Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/112174
Citations
Scopus Web of Science® Altmetric
?
?
Type: Journal article
Title: Identification of crop cultivars with consistently high lignocellulosic sugar release requires the use of appropriate statistical design and modelling
Author: Oakey, H.
Shafiei, R.
Comadran, J.
Uzrek, N.
Cullis, B.
Gomez, L.
Whitehead, C.
McQueen-Mason, S.
Waugh, R.
Halpin, C.
Citation: Biotechnology for Biofuels, 2013; 6(1):185-1-185-12
Issue Date: 2013
ISSN: 1754-6834
1754-6834
Statement of
Responsibility: 
Helena Oakey, Reza Shafiei, Jordi Comadran, Nicola Uzrek, Brian Cullis, Leonardo D Gomez, Caragh Whitehead, Simon J McQueen-Mason, Robbie Waugh and Claire Halpin
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.
Keywords: Multi-phase experiment; multi-environment trial; saccharification; barley; phenotyping; second generation biofuels
Rights: © 2013 Oakey et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
RMID: 0030063274
DOI: 10.1186/1754-6834-6-185
Appears in Collections:Agriculture, Food and Wine publications

Files in This Item:
File Description SizeFormat 
hdl_112174.pdfPublished version2.12 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.