A comparison of empirical BLUP with different considerations of residual error variance for genotype evaluation of multi-location trials

Abstract

The empirical best linear unbiased prediction (eBLUP) is usually based on the assumption that the residual error variance (REV) is homogenous. This may be unrealistic, and therefore limits the accuracy of genotype evaluations for multi-location trials, where the REV often varies across locations. The objective of this contribution was to investigate the direct implications of the eBLUP with different considerations about REV based on the mixed model for evaluation of genotype simple effects (i.e. genotype effects at individual locations). A series of 14 multi-location trials from a rape-breeding program in the north of China were simultaneously analyzed from 2012 to 2014 using a randomized complete block design at each location. The results showed that the model with heterogeneous REV was more appropriate than the one with homogeneous REV in all of the trials according to model fitting statistics. Whether the REV differences across locations were accounted for in the analysis procedure influenced the variance estimate of related random effects and testing of the variance of genotype-location (G-L) interactions. Ignoring REV differences by use of the eBLUP could result not only in an inflation or deflation of statistical Type I error rates for pair-wise testing but also in an inaccurate ranking of genotype simple effects for these trials. Therefore, it is suggested that in application of the eBLUP for evaluation of genotype simple effects in multi-location trials, the heterogeneity of REV should be accounted for based on mixed model approaches with appropriate variance-covariance structure.