Dealing with sources of variability in the data-analysis of phenotyping experiments with transgenic rice

The analysis of phenotyping experiments for transgenics deserves special attention. Experiments set up for the detection of interesting phenotypes among transgenic plants have to screen several primary events obtained by transforming with a particular transgene, since expression levels of the transgene differ considerably. Agronomically most interesting lines might have an intermediate level of transgene expression. Therefore, attention should be paid to all transformants and how their expression levels differ. Experimental design and the analysis of the data have to focus on the variability among lines and have to be able to detect small differences in quantitative traits. The mixed model is the most adequate approach to analyse data of phenotyping experiments because it reflects the structure and provides the researcher with important measures to allow broader inferences. The paper explains the model and illustrates it using a screening experiment carried out by the high-throughput phenotyping method of TraitMill(TM). Besides inference for a particular experiment and a particular set of lines, the output allows more general predictions for a wider set of non-tested lines. It quantifies the various sources of variability encountered and helps to understand the underlying process. It also helps to optimise the experimental set-up of future experiments. The model presented here has been implemented in the R-language and SAS. The scripts are attached.