nicholas George & Kim Tungate & Cameron Beeck & Michael Stamm, 2012. "Exploring Genotype by Environment Interaction in Winter Canola in North Carolina," Journal of Agricultural Science, Canadian Center of Science and Education, vol. 4(2), pages 237-237, February.
    6. Boby Mathew & Jens Léon & Mikko J Sillanpää, 2018. "Impact of residual covariance structures on genomic prediction ability in multi-environment trials," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-11, July.
    7. Joanne De Faveri & Arūnas P. Verbyla & Brian R. Cullis & Wayne S. Pitchford & Robin Thompson, 2017. "Residual Variance–Covariance Modelling in Analysis of Multivariate Data from Variety Selection Trials," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(1), pages 1-22, March.
    8. Alison B. Smith & Lauren M. Borg & Beverley J. Gogel & Brian R. Cullis, 2019. "Estimation of Factor Analytic Mixed Models for the Analysis of Multi-treatment Multi-environment Trial Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(4), pages 573-588, December.
    9. Brian R. Cullis & Alison B. Smith & Nicole A. Cocks & David G. Butler, 2020. "The Design of Early-Stage Plant Breeding Trials Using Genetic Relatedness," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(4), pages 553-578, December.
    10. Gambin, Brenda L. & Coyos, Tomás & Di Mauro, Guido & Borrás, Lucas & Garibaldi, Lucas A., 2016. "Exploring genotype, management, and environmental variables influencing grain yield of late-sown maize in central Argentina," Agricultural Systems, Elsevier, vol. 146(C), pages 11-19.
    11. Emi Tanaka, 2020. "Simple outlier detection for a multi‐environmental field trial," Biometrics, The International Biometric Society, vol. 76(4), pages 1374-1382, December.
    12. Johannes Forkman & Hans-Peter Piepho, 2014. "Parametric bootstrap methods for testing multiplicative terms in GGE and AMMI models," Biometrics, The International Biometric Society, vol. 70(3), pages 639-647, September.
    13. S. Hadasch & J. Forkman & W. A. Malik & H. P. Piepho, 2018. "Weighted Estimation of AMMI and GGE Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(2), pages 255-275, June.
    14. Johannes Forkman, 2013. "The use of a reference variety for comparisons in incomplete series of crop variety trials," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(12), pages 2681-2698, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0159371. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.