Genome and environment based prediction models and methods of complex traits incorporating genotype × environment interaction.

• Autor(a) principal: CROSSA, J.
• Data de Publicação: 2022
• Outros Autores: MONTESINOS-LÓPEZ, O. A., PÉREZ-RODRÍGUEZ, P., COSTA-NETO, G., FRITSCHE-NETO, R., ORTIZ, R., MARTINI, J. W. R., LILLEMO, M., MONTESINOS-LÓPEZ, A., JARQUIN, D., BRESEGHELLO, F., CUEVAS, J., RINCENT, R.
• Tipo de documento: Capítulo de livro
• Idioma: eng
• Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
• Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1143533; https://doi.org/10.1007/978-1-0716-2205-6_9
• topic: Genomic selection; Genome-enabled prediction; Models with G x E interaction; Melhoramento Genético Vegetal; Genótipo; Interação Genética; Genome; Genomics; Plant breeding; Genotype-environment interaction

Genomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and how the use of mechanistic computational modeling can derive the crop growth and development aspects useful for predicting phenotypes and explaining G × E.