Meta-analysis to the rescue - improved weighting methods for the statistical analysis of multi environment crop variety trials

点击翻译按钮获取中文摘要

Multi-environment trials (MET) play a key role in plant breeding and official crop variety testing. MET are routinely analysed by linear mixed models. Current best practice in the statistical analysis involves weighting of trials based on their precision. There are currently several unresolved problems related to weighting of information in MET. Here, we focus on the following five problems: (1) Weighted least squares estimates of genotype means based on a linear mixed model for MET may lead to estimates of differences among genotypes which are outside of the range of corresponding difference estimates in the individual trials. (2) Trials are often discarded when having a large coefficient of variation, which constitutes an extreme form of weighting. There is no agreed threshold for discarding trials, and it is not even clear that discarding trials improves precision. (3) It is quite common, especially with long-term data, that for some trials no variance information is available for weighting. There is currently no established method to deal with this missing-weight problem in MET analyses. (4) The weights for different trials may be correlated with the genotype-environment interaction pattern and hence the effect size, in which case a weighted analysis may lead to biased results. (5) Different types of trial may yield inconsistent results. For example, on-farm trials may lead different results than on-station trials. There is no established method for detecting such inconsistencies and for weighted combination of information in MET under inconsistency.This project proposes to use recent results from meta-analysis to approach these five problems. Methods of meta-analysis were mainly developed in the domain of medical statistics and comprise a host of methods to summarize the evidence from multiple published trials on the same type of research question. A key component of most established methods of meta-analysis is weighting of trials according to precision and hence there are many established methods of meta-analysis that can be used to solve these problems in MET. This opportunity has so far not been explored, however, because meta-analysis on the one hand and methods for analysis of MET on the other hand have evolved on largely disconnected paths over the past decades. Here, we will build on recent own work establishing the close connection between the two lines of research. A key finding of that recent work is that meta-analysis can be based on models that are very similar to models used for MET. These connections allow adapting methods of meta-analysis to the analysis of MET and thus developing solutions to the five weighting-related problems named above. The main focus of the project will be on variety trials, but we will also explore applications of newly developed methodology for meta-analysis in other areas of the agricultural sciences as well as in medicine.