Identification of crop cultivars with consistently high lignocellulosic sugar release requires the use of appropriate statistical design and modelling

Helena Oakey, Reza Shafiei, Jordi Comadran, Nicola Uzrek, Brian Cullis, Leonardo D. Gomez, Caragh Whitehead, Simon J. McQueen-Mason, Robbie Waugh, Claire Halpin

    Research output: Contribution to journalArticle

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    Abstract

    Background: In this study, a multi-parent population of barley cultivars was grown in the field for two consecutive years and then straw saccharification (sugar release by enzymes) was subsequently analysed in the laboratory to identify the cultivars with the highest consistent sugar yield. This experiment was used to assess the benefit of accounting for both the multi-phase and multi-environment aspects of large-scale phenotyping experiments with field-grown germplasm through sound statistical design and analysis. Results: Complementary designs at both the field and laboratory phases of the experiment ensured that non-genetic sources of variation could be separated from the genetic variation of cultivars, which was the main target of the study. The field phase included biological replication and plot randomisation. The laboratory phase employed re-randomisation and technical replication of samples within a batch, with a subset of cultivars chosen as duplicates that were randomly allocated across batches. The resulting data was analysed using a linear mixed model that incorporated field and laboratory variation and a cultivar by trial interaction, and ensured that the cultivar means were more accurately represented than if the non-genetic variation was ignored. The heritability detected was more than doubled in each year of the trial by accounting for the non-genetic variation in the analysis, clearly showing the benefit of this design and approach. Conclusions: The importance of accounting for both field and laboratory variation, as well as the cultivar by trial interaction, by fitting a single statistical model (multi-environment trial, MET, model), was evidenced by the changes in list of the top 40 cultivars showing the highest sugar yields. Failure to account for this interaction resulted in only eight cultivars that were consistently in the top 40 in different years. The correspondence between the rankings of cultivars was much higher at 25 in the MET model. This approach is suited to any multi-phase and multi-environment population-based genetic experiment.

    Original languageEnglish
    Article number185
    Pages (from-to)1-12
    Number of pages12
    JournalBiotechnology for Biofuels
    Volume6
    Issue number1
    DOIs
    Publication statusPublished - 21 Dec 2013

    Fingerprint

    Sugars
    Crops
    cultivar
    sugar
    crop
    modeling
    Random Allocation
    Experiments
    Saccharification
    Straw
    Population Genetics
    Statistical Models
    Hordeum
    Linear Models
    Enzymes
    experiment
    Acoustic waves
    germplasm
    heritability
    barley

    Keywords

    • Barley
    • Multi-environment trial
    • Multi-phase experiment
    • Phenotyping
    • Saccharification
    • Second generation biofuels

    Cite this

    Oakey, Helena ; Shafiei, Reza ; Comadran, Jordi ; Uzrek, Nicola ; Cullis, Brian ; Gomez, Leonardo D. ; Whitehead, Caragh ; McQueen-Mason, Simon J. ; Waugh, Robbie ; Halpin, Claire. / Identification of crop cultivars with consistently high lignocellulosic sugar release requires the use of appropriate statistical design and modelling. In: Biotechnology for Biofuels. 2013 ; Vol. 6, No. 1. pp. 1-12.
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    abstract = "Background: In this study, a multi-parent population of barley cultivars was grown in the field for two consecutive years and then straw saccharification (sugar release by enzymes) was subsequently analysed in the laboratory to identify the cultivars with the highest consistent sugar yield. This experiment was used to assess the benefit of accounting for both the multi-phase and multi-environment aspects of large-scale phenotyping experiments with field-grown germplasm through sound statistical design and analysis. Results: Complementary designs at both the field and laboratory phases of the experiment ensured that non-genetic sources of variation could be separated from the genetic variation of cultivars, which was the main target of the study. The field phase included biological replication and plot randomisation. The laboratory phase employed re-randomisation and technical replication of samples within a batch, with a subset of cultivars chosen as duplicates that were randomly allocated across batches. The resulting data was analysed using a linear mixed model that incorporated field and laboratory variation and a cultivar by trial interaction, and ensured that the cultivar means were more accurately represented than if the non-genetic variation was ignored. The heritability detected was more than doubled in each year of the trial by accounting for the non-genetic variation in the analysis, clearly showing the benefit of this design and approach. Conclusions: The importance of accounting for both field and laboratory variation, as well as the cultivar by trial interaction, by fitting a single statistical model (multi-environment trial, MET, model), was evidenced by the changes in list of the top 40 cultivars showing the highest sugar yields. Failure to account for this interaction resulted in only eight cultivars that were consistently in the top 40 in different years. The correspondence between the rankings of cultivars was much higher at 25 in the MET model. This approach is suited to any multi-phase and multi-environment population-based genetic experiment.",
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    Identification of crop cultivars with consistently high lignocellulosic sugar release requires the use of appropriate statistical design and modelling. / Oakey, Helena; Shafiei, Reza; Comadran, Jordi; Uzrek, Nicola; Cullis, Brian; Gomez, Leonardo D.; Whitehead, Caragh; McQueen-Mason, Simon J.; Waugh, Robbie; Halpin, Claire.

    In: Biotechnology for Biofuels, Vol. 6, No. 1, 185, 21.12.2013, p. 1-12.

    Research output: Contribution to journalArticle

    TY - JOUR

    T1 - Identification of crop cultivars with consistently high lignocellulosic sugar release requires the use of appropriate statistical design and modelling

    AU - Oakey, Helena

    AU - Shafiei, Reza

    AU - Comadran, Jordi

    AU - Uzrek, Nicola

    AU - Cullis, Brian

    AU - Gomez, Leonardo D.

    AU - Whitehead, Caragh

    AU - McQueen-Mason, Simon J.

    AU - Waugh, Robbie

    AU - Halpin, Claire

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    AB - Background: In this study, a multi-parent population of barley cultivars was grown in the field for two consecutive years and then straw saccharification (sugar release by enzymes) was subsequently analysed in the laboratory to identify the cultivars with the highest consistent sugar yield. This experiment was used to assess the benefit of accounting for both the multi-phase and multi-environment aspects of large-scale phenotyping experiments with field-grown germplasm through sound statistical design and analysis. Results: Complementary designs at both the field and laboratory phases of the experiment ensured that non-genetic sources of variation could be separated from the genetic variation of cultivars, which was the main target of the study. The field phase included biological replication and plot randomisation. The laboratory phase employed re-randomisation and technical replication of samples within a batch, with a subset of cultivars chosen as duplicates that were randomly allocated across batches. The resulting data was analysed using a linear mixed model that incorporated field and laboratory variation and a cultivar by trial interaction, and ensured that the cultivar means were more accurately represented than if the non-genetic variation was ignored. The heritability detected was more than doubled in each year of the trial by accounting for the non-genetic variation in the analysis, clearly showing the benefit of this design and approach. Conclusions: The importance of accounting for both field and laboratory variation, as well as the cultivar by trial interaction, by fitting a single statistical model (multi-environment trial, MET, model), was evidenced by the changes in list of the top 40 cultivars showing the highest sugar yields. Failure to account for this interaction resulted in only eight cultivars that were consistently in the top 40 in different years. The correspondence between the rankings of cultivars was much higher at 25 in the MET model. This approach is suited to any multi-phase and multi-environment population-based genetic experiment.

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    KW - Second generation biofuels

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