The phenomenon of genotype× environment (G× E) interaction in plant breeding decreases
selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction …

In genomic selection (GS), genotype× environment interaction (G× E) can be modeled by a
marker× environment interaction (M× E). The G× E may be modeled through a linear kernel …

One of the most widely used kernel functions in genomic-enabled prediction is the Gaussian
kernel. Selection of the bandwidth parameter for kernel regression has generally been …

One of the major issues in plant breeding is the occurrence of genotype× environment (GE)
interaction. Several models have been created to understand this phenomenon and explore …

Multi-environment trials are routinely conducted in plant breeding to select candidates for
the next selection cycle. In this study, we compare the prediction accuracy of four developed …

Genomic selection (GS) has become an important aid in plant and animal breeding.
Multienvironment (multitrait) models allow borrowing of information across environments …

Evidence that genomic selection (GS) is a technology that is revolutionizing plant breeding
continues to grow. However, it is very well documented that its success strongly depends on …

When information on multiple genotypes evaluated in multiple environments is recorded, a
multi-environment single trait model for assessing genotype× environment interaction (G× E) …

Genomic selection (GS) models use genome-wide genetic information to predict genetic
values of candidates of selection. Originally, these models were developed without …

In this study, we compared the prediction accuracy of the main genotypic effect model (MM)
without G× E interactions, the multi-environment single variance G× E deviation model …