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Genetic analyses using GGE model and a mixed linear model approach, and stability analyses using AMMI bi-plot for late-maturity alpha-amylase activity in bread wheat genotypes

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Abstract

Low falling number and discounting grain when it is downgraded in class are the consequences of excessive late-maturity α-amylase activity (LMAA) in bread wheat (Triticum aestivum L.). Grain expressing high LMAA produces poorer quality bread products. To effectively breed for low LMAA, it is necessary to understand what genes control it and how they are expressed, particularly when genotypes are grown in different environments. In this study, an International Collection (IC) of 18 spring wheat genotypes and another set of 15 spring wheat cultivars adapted to South Dakota (SD), USA were assessed to characterize the genetic component of LMAA over 5 and 13 environments, respectively. The data were analysed using a GGE model with a mixed linear model approach and stability analysis was presented using an AMMI bi-plot on R software. All estimated variance components and their proportions to the total phenotypic variance were highly significant for both sets of genotypes, which were validated by the AMMI model analysis. Broad-sense heritability for LMAA was higher in SD adapted cultivars (53%) compared to that in IC (49%). Significant genetic effects and stability analyses showed some genotypes, e.g. ‘Lancer’, ‘Chester’ and ‘LoSprout’ from IC, and ‘Alsen’, ‘Traverse’ and ‘Forefront’ from SD cultivars could be used as parents to develop new cultivars expressing low levels of LMAA. Stability analysis using an AMMI bi-plot revealed that ‘Chester’, ‘Lancer’ and ‘Advance’ were the most stable across environments, while in contrast, ‘Kinsman’, ‘Lerma52’ and ‘Traverse’ exhibited the lowest stability for LMAA across environments.

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Acknowledgements

This research was a contribution of the Department of Plant Science, South Dakota State University. The first author was supported by Monsanto through the Monsanto Fellowships in Plant Breeding program. This study was also partially supported by USDA-NIFA Hatch projects 1005459 and SD00H492-13. The authors would like to show their gratitude to Jonathan Kleinjan, the Spring Wheat Breeding crews, and fellow graduate students for their assistance with field trials and screening procedures.

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Authors and Affiliations

  1. Department of Plant Science, South Dakota State University, Brookings, SD, 57006, USA

    Golam Rasul, Karl D. Glover & Jixiang Wu

  2. Dairy and Food Science Department, South Dakota State University, Brookings, SD, 57007, USA

    Padmanaban G. Krishnan

  3. Bayer CropScience LP, Lincoln, NE, USA

    William A. Berzonsky

  4. Crop and Livestock Research Centre, Agriculture and Agri-Food Canada, Charlottetown, PEI, C1A 4N6, Canada

    Bourlaye Fofana

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  1. Golam Rasul
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  2. Karl D. Glover
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  3. Padmanaban G. Krishnan
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  4. Jixiang Wu
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  5. William A. Berzonsky
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  6. Bourlaye Fofana
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Correspondence to Golam Rasul.

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Rasul, G., Glover, K.D., Krishnan, P.G. et al. Genetic analyses using GGE model and a mixed linear model approach, and stability analyses using AMMI bi-plot for late-maturity alpha-amylase activity in bread wheat genotypes. Genetica 145, 259–268 (2017). https://doi.org/10.1007/s10709-017-9962-1

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  • DOI: https://doi.org/10.1007/s10709-017-9962-1

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