Improving the efficiency of multi-location field trials with complete and incomplete relationship information

The increasingly cost-efficient availability of 'omics' data has led to the development of a rich framework for predicting the performance of non-phenotyped selection candidates in recent years. The improvement of phenotypic analyses by using pedigree and/or genomic relationship data has however received much less attention, albeit it has shown large potential for increasing the efficiency of early generation yield trials in some breeding programs. The aim of this study was accordingly to assess the possibility to enhance phenotypic analyses of multi-location field trials with complete relationship information as well as when merely incomplete pedigree and/or genomic relationship information is available for a set of selection candidates. For his purpose, four winter bread wheat trial series conducted in Eastern and Western Europe were used to determine the experimental efficiency and accuracy of different resource allocations with a varying degree of relationship information. The results showed that modelling relationship between the selection candidates in the analyses of multi-location trial series was up to 20% more efficient than employing routine analyses, where genotypes are assumed to be unrelated. The observed decrease in efficiency and accuracy when reducing the testing capacities was furthermore less pronounced when modelling relationship information, even in cases when merely partial pedigree and/or genomic information was available for the phenotypic analyses. Exploiting complete and incomplete relationship information in both preliminary yield trials and multi-location trial series has thus large potential to optimize resource allocations and increase the selection gain in programs that make use of various predictive breeding methods.

Automated summary

The use of complete and incomplete relationship information can enhance the efficiency and accuracy of phenotypic analysis in multi-location field trials, leading to improved selection gain in breeding programs. This has significant potential for optimizing resource allocations and enhancing predictive breeding methods.