title: Genomic models with genotype × environment interaction for predicting hybrid performance: an application in maize hybrids.
abstract: A new genomic model that incorporates genotype × environment interaction gave increased prediction accuracy of untested hybrid response for traits such as percent starch content, percent dry matter content and silage yield of maize hybrids. The prediction of hybrid performance (HP) is very important in agricultural breeding programs. In plant breeding, multi-environment trials play an important role in the selection of important traits, such as stability across environments, grain yield and pest resistance. Environmental conditions modulate gene expression causing genotype × environment interaction (G × E), such that the estimated genetic correlations of the performance of individual lines across environments summarize the joint action of genes and environmental conditions. This article proposes a genomic statistical model that incorporates G × E for general and specific combining ability for predicting the performance of hybrids in environments. The proposed model can also be applied to any other hybrid species with distinct parental pools. In this study, we evaluated the predictive ability of two HP prediction models using a cross-validation approach applied in extensive maize hybrid data, comprising 2724 hybrids derived from 507 dent lines and 24 flint lines, which were evaluated for three traits in 58 environments over 12 years; analyses were performed for each year. On average, genomic models that include the interaction of general and specific combining ability with environments have greater predictive ability than genomic models without interaction with environments (ranging from 12 to 22%, depending on the trait). We concluded that including G × E in the prediction of untested maize hybrids increases the accuracy of genomic models.
authors.affiliation: Colegio de Postgraduados, CP 56230, Montecillos, Edo. De México, México.
authors.forename: Rocío
authors.initials: R
authors.lastname: Acosta-Pech
authors.affiliation: Biometrics and Statistics Unit of the International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, México DF, México.
authors.forename: José
authors.initials: J
authors.lastname: Crossa
authors.affiliation: Department of Epidemiology and Biostatistics, Michigan State University, 909 Fee Road, Room B601, East Lansing, MI, 48824, USA.
authors.forename: Gustavo
authors.initials: G
authors.lastname: de Los Campos
authors.affiliation: RAGT 2n, Analytics Research Team, 12510, Druelle, France.
authors.forename: Simon
authors.initials: S
authors.lastname: Teyssèdre
authors.affiliation: RAGT 2n, Analytics Research Team, 12510, Druelle, France.
authors.forename: Bruno
authors.initials: B
authors.lastname: Claustres
authors.affiliation: Colegio de Postgraduados, CP 56230, Montecillos, Edo. De México, México.
authors.forename: Sergio
authors.initials: S
authors.lastname: Pérez-Elizalde
authors.affiliation: Colegio de Postgraduados, CP 56230, Montecillos, Edo. De México, México. perpdgo@gmail.com.
authors.forename: Paulino
authors.initials: P
authors.lastname: Pérez-Rodríguez
mesh_terms: D004777:Environment; D059647:Gene-Environment Interaction; D018745:Genome, Plant; D023281:Genomics / Q000379:methods*; D005838:Genotype; D006824:Hybridization, Genetic; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D000069600:Plant Breeding; D020641:Polymorphism, Single Nucleotide; D003313:Zea mays / Q000235:genetics*