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

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Biotechnology for Biofuels

, 6:185

First Online: 21 December 2013Received: 02 August 2013Accepted: 06 December 2013


BackgroundIn 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.

ResultsComplementary 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.

ConclusionsThe 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.

KeywordsMulti-phase experiment Multi-environment trial Saccharification Barley Phenotyping Second generation biofuels AbbreviationsANOVAAnalysis of variance

DMDry matter

EBLUPEmpirical best linear unbiased predictor

FPUFilter paper unit

GWASGenome-wide association study

MBTH3-methyl-2-benzothiazolinone hydrazone

METmulti-environment trial

QTLQuantitative trait loci

REMLResidual maximum likelihood


Electronic supplementary materialThe online version of this article doi:10.1186-1754-6834-6-185 contains supplementary material, which is available to authorized users.

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Author: Helena Oakey - Reza Shafiei - Jordi Comadran - Nicola Uzrek - Brian Cullis - Leonardo D Gomez - Caragh Whitehead - Simon 


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