title: Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.
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 single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects [Formula: see text] 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 [Formula: see text] plus an extra component, F: , that captures random effects between environments that were not captured by the random effects [Formula: see text] 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 [Formula: see text] 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 [Formula: see text].
authors: Universidad de Quintana Roo, Chetumal, Quintana Roo, México.
authors: Jaime
authors: J
authors: Cuevas
authors: Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), 06600 México D. F., México j.crossa@cgiar.org.
authors: José
authors: 0000-0001-9428-5855
authors: J
authors: Crossa
authors: Facultad de Telemática, Universidad de Colima, C. P. 28040, Edo. de Colima, México.
authors: Osval A
authors: OA
authors: Montesinos-López
authors: Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), 06600 México D. F., México.
authors: Juan
authors: J
authors: Burgueño
authors: Colegio de Postgraduados, C. P. 56230 Montecillos, Edo. de México, México.
authors: Paulino
authors: P
authors: Pérez-Rodríguez
authors: Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan 48824.
authors: Gustavo
authors: G
authors: de Los Campos
mesh_terms: D001499:Bayes Theorem; D059647:Gene-Environment Interaction; D018745:Genome, Plant; D023281:Genomics; D005838:Genotype; D008957:Models, Genetic; D000069600:Plant Breeding / Q000706:statistics & numerical data*; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D012641:Selection, Genetic; D014908:Triticum / Q000235:genetics; D003313:Zea mays / Q000235:genetics
keywords: Gaussian kernel; GenPred; Shared data resource; genomic selection; kernel GBLUP; marker × environment interaction; multi-environment