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Chapter 9. Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction
| Creador: | Crossa, J. |
| Creador: | Montesinos-Lopez, O.A. |
| Creador: | Perez-Rodriguez, P. |
| Creador: | Costa-Neto, G. |
| Creador: | Fritsche-Neto, R. |
| Creador: | Ortiz, R. |
| Creador: | Martini, J.W.R. |
| Creador: | Lillemo, M. |
| Creador: | Montesinos-Lopez, A. |
| Creador: | Jarquín, D. |
| Creador: | Breseghello, F. |
| Creador: | Cuevas, J. |
| Creador: | Rincent, R. |
| Año: | 2022 |
| ISBN: | 978-1-0716-2204-9 |
| ISBN: | 978-1-0716-2205-6 (Online) |
| URI: | https://hdl.handle.net/10883/22080 |
| Lenguaje: | English |
| Editor: | Humana Press Inc. |
| Copyright: | CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose |
| Tipo: | Book Chapter |
| Lugar de publicación: | New York (USA) |
| Páginas: | 245-283 |
| Volumen: | 2467 |
| DOI: | 10.1007/978-1-0716-2205-6_9 |
| Palabras Claves: | Genome-Enabled Prediction |
| Palabras Claves: | Genomic Selection |
| Palabras Claves: | Models With G x E Interaction |
| Descripción: | 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. |
| Agrovoc: | FIELDS |
| Agrovoc: | GENOTYPES |
| Agrovoc: | GENOTYPE ENVIRONMENT INTERACTION |
| Agrovoc: | PHENOTYPES |
| Agrovoc: | PLANT BREEDING |
| Agrovoc: | PLANT GROWTH |
| Agrovoc: | MARKER-ASSISTED SELECTION |
| ISBN: | 978-1-0716-2204-9 |
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