Submitted to: Open Journal of Statistics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/27/2019
Publication Date: 6/30/2019
Citation: Wu, J., Jenkins, J.N., McCarty Jr, J.C. 2019. Revealing GE interactions from trial data without replications. Open Journal of Statistics. 9:407-419. https://doi.org/10.4236/ojs.2019.93027.
Interpretive Summary: An important component of crop trial data analysis is determining genotype by environment interactions. Most published crop trial data only report means for different environments rather than actual plot data. This makes it difficult to conduct statistical analysis. A new methodology is proposed in the study to impute replicated trial data sets to detect genotype by environment interactions from the original data. A published data set containing 28 genotypes, six environments, with three replications was used to evaluate our new imputation method. The phenotypic means and predicted random effects from the imputed data were compared with the original data results. The results from the imputed data were consistent with those from the original data. This indicates that imputed data from our proposed method can be used to reveal genotype by environment effects harbored in the original data. Therefore, this study could pave a way to detect statistical interactions and other related information from historical crop trial reports when replications are not included in such reports.
Technical Abstract: Detecting genotype-by-environment (GE) interaction effects or yield stability is one of the most important components for crop trial data analysis, especially in historical crop trial data. However, it is statistically challenging to discover the GE interaction effects because many published data were just entry means under each environment rather than repeated field plot data. In this study, we propose a new methodology, which can be used to impute replicated trial data sets to reveal GE interactions from the original data. As a demonstration, we used a data set, which includes 28 potato genotypes and six environments with three replications to numerically evaluate the properties of this new imputation method. We compared the phenotypic means and predicted random effects from the imputed data with the results from the original data. The results from the imputed data were highly consistent with those from the original data set, indicating that imputed data from the method we proposed in this study can be used to reveal information including GE interaction effects harbored in the original data. Therefore, this study could pave a way to detect the GE interactions and other related information from historical crop trial reports when replications were not available.