Increased prediction accuracy in XXXX breeding trials using a marker * environment interaction genomic selection model.

@entity3505 @entity5555
@entity5555 ( @entity5555 ) models use genome-wide genetic information to predict genetic values of candidates of selection. Originally, these models were developed without considering genotype * environment interaction(G*E). Several authors have proposed extensions of the single-environment @entity5555 model that accommodate G*E using either covariance functions or environmental covariates. In this study, we model G*E using a marker * environment interaction (M*E) @entity5555 model; the approach is conceptually simple and can be implemented with existing @entity5555 software. We discuss how the model can be implemented by using an explicit regression of phenotypes on markers or using co-variance structures (a genomic best linear unbiased prediction-type model). We used the M*E model to analyze three CIMMYT @entity3505 data sets (W1, W2, and W3), where more than 1000 lines were genotyped using genotyping-by-sequencing and evaluated at CIMMYT's research station in Ciudad Obregon, Mexico, under simulated environmental conditions that covered different irrigation levels, sowing dates and planting systems. We compared the M*E model with a stratified (i.e., within-environment) analysis and with a standard (across-environment) @entity5555 model that assumes that effects are constant across environments (i.e., ignoring G*E). The prediction accuracy of the M*E model was substantially greater of that of an across-environment analysis that ignores G*E. Depending on the prediction problem, the M*E model had either similar or greater levels of prediction accuracy than the stratified analyses. The M*E model decomposes marker effects and genomic values into components that are stable across environments (main effects) and others that are environment-specific (interactions). Therefore, in principle, the interaction model could shed light over which variants have effects that are stable across environments and which ones are responsible for G*E. The data set and the scripts required to reproduce the analysis are publicly available as Supporting Information.
