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

Biotechnology for Biofuels

Claire Halpin⁴  Robbie Waugh¹  Simon J McQueen-Mason²  Caragh Whitehead²  Leonardo D Gomez²  Brian Cullis³  Nicola Uzrek¹  Jordi Comadran¹  Reza Shafiei⁴  Helena Oakey⁴ 

[1]The James Hutton Institute, Invergowrie, Dundee DD2 5DA Scotland, UK

[2]Biology Department, Centre for Novel Agricultural Products (CNAP), University of York, Wentworth Way, York YO10 5DD, UK

[3]Computational Informatics, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, ACT 2600, Australia

[4]Division of Plant Sciences, College of Life Sciences, University of Dundee at The James Hutton Institute, Invergowrie, Dundee DD2 5DA, UK

关键词: Second generation biofuels;    Phenotyping;    Barley;    Saccharification;    Multi-environment trial;    Multi-phase experiment;   

Others  :  794248
DOI  :  10.1186/1754-6834-6-185

 received in 2013-08-02, accepted in 2013-12-06,  发布年份 2013

【 摘 要 】

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.

【 授权许可】

   
2013 Oakey et al.; licensee BioMed Central Ltd.

【 图 表 】

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