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Title Bayesian genomic prediction with genotype x environment interaction kernel models
 
Names Cuevas, J.
Crossa, J.
Montesinos-Lopez, O.A.
Burgueño, J.
Pérez-Rodríguez, P.
De los Campos, G.
Date Issued 2017 (iso8601)
Abstract The phenomenon of genotype · environment (G · E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G · E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G · E interaction are extensions of a singleenvironment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects ðuÞ that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model ðuÞ plus an extra component, f, that captures random effects between environments that were not captured by the random effects u: We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G · E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u.
Genre Article
Access Condition Open Access
Identifier http://hdl.handle.net/10883/18616