Abstract
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.
Highlights
In this study, a multi-parent population of barley cultivars was grown in the field for two consecutive years and straw saccharification was subsequently analysed in the laboratory to identify the cultivars with the highest consistent sugar yield
In each trial, the barley cultivars were grown in pots in the field in a polyethylene tunnel arranged in a spatial row-column design with five replicate blocks (Figure 1)
Our analyses illustrate the benefit of accounting for both the multi-phase and multi-environment aspects of an experiment in which we aimed to classify and rank barley cultivars according to the saccharification potential of their straw
Summary
A multi-parent population of barley cultivars was grown in the field for two consecutive years and 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. To date, little attention has been focused on the challenge of ensuring that the statistical design and analysis of these large and lengthy experiments are sufficiently robust to guarantee meaningful data that identifies the best performing genotypes/cultivars in a reproducible way Achieving such rigour is essential when the phenotypic data is intended to underpin subsequent genetic dissection of the genes and loci controlling saccharification as a quantitative trait, or identifying better cultivars for biofuel applications
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